NEW APPROACHES, METHODS, TECHNOLOGIES Collection Of

RUSSIAN ACADEMY OF SCIENCES Institute of Arid Zones, Southern Scientific Centre, Rostov-on-Don, Russia

OCEANOGRAPHY CENTER University of Cyprus, Nicosia, Cyprus

Research and Development Center “ScanEx”, Moscow, Russia

UNIGEO Consortium, Russia

Innovative Research and Production Enterprise "Innovative technologies sturgeon ", Rostov-on-Don, Russia

Council of Young Scientists of the Institute of Arid Zones SSC RAS

GEOINFORMATION SCIENCES AND ENVIRONMENTAL DEVELOPMENT: NEW APPROACHES, METHODS, TECHNOLOGIES

Collection of articles of the II International conference

(Limassol, Cyprus, May 5-9, 2014)

Rostov-on-Don 2014

Geoinformation Sciences and Environmental Development: New Approaches, Methods, Technologies. Collection of articles of the II International conference (May 5-9, 2014. Limassol, Cyprus). [The electronic resource] – Rostov-on-Don: Publishing house SSC RAS, 2014. – 146 p. ISBN 978-5-4358- 0089

ISBN 978-5-4358-0089

The Conference was presented new results on the prevention and prediction of emergency, development and application of information technologies and mathematical modeling for a comprehensive study of marine and terrestrial ecosystems, new methods and areas of application of remote sensing data in the geosciences. The Conference was allowed scientists and experts to get acquainted with the latest world achievements in the field of sustainable development problems and geoinformatics including theoretical and methodological aspects of geoinformational support for tasks of sustainable development, the role of remote sensing in support of sustainable development, mathematical methods and models in environmental research, environmental engineering and environmental management technologies.

Editorial board: Dr. (Geography) S.V. Berdnikov PhD (Technology) O.E. Arkhipova PhD (Geography) N.A. Yaitskaya

Materials are published with the highest reservation of authors’ editing

ISBN 978-5-4358-0089 © Sothern Scientific Centre of RAS, 2014

РОССИЙСКАЯ АКАДЕМИЯ НАУК Институт аридных зон, Южный научный центр, Ростов-на-Дону, Россия

ОКЕАНОГРАФИЧЕСКИЙ ЦЕНТР Кипрского университета, Никосия, Кипр

Инженерно-технологический центр “СКАНЭКС”, Москва, Россия

Консорциум “Университетские геопорталы (УНИГЕО)”, Россия

Инновационное научно-производственное предприятие "Инновационные технологии осетроводства", Ростов-на-Дону, Россия

Совет молодых ученых Института аридных зон ЮНЦ РАН

ГЕОИНФОРМАЦИОННЫЕ НАУКИ И ЭКОЛОГИЧЕСКОЕ РАЗВИТИЕ: НОВЫЕ ПОДХОДЫ, МЕТОДЫ, ТЕХНОЛОГИИ

Сборник статей II Международной научной конференции

(г. Лимассол, Кипр, 5-9 мая 2014 г.)

Ростов-на-Дону 2014

Геоинформационные науки и экологическое развитие: новые подходы, методы, Г35 технологии: сборник статей II Международной научной конференции (Лимассол, Кипр, 5–9 мая 2014 г.) [Электронный ресурс] – Ростов н/Д: Изд-во ЮНЦ РАН, 2014. – 146 с. ISBN 978-5-4358- 0089.

На конференции представлены новые результаты работ по предупреждению и прогнозированию чрезвычайных ситуаций, разработки и применению информационных технологий и математического моделирования для комплексного исследования морских и наземных экосистем, новым методам и областям применения данных ДЗЗ в науках о Земле. Конференция позволила ученым и специалистам ознакомиться с последними мировыми достижениями в области проблем устойчивого развития и геоинформатики, в том числе с теоретическими и методическими аспектами геоинформационного обеспечения задач устойчивого развития, ролью дистанционного зондирования Земли в обеспечении устойчивого развития, математическими методами и моделями в исследованиях окружающей среды, инженерной экологии и технологии рационального природопользования.

Редакционная коллегия: д.г.н. С.В. Бердников к.т.н. О.Е. Архипова к.г.н. Н.А. Яицкая

Материалы опубликованы с максимальным сохранением авторской редакции

ISBN 978-5-4358-0089 ©Южный научный центр РАН, 2014

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Contents

TECHNOLOGIES FOR EARLY DETECTION AND PREDICTION OF NATURAL AND TECHNOGENIC EXTREME EVENTS E.K. Nikolsky, T.O. Eriskina, М.S. Belyakova REMOTE METHODS OF MONITORING OF FIRES AND THEIR CONSEQUENCES 8 V.N. Orlyankin TECHNOLOGY OF EMERGENCE SITUATION SHORT-TERM FORECASTING WHEN FLOOD WITHIN THE RIVER VALLEY 13 E.P. Yankovich, N.V. Baranovskiy ESTIMATION OF FOREST FIRE DANGER CAUSED BY FOCUSED SUNLIGHT ACTION USING ARCGIS 17 V.A. Zelentsov, B.V. Sokolov, A.V. Ziuban, S.A. Potryasaev, J.J. Petukhova, I.N. Krylenko SYSTEM OF THE OPERATIONAL FORECASTING OF FLOODS ON THE BASIS OF INTEGRATED USE OF SPACE-GROUND DATA 21 G. Zodiatis, R. Lardner, H. Radhakrishnan, A. Nikolaides, S. Stylianou, X. Panayidou MYOCEAN COPERNICUS MARINE DOWNSCALING AND DOWNSTREAM APPLICATIONS: THE CYPRUS OCEAN FORECASTING SYSTEM AND THE MEDITERRANEAN OIL SPILL PREDICTION SYSTEM 27 D. Lubnin, E. Levin, A. Grechishev SOFTWARE AND TECHNOLOGY OF THE OPENED NETWORKED GEOINFORMATION PORTAL AND EDUCATIONAL CONTENT FOR THE PROCESSING OF THE DISTRIBUTED DATA IN EMERGENCY SITUATIONS RESPONSE APPLICATION SCENARIOS 28 I.A. Tretyakova, A.L. Chikin FORECASTING OF DANGEROUS DON DELTA FLOODING: PRELIMINARY RESULTS 33

BIODIVERSITY AND MARINE ENVIRONMENTAL MANAGEMENT V.N. Bocharnikov, S. Krasnopeev DEVELOPMENT AND PERSPECTIVES OF SOUTH OF RUSSIAN FAR EAST WETLANDS GEOGRAPHIC DATABASE USE FOR PROBLEM SOLVING OF STRATEGIC PLAN FOR CONSERVATION OF BIODIVERSITY 35 G. Fyttis, Y. Samuel-Rhoads, Irinios Yiannoukos, Leda Liyue Cai, G. Zodiatis PRELIMINARY STUDY OΝ THE ΜΑRINE BIODIVERSITY OF THE COASTAL AREA OF CYPRUS AND AN INVESTIGATION OF SEA SURFACE TEMPERATURE CHANGES IN THE LEVANTINE BASIN 39 L.D. Nemtseva ASSESSMENT OF THE RELATIONSHIP BETWEEN THE PLANTS BIOMASS AND NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) FOR EXAMPLE, ARID STEPPE LANDSCAPES 40 Yu. Tyutyunov, L. Titova SIMPLE INDIVIDUAL-BASED MODEL FOR PURSUIT-EVASION IN PREDATOR-PREY SYSTEM 43

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THE METHODS OF THE COMPLEX GEOMODELING OF NATURAL AND ANTHROPOGENICALLY TRANSFORMED GEOSYSTEMS O.E. Arkhipova, Е.А. Chernogubova, N.V. Likhtanskaya SPATIOTEMPORAL ANALYSIS OF THE INCIDENCE OF CANCER DISEASES AS AN INDICATOR OF MEDICAL AND ENVIRONMENTAL SAFETY: THE CASE STUDY OF THE ROSTOV REGION 49 O.D. Ajunova, S.G. Prudnikov, V.I. Zabelin, O.I. Kalnaya, T.P. Archimaeva, E.A. Domozhakova GIS USING FOR STATE ESTIMATION OF NATURAL OBJECTS IN VARIOUS STAGES OF THE KYZYL- TASHTYG POLYMETALLIC DEPOSIT EXPLORATION IN TYVA REPUBLIC 53 L. Usoltseva, V. Lushpei, Y. Vasyanovich, V. Murzin THE MONITORING OF SURVEYING WORK FOR OPENCAST MINING DEVELOPMENT ON PRIMORSKY REGION TERRITORY 56 T.V. Vatlina ASSESSMENT OF EPIDEMIC DANGER TO THE NATURAL FOCI DISEASES 61 E.Zh. Garmaev, A.K. Tulokhonov, B.Z. Tsydypov THE ASSESSMENT OF THE LAKE BAIKAL SHORELINE DYNAMICS USING REMOTE SENSING METHODS 63 D.O. Dushkova APPLICATION OF GIS-TECHNOLOGIES FOR ENVIRONMENTAL HEALTH HAZARDS MAPPING 68 A.F. Varfolomeev, S.P. Evdokimov THE STUDY OF THE SPATIAL DISTRIBUTION OF SOIL AREAS USING GIS TECHNOLOGIES IN SMOLENSK REGION 73 S.G. Pugacheva, V.V. Shevchenko GEOMORPHOLOGICAL FEATURES OF ANCIENT VOLCANIC TERRAIN OF MARS 78 Iu.F. Rozhkov, M.Y. Kondakova USING MULTISPECTRAL SATELLITE IMAGES FOR MONITORING OF THE FOREST ECOSYSTEMS STATE 83 A.V. Skripchinsky ASSESSMENT OF A CURRENT STATE OF RESERVOIRS ON THE BASIS OF REMOTE SENSING OF THE EARTH 88 V.V. Sorokina, V.V. Kulygin, S.V. Berdnikov TOTAL SUSPENDED SOLIDS, PARTICULATE ORGANIC MATTER AND SECCHI DEPTH IN THE SEA OF AZOV 93 O.V. Sukhova AN EVALUATION OF SEASONAL SNOW ACCUMULATION PROCESSES IN FORESTED AND OPEN AREAS 97 A.E. Tsygankova, S.V. Berdnikov, I.V. Sheverdyaev APPLICATION OF GIS-TECHNOLOGIES FOR IDENTIFYING THE THERMOHALINE VARIABILITY AT THE CENTURY SECTIONS OF THE BARENTS SEA 102 Yu.P. Yuronen, E.A. Yuronen, V.V. Ivanov TO A PROBLEM OF INFORMATION SYSTEM CREATION OF ENVIRONMENTAL MONITORING OF KRASNOYARSK REGION ON THE BASIS OF MODERN GIS-TECHNOLOGIES AND EARTH REMOTE SENSING DATA 107 V.V. Fomin, A.A. Polozok, R.V. Kamyshnikov WAVE AND STORM SURGE MODELLING FOR SEA OF AZOV WITH USE OF ADCIRC+SWAN 111

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V.V. Kulygin, V.V. Sorokina DEVELOPMENT OF MARINE ECOSYSTEM DYNAMIC MODEL OF THE SEA OF AZOV 116

NEW TECHNOLOGIES AND APPROACHES TO WORK WITH REMOTE SENSING DATA S.A. Arkhipov METHODICAL SOFTWARE FOR ADJUSTMENT OF THE ”RESOURCE-P” SPACECRAFT HYPERSPECTRAL SHOOTING EQUIPMENT AND FOR HYPERSPECTRAL DATA PREPROCESSING 118 A.A. Lagutin TOTAL METHANE MIXING RATIOS IN WEST SIBERIA FOR 2003-2013: RESULTS FROM AIRS/AMSU-AQUA AND CHEMISTRY TRANSPORT MODELS 121 D. Papazachariou, G. Zodiatis, A. Nikolaidis, S. Stylianou and D. Arabelos SATELLITE ALTIMETRY FOR MONITORING SEA LEVEL CHANGES IN THE EASTERN MEDITERRANEAN 122 A.V. Molochko, T.V Pyatnizyna, A.V. Fedorov, D.P. Khvorostukhin STEREOSCOPIC TERRAIN MODEL CREATION BASED ON A SINGLE SATELLITE (SPACE) IMAGERY AND VISUALIZATION OF GEOSPATIAL INFORMATION BY MEANS OF LENS RASTER 123 V.A. Ryzhkova, I.V. Danilova SPATIAL MODELLING OF FOREST REGENERATION DYNAMICS AND BIODIVERSITY USING REMOTE SENSING DATA AND GIS-TECHNOLOGY 127 Y. Samuel-Rhoads, G. Zodiatis, A. Nikolaidis, D. Solovyov MONITORING THE LEVANTINE BASIN THROUGH THE USE OF MULTIPLE SATELLITE REMOTE SENSING PRODUCTS: WITH A FOCUS ON AN INTERCOMPARISION OF THE NEW SMOS GLOBAL SALINITY DATA WITH MYOCEAN MODEL DATA 132 E.Zh. Garmaev, B.Z. Tsydypov, A.A. Ayurzhanaev, Zh.B. Alymbaeva, B.V. Sodnomov VEGETATION MAP FOR THE SELENGA RIVER BASIN ON THE BASE OF LANDSAT TM IMAGERY 133 D.V. Moiseev, G.N. Duhno, Yu.V. Fedorkova VERIFICATION OF REMOTE SENSING BASIC PRODUCTS WITH IN SITU DATA FOR THE KARA SEA 137 O.E. Arkhipova, N.A. Kachalina, Yu.V. Tyutyunov WEEDINESS ASSESSMENT OF ANTHROPOGENIC PHYTOCENOSES ON THE BASIS OF SATELLITE REMOTE SENSING DATA 141

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TECHNOLOGIES FOR EARLY DETECTION AND PREDICTION OF NATURAL AND TECHNOGENIC EXTREME EVENTS

REMOTE METHODS OF MONITORING OF FIRES AND THEIR CONSEQUENCES

______E.K. Nikolsky, T.O. Eriskina, М.S. Belyakova Nizhny Novgorod state university of architecture and civil engineering (NNGASU), Nizhny Novgorod, Russia. [email protected]

Abstract The article is devoted to topical issues of the technology of monitoring of fires and their consequences according to the satellite imagery and statistical data made in the Nizhny Novgorod region. The zoning of forest fires occurrence was made on a basis of the statistical data. The studies have determined the threshold temperature value for automatic fire detection by «ScanEx IMAGE Processor» software in accordance with ATVD-MOD-14 algorithm, the effectiveness of space monitoring of fires was analyzed, monitoring of fires in 2010 at the territory of Kerzhensky biosphere reserve was conducted, the vegetation damage from fires was estimated and the spectral brightness of damaged vegetation three years after the fire was investigated.

Keywords: Factors causing fires, Earth remote sensing, threat, risk, damage, ScanExImageProcessor software, automatic fire detection, spectral brightness, GIS methods

Territory of many countries of the world is threatened by forest fires. Duration of a fire season is associated with geographical features. The Nizhny Novgorod region having large reserves of natural and energy resources, presented in the form of minerals and peat reserves, huge areas of forests, is among the most exposed to these scourge Russian regions. Forest fires are the result of natural and anthropogenic factors (Fig. 1).

Figure 1. Factors causing forest fires

Background for the development of the forest fire is the vulnerability of the area which depends on: tree stand, soil, availability of hydrographic objects, temperature, and precipitation amount. On a basis of the analysis of these and other influencing factors the forest fire occurrence zoning was made (Fig. 2).

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Figure 2. Schematic map zoning of forest fires in the Nizhny Novgorod agglomeration for the spring period (month - May) according to the EMERCOM data for 2005-2011

Figure 3 shows a graph which reflects the area of natural fires in fire season. The correlation between the steppe and forest fires is traced, so it can be concluded that forest fires often occur as a consequence of the steppe and peat fires. In fire season there are two upper limits of forests burning which occur in May and August. These time periods are accompanied by burning of dry last year's grass, straw residues on fields and peat smoldering.

Figure 3. Area of fires at the territory of the Nizhny Novgorod agglomeration taken monthly during the period 2005-2011 years (Nikolsky, 2013)

The urgent task of securing forest fund is the conducting of aerospace monitoring. Modern methods of remote sensing (RS) using satellite technology can solve a wide range of scientific and practical problems in the field of environmental protection, environmental management, as well as monitoring of a fire situation. High efficiency of satellite observations, large spatial coverage, the ability to track the development of dynamic processes and relative cheapness (and in case of , for example, using MODIS system - free distribution) make it necessary and promising the use of remote sensing techniques for solving some problems also in the conditions of the Nizhny Novgorod region. One of the means of obtaining real-time data is NASA instrument Moderate Resolution Imaging Spectroradiometer (MODIS) launched aboard spacecraft «Terra» and «AQUA», which collects daily global systematic data on fires. MODIS spectral bands (total 36) cover a region of the spectrum of

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wavelengths from 0.4 to 14.4 micrometers. 250 meters shooting is conducted in two areas: 0.62-0.67 and 0.841-0.876 micrometers , 500 meters - in five zones of the visible and near-infrared regions, 1000 meters - in other areas of the spectrum. Radiometric resolution of original pictures are high - 12 bits. Detection of fires with the help of software «ScanExImageProcessor» is conducted by the algorithm АТВD-MOD-14 according to spectroradiometer MODIS data. Registration data are: - geographical coordinates of the area, at a satellite image their photos are recognized as a thermal anomaly (Longitude, Latitude); - Brightness temperature of the Earth's surface within a pixel referred to the point of ignition, coordinates of which are defined (T4, K); - pixel brightness, expressed in numerical form (Power, W/m2). The principle of automatic fire detection consists in determining the temperature difference between adjacent pixels in the 21st and 31st channels by calculation of template of fires. The result of calculation is presented in the file of *.log format which contains a certain amount of registration data. Before calculating the template of fires the temperature threshold value is input. The pixels having higher brightness temperature than the agreed one will be referred to the thermal anomalies and the records of this point will be input in the appropriate file. In this way, by changing the value of the threshold temperature different amounts of registration data can be received (Nikolsky, 2007). The graph clearly represents the relationship between the change in the set temperature and the number of pixels classified as thermal anomalies shown in Figure 4.

Figure 4. The dependence of the amount of thermal anomalies on the threshold temperature value

Basing on information about the fires provided by the EMERCOM and interpretive signs of actually existing fire, the value of the threshold temperature when the registration data obtained as a result of automatic fire detection algorithm are the most reliable is determined. The research value of brightness temperature was 316 K. Analysis of the registration data and official information about fires from the EMERCOM in the Nizhny Novgorod region showed the occurrence of fires in the absence of the data in the EMERCOM, which proves the effectiveness of space monitoring (Fig. 5). So out of 71 fires detected by photo 2.05.06 in the Nizhny Novgorod region, only 12 cases of fire were confirmed (17%) by the report of Head Department of the EMERCOM. In summer 2010 the territory of Kerzhensky biosphere reserve was damaged by forest fires. In order to monitor the damage to vegetation from wildfires the shots of its territory from satellites AQUA TERRA with MODIS radiospectroradiometer aboard were treated during August 10 th – 18 th. The image processing is carried out in the software ScanExImageProcessor. Image processing allowed not only to assess the area burnt but also to determine the average speed and direction of the fire (Fig. 5).

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Figure 5. Diagram of dynamics of the spread of fire according to MODIS

Satellite AQUA passes over the study area in average 2 hours later than TERRA satellite does. Fire way, passed on August 13th in 2 hours was 1.2 km and 1.3 km on August 14th. In this way, we can calculate the actual speed and predict the spread of fire. Average rate of spread determined by space- based data was 0.2 m/s. Assessment of the damage from the fire was carried out according to the LANDSAT-5 (7-4-2 combination). Overlaying the layer "fumes" on a vegetation of Kerzhensky biosphere reserve map made it possible to determine the composition of damaged vegetation (Fig. 6). Total area burnt was 20120.0 ha.

Figure 6. Evaluation of fire damage according to LANDSAT-5 data (left) using vector vegetation map (right)

Burnt areas of each type of vegetation were determined with the help of geoinformation methods. The greatest losses were recorded in the mixed forest zone (pine - birch). Areal characteristics affected by the fire stands are shown in Table 1.

Table 1. Assessment of damage from fire, ha Kind Square, ha Birch forests 249.5 Birch forests-osier forests 395.2 Birch forest-aspen forests-pine forests 23.2 Birch forests-black alder forest 882.9 Osier-beds 9.7 Aspen forests 17.5 Aspen forests-birch forests 810.6 Pine-forests 2524.9 Pine forests-birch forests 14861.0 Black alder-forest 0.6 Motley grass 344.9 Total 20120.0

Vegetation recovery process in burned areas can be analyzed by spectral brightness of vegetation. Spectral brightness of vegetation of the territory of the reserve burnt varies in its values at different

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wavelengths. The plants in the near infrared region are the most sensitive to stressful situations. In stressful conditions the reflectivity of leaves in the infrared zone falls. So, three years after the fire the brightness in the near-infrared zone remains lower than the brightness of intact vegetation (Fig. 7), and the spectrum curve in the red zone of the spectral brightness of the area subjected to fire lies above the curve of healthy vegetation which shows the stress state of the forest area (healthy leaves soak up the rays of the red zone of the spectrum for photosynthesis influenced by diseases, stress and aging, the reflectivity of the plants in the red zone is increased). However, in the study area in 2013 the brightness in the green zone of the spectrum increases, what shows a gradual revegetation (immediately after the fire the intensity in the green zone of the spectrum falls sharply).

Figure 7. Spectral brightness of mixed vegetation (pine - birch) three years after the fire and spectral brightness of healthy vegetation at different wavelengths

Figure 8 shows the satellite image of LANDSAT-8 on the territory of the left bank of the Volga river, including lands of Kerzhensky biosphere reserve. After connecting the channels of near infrared range the area of the forest burnt in 2010 is highlighted by bordeaux color.

Figure 8. The territory of the left bank of the Volga river before (Landsat 5 - June 26 th , 2010, combination 5-4-2) and after fires (Landsat 8 - July 4 th, 2013, the combination 6-5-3)

Spectral brightness is, to a certain extent, an indicator of the state of vegetation, so the research of objects brightness can be used in monitoring of the processes of revegetation at lands burned. Periodic updating of remote sensing data allows monitoring and mapping of territories which is significant for assessment of natural resource and areas management. Using satellite imagery provides a new opportunity for obtaining and using of objective information, which is important when working with such dynamically changing objects. The minimal time should be spent from the detection of forest fire till deciding to eliminate it. Using only satellite monitoring data in detection takes quite a long time meanwhile fire damages new areas. To prevent or reduce the risk of forest fires it’s necessary to carry out complex operations to diminish fire risk of natural and anthropogenic factors to the forest. Types of fire protection measures should be selected according to the vulnerability of the territory. In this way, it’s sufficient to set thermal imagers on towers or balloons in the forests far from human settlements. In forests located near

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settlements it’s necessary to arrange ground patrolling for monitoring of the forest fires. Competently made schedule observing fire hazard areas subject to the period of the fire season and the wind direction will allow detecting and taking measures to eliminate the forest fire. One of the major works to reduce the vulnerability of the forest fire is clearing forests. It’s necessary to conduct forest management activities (sanitary felling, clearing and logging sites, etc.), and also to hold special events for the construction, reconstruction and maintenance of various fire fighting facilities. Fire risk of forests is significantly reduced when combustible material is not accumulated at its territory. Most forest fires are caused by humans. Therefore it’s important to carry out work aimed at the development of standards of human behavior in a forest. It’s important to engage and encourage volunteering, intensification of fire safety propagation. Preventive work with the local community is one of the main fire safety measures (Efremov, 2012). To date, there is a practice of environmental insurance which can exist not only as an economic mechanism to regulate forestry activities but also as a mechanism for compliance with forest protection activities. In this way, the environmental insurance can not only compensate the economic losses from forest fires but also effectively stimulate the reduction of negative environmental load. Unfortunately due to the complexity of calculating economic losses (multiplicity of manifestations of prejudice and lateness) environmental insurance is not getting much support from insurers. Due to the importance of forest resources for society as well as for the state the interest in the development of environmental insurance practices should grow (Motkin, 2010). The problem of forest fires can not be reduced only to the fight against this natural process. Constant comprehensive monitoring (satellite, air, ground) of the state of forests and suppression of all fires detected is required.

References Nikolsky, E.K. Factors causing forest fires and risk zoning / E.K. Nikolsky, M.S. Belyakova // Privolzhsky scientific journal, №4 (28) / Nizhny Novgorod state university of architecture and civil engineering – N.Novgorod, 2013. – p.143-149. Nikolsky, E.K. Technological stages of space monitoring of forest fires according to spectroradiometer MODIS data/Е.К. Nikolsky, T.O.Eriskina// Privolzhsky scientific journal, №4 (4). / Nizhny Novgorod university of architecture and civil engineering – N.Novgorod, 2007.– 204 p. D.F. Efremov, Prevention and measures to prevent forest fires in the forest management system of the Russian Federation / D.F. Efremov [et al] – М.: The World bank, 2012. – 104 p. Motkin, G.A. Ecological insurance: results and perspectives / G.A. Motkin / Works of the Xth anniversary all-Russian and the Vth international conference / – М.: Research center «Ecoproject», 2010. – 71 p.

TECHNOLOGY OF EMERGENCE SITUATION SHORT-TERM FORECASTING WHEN FLOOD WITHIN THE RIVER VALLEY

______V. N. Orlyankin Scientific Geoinformation Centre (NGIC RAS), Moscow, Russia. [email protected]

Abstract Technology of rain floods short-term forecasting is offered. The short-term forecasting of floods suggests earliness calculation of discharges and water level for a limited concrete river valley place. The earliness forecast is determined by velocity and time channel lag of the flood wave. The author propose model for calculating at the flood wave velocity by the sum of flow rate in the main river and

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flood plain. Novelty of report consist in the estimated models of flood wave velocity and reduction coefficients of maximum discharges in the river places without tributaries.

Keywords: rain flood, short-term forecasting, flood wave velocity, river discharge, flow rate, river bed, flood plain, influx, mouth, gauge

Emergency situations (ES) are, in our case, natural catastrophes, which have significant adverse impacts on the vital activity of people, economic, social or natural environment. Hydrological ES (HES) arise cases of significant flooding in river valleys as a result of the river water level rising, causing material damage, damage to public health or causing of people deaths. HES take the second place by the number of human victims and material damage after the earthquakes, and the first place in the series of natural catastrophes about frequency of repeatability, the area of flooding distribution and the total average annual damage. Technology of the short-term forecasting of emergence situation caused mainly by rain floods is below, which are the most common in the world. The short-term forecasting of floods suggests earliness calculation of discharges and water level for a limited concrete place river valley - usually for a large settlement. The earliness of forecast - a few days, as is generally known, is determined by time channel lag of the flood wave passing from the upstream river information staff gauges to position of forecast. And the time lag of the flood wave depends on its velocity and the length of the area from the upstream information staff gauge till the position of forecasting. If there is no river basin stream-gauging observation network or it is rare, than determination of the flood wave velocity and the time lag is difficult task. It is considered that the flood wave velocity (U, m / sec.) fluctuates in limits (0.5 ... 1.5) · V, where V - the average flow rate in the compound cross river section (V m/sec). The author propose the following model for calculating of the flood wave velocity (U km / day) by the sum of flow rate in the main bed (Vb) and floodplain (Vf):

U = 86.4 (Qb · Vb + Qf · Vf) / (Qb + Qf) km/d (1) here Qb - average daily discharge of water in the main river bed, m3/sec:

Qb = b · hb· Vb , where b - average bed width on the rectilinear river lenght - the distance between floodplain edges, m, hb - the average depth of water in the high-water bed, m; hb = h + 0.1· b0,5 (h - water level above low-water level, m);

Vb = (hb0,67 · i0,5/nb) · (1.6 - h/h1%)k (2) where i – bed slope in shares of unit; nb - coefficient of roughness for channel bed ( from Table, Sribniy); h1% - the highest water level in flood 1% probability (the approximate evaluation):

h1% = 0.16 [b2 ̸ (B + b) · i]0,3 (3) where B - average width of floodplain (m ) in the area of the river valley with length 3B; degree k in formula (2) depends on the relative width of flood plains (B):

k = [(B + b) / b] 0,125 (4)

The right half of formula (2) - (1.6 - h ̸ h1%) k is used only when h ̸ h1% ≥ 0.6 or since beginning of flood waters into the lowest parts of the floodplain (back marsh), and if h ̸ h1% ˂ 0,6, it applies only classical formula for the flow rate:

Vb = (hb0,67 · i0,5 ) / nb .

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Average daily discharge of water on the floodplain (Qf,m3/sec):

Qf = B · hf · Vf , where hf – mean flow depth on the floodplain in the line gauge:

hf = 0.85 (h - Hf) (5) where h - the water level in the channel, m ( above low one); Hf - the height of floodplain edges above low water , m;

Hf = 0.11 [b2 ̸ (B + b) · i]0,3 (6) Vf - mean flow rate on the floodplain, m/s : 0,67 0,5 Vf = hf · (iK) ⁄̸ nf , where K - the coefficient of the meandering channel, nf - surface roughness coefficient for floodplain.

An example of calculating flood wave velocity on the Zeya River (left tributary of the Amur River) on the area the Selemdzha River influx into Zeya and till town Blagoveshchensk (mouth of Zeya) in flood peak in June 1953.

b = 700 m , B = 11300 m , i = 0.00012; K = 2 ; nb = 0.025; nf = 0.10; h = 8.8 m ; hb = 8.8 + 0.1·7000,5 = 11.4 m ; H1% = 9.8 m; Vb = 1.21 m / s ; Qb = hp · b · Vb = 9680 m3/sec. Hf = 6.3 m ; hf = 0.85 (8.8 - 6,3) = 2.12 m ; Vf = hf0,67 · (i K) 0,5 ̸ nf = 0.256 m/s; Qf = 11 300 · 2.12 · 0,256 = 6140 m3 / s; U = 86.4 ( 9680 · 1.21 + 6180 · 0.256) ⁄̸ ( 9680 + 6140 ) = 72.3 km/ day .

As the example the calculation of the highest water level for the Zeya River in Malaya Sazanka river section is given which is the highest in the twentieth century during rain flood in 1953. Three relatively large lateral inflow fall into Zeya River above Malaya Sazanka: Selemdzha River- 138 km above Malaya Sazanka, Dep River - 195 km above the Selemdzha River influx, Urcan River - 105 km above the Dep River influx. At 65 km above the Urcan influx Zeyskie Vorota river gauge was placed on the site on which Zeyskaya hydroelectric power station was built after 1953. Maximum water discharges reduce downstream on the Zeya River reach of channel with small inflow to reach an account of flood wave subsidence. Reduction coefficients of maximum discharges (k) calculated by our empirical formula (7):

k = 1 – 0.00003 · L [(B + b) ̸ b2] (7) where L - length of area without tributaries , B and b - average widths of the floodplain and river bed, correspondingly (all three parameters - or kilometers or meters). Formula (7 ) is applicable in the condition h> Hf. When h ˂ Hf (below overflow level)

k = 1 – 0.00004 L (8) All calculated k for four sections are equal : Zeyskiye Vorota gauge – Urcan river mouth k1 = 0.94 Urcan river mouth – Dep river mouth k2 = 0.95 Deep river mouth – Selemdzha river mouth k3 = 0.97 Selemdzha river mouth - Malaya Sazanka gauge k4 = 0.90

The advance of flood wave time on the Zeya River τ = L / U is also defined for 4reach of stream: τ1 = τ2 = τ3 = 1 day , τ4 = 2 days. Total from Zeyskiye Vorota till Malaya Sazanka Στ = 5 days.

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For the same 5 days flood waves (or water masses of any phases ) move to Malaya Sazanka from the Zarechnoe gauge on the Urcan River (34 km from the mouth) , from the Dep- Dolbyr gauge on the Dep River (240 km from the mouth ) and from the Stoyba gauge on Selemdzha River (326 km from the mouth) . For Urcan, Dep, Selemdzha rivers graphs of relation average daily water levels at gauges Zarechnoe, Dep-Dolbyr and Stoyba are plotted and dayly average discharges in the mouths of these rivers are calculated. Maximum daily discharge of water 14.VII.1953 by Zeyskiye Vorota denoted as Qz = 12600 m3/sec, discharges coming into Zeya River from Urcan River - Qu = 300 m3/sec (15.VII), from Dep River - Qd = 900 m3/s (16.VII), from Selemdzha River - Qs = 5500 m3/s (17.VII). Forecasted discharge by Malaya Sazanka - Qm.s. =? (19.VII).

Qm.s. = {[(Qz k1 + Qu) k2 + Qd] k3 + Qs} k4 = (9) ={[(12600 · 0.94 + 300) · 0.95 + 900] · 0.97 + 5500} · 0,90 = 15700 m3/sec.

(The actual maximum daily discharge of 19.VII.1953 in Malaya Sazanka - 15700 m3/sec.) . By the discharge rating curve Q = f (h) we obtain the forecast value of the highest water level h = 8.8 m, exceeding the output level of flood waters on the floodplain (the height of floodplain edges) 2.5 m (Hf = 6.3 m if Q = 8360 m3/sec). In recent decades, in place Zeyskiye Vorota gauge Zeyskaya hydroelectric station was created with flood-control reservoir. To prevent an emergency situation - the flooding in the most populated part of the Zeya River valley between the Selemdzha mouth and town Blagoveshchensk (or to reduce flood damage), regulating water removing from the Zeyskiy flood-control reservoir can be carried out in the following scheme, return forecasting one:

Qz ≧ {[(Qm.s. ̸ k4 – Qs) ̸ k3 – Qd] ̸ k2 – Qu} ̸ k1 (10) where Qm.s. = 8300 m3/sec (if Hf = 6.3 m) - maximum mean daily discharge of Zeya River (Malaya Sazanka) before the release of water to the flood plain , before the flooding occurring on the surface of the flood plain settlements.

Qz ≤ {[( 8300 ̸ 0.90 - 5500 ) ̸ 0.97 - 900 ] ̸ 0.95 - 300 } ̸ 0.94 = 2980 m3 /s. At the same admission of the water masses of the tributaries into Zeya , as it was in June 1953, the water flash of the Zeya reservoir should not exceed 2980 m3/sec. On the whole scheme of river discharge (Qx) forecasting in any line gauge “x” is following:

Qx = {[(Q0 k0 + Q1) k1 + Q2] k2 +…+ QN} kN and hx=f(Qx) (11) where Q0 – mean daily river discharge into upstream cross section of main river; Q1, Q2… QN – computed tributaries discharges in their month; N – quantity of the tributaries; k0, k1,… kN,- calculated reduction coefficients of maximum discharges in the river places with small intermediate inflow; hx – short-term forecasted average daily water level in any line gauge “x”.

The principal novelty of present report consist in the estimated models of flood wave velocity and reduction coefficients of maximum discharges in the river places without tributaries for the short- term forecasting of catastrophic rain floods.

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ESTIMATION OF FOREST FIRE DANGER CAUSED BY FOCUSED SUNLIGHT ACTION USING ARCGIS

______E.P. Yankovich National Research Tomsk Polytechnic University, Tomsk, Russia. [email protected] N.V. Baranovskiy National Research Tomsk Polytechnic University, Power Engineering Institute, Tomsk, Russia. [email protected]

Abstract This paper presents main aspects of using GIS toolset to estimate forest fire danger caused by focused sunlight action. In a range of researches the possibility of forest flammable material ignition by focused sunlight action is shown and new methodic of assessment of forest fire danger caused by this factor is proposed. Geoinformation system that allows estimation of forest fire danger caused by focused sunlight action was created on basis of developed methodic and ArcGIS application. The system combines taxation description of forest areas database, quarterly forestry map and tools of data processing for assessment and classification of plots by level of fire danger caused by focused sunlight action. As a pilot work, estimation of forest fire danger of Timiryazevskoe forestry of Tomsk region was carried out. The system is capable of evaluating probability and classification of fire danger and can be used for early detection and prediction of disasters of natural and technogenic origin.

Keywords: Geoinformation system, ArcGIS, Forest Fire Danger, Focused Sunlight

High level of fire danger in multiple countries of world community became the reason of intensification of researches in development of different methods and systems for forest fire danger prediction and assessment (Kuznetsov, Baranovskiy, 2009). Scientists and specialists of different fields devise approaches to this issue. Forest fires originate from different sources of natural and anthropogenic character. Analysis of possible causes of forest fire initiation allows distinguishing of such factor as focused sunlight action (Kuznetsov, Baranovskiy, 2013). Previously it was shown theoretically (Baranovskiy, 2011) and experimentally (Baranovskiy, 2012) that ignition of forest flammable material caused by concentrated sun radiation is possible. Sun energy can be concentrated by glassware, it’s particles and large blobs of resin of coniferous trees. Physically proven method of forest fire danger assessment by classification forest areas on basis of taxation data was developed as a result of mathematic modelling and numerous experiments of studying processes of forest material ignition by focused sunlight (Baranovskiy, Yankovich, 2014). Classification principle is based on consecutive exclusion of low-flammable areas of forest territory (Baranovskiy, 2013). According to (Baranovskiy, 2013) low-flammable under influence of focused sunlight areas include: road network and water bodies, since there is no forest flammable materials and ignition is impossible; water-saturated swamps, since high moisture prevents inflammation; deciduous and mixed forests, since leaves do not inflame under any conditions of focused sunlight action and in mixed forests needles are distributed as scattered layers which makes ignition impossible. Areas with predominance of young coniferous trees are also attributed to low-flammable, since large resin blobs are highly unlikely. Thus, fire danger caused by focused sunlight action is typical for old coniferous forests (Baranovskiy, 2013). According to method (Baranovskiy, 2013) classification of forest plots can be carried out on basis of forest taxation data. Such data includes description of composition of every plot included in forest quarter. Classification of forest plots allows evaluation of forest fire danger caused by focused sunlight action using following formulae (Baranovskiy, Yankovich, 2014):

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N P(С)  П Nм where P(C) – probability of forest fire occurrence based on vegetation conditions, N(f) – amount of fire endangered plots in quarter, N(t) – total amount of plots in quarter. In different states of the world work on creating and improving methods of forest fire prediction is carried on. Analysis of modern systems of forest fire danger assessment shows that systems, which take into consideration focused sunlight action, do not exist at this moment (Kuznetsov, Baranovskiy, 2009). Canadian, American and European systems of forest fire danger assessment are based on analysis of huge massive of statistical information on large forest territories. They consider anthropogenic load and thunderstorm activity as the reasons of forest fire occurrence. Influence of focused sunlight is not taken into account. This article describes technology of assessment of forest fire danger caused by focused sunlight action and differentiation of various zones of inflammation danger based on analysis of forest taxation data in geoinformation system. Named system of assessment of forest fire danger caused by focused sunlight is based on integrated system of spatial data processing “ArcGIS”. This software is a scalable system for creating, manipulating, integrating and analyzing of geographic data. Since data used is in different formats, the best choice to unite and store such information is geodatabase (GDB). As a pilot research estimation of forest fire danger caused by focused sunlight action of Timiryazevskoe forestry, Tomsk region (Russia) was made. The Timiryazevskiy mechanised timber enterprise of Tomsk management is located by forests in interfluves of two big rivers, the river Ob and the river Tom' in territory of three administrative areas of Tomsk regiona - Tomsk, Shegarskiy and Kozhevnikovskiy areas. Extent of territory of timber enterprise from the North on the South - 64 km, from the West on the East - 50 km. The Timiryazevskiy timber enterprise is formed in 1966 on the basis of the order of the Ministry of forestry of RSFSR № 261. Timber enterprise forests basically are presented by a uniform large forest, except isolated cedar forests near villages - Zorcalcevo, NizhneSechenovo and Gubino. On forest division into districts of Western Siberia the territory of Timiryazevskiy timber enterprise concerns a zone of the Southern taiga (Obsko-Tomsk cedar-pine forest districts). On the agroclimatic division into districts of Tomsk region accepted by Tomsk branch Sibgiprozema the timber enterprise territory is carried to the is moderate-humidified area. Duration of the vegetative period makes 120 days. The most widespread soils in timber enterprise territory are podsolic and dern-podsolic (58%). On mechanical structure prevail sandy (99%). Dern-podsolic soils make 37.3%. In timber enterprise territory almost 20% occupy bogs of which the wet make 35.5%. Prevailing main breed is the pine – 39.6%; the aspen – 26.2% and a birch – 21.2%; the cedar, a larch, a fur-tree and a fir make - 13%. According to methods (Baranovsky, 2013) minimal requirements to assess forest fire danger caused by focused sunlight include following attributive data: quarter and plot numbers, type of trees, age of forest stand. This data may be received from standard taxation books. For displaying of assessment on vegetation conditions information on spatial location is required. Mathematic modeling in geoinformation system is implemented by module of quantitative evaluation of forest fire danger caused by focused sunlight. This module is written in built-in Python programming language of ArcGUS software. Geoinformation system functionality allows (Baranovsky, Yankovich, 2013): Assessment of fire danger of forest plot by taxation data Quantitative evaluation of forest fire danger of quarter caused by focused sunlight action Classification of forest areas on level of fire danger under conditions of influence of concentrated sun radiation on forest flammable material Creation of thematic maps by levels of forest fire danger of the quarter Main task of geoinformation system is quantitative assessment of possibility of forest fire emergence based on taxation data of forest plots, statistical information using remote sensing data. Components of the system:

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Subsystem of data entry and storage – file geodatabase, which includes classes of spatial objects, remote sensing pictures, separate tables. Subsystem of visualization and editing. Subsystem of analysis, which includes set of additional tools: - Mapfiredanger.tbx – addresses tasks of estimation of forest fire probability with the help of taxation descriptions and visualization of resulting information as a map. Input data: Polygonal class of spatial features containing information about location of forest quarters MS Excel tables containing taxation description of forest plots Layer template file Output data is presented by autonomous table with estimation of fire danger probability of each forest quarter (Fig. 1), map of fire danger levels (Fig.2).

Figure 1. Autonomous table with forest fire danger of quarter results

It is necessary to mention that for classified estimation of forest fire danger caused by focused sunlight action total probability interval ranging from 0 to 1 is divided into five classes, which allows assessment of forest fire danger qualitatively.

Figure 2. Schematic map of forest fire danger caused by focused sunlight

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Figure 2 shows that only insignificant part of forest areas is characterized by extreme and high levels of fire danger caused by focused sunlight. In Timiryazevskoe forestry such areas are clystering around Timiryazevskoe town. Developed system accomplishes its main function: classifies forest areas on level of fire danger under conditions of concentrated sun radiation influence on forest flammable material. Application of the system results in improvement of fire danger control, allows selection of optimal locations for video cameras placement. Implementation and operation of geoinformation systems for estimation of forest fire danger caused by focused sunlight does not require significant funding, since data used already exists in standard taxation description.

Acknowledgments Work is executed with financial support of the state contract of the Ministry of Education and Science within FCP «Researches and developments in priority directions of development of a scientifically-technological complex of Russia on 2007 - 2013». The state contract № 14.515.11.0106.

References Kuznetsov G.V., Baranovskiy N.V. Prognoz vozniknoveniya lesnykh pozharov i ikh ekologicheskikh posledstviy. Novosibirsk: Izd-vo SO RAN. [Forecast of forest fire occurrence and their ecological consequences. Novosibirsk: Publishing house of the Siberian Branch of the Russian Academy of Science], 2009. 301 P. Kuznetsov G.V., Baranovskiy N.V. Focused sun's rays and forest fire danger: new concept // Proceedings of SPIE. 2013. Vol. 8890, paper 889011; doi:10.1117/12.2033929 Baranovskiy N.V. Numerical study of the ignition of forest fuel layer focused sunlight action [Chislennoye issledovaniye zazhiganiya sloya lesnogo goryuchego materiala sfokusirovannym potokom solnechnogo izlucheniya] // Butlerov Communications [Butlerovskiye soobshcheniya]. 2011. Vol. 26. N 11. P. 53 – 60. Baranovskiy N.V. Experimental research of forest fuel layer ignition by the focused sunlight [Eksperimentalnye issledovaniya zazhiganiya sloya lesnykh goryuchikh materialov sfokusirovannym solnechnym izlucheniem] // Fire and Explosion Safety - . 2012. Vol. 21. N 9. P. 23 - 27. Baranovskiy N.V., Yankovich E.P. Methodical and technical bases of use of the land taxation data of large forests with a view of a quantitative estimation of forest fire danger. [Metodicheskiye i tekhnicheskiye osnovy ispolzovaniya dannykh nazemnoy taksatsii lesnykh massivov v tselyakh kolichestvennoy otsenki lesnoy pozharnoy opasnosti]// Ekologicheskie sistemy i pribory [Ecological systems and devices]. 2014. N 3. P. 3 - 12. Baranovsky N.V. New approach to an estimation of fire danger of large forests in the conditions of action of the focused sunlight [Novyy podkhod k otsenke pozharnoy opasnosti lesnykh massivov v usloviyakh deystviya sfokusirovannogo solnechnogo izlucheniya] // Fire and Explosion Safety - . 2013. Vol. 22. N 1. P. 24-30. Baranovskiy N.V., Yankovich E.P. Estimation and mapping of the forest fire danger caused by action of the focused sunlight, in geographical information system [Otsenka i kartografirovanie lesnoy pozharnoy opasnosti, obuslovlennoy deystviem sfokusirovannogo solnechnogo izlucheniya, v geograficheskoy informatsionnoy sisteme] // Devices and systems. Management. Control. Diagnostics.[Pribory i sistemy. Upravlenie. Kontrol. Diagnostika]. 2013. N 12. P. 8 – 15.

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SYSTEM OF THE OPERATIONAL FORECASTING OF FLOODS ON THE BASIS OF INTEGRATED USE OF SPACE-GROUND DATA

______Zelentsov V.A. University of Aerospace Instrumentation, St-Petersburg, Russia Sokolov B.V. Institute of Informatics and Automatics of RAS, St-Petersburg, Russia, University ITMO, St-Petersburg, Russia Ziuban A.V., Potryasaev S.A. Institute of Informatics and Automatics of RAS, St-Petersburg, Russia Petukhova J.J. Technical University, Riga, Latvia Krylenko I.N. Moscow State University, Moscow, Russia

1. Introduction Flooding is one of natural disasters that often cause significant economic losses, human and social tragedies. Floods may be caused by different reasons, such as snow and ice melting in rivers in the spring causing freshet; heavy raining in the nearby areas, and wind-generated waves in the areas along the coast and river estuaries. Therefore, flood forecasting and its effective control is always a huge challenge for governments and local authorities (Vasiliev, 2012). A system of monitoring and flood forecasting allows to determine in advance the estimated area of the spill and thus significantly increase the safety and reduce the economic damage caused by the floods. Forecasts of river flow may be developed in the short term, over periods of a few hours or a few days, in the medium term, for several weeks, and in the long term, up to nine months (Potryasaev et al, 2013). An efficient flood alarm system based on a short-term flow forecasting may significantly improve public safety, mitigate social damages and reduce economic losses associated with floods. Recent advances in spatial modeling of floods, modern geo-information systems and remote sensing of the Earth opens up new promising areas of flood control due to significant improvements in the quality of the simulation result.

2. The main approaches to solving the problems of monitoring and flood forecasting Currently there are two types of problems associated with flood – task of flood monitoring and the task of the forecast. Elements of a decision of the prediction problem may be present, but they are based primarily on expert knowledge. Mastered the task of predicting flooding of a river valley can be solved by scientific justification and the creation of new technological solutions. They are based on the methods of mathematical modeling of the studied dangerous hydrological phenomena. Taking into account complex use of the opportunities given by geo-information technologies and methods of remote sensing of a earth's surface, results of modeling are able to afford to achieve necessary detail in the characteristic of spatial variability of a water mode on all length of the river or on its concrete site, and also to provide consumers with necessary operational anticipatory data (Alabyan, 2004; Krylenko, 2005; Kussul et al, 2010). Need of performance of anticipatory modeling on the basis of which to services of emergency situations and the population will provide working data about the level of floods in the range of time from several hours up to several days to a dangerous case, is extremely important. The purposeful solution of a problem of a prediction of the size of a possible flood, assessment of risk of its emergence,

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the preliminary notification and evacuation of the population requires creation of specialized systems of forecasting of floods.

3. Structure of system of forecasting of floods On the basis of the made analysis of experiment of the solution of a considered task in Russia and the countries of the European community it is possible to allocate three main components of system of forecasting of floods (Vasiliev, 2012): 1. System of collecting, preprocessing and transfer of hydro-meteorological information to the forecasting center. 2. The information modeling system which is a basis of functioning of the center of forecasting and turning on two blocks: models of movement of water along the riverbed network and model of distribution of water on a relief. 3. System of visualization of results and their distribution. The basis of the system being access to the results of simulation based on three components: a management application, service bus and intelligent interface. The special role in system of monitoring and anticipatory modeling of floods is played by the service representing the intelligent interface to a set of hydrological models. Its main purpose is the selection of a particular model for calculation of water distribution based on contextual information (accuracy of the original data , the dynamics of flooding for a rapid results, etc.). Classification of the models used, see document «Hand book on good practices for flood mapping in Europe», published on the website of the European Commission (http://ec.europa.eu/environment/water/flood_risk/flood_atlas). The service-focused architecture of system of monitoring and anticipatory forecasting of floods and weak connectivity of program modules peculiar to it allows to transfer developed system to a format of the "cloudy" appendix realized as service (Software as a Service, SaaS), by means of process of virtualization of resources of hardware. To cloud computing essential increase of flexibility of hardware-software realization is a consequence of transition. In particular, the modules realizing algorithms of monitoring, forecasting and decision-making support, can be distributed considerably territorially and structurally, that is, to be carried out at the computing capacities which are not only in the different cities and the countries, but also belonging to the different organizations. Thus synthesized system of monitoring from the point of view of the end user will function as a unit the local solution. Efficiency of the analysis of a situation and taking measures to management of a situation often depends on speed of access to information which is available in the monitoring system. Despite the general trend towards the establishment of mobile clients of various industrial systems, many existing and widely known systems in the world implement a monitoring system "tied" to the situational center user interface.

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Figure 1. Structure of the system access to the results of monitoring and proactive modeling flood through Geo- portal

Applicable service-oriented architecture enables to organize access to operational information monitoring system from almost any point in space and on any device if Internet is available. Unlike the given approach, use of the intelligent interface allows to use any hydrological models, namely their realization. It is required to describe process of transformation of the input and output data of a format of developed system of monitoring of floods in a private format specified model for possibility of use of a concrete program's realization of hydrological model.

4. Practical results. Forecasting and modeling To predict the river water levels for the upcoming period of 12 hours on a daily base, a trend- adjusted exponential smoothing model is applied to observed water hourly level time series. By application of a symbolic regression method, a model for converting the water level into the water flow discharge in m³/s was created. To determine the functional dependency between the water flow discharge in the river and its water level within the forecasting horizon, several scenarios of modeling such as linear, nonlinear regression models and a symbolic regression were experimentally tested (Kussul, 2010). Finally, a symbolic regression-based method implemented in Heuristic Lab optimization framework (Alabyan,

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2004) has been selected. In order to train the model, historical data on water level forecasts for the previous intensive flooding period in March-April 2010 were used. A web service for recalculation of the river water level into the water flow discharge was created providing hourly receipt of the water discharge in the river. In fact, forecasts of the water levels are transformed into forecasts for the water discharge values. The forecasting accuracy of the river water flow discharge was within 95% confidence interval (Fig. 2).

Figure 2. Empirical data versus model-based forecasting results

A LISFLOOD hydrological model is developed to simulate water flows in the riverbed of Daugava and within the channel network by integrating the digital map of the relief of the specified area and obtained hydrological characteristics of the river. To increase an accuracy of flood forecasts, 3D elevation model data with low vertical resolution that does not exceed 1 meter is used. Consequently, based on the water flow discharge data and a digital elevation model, LISFLOOD-based hydrological model is built which allow forecasting inundation territories along the river basin (Zelentsov et al, 2013; Potryasaev et al, 2013). To test and validate the model, the flood simulation results have been compared with available historical data on the flooded zones in the research area in March-April, 2010 (Fig.3). The bounds of the inundation area from simulation experiments are close to historical data, and a forecast error is less that 10%.

Figure 3. Matching between simulation results and historical data from 2010 year

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Then calibration of the model has been performed in two steps: by using the image received from the satellite Radasart-1 to precise the current state of the river channel; and by using data crowdsourcing technology – photos and video materials downloaded by socially active residents of the Daugavpils district through a created web service http://daugava.crowdmap.com on open-source platform. Performed real-time experiments with the developed model allowed achieving about 90% confidence in flood forecasts regarding significant objects which were actually inundated later on (Fig.4). The high forecast precision is achieved through continuous updating of input parameters and arising out short-term forecasts (Zelentsov et al, 2013).

Figure 4. Crowdsourcing: sample experimental results

5. Post-processing and visualisation of modelling results The LISFLOOD-HP hydrological model generates 12-hour forecasts of inundation zones hourly (Fig. 5). The results of flood simulation are presented as a raster map (Fig. 6) with information about the depth of water in the flooded territory which was automatically vectorized to provide compatibility with the external GIS software and storing in the data base archival information about flood dynamics.

Figure 5. Sample flooding forecast in Daugavpils: 26.04.2013, 20:30

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Figure 6. Raster map of the flooded territory

6. Conclusions The advantages and disadvantages of existing approaches to building a system of access to spatial data, The generalized structure of the system access to the results of monitoring and proactive modeling of floods through the geo-portal. The review of the state-of-the-art in river flood flow forecasting and simulation allows defining the most efficient models and tools for water flow forecasting and river simulation. The advanced approach is based on processing and integrated use of heterogeneous data from both space and ground-based information sources The experimental results showed a high forecasting accuracy. The flood monitoring and forecasting system allows significantly improve the social security and decrease economic damage caused by floods.

7. Acknowledgments The research has been supported by the Russian Foundation for Basic Research (grants 13-07- 00279, 13-08-00702, 13-08-01250, 13-07-12120-ofi_m), Department of nanotechnologies and information technologies of the RAS (project 2.11), by ESTLATRUS projects 1.2./ELRI-121/2011/13 «Baltic ICT Platform» and 2.1/ELRI-184/2011/14 «Integrated Intelligent Platform for Monitoring the Cross-Border Natural-Technological Systems» as a part of the Estonia-Latvia-Russia cross-border cooperation Program within European Neighborhood and Partnership instrument 2007-2013, grant 074-U01 supported by Government of Russian Federation).

References Vasiliev O.F. Creation of systems operational forecasting of floods and flood. Bulletin of the Russian Academy of Science. 2012. Volume 82. Number 3. P. 217-251. S. Potryasaev, V. Zelentsov, J. Petuhova, Y. Merkuryev, S. Rogachev. Integrated Space-Ground Floods Monitoring. The 1st International Workshop on Innovation for Logistics, WIN-LOG 2013. November 14-15, 2013, Campora S. Giovanni, Italy. Edited by Francesco Longo, Francesco De Bonis, Yuri Merkuryev, Manfred Gronat. P. 1-5. Alabyan A.M. Information technologies in hydrology. // Misc. Hydro-ecology: Theory and Practice. (Problems of Hydrology and Hydro, vol. 2), M: Geography Faculty of Moscow State University, 2004, p.476-482. Krylenko I.N. Experience of using satellite imagery for computer simulation of flooding areas during floods on the rivers. //Earth from Space - the Most Effective Solutions, II International Conference, Abstracts, Moscow, publ. BINOM, 2005, p.104-106.

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N. Kussul, B. Sokolov, Y. Zyelyk, V. Zelentsov, S. Skakun, A. Shelestov. Disaster Risk Assessment Based on Heterogeneous Geospatial Information. // J. of Automation and Inf. Sci., vol. 42, no. 12, pp. 32-45, 2010. http://ec.europa.eu/environment/water/flood_risk/flood_atlas Zelentsov V.A., Petukhova Y.Y., Potryasayev S.A., Rogatchev S.A. Technology of operative automated forecasting the flood during the spring flood // Thematic issue of the journal "Proceedings of SPIIRAS" Technology and examples of problem solving processes automation and intellectualization of land- aerospace Monitoring, Issue 6 (29), 2013, p. 40-57, Saint-Petersburg, Russia. M.Yu. Okhtilev, V.A. Zelentsov, S. A. Potryasayev, B.V. Sokolov. Concept of pro-active management of difficult technical objects and technologies of its realization//News of Universities. Publ. Priborostroenie. Saint-Petersburg, 2012. – No. 12. P.73-75.

MYOCEAN COPERNICUS MARINE DOWNSCALING AND DOWNSTREAM APPLICATIONS: THE CYPRUS OCEAN FORECASTING SYSTEM AND THE MEDITERRANEAN OIL SPILL PREDICTION SYSTEM

______G. Zodiatis, R. Lardner, H. Radhakrishnan, A. Nikolaides, S. Stylianou, X. Panayidou, on behalf of MyOcean and MEDESS4MS consortiums Oceanography Center, University of Cyprus, Nicosia, Cyprus

One of the EU space programs is the Copernicus (former known as Global Monitoring for Environment and Security-GMES) aiming to setup operational services related to land, ocean, atmosphere, emergency, security and climate changes, providing access to monitoring and forecasting information at regional and global levels and assist citizen’s protection in cases of emergency, search and rescue operations, response in pollution, etc. The Copernicus services are based on earth monitoring data, collected from space, air, ocean, land and providing information in the form of maps, datasets, reports, targeted alerts, etc. MyOcean is the major component of the Copernicus Marine Service. The aim of the MyOcean (2009-2015), is to create a pan-European ocean monitoring and forecasting service and its transition to a long-term operational service. MyOcean involves 59 partners from 28 countries. Using information from both satellite and in situ observations, MyOcean provides analyses and forecasts daily on marine environment. MyOcean data are made available freely and openly to users around the world via the MyOcean web portal http://www.myocean.eu/ in 4 areas of benefits: Maritime Safety, Marine Resources, Coastal and Marine Environment, Weather, Climate and Seasonal Forecasting. Over the last years, MyOcean has successfully demonstrated the use of the service to EU, national agencies, operational centres, research institutes and many others. The Cyprus Oceanography Center developed and operates the Cyprus coastal ocean forecasting and observing system, well known as CYCOFOS (www.oceanography.ucy.ac.cy/cycofos) in cooperation with ocean networks such as MONGOOS and MyOCEAN, within the scopes of Copernicus marine service. CYCOFOS has been operational since early 2002 and is upgraded constantly, following forefront numerical techniques, as for example the new parallel flow model based on MPI technology, as well its coupling with waves. CYCOFOS uses the MyOcean MFS regional products of the Mediterranean for high resolution downscaling. CYCOFOS provides forecasts including currents, temperature and salinity, sea level, significant wave height, swell and tides. The CYCOFOS flow forecasts cover the Eastern Mediterranean and the Levantine Basin, as well as the entire Mediterranean and the Black Sea, regarding the waves and tides forecasts. Through the use of the MyOcean MFS regional and CYCOFOS forecasts and the well established MEDSLIK oil spill model, the response agencies are assisted in the frame of national and sub-regional contingency plans for combating major oil spills. MEDSLIK provides near real time predictions for oil spills with information

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on where the spill will move, the time it will take to reach the coast and how the spill will change through its dispersion. Thanks to the MyOcean regional and downscaled systems, such as CYCOFOS, it was made possible to implement a multi model oil spill prediction service for the entire Mediterranean within the frame of the MEDESS4MS project. The latter is implemented in close cooperation with major agencies in oil spill pollution response at national and regional level. MyOcean, downscaled and downstream systems, such as CYCOFOS and MEDESS4MS (www.medess4ms.eu) will assist the response agencies and not only, to mitigate the permanent risks from possible incidents associated with the heavy maritime traffic in the Mediterranean and with the offshore installations related to the hydrocarbon exploitation, especially in the Levantine Basin.

SOFTWARE AND TECHNOLOGY OF THE OPENED NETWORKED GEOINFORMATION PORTAL AND EDUCATIONAL CONTENT FOR THE PROCESSING OF THE DISTRIBUTED DATA IN EMERGENCY SITUATIONS RESPONSE APPLICATION SCENARIOS

______D. Lubnin Moscow State University of Geodesy and Cartography, Moscow, Russia E. Levin Michigan Technological University, USA A. Grechishev Moscow State University of Geodesy and Cartography, Moscow, Russia

Abstract Paper outlines some major technological solutions that were created during development of the opened networked geoinformation portal and educational content based on deploying remote sensing data and technologies for decision support in natural and manmade emergency situations response application scenarios. Research was performed in research and educational center “Geomonitoring” at Moscow State University of Geodesy and Cartography in Moscow, Russia.

Keywords: geoportal, remote sensing, emergency situations, open-sources geoinformation instrument, geospatial educational content

Introduction Emergency situations may arise as a result of accidents, disasters of various kinds, development of natural hazards and require immediate response, which implies, among other things consolidation of forces and resources of different organizations and agencies. In many countries, to perform related in purpose, time and place of action for the timely receipt of information on the occurrence of emergencies, notification of the population and stakeholders, situation assessment, decision making and disaster management organization set up special services, which are used for such purposes all available resources including GIS solutions and satellite imagery. Various information services operate to support emergency response decision making. In Russia, those include geoportals "Kosmoplan" and "Cascade", as well as supporting internal GIS systems. Sometimes disasters can affect the territory of several coutries, in connection with which international agreements are signed on mutual cooperation for the benefit of pre-emption, emergency response and disaster management, for example UN-SPIDER (United Nations Platform for Space-based Information for Disaster Management and Emergency Response) - UN platform to harness space information in appropriate situations (http://www.un- spider.org/). International programs and domestic emergency response system bring undoubted

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benefits, but often ordinary user or volunteer opportunities available are limited due to fact that most of the operational information is not publically available. Nowadays of advanced technology era virtually every resident of the economically developed country is in possession of the smartphone with the capabilities of a mini-computer and high-speed internet access, that allows anyone to maintain an online mode with a lots of applications. To this end, a logical development is the emergence of so-called crowdsourcing - a promising innovation, implying decision socially significant problems by volunteers. In other words, volunteers in their spare time collect and analyze data, create the content of certain subjects, looking for ways to solve problems, often using the Internet and mobile devices (Future U.S. …, 2013). Interactive scalable networks formed by enthusiasts allow both professionals and a wide range of users to access, share and visualize the information of interest and collaborate on the design, data collection and testing of the results and mobile devices (Heipke, 2010).

Research description and results One of the projects being implemented in innovative scientific-educational center "Geomonitoring" Moscow State University of Geodesy and Cartography, - was challenged in developing of the open network of geoinformation tools and creating an educational context through the use of data and remote sensing technologies to support decision making in natural and manmade emergency situations. The project has been started in 2012 within the framework of state supported university grant (research grant 14.B37.21.1243 supported by the Ministry of Education and Science of the Russian Federation) in collaboration with Michigan Technological University (Michigan Tech, Houghton, MI, USA). Our research goal was established as a study of the possibility to create an open interactive system for training in use of the remotely sensed data for natural and man-made emergency situations response (Лубнин et al, 2013). It was supposed to create the technological opportunities for contacting emergency services and regular non-professional users, who are able and willing to provide operational assistance, including being in the place of emergency occurrence. Knowledge Base of the training data was initially focused on providing assistance to non-professional users in obtaining the necessary up to date information for optimal execution of spatial data transactions. Project was culminated in proof-of-concept and working system prototype. The principle of the open networked system operation assumes that after an emergency user collects all available public information, formed as a set of spatial layers which can be combined. These data are necessary for a preliminary assessment of the situation; identify the hearth and emergency action plan, which steps may be leaving on the terrain with unmanned aerial vehicles (UAVs), reordering fresh satellite and aerial imagery, etc. New incoming data sets are preprocessed by operator, then placed in a database and uploaded to a web site, and also depending on the type become available in the form of web services, organized by the WMS, WFS and WFS-T standard protocols. Simultaneously vector data are available for authorized users to edit in both the website and via mobile app modes through the use of WFS-T. That allows users to make operational changes to complement the overall operational picture of what is happening. System prototype workflow which is realized presently is presented at Figure 1.

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Figure 1. Schematic diagram of the open interactive system for training in use of the remotely sensed data for natural and man-made emergency situations response

Deploying in project of the combined system architecture provides interoperability of GIS data and applications of different user levels - from personal computers to mobile devices. System prototype consist of two major components: 1) GIS server and 2) Web-portal. The first component is based on GeoServer technology, that generates unique portal content and runs it. The second is based on the Leaflet Library that provides the tools and platform to combine spatial data from different sources. For choosing software for the system prototype we took into account a number of requirements including: a) the need to use software with open source support for the latest web technologies and standards for data transmission (eg, WMS, WFS, WFS-T);b) the ability to work via mobile devices. Currently, most of large companies, in particular Google Maps and "Yandex.Maps" are moving their applications with Flash technology for applications and libraries to JavaScript, which lets one to use all the benefits of AJAX and HTML5. Approach of building interactive user interfaces with AJAX web applications involves "background" data exchange from a web server that allows the upgrade information without reloading of the web page completely and thus causes an increase in performance and usability of the application. Another important feature of AJAX is deploying of the CSS, DOM , JavaScript and DHTML technologies to dynamically change the content of the page. Thus, the transition to AJAX allows to save bandwidth, reduce server load and speed up the interface reaction (http://www.adaptivepath.com/ideas/ajax-new-approach-web-applications). Besides the version of Android 4.0 and above do not support Flash technology to the fullest, which entails the use of mobile devices become obsolete and the inability to use all the power of the application. With that said the development of the web portal cartographic component library written in JavaScript, including lead OpenLayers and Leaflet was considered as a base. As the optimal technology for displaying and manipulating geospatial data service was chosen a Leaflet, providing greater stability and operation speed on mobile platforms, which is especially important for the geoportal demanded to work in place of the emergency situations. Due to the small size and modular architecture geoportal platform focuses primarily on performance. During its assembly and compilation one can select modules and extensions required for

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specific tasks. If one need any new tools Leaflet allows additional functionality to build a platform (Лубнин et al, 2013). Interface of the developed geoinformation tool is shown at Figure 2.

Figure 2. Interface of the open interactive system for training in use of the remotely sensed data for natural and man-made emergency situations response

Initial testing of the system prototype mobile version performed using standard Web browsers that supports the Android operating system, and displaying geoportal on mobile devices, a number of problems whose solution was technology involved creating applications for mobile devices, PhoneGap, which is a ready frame (framework) extending the functional language Java (Easily create apps …). GIS-server is an important part of opened geoinformation service. Its functionality is to establish web services with unique content in accordance with internationally approved standards of data transmission. As result of the available software platforms analysis were allocated two technologies most suitable for system implementing: GeoServer and MapServer, differing from other multiplatform support technologies by more file formats, databases, and Web services standards support, as well as lower license restrictions (http://www.geoserver.org; http://www.mapserver.org). The final choice was made in favor of GeoServer software platform, because this product allows to create Web services of WFS-T standard without any additional software (in MapServer deploying TinyOWS). Useful advantage that is included in GeoServer is visual control of system configuration files and projects descriptions. For stable and efficient operation of the web site and a GIS server one need to use a web server, which will process the data via HTTP and maintain the necessary Internet services that provide full functionality of the network segment on which the project is based. From our system the perspective were considered optimal web servers, mostly able to generate and process a variety of dynamic content, while providing the user with access to the already created static material. To this end Microsoft Internet Information Server and Apache HTTP Server were selected. Since our server installed with licensed Microsoft software the beginning of proprietary MS IIS is not a drawback, besides the deployment and administration of MS IIS convenient for the personal and performs faster. All MS IIS advantages are relevant for a family of operating systems All the benefits of it are relevant for the All the benefits of it are relevant for a family of operating systems Windows

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operating systems family. As a result a choice of MS IIS for Windows is obvious. When one needs to deploy the platform different than Windows, the most rational choice seems to be Apache HTTP Server. To accelerate and optimize work with spatial information, it was decided to use spatial database and management tools. Whereas traditional databases can store and process only the numeric and symbolic information, the spatial database can store holistic spatial object that combines traditional descriptive or attribute data types and topology (Shekhar, Chawla, 2002). Controls for spatial databases often offered as additional features and extensions to traditional DBMS, among which the most popular are PostgreSQL, Microsoft SQL Server, MySQL, Oracle, IBM DB2, but not all of them are freely available and open source. For example, such IT-market giants like Microsoft and Oracle, see the main purpose of making a profit from the sales of the DBMS, so the source code is closed, and the express-versions are significantly limited in scope (http://msdn.microsoft.com/ru-ru/sqlserver; http://www.oracle.com/ru/products/database/overview/index.html). Initially “free” relational database management system MySQL is also not suitable for our project because since January 2010, its development and dissemination has undertaken by Oracle which set its own licensing procedure (http://www.oracle.com/ru/products/mysql/index.html). It is worth noting that despite the serious limitations in free versions of their DBMS is quite possible to use - it all depends on the amount of used information. Most of the limitations are associated with the scaling of the system and the database access speed. Thus, due to set of all criteria for our project it was chosen free object-relational database management system PostgreSQL with extension for PostGIS spatial data management. Knowledge base is necessary for the comprehensive training in GIS and remote sensing data processing, continuous improvement of personnel skills and abilities, as well as to support a wide range of users who rely on these skills during emergencies and elimination of their consequences. Knowledge Base is a key element of specialized trainings.

Conclusion and future research Appending a knowledge base is a permanent ongoing process that is caused by the emergence of new data and their processing algorithms. In addition, each geographic area has its own characteristics and development of emergency which are problematic for taking into account in a short time. Therefore, for our project it was created a test-unit of the knowledge base and database sampling floods that occurred in Russia during 2012-2013. The test unit includes: archived satellite imagery, various thematic maps, UAV imagery, links to public GIS products and trial versions of remote sensing data processing, guidelines on the use of specialized software, a description of the fundamentals of primary and thematic processing of satellite and other remote data sensing, news archives for specific emergencies, photos and videos, descriptions of typical emergencies and behavior in the event of such, lecture materials, etc. Presently research described in this paper continues on the research project as part of a Moscow State University of Geodesy and Cartography fundamental research entitled "Development of a method of integration of multispectral remote sensing data from the optical range radar data to extract complex geospatial information for monitoring disaster area" (Grant 10.9220.2014/04.02.2014 ).

Acknowledgements This study is supported by The Ministry of Education and Science of Russian Federation under research grants 14.B37.21.1243 and 10.9220.2014.

References Future U.S. Workforce for Geospatial Intelligence. – Washington: Academic press book, 2013. http://www.nap.edu/catalog.php?record_id=18265. Heipke C. Crowdsourcing geospatial data // ISPRS Journal of Photogrammetry and Remote Sensing. – 2010. – № 65(6). – P. 550-557. Лубнин Д.С., Кострюков И.С., Стоволосов Е.В., Левин Е.Л. Открытые платформы для публикации геоинформации в Интернете и представление трехмерных пространственных

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данных на мобильных устройствах // Мат-лы междунар. конф. «Экология. Экономика. Информатика». – Ростов-на-Дону: Изд-во ЮФУ, 2013. –Т. 2. – С. 278–286. (In Russian) Garrett J.J. Ajax: A New Approach to Web Applications. http://www.adaptivepath.com/ideas/ajax-new-approach-web-applications. Easily create apps using the web technologies you know and love: HTML, CSS, and JavaScript. – http://phonegap.com. http://www.geoserver.org. http://www.mapserver.org. Shekhar, S., Chawla, S.: Spatial Databases: A Tour, Prentice Hall, 2002/2003, p. 262, ISBN 0-13- 017480-7 http://msdn.microsoft.com/ru-ru/sqlserver. http://www.oracle.com/ru/products/database/overview/index.html . http://www.oracle.com/ru/products/mysql/index.html. http://www.un-spider.org/.

FORECASTING OF DANGEROUS DON DELTA FLOODING: PRELIMINARY RESULTS

______I.A. Tretyakova, A.L. Chikin Institute of Arid Zones of the Southern Scientific Center of RAS, Rostov-on-Don, Russia. [email protected]

Abstract Currently, mathematical models are widely used to deal with hydrological problems. This paper presents the preliminary results of the flooding simulation in the Don Delta caused by wave surges.

Keywords: wind surge, Don Delta, water level, mathematical modeling

Delta Don is characterized by the particular complexity of hydrological processes running there. The determining influence on the level mode of the delta and the seaside provide surge phenomena. Winds from the western component cause surges from the Taganrog Bay and sea level rise on the Don River, winds from the eastern component cause eviction and water level decrease. Floods caused by surge waves do huge economic damage to the region. Thus, on April 12, 1997 a rise of water level on the Don River was induced by a strong and continuous south-west wind with the speed ranging from 15 to 43 m/s. The amount of water level rise exceeded by almost three meters average annual level. A mass evacuation of people was carried out. On the night of March 1, 2005 the level again exceeded 2.5 m point again. The rise was induced by the sustained impact of western and southwestern wind with 8 to 16 m/s speed. In the Azov district 20 transmission towers were shot down by ice. Kilometers of coastal fortifications were destroyed; dams washed away, roads damaged, wooden piers and bridges destructed. Pond farms suffered damage as well. Cattle died: horses, cows. On the night of September 30 to October 1, 2010 level rise of more than 3 m was caused by a sharp change of the south-east wind in the south-west, whose speed reached 25 m/s. Donskoy farm, coastal streets of Kagalnik village, Pavloochakovskaya and Chumburskaya spurs were flooded. On the 24-25 March 2013 with a strong south-westerly wind of 10-15 m/s, reaching 25 m/s at peaks the Don River level rose to by 2.8 m resulting in 20 flooded settlements and more than 2400 households. Material damage to the population and economy of the region was worth more than 0.5 billion rubles (Extreme flooding …, 2014). Extreme surges with marks higher than 2.5 meters above sea level occur on average once every ten years. Surges reaching approx. 2 m above sea level occur almost every year.

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Due to the rather frequent recurrence of floods caused by surges there is an obvious need for short-term forecasts of water level rises. For that purpose a mathematical model of the Sea of Azov is used. The proposed approach allows for finite difference methods using a uniform rectangular grid to be used without preliminary conversion of the computational domain. Advantages of this model are its simple mathematical description, as well as easy numerical implementation (Chikin, 2005). The initial data for the level calculation of the Don Delta is the bathymetry of the Sea of Azov. As meteorological data wind situation in Taganrog is used. Information on wind speed and direction were obtained from public sources including web sites of FGBI "RIHMI-WDC" and http://rp5.ru for March 2014. Time resolution is 3 hours. To verify the model, data on level observations for March 2014 in the delta of the Don were used from an automatic level measurement unit located in Donskoy farm. This information is a level measurement recorded every 10 minutes and is available on the website "Flood Monitoring in the Krasnodar region". Calculations were performed in the center of HPC YUGINFO SFU.

Figure 1. Levels at the Donskoy farm for 13-th to 19-th March, 2014

In Figure 1, a wide-dashed line indicates general calculation based on weather station data, a short-dashed line denotes the result based on wind forecast data, the solid line shows the level taken as a control unit from the automatic station in the Donskoy farm. Calculation results are presented for the period from 13 to 19 March, 2014. During this period, an overrun of the adverse level could be observed. One can see that the calculated and natural curves coincide, which suggests possible application of this method for short-term forecasts of extreme flooding caused by wave surges. In the future, in order to improve accuracy of the results it is planned to set wind field over the Sea of Azov as an input data. There are two possible options: 1 - interpolation based on the original data from eight weather stations around the Sea of Azov; 2 - setting the wind field directly. The simulation results can be applied in systems for short-term forecasts of hydrometeorological risks.

References Extreme flooding of Don delta in spring of 2013: Chronology, formation conditions and consequences / G.G. Matishov, A.L. Chikin, S.V. Berdnikov [etc.] // Newsletter of the Southern Scientific Center of RAS / Ch. Ed Acad.G.G. Matishov. - Moscow: Nauka, 2014. - Vol.10, Nr, 1. - p. 17- 24. Chikin A.L. On one method of calculating the flow parameters in reservoirs with high depth heterogeneity // Water Resources/ Ch. Ed.Khublaryan M.G.Moscow: Nauka, 2005. - V.32, Nr. 1. - p.55- 60.

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BIODIVERSITY AND MARINE ENVIRONMENTAL MANAGEMENT

DEVELOPMENT AND PERSPECTIVES OF SOUTH OF RUSSIAN FAR EAST WETLANDS GEOGRAPHIC DATABASE USE FOR PROBLEM SOLVING OF STRATEGIC PLAN FOR CONSERVATION OF BIODIVERSITY

______V.N. Bocharnikov, S. Krasnopeev Pacific Institute of Geography, Far Eastern Branch RAS, Vladivostok, Russia. [email protected]

Introduction Ramsar Convention (http://www.ramsar.org), which provides the framework for national action and international cooperation for the conservation and wise use of wetlands and their resources represents one of the most authoritative agreements in the sphere of global nature protection policy. Last decisions of the Conference of the Contracting Parties and cooperative actions which are planned in the course of co-operation with Convention of Biological Diversity fix attention of the Parties which take part in the agreement at the question of inventory, monitoring, research and potection of wetlands. The most actual methods are GIS technologies and remote sensing data which developed successfully on global level but coordinated weekly and unsufficiant for realization on national and regional level (Sherbinin & Gin, 2001). We consider that existing experience is interesting for projects carried by Convention of biodiversity. It is known that the main working document which has adopted by the Parties of the Convention is Strategic Plan for Biodiversity on 2011-2020. The mission is ‘’Take effective and urgent action to halt the loss of biodiversity in order to ensure that by 2020 ecosystems are resilient and continue to provide essential services, thereby securing the planet’s variety of life, and contributing to human well-being, and poverty eradication”. Thematic programs include Agricultural Biodiversity, Dry and Sub-humid ands Biodiversity Forest Biodiversity, Inland Waters Biodiversity, Island Biodiversity, Marine and Coastal Biodiversity and Mountain Biodiversity. In the framework of the Convention 20 key targets for conservation and sustainable nature use carried at IT (?) were minimized to 5 strategic aims. These targets provide aspiration for achievements the aims on global level and include flexible structure for formation list of national and regional targets. The Parties are proposed to make their own targets in the framework of this flexible structure.

Material and Methods Geographical database of wetland of South of Far East of Russia was created by authors in 2004- 2005. This work was part of national project of preparation of inventory reports for condition of wetland in Russia, in particular, under supervision of V. Bocharnikov collective work was undertaken for preparation of standardized by criteria of Ramsar Convention descriptions of important wetlands in the frames of administrative area of Primorsky, Khabarovky krai, Sakhalinskaya, Amurskaya and Evreyskaya autonomous oblast (Wetlands…, 2005). In this project all technical work which enabled experts of wetland all necessary primary material (topographic and thematic map, satellite images) was made at the basis of GIS technologies. For organization collective work possibility of change of materials which were connected with preparation, editing texts and cartographic document was enabled for all described objects. The frameworks, area and length of coastline of wetlands were specified for all objects by GIS technologies.

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Special attention was paid to classification of wetlands for criteria which was established by Ramsar convention. Calculation of areas of wetlands defined by experts at the basis of analysis of topographic maps and interpretation of remote sensing data was made. It was impossible to eliminate remarkable qualities differences in many cases which were explored. In this context special cartograms as basis for further research and monitoring were constructed. Great work was done for estimation of structure, number and distribution of groups of waterfowls and water birds. The places of concentration of waterfowls in the period of seasonal migration, species composition and number of colonial nesting birds were defined as a result of synthesis of all date and calculation of GIS technologies. The work carried out permits gives not only good representation of biodiversity of coastal and marine and internal freshwater ecosystems but actual place of the frontiers of Far Eastern eco-regional complex which had proposed was precised (Bocharnikov et al., 2005).

Results As a basis for this work we can mention that active efforts for elaboration and development key elements for spatial data infrastructure which are based technologically at interoperable geospatial web-services were applied by scientists from Institute of Geography of Russian Academy of Science since 2006. This approach permits realize the conception of “open access” to spatial data. Then we can consider that implementation of open data is a rapidly emerging trend enables execution of requirement for data used for scientific analysis: • data transparency regarding data collection methods, data semantics, and processing methods; • verifiability; • unification of observations; • Data Sharing & Cross- Disciplinary Studies; • Longitudinal studies/Community standards will help ensure long-term consistency of data representation; • Maximizing value. We consider that these technologic decisions let satisfy growing demand of Society to discover, access and use Earth Information, in a seamless and effective way. To make these possibilities available for wide range of users, client web application was created in the institute - http://gis.dvo.ru/web/ , prototype of geoportal http://gis.dvo.ru/portal/ which gives access to catalog of meta-data (http://gis.dvo.ru/geonetwork) and Web mapping services, to services to Web-cartography. Map Servers provide the display-ready information to the Client. We should mention that functionality was realized at first stage which enables decision of group of tasks which can be referred to the category “business for Customer” namely at the base of open source software “Geoserver” web-service for publication of was evolved. Map Server (http://gis.dvo.ru:8080/geoserver/wms) provide the display-ready information to the Client or Clients. It produce a map as a picture, as a series of graphical elements, or as a packaged set of geographic feature data. Resource of a core spatial data was formed and forming including actual remote sensing data Landsat 8 and Canopus-V No.1. Access to web-services is available by specialized (?) which can be Desktop software or clients Web applications. Functionality was successfully realized at second stage of the tasks can be attribute to the category Business to Business, namely transactional Web-service of publication of spatial features (spatial features) OpenGIS® Web Feature Service transactional (WFS, WFS-T) - (http://gis.dvo.ru:8080/geoserver/wfs?). As a result user gives possibility to edit the properties of spatial features (geometric and attributives), create new objects, work collectively on projects, make analysis.

Discussion One of the main targets of the Convention where wide use of GIS technologies and remote sensing data is required Target 14: By 2020, ecosystems that provide essential services, including

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services related to water, and contribute to health, livelihoods and well-being, are restored and safeguarded, taking into account the needs of women, indigenous and local communities, and the poor and vulnerable. The following indicators are relevant to Target 14 and are ready for use at a global level. Most relevant indicators: • Population trends and extinction risk trends of species that provide ecosystem services. • Trends in benefits that humans derive from selected ecosystem services. • Trends in proportion of the population using improved water services. • Trends in proportion of total freshwater resources used. Other relevant indicators: • Trends in abundance of selected species. • Trends in extinction risk of species. • Trends in coverage of protected areas. • Trends in protected area condition and/or management effectiveness including more equitable management. • Trends in representative coverage of protected areas and other area based approaches, including sites of particular importance for biodiversity, and of terrestrial, marine and inland water systems. • Status and trends in extent and condition of habitats that provide carbon storage. • Extinction risk trends of habitat dependent species in each major habitat type. • Trends in extent of selected biomes, ecosystems and habitats. • Extinction risk trends of coral and reef fish. • Population trends of habitat dependent species in each major habitat type. • Trends in incidence of hypoxic zones and algal blooms. • Trends in water quality in aquatic ecosystems. • Trends in population and extinction risk of utilized species, including species in trade. • Trends in proportion of utilized stocks outside safe biological limits. Monitoring progress towards this target entails assessing the status of ecosystems as well as trends in the services they provide. Status and trend information exists for many habitat types as well as for certain types of ecosystem services, in particular provisioning services. The types of ecosystem services that need to be monitored will likely vary from country to country and change over time as a result of societal needs. However, some ecosystem services, such as the availability of clean water and adequate food, will be of universal concern. These positions are considered as scientific problems: While relatively good information on provisioning services, particularly those which are marketable, exists, there is relatively little information on trends in the delivery of regulating, cultural and supporting services. Our current inability to monitor the delivery of these types of ecosystem services at the global level represents a major gap. More detailed information on the links between the condition of ecosystems and human well-being as well as between ecosystems and the provision of water would assist with monitoring progress towards this target. Furthermore, much of the information that is currently available is from developed countries. Efforts to improve the geographic coverage of the existing data would enhance our ability to monitor progress towards the attainment of this target. Our experience and existing geographical base let make these conclusions: - it is reasonable implementation of measures at the basis of geo-spatial web-services realizing the conception of unlimited access to geo-spatial data for organization of effective collective work of experts and specialists which work with complex problems of inventory, monitoring and research of biodiversity, realization of ecosystem services; - elaboration, coordination and use of various schemes of classification which permit characterize many geographical, biotic, social, economic characteristics of regional ecosystems in a flexible manner, adequately is necessary; - use of remote sensing data permit make monitoring and inventory works on-line.

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Figure 1. Russian Far East Wetland types area and its density

Figure 2. The most important Far Eastern Wetlands identified by topographic maps, using Landsat and expert opinions

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Conclusion We should aware of that we live in the time of geo-spatial revolution when global informational infrastructure for public and industrial activity which reflects our understanding of natural, social and cultural dynamics in the all regions of the world speeds up. It is necessary to mention that sustainable progressive advance is observed in the direction of active implantation of new technologies of governance of spatial data to the regional daily life. It was evidently shown in the first reports of third millennium when mobile technologies started to spread on a massive scale, GPS mapping and various cartographic services on-line. Far East of Russia is not exception, accelerates widening number instruments and applications which based at GIS systems can enable sustainable and safe present and help to feel confident about the future.

References Alex de Sherbinin and Chandra Giri. 2001. Remote Sensing in Support of Multilateral Environmental Agreements: What Have We Learned from Pilot Applications? // Remote Sensing in Support of Multilateral Environmental Agreements. Prepared for presentation at the Open Meeting of the Human Dimensions of Global Environmental Change Research Community, Rio de Janeiro, 6-8 October 2001. http://sedac.ciesin.org/rs-treaties/adesherbinin_riopaper.pdf. Bocharnikov V.N., Martynenko A.B., Gluschenko Yu.N. et al. The biodiversity of the Russian Far East Ecoregion Complex. Ed. P.G. Gorovoi. Vladivostok: Apelsin, 2004. 292 pp. (In Russian) Wetlands in Southern Far-Eastern Russia. Wetlands in Russia. Vol. 5. Ed. V.N. Bocharnikov. Wetlands International, Moscow. 220 pp. (In Russian) Lance McKee. “Geospatial Data for Sustainability: Ensuring Universal Access”. OGC, January 2010. Presentation at George Perkins Marsh Institute, Clark University. Available at: http://portal.opengeospatial.org/files/?artifact_id=37254 Krasnopeev S.M. Otyt razvertyvaniya kluchvykh elementov IPD na baze veb-slughb v TIG DVO RAN // Infrastructura sputnikovykh geoinformattsionnykh resursov I ikh integratsia. Sb. Nauch. Statei pod red. M.A. Popova I E.B. Kudasheva. Kiev: Karbon-Service, 2013. P. 18-27. (In Russian).

PRELIMINARY STUDY OΝ THE ΜΑRINE BIODIVERSITY OF THE COASTAL AREA OF CYPRUS AND AN INVESTIGATION OF SEA SURFACE TEMPERATURE CHANGES IN THE LEVANTINE BASIN

______G. Fyttis, Y. Samuel-Rhoads Oceanography Centre, University of Cyprus, Nicosia, Cyprus Irinios Yiannoukos Department of Marine Sciences, University of the Aegean, Mytilene, Lesvos island, Greece Leda Liyue Cai School of Ocean Sciences, Bangor Universit, Menai Bridge, Anglesey, UK G. Zodiatis Oceanography Centre, University of Cyprus, Nicosia, Cyprus

Cyprus is the closest Mediterranean island to the Suez Canal, and has been extensively affected by Lessepsian migrants, especially during the last decades. Some of the nonindigenous species have already successfully established themselves in the coastal waters of Cyprus. This establishment maybe facilitated by the increases in Sea Surface Temperatures, which have been recorded by satellites, to occur in the Mediterranean Sea. This study aims to observe and record marine biodiversity, with an

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emphasis on non-indigenous species, in the coastal area of Cyprus, as well as investigate the increase in SST due to climatic changes in the Levantine with the use of satellite remote sensing data. Six stations were studied in July 2013, following the CIESM Tropical Signals project methodology, three in Ayia Napa (Southeast coast) and three in Larnaca (South coast). Each station was divided into two zones parallel to the shoreline (100 m each) with 100m distance between them. Timed observations of 15 minutes in the two 100m zones, were made both in the intertidal (the surf and swash zone, from 0 to 0.5 m depth) and the subtidal zones (shallow waters, 0 to 3m depth) by snorkeling. Preliminary results showed a total number of 86 taxa of which 13 were non-indigenous (one macrophyte, six invertebrates and six fish). Ayia Napa presented higher total abundance and slightly higher percentage of non- indigenous species (17.74%) in comparison with Larnaca (15.51%). All non-indigenous species that were recorded are Lessepsian migrants. Analyses of annual mean satellite sea surface temperature (SST) data of the NOAA/NASA Advanced Very High Resolution Radiometers (AVHRR) and processed by the SST Pathfinder program, indicate that over the last years a general warming has occurred over the Levantine Basin, and occurred at an average rate of approximately 0.06°C/year. The SST variability is characterized by a broad, basin-wide warming (EOF mode 1) occurring at both seasonal and interannual time scales, and a weaker dipole pattern that fluctuates at similar time scales (EOF mode 2). The monitoring of the abundance of non-indigenous species and the study of their impact on the native marine biodiversity is necessary for the management and protection of the marine environment of Cyprus. Further research is needed to identify the extent of the impact of each non- indigenous species to the native marine biodiversity and how their increasing numbers will affect the marine trophic web, as well as the correlation between SST increases and the impact of non-indigenous species in the region.

ASSESSMENT OF THE RELATIONSHIP BETWEEN THE PLANTS BIOMASS AND NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) FOR EXAMPLE, ARID STEPPE LANDSCAPES

______L.D. Nemtseva Institute of Arid Zones of the Southern Scientific Centre RAS, Rostov-on-Don, Russia. [email protected]

Abstract This paper discusses the relationships for plants biomass vs. NDVI in vegetation cover arid steppe landscapes. The Normalized Difference Vegetation Index (NDVI) was tested for evaluating plants biomass of in the wild landscape coastal territory of Lake Manych-Gudilo. The values of NDVI were recalculated of the relative values in the absolute values of biomass (g/m²).

Keywords: plants biomass; arid steppe landscapes; Normalized Difference Vegetation Index

Introduction Currently, plant and soil cover arid steppe of coastal territory of Lake Manych-Gudilo experiencing strong anthropogenic pressure, which leads to the impoverishment of the species composition and reduce the productivity of rangelands. In this study, approach has been developed to estimate plants biomass using remotely sensed data. The Normalized Difference Vegetation Index (NDVI) was tested for evaluating plants biomass of in the wild landscape coastal territory of Lake Manych-Gudilo. The results showed great potential for estimating plants biomass. The objective of this study is to establish relationships for plants biomass vs. NDVI in vegetation cover arid steppe landscapes.

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Materials and methods During the growing season in 2009 (16-22 April, 13-20 May, 15-18 June), we used the established research center "Manych", which is part of the Southern Scientific Center of Russian Academy of Sciences (SSC RAS). The research facility "Manych" is located in the place Manych, Orlovskiy district of Rostov region. During the field work was laid down 40 study sites. They are all approximately 100 m2. At each site were conducted geobotanic study, namely, described the species composition of vegetation, and defined the fraction of vegetative cover in percent, length of stand, obtained the locations for sets using the Global Positioning System (GPS). Within each of 40 study sites, 2-3 small plot areas (25 cm × 25 cm) were established, which represented all major occurrences of species of plant within each large sit and collected the experimental cut of sample plants. Sample plants were transported to the laboratory, in the research facility "Manych", where they were weighed. Works were carried out using standard guidelines for field geobotany (Gorbachev, 1974). Date images coincide with periods of the expeditions. Software products were used ArcGIS and ENVI. In work were used multispectral satellite images of the Landsat 5 (TM) and P6 AWiFS. Normalized Difference Vegetation Index (NDVI) was calculated by the software ENVI according to satellite images. Normalized Difference Vegetation Index (NDVI) was first described by Rouse J.W. in 1973 (Rouse et al, 1978). Gitelson A.A. et al. (2006, 2014) found strong relationships between gross primary production, different vegetation indices, and canopy chlorophyll content in maize and soybean (Gitelson et al, 2006; Gitelson et al, 2014; Rundquist et al, 2014). Chlorophylls are vital pigments for photosynthesis of plant. Absorption and reflect by chlorophyll provides the necessary link between remote sensing observations and canopy state variables that are used as indicators of photosynthetic activity. Thus, NDVI is based on the principle that the pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 400 to 700 nm) for use in photosynthesis, on the other hand, strongly reflects near-infrared light (from 700 to 1100 nm). The NDVI Normalized Difference Vegetation Index was calculated as:

NDVI= (+)/( - ) (Gorbachev, 1974). Where, are reflectances in the ranges 770 to 900 nm (near infrared), 620 to 690 (red) nm. Calculations of NDVI for each pixel of image always result in a number that ranges from -1 to +1; however, no green leaves give a value close to 0. A 0 means no vegetation and close to +1 (0.8 - 0.9) indicates the highest possible density of green leaves (Knijnikov ey al, 2004). By so doing, as a result of calculations NDVI, were obtained index image. NDVI values do not give absolute quantitative indicators, and only the relative evaluation of the properties of vegetation cover. In this regard, their interpretation was carried out with the involvement of field data and conversion into absolute values (g/m2). The next step was the mapping study sites in the form of a vector point layer on the map in GIS, in accordance with their coordinates. Then, the index of the image NDVI values were extracted using the software ArcGIS (Spatial Analyst Tools) vector layer-studied sites. As a result, point plots were built relationships vegetation index NDVI and biomass plants.

Results and discussion As can be seen in Figure 1, the data for different months (April, May, June) are combined into separate point clouds. This involves a change of aspects of steppe and chlorophyll content in vegetation. In this study, field ground-truth observations were conducted during periods of the growing season, which correspond to phenological aspects of the arid steppe on the classification of V.V. Alekhine (1951) (Alekhine, Rastitelnost, 1951): April – middling vernal aspect, May – serotinal aspect, June – middling summer aspect.

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Figure 1. Relationship between the plants biomass and normalized difference vegetation index (NDVI)

During the work has been established strong linear relationship between canopy chlorophyll content and NDVI. However, this relationship exhibits hysteresis due to the leaf chlorophyll content varying over a growing season. According to data for the month of April (middling vernal aspect) was found relationship between biomass and NDVI with the highest coefficient of determination (R2) in excess of 0.75. In May (serotinal aspect), the coefficient of determination (R2) was 0.73. In June (middling summer aspect) value of the coefficient of determination (R2) was negligible. The relationship between NDVI and biomass was decreased from April to June because the bright green plants content high chlorophyll during vegetative growth in spring and plants with lower chlorophyll contents during summer phenological stages. Vegetation biomasses (g/m2) were calculated from the regression formula and thus NDVI values were interpreted in different phenological phases (Table 1). As can be seen in Table 1, that NDVI and total chlorophyll values change gradually during the growing season (Nemtseva, Bespalova, 2011).

Table 1. Conformity of relative values of NDVI and absolute values of biomass vegetation (g/m2) May, April, biomass, June, biomass, Average values NDVI biomass, g/m2 g/m2 biomass, g/m2 g/m2 0.3 - - 250 250 0.35 - - 350 350 0.40 150 - 600 400 0.45 250 400 750 450 0.50 350 900 900 700 0.55 450 1400 1100 900 0.60 550 1900 1250 1200 0.65 650 2400 1400 1500 0.70 750 2900 1600 1800 0.75 850 3400 1750 2000

Summary In this paper, remote sensing method used to estimate the biomass of vegetation in the territory of lowland steppe lake basin Manych - Gudilo. Identified periods (April and May), the most favorable for biomass estimation using remote sensing data. A distinctive feature of this approach is that it not only facilitates research but also yields useful products for agriculture. The values of NDVI were recalculated of the relative values in the absolute values of biomass (g/m2).

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References Gorbachev, B.N. Rastitelnost i estestvennie kormovie ugodia Rostovskoi oblasti (Vegetation and natural forage lands of the Rostov region) / B.N. Gorbachev. - Rostov-on-Don: Rostov Publishing House, 1974. - 152. Rouse, J.W., Jr., R.H. Haas, J.A. Schell, and D.W. Deering. 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Prog. Rep. RSC 1978-1. Remote Sensing Cent., Texas A&M Univ., College Station. Gitelson, A. A., A. Viña, S. B. Verma, D. C. Rundquist, T. J. Arkebauer, G. Keydan, B. Leavitt, V. Ciganda, G. G. Burba, and A. E. Suyker (2006), Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity, J. Geophys. Res., 111, D08S11, doi:10.1029/2005JD006017. Gitelson, A. A., Y. Peng, T. J. Arkebauer, J. Schepers. (2014). Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production, Remote Sensing of Environment, 144: 65–72. Rundquist, D. C., A. A. Gitelson, B. Leavitt, A. Zygielbaum, R. Perk, G. P., Keydan. (2014). Elements of an Integrated Phenotyping System for Monitoring Crop Status at Canopy Level, Agronomy 2014, 4(1), 108-123; doi:10.3390/agronomy4010108. Knijnikov Y.F. Aerokosmicheskie metody geograficheskih issledovaniy (Aerospace methods of geographical research) / Y. F. Knijnikov, V.I. Kravtsova, O.V. Tutubalina. – M.: Akademia, 2004. – 336 p. Alekhine V.V. Rastitelnost SSSR v osnovnih zonah (Vegetation in the main areas of the USSR) / V.V. Alekhine. - Moscow: Publishing House of the "Sovietskaya nauka", 1951. - 512. Nemtseva L.D., Bespalova L.A. (2011) Study of the interrelationship between the above-ground biomass of vegetation and vegetation index in the valley Manych-Gudilo // in Proc. Int. Sci. Conf. "Study and Exploration of Marine and Land Ecosystems in Arctic and Antarctic", Rostov-on-Don, July 6-11, 2011 (Yuzhn. Nauchn. Tsentra Ross. Akad. Nauk, Rostov-on-Don, 2011), pp. 421-424.

SIMPLE INDIVIDUAL-BASED MODEL FOR PURSUIT-EVASION IN PREDATOR- PREY SYSTEM

______Yu. Tyutyunov Institute of Arid Zones, Southern Scientific Center of the Russian Academy of Sciences, Vorovich Research Institute of Mechanics and Applied Mathematics, Southern Federal University, Chair of global information systems, Faculty of High Technologies, Southern Federal University, Rostov-on-Don, Russia. [email protected] L. Titova Vorovich Research Institute of Mechanics and Applied Mathematics, Southern Federal University, Rostov-on-Don, Russia. [email protected]

Abstract We present the results of simulations with an individual-based model describing spatial movements of animals in predator–prey system within a closed rectangular habitat. Movement of each individual animal includes random and directed components. The latest one is determined by the gradient of cue field that is the sum of cues issued by each individual of the antagonistic species. That is, the cue field of all predators is the repelling stimulus for prey, while the cue field of all prey individuals is assumed to be attractive stimulus for predators. The movements of each animal in the model are stimulated by local conditions only, so any collective behaviour emerges owing to self-organization. It is shown that the proposed pursuit-evasion model easily mimics spatial patterns observed in natural predator-prey communities of fish species.

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Keywords: simulation, animal spatial behaviour, spatial movements, spatial pattern, spatial dynamics, spatial heterogeneity, spatial aggregation, interactive behaviour, attraction, repulsion, self- organisation, herd, swarming, schooling

Introduction Spatial heterogeneity is considered as an intrinsic property of any natural ecosystem. It is observed in different biotopes and can be caused by various factors (Grünbaum, Okubo, 1994; Camazine et al., 2003). These factors include environmental gradients influencing ecosystem productivity directly (Turchin, 1998; Camazine et al., 2003), temporal local variations of population reproduction due to periodic or irregularly oscillating external forces (Dombrovsky, Markman, 1983; Medvinsky et al., 2001), periodic boundary conditions (Venturino, Medvinsky, 2001), spatial instability induced by interplay between local kinetics and spatial fluxes of population densities in biological communities (Svirezhev, 1987; Okubo, Levin, 2001; Murray 2003; Petrovskii, Li, 2005), etc. In our opinion, the most interesting case of population heterogeneity is patchiness caused by spatial behaviour of animals. Here we will consider self-organized spatial structures that emerge due to pursuit-evasion activity of interacting prey populations of predator and prey species.

Biological phenomena of animal interactive behaviour Studying collective movements in fish populations, Pitcher and Parrish (1993) classified the behaviour patterns and described several typical examples of observed spatial configurations (Fig. 1), caused, in particular, by mutual attraction-repulsion interactions of prey and predators (see also Parrish et al., 2002; Ritz, 1991; Lee et al., 2006).

Figure 1. Typical behavioural patterns observed in fish populations (Pitcher, Parrish, 1993)

It is important noting that in nature, pursuit-evasion movements occur at much faster temporal scale than birth/death processes, and even faster than consumption of prey by predators. Indeed, the predation efficiency (their ability to capture prey after an attack) may be quite low for some species, especially if the prey exhibit active escaping behaviour that reduces the risk of predation (Lee et al., 2006; Ritz, 1991). Field observations reveal that only few attacks performed by the predators pursuing

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their prey are turn to be successful (MacKenzie, Kiørboe, 1995; Hammerschlag et al., 2006; Roth and Lima, 2007). In combination with active evading behaviour of the prey this leads to the fast emergence of a spatial cellular structure presented in Fig. 2, that referred to as vacuole pattern in the classification of Pitcher and Parrish (1993).

Figure 2. Typical spatial pattern (vacuole) observed in aquatic predator-prey systems

Model and simulations We present a simple individual-based simulation model that easily mimics spatial configurations of animal distribution typical for such dynamical systems. Simple (minimal) mathematical models able to explain complex patterns of spatiotemporal dynamics of population systems should be considered as essential blocks constituting a basis of GIS- oriented knowledge systems intended for describing large-scale biocenoses. The individual-based (Lagrangian) approach to modelling population dynamics allows taking into account behavioural

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mechanisms that cause the clustering in predator–prey systems consisting of not very abundant populations. Lagrangian models of individual particle movements are especially useful for studying systems with low population densities that does not allow applying the Eulerian taxis-diffusion- reaction models (e.g., Tyutyunov et al., 2007). Let us describe the basic hypotheses and modelling assumptions. More details of the model and its applications are given in (Tyutyunov et al., 2008, 2013). In the model, space is continuous and time is discrete t  0,1, 2,. We consider populations of prey and predators in a closed rectangular area   0, Lx  0, Ly  with reflective boundaries  and consisting of N and P individuals respectively. The states of each prey i i  1,2,, N  and predator N P N P j j  1,2,, P at time t are given by their coordinates xi,t , x j,t  and their velocities v i,t , v j,t (the upper index N and P stands for prey and predator respectively). It is assumed that every individual release a specific cue — either a substance (scent, pheromone, exometabolite) or waves (sound, water vibrations sensed by the lateral line of fish) — that spreads and decays much faster than the animal moves from one position to another. Then the distribution of individual cue can be taken to be normal, centered at the individual location point xi,t , with root-mean- square deviations  N for the prey and  P for the predator respectively. The cue of the whole population ( S N x,t for the prey and S P x,t for the predator) in every point of the model space is determined by summation of the cues issued by all individuals. P P P P P The moves of an i -th predator are described by equation xi,t1  xi,t  ξ i,t  vi,t , where x i,t is the P vector specifying the position of the individual in discrete moments t  0,1, 2,.. .; ξ i,t is the random vector uniformly distributed inside a circle of radius  P , modeling random motion; while P P P vi,t   PS N xi,t  P vi,t1 is the vector of directed movement of the predator along the gradient of P prey cue S N in point xi,t . For prey the formulae are analogous, differing only by the swapping the places of indices N and P . The taxis coefficients  N and  P specify the intensity of directed movements;  N , P 0,1 set their inertia. The direction is set by the sign:  N  0 because predator cue is a repellent for prey;  P  0 , as prey cue is attractant for predators. In this way, by changing the parameter values, we can model various strategies of motion of predators and their prey inside the rectangle, obtaining a variety of spatial patterns, including those shown in Fig. 1. In particular, Fig. 3 represents snapshots of simulations performed with the following parameter values: Lx 10 , Ly  6 , N  5000 ,  N  0.5,  N  0.1,  N  0.03,  N  0.05 , P  25 ,  P  0.35 ,

 P  0.15 , N 0.003,  P  0.05 . The obtained results are qualitatively similar to the behavioral patterns observed in natural predator-prey system (see Fig. 2), and schematically shown in Fig. 1 (split, join, avoid, hourglass, herd, etc.).

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Figure 3. Screenshots showing distribution of predators (larger points) and prey (smaller points) over 2D density plot of the movement stimulus (prey and predator cues in the left and right panes respectively)

References Camazine, S. Deneubourg, , J.-L., Franks, N., Sneyd, J., Théraulaz, G., Bonabeau, E. Self-Organization in Biological Systems. Princeton University Press, Princeton and Oxford, 2003. 538 p. Dombrovsky, Yu.A., Markman, G.S. Spatial and Temporal Ordering in Ecological and Biochemical Systems. Rostov State University, Rostov-on-Don, 1983. 118 p. Grünbaum, D., Okubo, A. Modelling social animal aggregation / in: S. Levin, (ed.) Frontiers in Mathematical Biology. Springer, New York, 1994. P. 296–325. Hammerschlag, N., Martin, R.A., Fallows, C. Effects of environmental conditions on predator–prey interactions between white sharks (Carcharodon carcharias) and Capefurseals (Arctocephaluspusillus pusillus) at Seal Island, South Africa. Environmental Biology of Fishes, 2006. Vol. 76, No. 2–4, 341–350. Lee, S.-H., Park, J.H., Chon, T.-S., Pak, H.K. Prey-flock deformation under a predator attack. Journal of the Korean Physical Society, 2006. Vol. 48. P. S236–S240. MacKenzie, B.R., Kiørboe, T. Encounter rates and swimming behavior of pause-travel and cruise larval fish predators in calm and turbulent laboratory environments. Limnology and Oceanography, 1995. Vol. 40, No. 7. P. 1278–1289. Medvinsky, A.B., Petrovskii, S.V., Tikhonov, I.A., Venturino, E., Malchow, H. Chaos and regular dynamics in model multi-habitat plankton – fish communities. Journal of Biosciences, 2001. Vol. 26, No. 1. P. 109–120.

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Murray, J.D. Mathematical Biology. II: Spatial Models and Biomedical Applications. Springer- Verlag, New York, 2003. 811 p. Okubo, A., Levin, S.A. Diffusion and Ecological Problems: Modern Perspectives. Springer-Verlag, New York, 2001. 467 p. Parrish, J.K., Viscido, S.V., Grünbaum, D. Self-organized fish schools: an examination of emergent properties, Biological Bulletin, 2002. Vol. 202. P. 296–305. Petrovskii, S.V., Li, B.L. Exactly Solvable Models of Biological Invasion. CRC Press, Boca Raton, London, New York, Washington, 2005. 217 p. Pitcher, T.J., Parrish, J.K. Functions of shoaling behavior in teleosts / in: T.J. Pitcher, (ed.) Behaviour of Teleost Fishes, 2nd ed. Chapman and Hall, London, 1993. P. 363–439. Ritz, D. The benefits of a good school: When it comes to schooling, invertebrates such as shrimps and squids do it like fish, and probably for the same reasons. New Scientist, 1991. Vol. 126, No. 1761. P. 41–43. Roth, T.C., Lima, S.L. Use of prey hotspots by an avian predator: purposeful unpredictability? The American Naturalist, 2007. Vol. 169, No. 2. P. 264–273. Svirezhev, Yu.M. Nonlinear Waves, Dissipative Structures and Catastrophes in Ecology, Nauka, Moscow, 1987. 368 p. [in Russian]. Turchin, P. Quantitative Analysis of Movement: Measuring and Modeling Population Distribution in Animals and Plants. Sinauer, Sunderland, 1998. 406 p. Tyutyunov, Yu., Titova, L., Arditi, R. A minimal model of pursuit-evasion in a predator-prey system. Mathematical Modelling of Natural Phenomena, 2007. Vol. 2, No. 4. P. 122–134. Tyutyunov, Y., Titova, L., Arditi, R. Predator interference emerging from trophotaxis in predator– prey systems: an individual-based approach. Ecological complexity, 2008. Vol. 5, No. 1. P. 48–58. Tyutyunov, Yu.V., Titova, L.I., Berdnikov, S.V. A mechanistic model for interference and Allee effect in the predator population. Biophysics, 2013. Vol. 58, No. 2. P. 258–264. Venturino, E., Medvinsky, A.B. The role of periodic boundary forcing in plankton pattern formation. Ecological Modelling, 2001. Vol. 140. P. 255–270.

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THE METHODS OF THE COMPLEX GEOMODELING OF NATURAL AND ANTHROPOGENICALLY TRANSFORMED GEOSYSTEMS

SPATIOTEMPORAL ANALYSIS OF THE INCIDENCE OF CANCER DISEASES AS AN INDICATOR OF MEDICAL AND ENVIRONMENTAL SAFETY: THE CASE STUDY OF THE ROSTOV REGION

______O.E. Arkhipova, Е.А. Chernogubova, N.V. Likhtanskaya Institute of Arid zones of the Southern Scientific Center of Russian Academy of Sciences, Rostov-on-Don, Russia

Abstract Model of conditionality of distribution of oncological diseases as ecology-dependent pathologies is based on ArcGis 10.1. The constructed model has provided a number of results, significantly expanding the scope and use of medical and environmental monitoring. Analysis of the incidence of cancer in the Rostov region performed between 2001 and 2012. It has been showed that regions and cities of Rostov region characterized by a statistically significant increase in morbidity. On the example of Rostov region it has been showed that the level of cancer is the indicator of health and environmental safety areas.

Keywords: cancer, environmental-dependent pathology, spatial distribution, GIS technology

Introduction Fundamental criteria of the environmental well-being areas are quality of life and level of health. That category of health is now as an indicator of environmental performance and compliance with the scientific and technological progress. Health of the population within the biological norm is a function of economic, social and environmental conditions and can be considered as the primary biological indicator of environmental risk and an important component of environmental monitoring (Gichev, 2003; Revich et al, 2004). It should be noted that the thesis "Promote health for all through a healthy environment)" (Healthy People 2020….), is a worldwide standard. In the last decade, new research area devoted to the study, analysis and proof of health depending on the state of the environment - environmental epidemiologists were formed. The objects of study "Environmental Epidemiology" are environmentally caused diseases and pathological conditions associated with exposure to human hazards of its habitat, including - natural and man-made (Zueva, Yafaev, 2005; Oleynikova et al, 2006). Worldwide organization within the field of health care, increasingly rely on GIS technology which provide solutions that improve performance in this important for our life and economy of the region. In addition, the use of network and cloud technologies for processing spatially distributed GIS data is of special interest, which is the most important motivation for research in this area.

The Tasks A major problem the last few decades in Russia and around the world is the growth of malignant tumors. The incidence of cancer in the Russian Federation from 2002 to 2012 increased from 317.2 to 367.3 per 100 thousand population in Russia (Chissov et al, 2013). In the Rostov region the incidence of malignant neoplasms of the population in 2012 was 392.5 per 100 thousand. It should be noted that in 2012, in the Southern Federal District of deaths from cancer is second only to cardiovascular disease

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and was in the Southern Federal District and 203.2 Rostov region - 197.9 cases per 100 000 population (Chissov et al, 2013; Kaprin et al, 2014). The factors that have a direct or indirect impact on the dynamics and structure of malignant tumors should be classified as environmental factors, among which we can distinguish natural, man- made, and the socio-economic and demographic factors (Bobrov, 2010; Veremchuk et al, 2012; Boffetla, Nyberg, 2003; De Santis, Di Doll, 2004). According to the International Agency for Research on Cancer, the occurrence of 85% of human cancers due to lifestyle factors and exposure to environmental carcinogens to which the human body is not evolutionarily prepared (Imyanitov, 2009; Chernogubova et al, 2009; Muir, 2006; Jemal et al, 2011). Determining influence on the incidence of malignant neoplasms have anthropogenic factors. In most cases, their effects are to reduce the adaptive capacity of the organism. Anthropogenic impacts due primarily to environmental pollution with polycyclic aromatic hydrocarbons, aromatic amines, aminoazo compounds , nitroarenes, nitroso compounds, heavy metals and their compounds, fibrous and non-fibrous silicates and radionuclides, which are toxic, carcinogenic, mutagenic action (Putilina et al, 2009). Increase in cancer incidence is also exacerbated by demographic and socio-economic processes, such as depopulation and aging, sex and age composition, quality of life, population, dynamics of industrial production, the quality and availability of health care (Chissov et al, 2013; Hirayama, 2002). Thus, cancers are environmental indicators pathology, a highly significant indicator of the social and health status of the general population. Therefore, we set a goal to analyze the spatial distribution of the occurrence of the most frequently diagnosed form of cancer and the reasons oncological epidemiological situation at the southern macro-region using methods of geoinformation technologies. As an example we use the Rostov region.

Materials and Methods The statistics of the primary detection of cancer in 4 locations in 43 regions and 16 cities of Rostov region for 10 years from 2001 to 2012, provided FSBI "Rostov Cancer Research Institute" Russian Ministry of Health and GBU RO "Medical Information-Analytical Center of Rostov region ", were used as research materials. Multidimensional task classification of objects in multi-dimensional space was solved by the cluster analysis. This solution allows you to partition the whole data set at differing but homogeneous within the group, based on the values of intra-and inter-group variances. To perform clustering in the cluster analysis were used the following methods: tree clustering, K - means clustering, Two-way joining. To reduce the amount of data in a multidimensional model of both the number of objects and the number of features and select a subset of the most significant features of their original set factor analysis was used. To identify the factors that most significantly for Rostov region whole array of medical statistics data was subjected to factor analysis. It was produced for each cluster individually, and allowed us to determine the number of hypothetical factors that significantly influence on morbidity in each cluster and factors loadings. Statistical study of the dependencies for the study of the nature and structure of the relationship between the parameters were analyzed by regression, correlation, variance and covariance analysis. Statistical analysis was performed using a standard software package STATISTICA 6.1, and methods of spatial statistics Arcgis 10.1 Esri. For mathematical and cartographic modeling also an array of statistical information by the Federal State Statistics Service and its territorial bodies FGUZ "Federal Center of Hygiene and Epidemiology» has been used. The need for integration of diverse information about objects in the environment, for example the spatial distribution of the factors and areals caused habitat, caused the use of GIS capabilities to store information in a single logical space - geodatabase. Structuring information in a geodatabase to allow a single unified position to organize storage and access data describing the different sides of the spatially extended objects: their spatial characteristics, and the dynamics of the processes occurring in these objects (time series data) and their mathematical models and methods of obtaining and processing data. Offered powerful GIS spatial analysis, modeling, and visual imaging allowed to provide

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comprehensive support for the problem, broaden the research and surveys, to report the results in an easy to understand further work and cartographic form (Arkhipova, 2009; Arkhipova et al, 2013).

Results and Discussion For integrated quantitative assessment of health status, the temporal and spatial distributions of disease have been analyzed and the territory of primary health risk assessment has been identified. Analysis of the results revealed oncogenic dangerous territory. All districts and towns of the region, depending on the number of patients (based on 100 thousand population) in each of them, were divided into 3 groups): Group 1 - the city and the areas where the number of cases is less than the average for cities and districts of the Rostov region-oncogene situation in these cities and regions called "background"; Group 2 - cities and regions in which the number of patients exceeds the average for the cities and districts of the Rostov region - the city and the areas with oncogenic situation "high risk"; Group 3 - areas where the average number of disease (based on 100 thousand population) is below average for the cities and districts of the Rostov region - a group of "minimal risk". Analysis showed that the incidence of all forms of cancer except lung cancer in cities and districts of the Rostov region from 2001 to 2012 significantly increased. The areas of Rostov region with oncogenic situation "high risk" - Belokalitvenskiy, Kamensky, Salsky, Semikarakorsky, Peschanokopskiy, Tselinskii districts, where the incidence of at least one of the analyzed cancer significantly higher than the average for the region. Attention is drawn to the fact that in 2012 in Belokalitvenskiy, Kamensky district areas noted an increased incidence of two of the four nosological forms of cancer. Among the cities of Rostov region high cancer rates observed in Gukovo, Taganrog and Rostov- on-Don. It should be noted that the Taganrog "led" by the level of lung cancer, breast cancer, colon cancer and prostate cancer. Identified as "low risk areas of cancer," where detection of lung cancer, breast cancer, colon cancer and prostate cancer were significantly lower than those for districts of the Rostov region. These areas include the Verkhniy Don, Myasnikovsky, Oblivskaya, Remontnenskiy, Soviet, Ust-Donetsk, Bagayevskaya, Dubovsky, Tselinskii, Veselovsky Krasnosulinskiy, Tarasovskiy Egorlykskiy, Morozovskiy and Octiabrskiy regions. Thus, it can be assumed that there are common for all types of cancer factors that trigger the high incidence of cancer in the Belokalitvenskiy, Kamensky district and in Taganrog and specific - for each type of cancer, which vary in different regions of Rostov region. The results of the factor analysis and correlation indicate about common external causes of the high risk of cancer pathology in these areas. Analyzing the causes of the high incidence of cancer in some towns and districts of the Rostov region interconnection cancer rates with the environmental situation in a given area cannot be ignored. State of the environment in the ecological situation can be assessed as satisfactory, tight, critical, crisis (the zone of ecological emergency) and catastrophic - a zone of ecological disaster. Most human pressure on the environment observed within agro industrial Rostov agglomeration which includes such cities as Rostov-on-Don, Taganrog, Novocherkassk, Azov, Bataysk and Aksai. The environmental situation in these cities as a whole can be evaluated as critical, and in the industrial zones of Novocherkassk, Rostov-on-Don, Azov - as critical. East Donbass also has highly technogenic loading by the coal mining industry. The ecological situation in the region as whole has been estimated as critical. In industrial areas of cities Sahkhty, Novoshakhtinsk, Gukovo Kamensk- Shakhtinsky (chemical industry), Belaya Kalitva and Krasny Sulin (metallurgy and metalworking) - too. Several districts of the Rostov region specializes in the production of fruits and vegetables, Azov, Aksay, Bagayevskaya, Semikarakorsk, Veselovsky Martynovsky, Volgodonsk. For these areas is characterized by air, water and soil residues of pesticides and fertilizers. Environmental situation here can be characterized as tense, on irrigated land - as critical. In addition, in the area is the few centers with the critical environmental situation. This is the eastern part of the delta area and Tarasovskoye Don, which produces natural gas, the city of Rostov NPP and Volgodonsk, industrial centers Chertkov, Millerovo, Morozovsk, Zimovniki, Proletarskiy, Salsk and others. The regions with the critical

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environmental situation due to severe under natural conditions (lack of safe drinking water) include the eastern part of the Rostov region (Remontnenskiy, Zavetinsky, Orlovskiy, Zimovnikovskiy, Dubovskiy) (Veremchuk et al, 2012; Boffetla, Nyberg, 2003). In complex influence to human the quality of drinking water, air, noise pollution are the priority environmental factors from the perspective of harm to public health. According to the results of ranking health implications in 5 cities of Rostov region in 2012 is estimated to be of strain. Priority environmental factors are in the cities. Azov, Taganrog - the quality of drinking water, air, soil, the level of noise pollution, in the cities. Novocherkassk, Rostov-on- Don - the quality of drinking water, the soil, the level of noise pollution, in Novoshakhtinsk - the quality of drinking water, air and soil. In the 11-city sanitary-hygienic situation is assessed as unsatisfactory. Priority environmental factors: the quality of drinking water and soil - Bataysk, the quality of drinking water, and high noise pollution - in Volgodonsk, the quality of drinking water in the cities. Aksay, Belaya Kalitva Gukovo, Donetsk, Kamensk Shakhtinsky, Krasny Sulin, Millerovo Salsk Zverevo, Shakhty (Boffetla, Nyberg, 2003). It should be noted that the frequency of detection of primary cancers analyzed in areas of critical and challenging environmental situation is significantly higher than in relatively prosperous areas of ecology.

Conclusion 1. Created on the basis on ArcGis 10.1 model of conditionality of distribution of oncological diseases has provided a number of results, significantly expanding the scope and use of medical and environmental monitoring. 2. Complex estimation of health and environmental safety areas on the basis on a systematic analysis using the mathematical methods of evidence-based medicine, taking into account the totality of internal and external factors to determine the priority of the impact of environmental factors and the level of the organism's response. These results may provide a basis for targeted analysis of the factors that increase the risk of cancer in the region and we have identified the development of a strategy based on this monitoring and the prevention of cancer in areas of the Rostov region, the introduction of medical and social programs aimed at early detection and, therefore, increase treatment of these social diseases.

Acknowledgments This work was partially supported by internal grants Southern Federal University, a program of the Presidium of RAS № 32 "Fundamental Problems of modernization time. The macro-region in the face of rising tensions" in 2014.

References Gichev Y.P. "Environmental pollution and environment-related human pathology," Analyt. overview of SPSL. Novosibirsk, 2003. No. 68. 138 p. Revich B.A., Avaliani S.L., Tikhonova G.I. "Environmental Epidemiology" M "Academy", 2004. 384 p. «Healthy People 2020 Environmental Health». url: http://healthypeople.gov/2020/about/default.aspx Zueva L.P., Yafaev A.D. "Epidemiology" (textbook). SPb.: Folio, 2005. 745. Oleynikova E.V., Nagorny S.V., Zuev L.P. "Environmental Epidemiology in addressing environment and health monitoring," Public health and the environment: the newsletter. 2006. T. 158. 5:23-28. Chissov V.I., Starinsky V.V., Petrova G.V. "Cancer in Russia in 2011 (morbidity and mortality)," M. Russian Ministry of Health, 2013. Kaprin V.I., Starinsky V.V., Petrova G.V. "The state of cancer care to the population of Russia in 2012." M. Russian Ministry of Health, 2014. Bobrov A.A. "The influence of environmental factors on the incidence of malignant tumors of the population of the Yaroslavl region" Ecological-deoendent disease: Proceedings of the Second Scientific-

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Practical. conference "The influence of anthropogenic pollution of the environment on human health." Yaroslavl: Publication TSB CEA, 2010. Pp. 25 – 27. Veremchuk L.V., Kiku P.F., Millstones M.V., Yudin S. "Environmental dependence spread of cancer in the Primorye Territory." Siberian Journal of Oncology. 2012. 1:19-25. Boffetla P., Nyberg F. «Contribution of environmental factors to cancer risk». Br. Med. Bull. 2003. Vol. 68: 71-94. De Santis M., Di Doll R. «Ionizing radiations in pregnancy and teratogenesis: a review of literature» Ed. J.G. Forter. Philadelphia Toronto, 2004. P. 103–121 (The epidemiology of cancer). Imyanitov E.N. "The fundamental laws of tumor growth," Journal of RCRC. NN RAMS. 2009. T. 20. 1:89-90. Chernogubova E.A., Braslavskaya I.V., Golikov A. "The role of serine proteases in the pathogenesis of prostate cancer" Journal of Southern Scientific Center RAS. 2009. 1: 81-93. Muir C.S. “Etiology of cancer” Accomplishments in cancer research Eds. J.G. Fortner, J.E. Rhads. Philadelphia. 2006. Vol. 11. P. 108-121. Jemal A., Bray F, Center M. M., Ferlay J., Ward E., Forman D. “Global cancer statistics” Cancer Journal for Clinicians. 2011. Vol. 1, 2:69–90. Putilina V.S., Galician I.V., Yuganova T.I. "Adsorption of heavy metals by soils and rocks. Sorbent characteristics, conditions, parameters and mechanisms of adsorption: analyte”. Review of Novosibirsk SPSL, Ecology. 2009. No. 90. Hirayama T. “Life-style and cancer: from epidemiological evidence to public behavior change to mortality reduction of target cancer” Natl. Cancer Inst. 2002. Vol. 1:65–74. Arkhipova O.E. "The concept of a regional environmental information system for monitoring" Information Technology 2009 5:62 – 67. Arkhipova O.E. Chernogubova E.A. Tarasov V.A., Likhtanskaya N.V., Kit O.I., Eremeeva A.A., Matishov D.G. " The level of oncological diseases as an indicator of medical-ecological safety of territories (the Rostov region being exemplified) Journal of Southern Scientific Center RAS. 2013. Vol.9, N3: 7-14.

GIS USING FOR STATE ESTIMATION OF NATURAL OBJECTS IN VARIOUS STAGES OF THE KYZYL- TASHTYG POLYMETALLIC DEPOSIT EXPLORATION IN TYVA REPUBLIC

______O.D. Ajunova, S.G. Prudnikov, V.I. Zabelin, O.I. Kalnaya, T.P. Archimaeva, E.A. Domozhakova Tuvinian Institute for Exploration of Natural Resources of Siberian branch of the Russian Academy of Sciences, Kyzyl, Russia. [email protected]

Abstract The paper describes GIS using for impact estimation on the environment in connection with the construction of "Kyzyl-Tashtyg deposit" lead-zinc mining and processing enterprise in the Republic of Tyva in stages of the analysis of the natural environment baseline of the deposit area, the project for impact estimation on the environment (IEE) of the Kyzyl-Tashtyg mining and processing complex construction and environmental state monitoring during deposit development.

Keywords: GIS technology, geoecology, anthropogenic landscapes

There are several objects with anomalous anthropogenic pollution of man-caused factor in Tuva. One of such objects is the exploration area of Kyzyl-Tashtyg pyritic copper-lead-zinc ores deposit in an ecologically clean area of Tuva Republic. Urgent task is the study of changes in the environment caused by human factors bearing negative consequences for the landscape area. The analysis of the background ecological state of natural resources in the area of shore field and the adjacent territory of the possible impact of future mining and processing plant was carried out at

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the first stage of the research. The resulted background characteristics formed the basis for the project basis for Environmental impact assessment (EIA) caused by the construction of mining and processing complex; and they are actual base of an environmental control and monitoring of the work area. GIS "Natural conditions of the construction territory of Kyzyl-Tashtyg mining and processing plant" is worked out on the basis of the software GIS ArcGIS9. GIS includes all components of the environment of the explored area. Lithosphere state data, recorded dangerous exogenous geological processes, hydrochemical parameters of surface waters and ground waters, snow, characteristics of the soil and vegetation, areal radiation levels, quantitative indicators of biota are added to GIS data-base. Geographical support of geoinformation system includes a standard set of layers of large-scale with notation of mining and processing objects on the map. The mentioned project was the basis of geoinformation ground providing efficient access to the multiscale topographical data, the concentration of multiple indicators of various natural environments on a single set, interpreting the results of research with the creation of the resulting maps on their basis. Base points are marked on a grid sampling at a pitch of 1 km. in the course of the soil state analysis. The network covers the territory of mining and processing complex and the area adjacent to the mining allotment. Map series of chemical elements distribution in soils are created as a result of interpolation by the inverse distance method. The present part of the research work characterizes the territory before the stage of its exploration. GIS support at the stage of environmental impact assessment (EIA) of the construction of Kyzyl- Tashtyg mining and processing plant involves creating a model of manifestation probability of negative impact in natural landscape of the territory. There are various forms of pollution impact on the environment during mining and processing works of the plant. Such impact contains the following: large areal violation of the earth's surface with the removal of mineral resources, the removal of economically valuable and biologically productive areas of lands, vegetation and soil mantle, the transformation of natural landscapes and the formation of man-made relief, violation of hydrogeological and hydrological conditions of the territory, the emission of harmful substances into the atmosphere (dust, gas and fly ash), inhibition of biological resources, violation of natural habitat of animals, birds, etc. (Fig. 1). Analysis of the changes in ecosystem components, identification of possible adverse impacts of mining and processing plant as a factor in violation of natural landscapes allowed to allocate in a GIS environment zones of direct and indirect impacts of the plant construction, to identify and to calculate the area of loss and damage of biological and economic productivity.

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Figure 1. Model of natural and man-made landscape on the construction territory of the plant

Monitoring for identification of the sources and consequences of environmental contamination, recommendations projecting for preventing or minimization of adverse impacts on the environment are carried out at the present time on the construction territory of the plant. Basic research methods are field investigations of changes in natural ecosystems caused by the construction of mining and processing complex objects, as follows: dangerous manifestation of exogenous processes, state of surface watercourses, soil landscape and vegetation cover, observation of fauna changes. Sampling of surface waters and groundwaters, soil and vegetation is carried out during fieldwork for pollution concentration determining by heavy metals and manmade components using analytical methods. Quantity and quality assurance monitoring of drinkable water and wastewater is planned on the objects of the mining plant. The work is carried out according to the contract with the mining company "Lunsin". The scheme of the monitoring locations of surface and groundwaters is worked out, data of waters chemical parameters is carried out with GIS support. Monitoring of exogenous geological processes is carried out in the area of construction and blasting operations by route-visual inspection (land-based, remote controlled) using fixation instruments of monitoring points on the special support of the observation network. Observations data are recorded in the GIS database, current maps of monitoring observations are obtained. Full state assessment of complex natural and man-made objects is based on the control results of the different environments characteristics. It is for relevance promotion of combining quantitative and qualitative characteristics. Further monitoring data will be designed for making management decisions by future Kyzyl- Tashtyg mining and processing plant administration as well as for summarizing them by state monitoring service.

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THE MONITORING OF SURVEYING WORK FOR OPENCAST MINING DEVELOPMENT ON PRIMORSKY REGION TERRITORY

______L. Usoltseva, V. Lushpei, Y. Vasyanovich, V. Murzin Far Eastern Federal University, Vladivostok, Russia

Keywords: Satellite imagery, decryption, opencast mining, surveying work, GNSS

The possibility of linking the modern methods of Surveying to ensure monitoring of the open pit mining to improve industrial safety in the Primorye Territory, as well as their use in the educational process.

Industrial safety in the management of open pit mining depends to a large extent on the methods used and the assessment methods and the stability of pit slopes dumps in complex mining and hydrogeological conditions. Their lack of elaboration complicates opencast mining, lowers their security is the need for additional geotechnical studies and develop new solutions to correct a project that leads ultimately to higher prices for overburden removal and emergency. This problem is most acute for coal deposits, both because coal deposits compared with ore have a greater variety of geological, geotechnical and hydro-geological conditions, complicated by the presence of layered structures and strong faulting. Second, the combination of disorders and abnormal deposition of layers in the zone of tectonic processes are difficult to predict at the design stage career options that provide adequate reliability and exclusive manifestation of landslides. Under these conditions, great importance attaches to the monitoring work that would quickly visualize and assess the situation prevailing in the career. Thus, high precision instrumental observations using electronic total stations and laser scanner, forming a geographic information system based on mining and graphic documentation and the results of field observations, as well as a retrospective analysis and three-dimensional visualization of the results can also be supplemented by the results of decoding satellite images and photos on the territory of the open pit mining. Currently, many surveying services for mining companies working in long-obsolete technologies, which significantly reduces their competitiveness over similar foreign enterprises and ultimately exacerbates the problems of industrial safety open pit mining. It is therefore necessary to ensure their development in terms of how modern technological solutions and cost-effectively. This article discusses the modern trends to solve problems surveying monitoring opencast mining, comprehensive utilization of which will create a highly reliable monitoring system, improve industrial safety in mining and mining complex hydrogeological conditions.

Possible practical applications «GNSS» First of all, when performing any types of geodetic and surveying works problem arises by definition rectangular coordinates and elevation points plan-height study. Contemporary and modern to make this work with the use of satellite positioning systems. The method consists in the fact that the receivers simultaneously measure code range and carrier phase to the same satellites, thereby eliminated errors introduced by the Earth's ionosphere. Positioning accuracy in this way reaches 0.6 cm horizontal and 0.9 cm in height. Accuracy can be improved by using a larger number of receivers in base stations as. In general, we can say that now RTK technology for surveying reached the level of production activities and is widely used around the world. Despite the fact that RTK has a specific level of application and can be considered only as a supplement to traditional methods of shooting, this technology has a bright future, and there is considerable potential for further development. Important links in the chain determine the rectangular coordinates and elevation using a GNSS software. It was he going finishing of "raw" data from the receivers, and as a result we obtain the equation in rectangular coordinates of the local coordinate system.

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To date, there are many different specialized software modules. Below are the main ones: Trimble Geomatics Office, Trimble Business Center, Leica Geo Office, JUSTIN (company JAVAD), Spectra Precision, etc. Using High Resolution Satellite Imagery. Another option, which can be successfully applied to open pit mining is the use of high resolution satellite images for surveying activities in the development of mineral deposits open method. Space monitoring is repeated continuously receiving information on the qualitative and quantitative characteristics of mining objects and processes running on them with accurate geo- referenced by data derived from satellites. Raster imagery is possible to load different vector editors. Loaded bitmap image can be scaled and applied on a topographic survey produced. As a result, it is possible to control the picture was taken, or supplement. Figure 1 shows an example of a satellite image overlay in AutoCAD (picture made in winter) for performing the topographical survey of the career in the vector. It is clearly seen that the contours of the upper and lower slope brovok (highlighted in orange) coincide with the characteristic lines in the picture.

Figure 1. Satellite image of the career, worked the Pushkarevskoe andesites field superimposed on a topographic survey

It is important to remember that these companies provide static information, ie satellite imagery shows not in real time. To work with archival data, there are many specialized programs, the most convenient is a free program SAS Planet. It is designed to view and download high-resolution satellite imagery and conventional maps. The rise in popularity of satellite images does not mean that paper maps are fading. They complement each other. "Card" of the future will be a product that combines the space and aerial photographs, based on them thematic layers, as well as a terrain model allows you to turn the card into a three-dimensional image of the terrain. All these components are combined in the concept of "geographic information systems" (GIS). Digital technology, simplifying the procedure of data processing led to the mapping of reality is not just static objects and terrain, but the processes occurring in a certain area in particular in open mining. One of the necessary directions in operational decision surveying tasks, as well as a retrospective analysis of the situation is to create plans based on digital paper mining and graphic documentation. All mining enterprises have mining surveying archives. They are classified maps, plans, diagrams, plates, made of paper or on a rigid foundation. Information that they contain is valuable and used periodically.

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When you transfer this information in digital form it is possible to quickly, without compromising the accuracy and quality of the original edit, copy, print at any scale all images, or some of its elements, as well as create digital archives of data, information which can be stored indefinitely, while maintaining The accuracy and quality of the original document. To perform digitizing used specialized software. As a result of these operations is obtained quality bitmap image as close to the original, which can already be placed in an electronic archive and subsequently used in a variety of CAD and GIS systems. Created bitmap is also converted into a vector format. Thus, as a result of scanning and digitizing calibration can create and maintain databases of digital plans for mining and graphic materials that are not affected by what or deformation changes. Furthermore, on the basis of GIS generated an opportunity to find the most efficient, systematic ized and used in everyday work all the accumulated material.

Create three-dimensional models of open cast mining One of the essential areas is to create digital plans and 3D models of the mining industry, given the current trend of transition from two-dimensional to three-dimensional graphics, which has already secured a number of normative documents in international and domestic practice. Computer simulation of mining objects is performed using graphical surveying and documentation of field survey data directly. When solving problems volume digital model is converted into a mathematical, with information density pattern increases hundreds of times by an additional calculated points. Generally the surface are created using triangulation networks. They are formed from triangles sides of these triangles form a line network (Fig. 2). It is important to note that the creation of three-dimensional surface models advisable when there are only complex digital information.

Figure 2. Triangulation net generator of 3D surface model

The following is an example of creating a three-dimensional model Baranovsky career andesite- basalts of "Terekhovka ZBI" (Primorsky Krai). Digital model created in the career program AutoCAD Civil 2012, the distance between pickets sustained as required "Instructions" and were 15 - 30 m. To adjust the model produced data compression using linear interpolation to the distances between points in 5 - 7 m, and the job of structural lines that provided sufficient accuracy. Figure 3 shows an example of a fragment of a career plan in the form of a two-dimensional vector made using AutoCAD Civil 2012. On the basis of computer geometrization Baranovsky career was established all the necessary package of mountain-graphic documentation in electronic form. Figure 4 shows the 3D model Baranovsky career. View display - Net triangulation. Figure 5 shows processed and visualized geometrization Baranovsky career program AutoCAD Civil 2012.

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So, as a result of scanning and digitizing calibration Created and updated database of digital plans for mining and graphic materials in a GIS environment, which will not be subject to any deformation changes.

152 .75 153 .03 149.67

14 8 .15 155 .06 15 4 .00

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147.03 155 .35 149.94 150.11 14 5 .71 15 0 .10

.31 144 .53 15 2 .74 15 9 .07

154 .43 15 9 .58 14 5 .27 156 .05 154 .32 14 3 .01 147.23 15 6 .70 14 9 .91 14 4 .31 139 .05 14 1.42 14 3 .68 145 .15 14 7.92 15 0 .42 13 6 .91 14 2 .06 14 9 .70 14 2 .91 1 14 2 .80 136 .05 147.47 14 5 .17 15 0 .29 14 2 . 224 14 1.19 14 0 .46 14 0 .62 МТ-3 13 6 .94 137.73 14 0 .95 139 .45 МТ-1 15 6 .00 14 9 .61 14 0 .29 151.15 160.559 139 .45 14 2 .73 13 9 .69 14 0 .28 13 8 .58 14 9 .62 13 6 .94 14 9 .45 13 9 .46 14 0 .80 143.64 139 .23 140 .33 13 6 .81 14 0 .49 14 2 .96 14 1.36 13 8 .87 14 0 . 68 136 .95 13 8 .18 13 9 .77 13 6 .92 13 6 .67 14 1.28 14 9 . 78 13 9 .66 14 0 .80 14 1.04 13 8 .73 14 0 .52 14 9 .11 155 .30 13 6 .96 13 9 .22 14 8 . 80 157.37 13 9 .94 13 9 .89 155 .30 13 9 .26 13 8 .45 157.34 14 1.45 14 8 .24 14 0 .39 13 9 .77 15 4 .48 14 0 .57 14 1.95 14 2 .02 14 0 .25 155 .09 143 .51 14 1.58 147.88 14 1.57 14 5 .48 14 7.97 14 2 .22 14 1.93 14 0 .92 15 4 .19 15 7.12 14 8 .25 146.92 14 4 .09

14 5 .69 15 4 .41 14 9 .46 145 .30 14 6 .64 153 .77 МТ-2 14 5 .48 151.5 48

14 9 .51 14 6 .7 146.66 14 6 .27 15 6 .40 15 0 .82

14 9 . 98 14 8 .71 14 6 .42 15 3 .86 153 .90

14 9 .22 152 .51 152 .83 150 .15 155 .42

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152 .51 153 .55

153 .55 Figure 3. Fragment career as a two-dimensional vector, made using AutoCAD Civil 2012

Furthermore, on the basis of the generated digital databases it is possible to find the most efficient, organize, and use in their daily work the accumulated material and retrospective analysis that is the basis of the monitoring of open cast mining in the integration of tools for manual remote sensing materials and three-dimensional visualization capabilities. The foregoing methods of surveying and monitoring open pit mining linking them have been tested in practice and, at low cost, proven effective and have been used in the educational process.

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Figure 4. Displays a network triangulation Baranovsky career

Figure 5. Processed and visualized geometrization Baranovsky career program AutoCAD Civil

References Vasyanovych Y.A., Usoltseva L.A., V.A. Murzin Possible practical applications of GNSS technology in open cast mining. - "Problems of georesources Far East." Mining information-analytical bulletin (scientific and technical journal), № 5 (May) 2012 (Special Issue), p. 3-18. - Moscow: Publishing House. "Mountain Book". Vasyanovych Y.A., Usoltseva L.A., V.A. Murzin Using high resolution satellite images for surveying activities in the development of open-pit mining in the Primorye Territory. - "Problems of georesources Far East." Mining information-analytical bulletin (scientific and technical journal), № 5 (May) 2012 (Special Issue), p. 19-30. - Moscow: Publishing House. "Mountain Book". Vasyanovych Y.A., Usoltseva L.A., V.A. Murzin Formation of GIS-based mining digitized graphic documentation for monitoring opencast mining. - "Problems of georesources Far East." Mining

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information-analytical bulletin (scientific and technical journal), № 12, 2012 (Special Issue), p. 97-118. - Moscow: Publishing House. "Mountain Book". Vasyanovych Y.A., Usoltseva L.A., V.A. Murzin Create three-dimensional models of open pit mining. - "Problems of georesources Far East." Mining information-analytical bulletin (scientific and technical journal), № 12, 2012 (Special Issue), p. 79-96. - Moscow: Publishing House. "Mountain Book". Antonovich K.M. Use of satellite navigation systems in geodesy. In 2 T. 1. Monograph / KM Antonovich; HPE "Siberian State Academy of Geodesy." - Moscow: FSUE "Kartgeotsentr", 2006. - 334. T. 2. Monograph / KM Antonovich; HPE "Siberian State Academy of Geodesy." - Moscow: FSUE "Kartgeotsentr", 2006. - 360. Kashkin V.B. "Remote Sensing of the Earth from space. Digital image processing: a tutorial "," Logos ", M.: 2001 - 253s.

ASSESSMENT OF EPIDEMIC DANGER TO THE NATURAL FOCI DISEASES

______T.V. Vatlina Smolensk State University, Smolensk, Russia. [email protected]

Keywords: natural foci diseases; epidemic danger; territory; landscape; GIS; map; assessment

The using of GIS technology helps to research the medical-geographical and epidemiological characteristics of the natural foci diseases. Among them there are environmental conditions for the occurrence of natural focal diseases, features of their spatial distribution. The examples of GIS application in complex medical-geographic research for determination of epidemic danger of natural foci infection are given. Natural foci diseases in the absolute number of cases inferior to other nosological entities, but because of the high epidemiological risk they require close attention (Wilks et al, 2003). Therefore, it is important to assess the current situation on the natural foci diseases by the example of Smolensk region that is located in the European part of Russia. The following natural foci diseases were identified in Smolensk region: leptospirosis, tularemia, rabies, haemorrhagic fever with renal syndrome, Q fever, tick-borne Lyme disease, pseudotuberculosis. The manifestation of these nosological entities was caused by favorable conditions for the circulation of the pathogen. Spreading areas of these diseases are associated with certain landscapes, habitats of insect vectors and warm-blooded carriers. Transmission mechanism is also affected by human activities and their relationship with nature. Multi-temporal cartographic representations of medical-geographical situation in respect of these diseases make it possible to assess the dynamics and spatial and temporal variations in incidence of population and animals, the relevance of certain infections and infestations, conduct monitoring studies, etc. The contribution of different natural components to the preservation and circulation of the pathogen is not equal. Thus, relief creates the conditions for the existence of pathogens providing the landscape with water. Ravine and gully system which is widely represented in Smolensk region provides with strong preservation of pathogen in nature, as far as these forms of relief haven’t been affected by plowing, that violates the structure of parasitic systems. Climatic conditions determine the seasonal dynamics of contamination by most natural focal infections, as the rhythm of the life cycle of pathogens and carriers connected to them. Presence of a well-developed hydrographic system in Smolensk region determines the favorable conditions for the development of the number of natural focal infections, such as leptospirosis, tularemia. There are pathogenic microbes, viruses, actinomycetes, protozoa, etc. preserved in the soil for a long time; the soil is also a habitat for carriers of vector-borne diseases. Extremely diverse and ambiguous is the ecological role of biota: vegetation

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forms habitat and fodder supplies for animals, some of which may be keepers and carriers of epidemic disease pathogens; many mammals and birds have a direct or indirect impact on human health, as they are the circulators of pathogens of infectious diseases. Thanks to GIS technologies there is an opportunity of conjugated analysis of mentioned natural factors with epidemiological and social-economic indicators. This will allow to carry out the identification of features of the spatial arrangement of natural foci and identify the degree of risk for population to develop the natural foci infections. By using GIS it is also possible to simulate the spatial structures of areas of natural foci diseases and a solution of prognostic problems on this basis. Every natural focal infection is characterized by certain physical and geographical parameters that determine the features of its spatial distribution. For example, the relief features and the moisture degree of the territory have the impact on the spread of tularemia (Rothschild, Kurolap, 1992; Tokarevich et al, 1975; Tularemia, 1980; WHO, 2007). In order to detect the spread of tularemia in Smolensk region we have considered the following factors: altitude, depth of groundwater, soil body divisibility and the type of soil. It should be noted that in all areas of Smolensk region the cases of the population infection were reported, but the areas free from infection stand out among the individual areas of continuous spread of natural foci. At the Stage I of the study the selection of the study subject and cartographic basis were justified, sampling and analysis of statistical data were conducted, analyzed environmental indicators were defined, and landscape basis was selected. At the Stage II MapInfo 11.0 software created database including: geographical coordinates of places of human infection with tularemia and places of infection picking from the environment; the time of the disease registration; the number of cases; the source of infection, etc. The result was a cadastral reference map. For generalization of the information received we used formal and territorial approach (Malkhazova, 2001). The entire territory of Smolensk region was divided into 3600 squares with 5x5 km side. Such size of the square is the most convenient when using it for the whole territory of the region. If there were found places within the square where the infection with tularemia or picking of tularemia pathogen had happened, it was considered dangerous because of tularemia infection and it was shaded. Other squares were left unshaded. As a result the tularemia nosoarea maps were created (350 squares were allocated), on the basis of which further analysis was conducted. At the Stage III on the basis of ArcView, ArcGIS software the selected features of the natural environment were analyzed. First, the prevailing heights to which the foci of tularemia confined were identified. Then the histogram of focal territories distribution by hypsometric gradations was constructed. It can be concluded that despite the significant share of the absolute height marks in the region relief 170-180 m, most of the known foci are located at altitudes of 190-220 m. Below 170 m the amount of focal areas is drastically reduced, and below 150 m they are not registered any more. Above 240 m focal areas are drastically reduced, and there aren’t any foci higher than 270 m. In order to assess the degree of tularemia dependence on qualitative and quantitative indicators of landscape water supply, features of tularemia foci distribution depending on the depth of groundwater have been traced. The following depth intervals were selected as the most reasonable: 0- 1 m, 1-2 m, 2-3 m; all values deeper than 3 meters. Analysis of the distribution of focal areas by gradations of groundwater depth shows that the vast majority of focal areas is located in territories with the groundwater depth of more than 3 m. In the territories with the depth of groundwater above the value of 1 m the occurrence of focal areas is drastically reduced. Analysis of tularemia distribution depending on quality of the soil of Smolensk region has shown that more often tularemia occurs on sod-podzolic, alluvial and sod-podzolic gley soils. There are certain types of soil, where the infection was not observed - these are sod and modal podzol soil; sod-podzolic soil with the second humus horizon; sod-gleyic soil; bog transition peaty soils on small and medium turfs; bog lowland peaty soil on small and medium turfs; eroded and drift soils of ravines, gullies, floodplains of small rivers and adjacent slopes. These features can be considered as a prerequisite for the existence of infection in nature. As a result of geoinformation simulation the spatial distribution of tularemia foci was assessed on a landscape basis (Evdokimov, Kovalev, 2011), which allows to identify the most dangerous

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landscape provinces in terms of population infection: Smolensk-Vyazemskaya and Dvina-Dnieper provinces that have similar physical and geographical features; rolling and undulating plains with significant degradation areas and valley system density from 200 to 300 m/km2, with an average bogging; Smolensk-Krasninsky landscape - elevation strongly indented in ravines and gullies, valley system density is 400-500 m/km2, in some areas more, bogging is insignificant and comprises 4%. Thus, the features in the spatial distribution of the tularemia focal areas are as following: - the largest amount of focal areas is located at altitudes of 190-220 m; - the majority of the focal areas is located in areas with groundwater depth more than 3 m; - the majority of the territories occupied with tularemia is confined to the regions with high soil body divisibility; - the majority of the known foci is located within landscapes with similar characteristic features (rolling and undulating plains with significant degradation areas and valley system density from 200 to 300 m/km2, with an average bogging). Revealed relations can be considered as a prerequisite for the existence of infection in nature. The occurrence of focal areas of tularemia in certain landscape conditions and the composition of the tularemia carriers allow us to conclude that the majority of them in Smolensk region belong to the meadow-field type. The obtained results revealed the most epidemiologically dangerous areas of Smolensk region and studied the impact of some landscape and environmental conditions on the tularemia distribution.

The reported study was partially supported by RFBR, research project No. 14-05-31109

References Evdokimov S.P, Kovalev D.V. Landscape differentiation in Smolensk region // Proceedings of the Smolensk State University. 2011. № 3. P. 324-331. Malkhazova S.M. Medical and geographical analysis territories: mapping, assessment, prediction. 2001. 240 p. Rothschild E.V. Kurolap S.A. Prediction of zoonoses’ foci activity. 1992. 184 p. Tokarevich K.N., Vershinsky B.V., Perfil'ev P.P. Essays about landscape geography zooanthroponoses (European North of the USSR). 1975. 168 p. Tularemia. Ed. by N.G. Olsufyev. 1980. 450 p. Wilks D., Farrington M., Rubenstein D. The Infectious Diseases Manual. Blackwell Science, 2003. p. 432. P. 307–308. WHO. Guidelines on Tularaemia. WHO Press, 2007. 116 p.

THE ASSESSMENT OF THE LAKE BAIKAL SHORELINE DYNAMICS USING REMOTE SENSING METHODS

______E.Zh. Garmaev, A.K. Tulokhonov, B.Z. Tsydypov Baikal Institute of Nature Management, Siberian Branch of the Russian Academy of Sciences, Ulan-Ude, Russia. [email protected]

Abstract Investigations of coastal line change in the region of Yarki Islands (Northern Baikal) on the base of remote sensing data are carried out. Automated classification of Landsat multitemporal imagery is carried out. As a result, vector layers of coastline are created. Systematic reduction of area the sand bar Yarki is observed. Sharp daily fluctuations of water level as a result of wind-surge and seiche oscillations reach up to 20-30 cm. Difference of simultaneous observations at different observation points reaches 19 cm. Increasing the number of gauging stations on the eastern shore of the Lake Baikal is offered.

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Keywords: remote sensing, multispectral image, interpretation, automated classification, vector layer, coastline, water level, gauging station

Introduction After commissioning of the Irkutsk hydroelectric power station in 1957 and of the subsequent cascade of the Angara hydroelectric power stations (the Bratsk and the Ust-Ilim), by 1959, the average level of Lake Baikal rose by more than 1.2 m in relation to its natural level (455.61 m; here and further, measured in the Pacific Ocean system), which has led to the formation of a new hydrological regime of the lake (Galaziy, 1988). Thus, at present, the level of Lake Baikal for the most part does not depend on natural factors but is heavily influenced by the Angara hydroelectric system (Monitoring …, 1991). The intra-annual fluctuations in the lake level increased from of 82 cm (under its natural regime) to 94 cm (after the control implementation) (Atlas of Lake Baikal, 1993). The rise of the lake level had a negative impact on the productivity of the aquatic flora and fauna, biodiversity, water birds and animals, and has led to the erosion of the coastline. The end-result of the construction of the Irkutsk hydroelectric station is the transformation of Lake Baikal into an artificial reservoir with all the ensuing consequences. During high-water years of the mid-1990s, there were level marks well above 457.0 m. As a consequence, there was a mass destruction and erosion of the coastline of the low eastern coast (coastal forests, recreation areas, beaches, and coastal structures), water logging, and flooding of agricultural land and settlements. There has been a widespread environmental damage to the natural biological complex of the lake system. There are estimates of the economic damage to the economy of Buryatia; there is also a negative experience with the litigation of JSC «Irkutskenergo» (Hydropower …, 1999). In order to prevent such processes, the Government of the Republic of Buryatia in agreement with the Irkutsk Oblast Administration initiated the adoption of the 2001 Resolution of the Government of the Russian Federation № 234 «On the limits of the water level in Lake Baikal under economic and other activities». This by-law of the Federal Law «On Protection of Lake Baikal» regulates the lake level fluctuations in the range of 456.0 to 457.0 m and minimizes ecological and economic backlash on the lake coast line.

The change of the coastline of Yarki islands A real threat of a complete destruction of the Yarki islands requires individual consideration; these islands separate the open Baikal from the Verkhneangarsky shoal. The Yarki is a sandy island system of 17 km in length and 200 m in width. The sandbar of the island is near the village Nizhneangarsk; the mainland is separated from the islands by the estuaries of the Kichera and the Upper Angara. If the lake level approaches 457.0 m and under 3-5 day-straight specific wave conditions, the Yarki island system could just disappear and the cold Baikal water could destroy the entire unique ecosystem of the shallow area of the Upper Angara and the Kichera deltas, including the Upper Angara race of omul; the length of Lake Baikal could extend 40-40 km to the north. There will be changes to the entire lake basin and to its water level regime. It can be seen that the islands continue to deteriorate and that the coastline is primarily destructed on the lake’s side. The main cause of the destruction of the Yarki is the rise in the water level and wave impact. The amplitude of the fluctuations of the water level at its 1 m rise and with controlled runoff has increased in the long-term. This has led to the activization of abrasion processes (The dynamics …, 2011). It is important to emphasize that the catastrophic activization of the coastal processes observed at present has been specifically caused by technogenic factors: the backwater effect of the hydrocomplex. The intense destruction of vegetation by recreating people in the Yarki has enhanced dune deflation and island flattening (Vicka et al, 2006). The paper analyzes changes in the coastline of the Yarki islands based on remote sending data. Regular space image collection is an objective and timely representation of the conditions of the Earth’s surface and of its changes; modern geoinformation technologies of space imagery processing provide for precise georeferencing of multi-temporal data for studies of changes occurring on the Earth’s surface. We have chosen the algorithm of the automated classification of satellite imagery for the

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optimal interpretation and automated zonal classification of the territory. The procedure for satellite interpretation used in our work involved: 1) download of orthorectified Landsat imagery from the Internet; 2) image processing – synthesis of the RGB- composites, coordinatewise isolation of necessary fragments; 3) objects’ interpretation (thematic classification followed by generalization); 4) creation of thematic layers and editing; 5) compilation of the final map of the coastline changes of the sandbar. Image processing was conducted using ENVI 4.7 software (www. ittvis.com/ENVI) that includes the most complete set of functions for remote sensing data interpretation and GIS processing. The thematic vector layers created were processed using GIS-software ArcView 3.3. Mapping of the Yarki island system was based on the multi-temporal and multi-spectral imagery. The data were downloaded from the geoportal of the U.S. Geological Survey (USGS) through the GloVis search system; three «summer» scenes of the Landsat platform on the area of Yarki islands (path = 131, row = 21) were downloaded because the summer period is characterized by stability, duration, and the best quality of light conditions: from July 3, 1994, August 12, 2000, and August 29, 2009. Spatial resolution of images is 15-30 m/pixel. Mandatory requirement for downloading the images was a complete lack of cloudiness (0%), high quality (Qlty = 9) and a sufficient level of image preparation (level L1T – orthorectification, radiometric and atmospheric correction). Using the freely available data determines the easy extension of time series of dynamics of natural and man-made objects in subsequent years. Due to the nature of the Landsat satellite paths, it was not possible to download scenes for the same time-period. Even if the scenes were close in date, other factors interfered: cloudiness and poor image quality; in any case, we tried to obtain the scenes close in dates. One of the main directions of the use of multiband images is the synthesis of color images for visual interpretation with the subsequent automated classification. It is feasible to conduct object definition and delineation using images with intentional false color rendering. We used synthesis of: the near infrared spectral band – red color, the first middle infrared band – green color, and the red visible band – blue color, i.e., we created a pseudo-colored RGB-composite with a combination of channels 4:5:3. This combination of channels allows distinct differentiation of the landwater boundary and accentuation of hidden details poorly visible with the channels in the visible band only. The fragments (21.5 × 11.7 km) that completely encompass the Yarki were cut from the obtained RGB- composites. We used the supervised (i.e., with training) classification with rectangular method to isolate coastline. This method is used in cases when values of spectral brightness of different objects practically do not overlap and there are only few classes present. Two types of the standard sites were selected as the training sets: water surface and land. For each type of the standard sites, we have calculated the average value of pixel brightness and the standard deviation of brightness. The maximal standard deviation from the brightness mean in the «water» class did not exceed 2, which is associated with a relatively uniform characteristics of this object class. The downside of this isolation method is a partial overlap of the brightness parameters of the sandbar shoal and land; these overlaps were eliminated in subsequent processing (Tulokhonov et al, 2010). In addition, in order to differentiate between the land and water surfaces, we used the algorithm of the unsupervised classification (the ISODATA method – the Iterative Self Organizing Data Analysis Technique). It is feasible to use this algorithm in the absence of prior information on a survey object. The method allows delineating contours with a non-contrast (in terms of spectral brightness) structure. We have selected the optimal (in our opinion) parameters: number of classes – 2, maximal number of iterations – 20, convergence limit (a number of pixels that change their class with the next iteration) – 5%, maximal standard deviation from the mean – 13, minimal number of pixels for class isolation – 3, maximal standard deviation inside a class – 5, and minimal spectral distance – 5 pixels. The land areals on the images obtained through the unsupervised and supervised classification appeared to be similar. This is due to the fact that land on the images is relatively uniform in spectral brightness and the water-land boundary is defined clearly because of the reflective properties of the water surface in the utilized combination of the Landsat channels.

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After the post-classification using «majority analysis», the obtained raster images were converted into the vector format. Then, the interpreted contours of the water and land sites were edited in order to obtain only the vector layers of the sandbar. As a result, three multi-temporal vector layers have been obtained: for 1994, 2000, and 2009. The areas of the vector layers of the sandbar Yarki are equal 2.524 km2 in 1994, 2.052 km2 in 2000 and 1.855 km2 in 2009. There is a distinct systematic reduction in the area of the Yarki sandbar in 2009 compared with 2000 (by 0.2 km2) and 1994 (by 0.7 km2) (Fig. 1, 2).

Figure 1. Comparison of the vector layers of 1994 and 2009: changes of the Yarki sandbar

Figure 2. Comparison of the vector layers of 2000 and 2009: changes of the Yarki sandbar

The conclusions of this paper are preliminary and at this time, it is difficult to speak with certainty about a consistent coastline retreat over a hydrological year based only on the analysis of the remote sensing data. It is difficult to quantitatively assess the rates of modern processes of coastal retreat, especially considering the fact that filling and drawdown of Baykal do not occur at a strictly uniform time (it depends on the water content in a particular year, the timing of the filling of the reservoir, the conditions of release of water through the locks of the Irkutsk dam, etc.). For ascertaining the rate of coastal erosion (in m/year) and the situation forecast, further monitoring work is required.

Wind and wave conditions Wave activity within the entire extent of the shore from the village Nishzneangarsk to the Dagara with the estuarial part of the Upper Angara River, has been already incapable of supplying the inflow of suspended sandy particles with floodwater. Consequently, the volume of the incoming sedimentary material not only does not compensate the occurring destruction of the Yarki islands, but does not even maintain the conditions that had existed prior to the construction of the Irkutsk hydroelectric system before the rise of the lake level to the top of the floodcontrol capacity (Potemkina, Suturyn, 2011).

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According to the estimate of A.L. Rybak who has conducted lithodynamic research in this area, the long-term annual rate of erosion of the islands is 0.8-1.0 m/year. Consequently, the complete destruction of the Yarki may occur in the nearest 30-40 years (Ymetkhenov, 1997). Many islands of the Yarki system have been destructed by wave impact. In the fall storm period and at southern winds, surging associated with the nature of the spatial contours of the lake and the wind regime determine a substantial increase of the lake level in its northern part over the limit levels identified in the federal law. Specifically this situation causes the intense destruction of the Yarki island system – there are substantial changes in the sand islands and in bay-, beach-, and shoal-bars. Moreover, even at the existing water level regime, the destruction of the Yarki in the nearest future may cause the expansion of the cold Baikal water further north with the catastrophic consequences to the biota of the estuarial areas of the Kichera and the Upper Angara. Surging and seiche vibrations are specific for the Lake Baikal. It varies in the different parts of the lake, depends on the strength, direction and duration of the wind. Surging vibrations intensify during the fall season at the period of the most significant storm activity. Seiche water level fluctuations occur almost every day, and the ice cover does not prevent their formation and does not impact to their duration and period. One of the main reasons of this is the difference between the atmospheric pressure at different ends of the lake Baikal and the wind activity. Daily water level fluctuations obtain the marks up to 20-30 cm. These circumstances give reason to preserve ecologically balanced system and to raise the question of the control of the water level regime within the average longterm fluctuations that existed before the construction of the Baikal Irkutsk hydropower complex, when fluctuations were in the range of 82 cm. Of course, this greatly complicates the task of hydropower electricity generation and requires its seasonal spread. However, this scenario is realistic, provided a sufficient number of hydrological stations exist and accurate weather forecasts are possible to allow storing in advance or releasing water of the Irkutsk reservoir.

Level of Lake Baikal The determination of the lake level currently carries out at only one point – at the Port Baikal (source of Angara River). Additional measurements are made for control marks on the other six stations of SE «Buryat Center of hydrometeorology and monitoring of environment» (BCGMS), which located on the eastern and northern coasts. According to these incomplete data, difference between simultaneous observations at various observation points reaches 19 cm. The level of measurement at Baikal can exceed the mark of eastern coast stations, that distant less than 50 km, which is contrary to the laws of Hydrophysics. According to the same data, the average daily level of 456.04 m stays unchanged for the separate decades, while the marks on the other posts fluctuates in a wide range. These circumstances lead to the conclusion that the observations performed on gauging station Baikal, which is located on the territory of Irkutsk Oblast, are not quite correct and reflect the departmental interests of «Irkutskenergo». Because its power generation depends on the volume of the Baikal water that discharged through the hydroelectric power station.

Conclusion With a lack of hydro-meteorological information, the need to increase the productivity of shallow waters of Baikal, and development of the tourism industry in the special economic zones, it is necessary to be more careful in respect to the coastal processes. This problem can only be solved with the mandatory legislative decrease of the amplitude of the minimal and maximal levels of the Lake Baikal water to the natural conditions prior to the construction of the hydropower system. In order to obtain timely information on the water level regime, to improve forecasting, and to avoid the influence of different natural phenomena on the water level of the lake within its entire area, it is necessary increase to the number of the gauging stations on the eastern and northern coasts of Lake Baikal. First, at the sites that protected from the intense wind activity. We offer to create four new hydropositions on the eastern and northern shores of the lake: 1) in Istomino in the delta of Selenga River, 2) on the shore of Chyvyrkuysky bay; 3) on the shore of Barguzynsky bay; 4) in the Dagarskaya bay to the north of the

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Yarki island system. Thus, the exploitation of the Angara hydroelectric power chain can be more efficient, the water transport along the Yenisei River can be increased, and losses at the catastrophic events in the basin of Selenga River can be reduced. Also, the acquisition of reliable and distributed hydrometeorological data system and its correct interpretation become more relevant in connection with the plans of hydraulic engineering in Mongolia.

This work was partially supported by RFBR project 12-05-98066-r_sibir_a «Comprehensive researches of land degradation and desertification of the Baikal region using geographic information technologies».

References Galaziy G.I. Lake Baikal in questions and answers. – M.: Mysl., 1988. – 221 p. Monitoring the status of Lake Baikal / Izrael Yu.A., Anokhin Yu.A. – Gidrometeoizdat, 1991. – 262 p. Atlas of Lake Baikal. – Moscow: Federal Service of Geodesy and Cartography of Russia, 1993. – 160 p. Hydropower and the Lake Baikal ecosystem conditions / Atutov A.A., Pronin N.M., Tulokhonov A.K. (Ed-in-Chief.). – Novosibirsk: Publishing House of RAS, 1999. – 281 p. The dynamics of the shores of Lake Baikal under the new water level / Pynygyn A.A. (Ed.). – Moscow: Nauka, 1976. – 88 p. Vicka S., Kozyreva Ye.A., Trzhtsinsky Yu.B., Shchipek T. The islands Yarki in Baikal – an example of the modern transformation of landscapes. Irkutsk-Sosnowets: IEC SB RAS. – 2006. – 69 p. Tulokhonov A.K., Tsydypov B.Z., Garmaev Ye.Zh., Andreev S.G. Dynamics of the shoreline of Lake Baikal on multi-temporal satellite images Landsat (an example of the Selenga delta). – Ulan-Ude. Deltas of Eurasia: the origin, evolution, ecology, and economic development. –2010. – pp. 103-110. Potemkina T.G., Suturyn A.N. Geo-ecological aspects of the conservation of the sandbar Yarki (Northern Baikal) // Environmental Engineering. – 2011. – № 6. – pp. 52-61. Ymetkhenov A.B. The nature of the transition zone on the example of the Baikal region. – Novosibirsk: Publishing House of the SB RAS, 1997. – 231 p.

APPLICATION OF GIS-TECHNOLOGIES FOR ENVIRONMENTAL HEALTH HAZARDS MAPPING

______D.O. Dushkova Moscow State University, Faculty of Geography, Moscow, Russia. [email protected]

Abstract Geographical information systems (GIS) provide a powerful technology for carrying out environmental health hazard mapping and for displaying and communicating the results. The World Health Organization defines environmental health hazard mapping as a set of methods for mapping and analyzing the distribution, character and magnitude of environmental conditions and processes which might pose significant threats to human health. Databases concerning population exposure to environmental pollution in the North of Russia are developed. Using the GIS-technologies, we analyzed the relationship between the level of industrial pollution, the degradation of landscapes and human health impact in the industrial regions of the Russian North. This territory is known for a number of reasons from an international and regional (Russian) perspective: it is one of the most developed regions in the whole Arctic and one of the largest national suppliers of natural resources as well as a unique biosphere reserve. Main types of economic activities causing environmental health hazards and main ecosystem disruptions centers were identified. A classification of environmental

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health hazards for studied regions was done. Technogenic impact and ecological risks were assessed and linked to human morbidity, especially to ecology-connected morbidity indicators. A number of maps on the environment state and human health characteristics, based on the obtained data and the applied methods of mathematical and cartographical modeling, were created on the different scale levels (local, regional etc.). It was shown, that such kind of research provides useful information in support of environmental health policies, and it will also help to instil a more strategic, forwardlooking and information-based approach to decision-making amongst those concerned.

Keywords: GIS, environment, human health, environmental health hazard mapping, the Russian North

The study of environmental health has a long tradition. As early as 400 BC, Hippocrates said that one's health depends on the air one breathes, the water one drinks, and the environment in which one lives (cited from Prokhorov, 2007). According to WHO (1997) environmental health addresses all the physical, chemical, and biological factors external to a person, and all the related factors impacting behaviours. Environmental health research includes the assessment and control of those environmental factors that can potentially affect health. It is targeted towards preventing disease and creating health- supportive environments (WHO 2014). Conducting environmental health studies has many difficulties. For example, it is often difficult to have an accurate assessment of exposure and to measure the impact of the environment on health outcomes etc. GIS technology is helping environmental health researchers address these challenges and many others. A Geographic information system (GIS) is an integrated collection of computer software and data used to view and manage information connected with specific locations, analyze spatial relationships, and model spatial processes (Wade and Somer, 2006). GIS provides new opportunities for environmental health issues to study associations between environmental exposures and the spatial distribution of disease. GIS is a powerful computer mapping and analysis technology capable of integrating large quantities of geographic (spatial) data as well as linking geographic with nongeographic data (e.g., demographic information, environmental exposure levels) (ESRI 2011). In compare to environmental health study the concept of environmental health hazard mapping is relatively new. The term hazard may be defined as follows. Hazard refers to those factors or conditions which have the potential to pose a threat to human wellbeing and health. A set of methods for mapping and analyzing the distribution, character and magnitude of environmental conditions and processes which might pose significant threats to human health represent environmental health hazard mapping. In this meaning mapping refers to more than simply the production of maps. It is an often lengthy and complex analytical process, it expresses this reality depends upon the decisions made, and the methods used, during this mapping process (ESRI 2011). Many examples of environmental health hazard mapping, which match the definition given above, already exist in different parts of the world. Indeed, in many countries (UK, USA, Australia, Philippines, Indonesia etc.) hazard mapping is an integral part of national or regional planning and health protection policies. For Russia it is a relatively new tradition. There are many examples of the maps of environmental situation, few in number of environmental hazards. But they involve only mapping of the distribution and magnitude of environmental hazards with the capacity to affect health, without consideration of the population. This is based solely on information on the environment. Environmental health hazard mapping focuses attention on the environment and supports an essentially precautionary approach to policy and intervention. Its advantages include that it shows potential risks to health before they occur. In this paper we provide an overview of some of the capabilities of GIS technology for the environmental health issues. Using experiences and methods of studies already exist (Briggs, 2000; ESRI, 2011; Kistemann et al, 2002; WHO, 1997, 2014) we have tried to adapt them to the reality of Russia. Special attention was paid to the industrial areas of the North of Russia where on account of strong anthropogenic influence negative natural changes were shown to have occurred and led to an unfavorable ecological situation. The negative environmental changes that have ensued from

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industrialization in the Far North of Russia over the course of the 20th century and into the present have led to a major decline in the state of health of the local population. On the one hand, the North of Russia is a region which is characterized by great sensitivity of the ecosystems. On the other hand, it is marked by higher demands on people’s ability to adapt to environmental conditions. The study was carried out within the framework of several projects: “Analysis of modern nature management impact on ecological parameters of natural capital of the Russian North” (2008-2010) supported by grant of Russian Foundation for Basic Research and “Peculiarities of using nature in connection with the conditions of human health in the industrial regions of the Russian North with the goal of sustainable development” (2010-2012) supported by the grant of Moscow State University for young scientists. Basic materials for this study were received from long-term field works (2000–2009), recent regional reports on natural environment situation, and primary data received from regional scientific institutes and health services. The Russian North belongs to the circumpolar region with rapidly developing economy. Its territory including marine economic zone and continental shelf occupies 60% of the Russian Federation. The development of the Russian North is focused on exploitation of natural resources. The total volume of industrial production of the region is twice more than Russian average. Northern ports and transcontinental corridors have international significance (Evseev et al, 2000). The leading role in industry plays non-ferrous metallurgy presented by “Norilsk Nickel” concern. Its factories are situated in Nickel, Zapolyarny, Monchegorsk, and Norilsk. Large iron ore processing plant is situated in Olenegorsk. A large mining company extracting and processing apatite-nepheline ores is situated in Kirovsk and Apatity. Pulp and paper plants and timber processing industry are situated in Arkhangelsk, Novodvinsk, and Koryazhma. Economic development was not accompanied by adequate understanding of environment impact danger and necessity of protection measures. Scientific data concerning sustainable maximal levels of environment pollutants regarding various ecosystems and different climatic zones were not taken into consideration. This caused heavy pollution loads and ecosystem disruptions. Ecological hot spots appeared in Russian northern cities Monchegorsk, Arkhangelsk, Vorkuta, Norilsk, etc. The detailed results were presented in previously research (Kosenkova et al, 2005; Dushkova and Evseev, 2011, 2012). The major human health hazards in the studied regions are connected with environmental pollution and disruption. High degree of environmental health hazards at the Russian North caused by the following pollutants: sulfur and nitrogen oxides, heavy metals, oil and polyaromatic hydrocarbons, and radionuclides. The problems mentioned above become of special importance regarding natural conditions of the territory, which belongs to high latitudes, is situated beyond the Polar Circle almost everywhere. Transpolar position explains nature peculiarities of the Russian North, an area with fragile ecosystems. They are as follows: low speed of biogeochemical turnover; severity of climate; unstable permafrost at vast territories; low soil and water self-purification potential; low speed of self-recovery of disturbed ecosystems; low biodiversity; slow restoration of disturbed soils; slow restoration of many plant communities (up to several centuries). This study presents classification and assessment of technogenic impact and ecological hazards at the regions of the Russian North supplemented by casual chain analysis of the system “environment – human health” regarding ecology-connected morbidity indicators. Various categories and examples of environmental health hazards at the North of Russia with the consequences for human health are presented in the table 1.

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Table 1. A classification of environmental health hazards at the North of Russia Category Examples of hazards Health risks Natural hazards Avalanches Primarily direct injuries from avalanches; includes rock and snow avalanches Earthquakes Includes direct injury from effects of earth tremors (e.g. building collapse), and indirect effects (e.g. of flooding, epidemics and famine) Flooding/storms Includes direct effects of drowning and injury by floods / storms, and indirect effects of water contamination, famine and epidemics Hurricanes/wind Primarily direct effects of injury (e.g. by collapsing buildings), but may also include longer-term effects of famine and contamination/loss of water supplies Lightning strikes Direct injury Soil erosion Primarily famine and poor diet due to effects on desertification food supply UV radiation Skin cancer Atmospheric Outdoor air pollution Wide range of respiratory, pulmonary and cardio-vascular hazards illnesses and cancers Water-related Surface water pollution Primarily diarrheal and gastro-intestinal diseases, but may hazards also include chemical poisoning Drinking water Gastro-intestinal and urinary diseases; rarely chemical contamination poisoning Food-borne Biological contamination Wide range of diseases of the digestive system hazards Chemical contamination Diseases of the digestive and urinary systems; rarely chemical poisoning Vector-borne Water-related vectors Infectious and parasitic diseases hazards Animal-related vectors Infectious and parasitic diseases Occupational Industrial pollutants Wide range of respiratory, pulmonary and cardio-vascular hazards illnesses and cancers; chemical poisoning Occupational accidents Acute and chronic physical injury Infrastructural Traffic accidents Physical injury (to vehicle occupants and pedestrians/ hazards cyclists) Industrial accidents Primarily acute physical injury (e.g. by fire, explosions), chemical poisoning and respiratory effects Contaminated land Mainly diseases of digestive and urinary system

Methodic was developed in accordance with the scientific approaches to a problem (Briggs, 2000, Kistemann et al, 2002, WHO, 2014) and concerns the steps presented in the figure 1. Physical- geographical conditions understood as natural hazards combined with heavy technogenic load are to blame for serious health problems in the regions of the Russian North. They explain heightened sensitiveness of northern population to environmental quality. Migrants experience more adaptation difficulties than locals. Pollution produces heightened health hazards in the unfavourable environmental background. Investigations of morbidity and environment pollution correlations showed their close connection, especially for ecology-connected morbidity indicators: respiratory diseases, cardiovascular and blood diseases, digestion diseases, cancer, and inborn diseases (Dushkova and Evseev, 2012).

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Figure 1. The steps in environmental health hazards mapping

In the course of a long-term research project, databases concerning population exposure to natural and technogenic hazards are developed using geographic information systems to map these factors in the North of Russia. In particular, environmental damage was shown to be a result of industrial nature-use and its influence on human health and quality of life, analyzing their causes. A number of retrospective and forecasting maps, based on the obtained data and the applied methods of mathematical and cartographical modeling, were created. The studies enabled to reveal 4 zones of heightened environmental health hazards on the Russian North: several cities in Murmansk and Arkhangelsk regions, as well as Norilsk and Vorkuta. These zones are distinct centers of social-ecological tense situation. These centers belong to technogenic anomalies which negative health impact is considerably higher than that of either geographical or geochemical factors. It was found out that population health, indicators of ecologically determined pathology for adults and children, death rate and life time differ greatly in these four regions from the rest of the territory. It was demonstrated that unique territories of the Russian North with rich natural resources are playing an important role in global ecological processes and need specific approaches to economic development. One of the most influential uses of environmental health hazard mapping is to inform local communities. Actually it is a basis for enhancing public participation in environmental health protection (WHO, 2014). As examples are the maps and databases which were done in the framework of the project named earlier. The maps are used to raise awareness in local communities and to lobby state and federal governments and industry. This relates to sites regarded as having potential risks of exposure to humans.

References Briggs, D. (2000) Environmental health hazard mapping for Africa. WHO-AFRO, Harare, Zimbabwe. Dushkova, D. and A. Evseev (2012) The Russian North: Environment and Human Health Risk Assessment. In: Kremers H and Susini A (eds.) Risk Models and Applications. Collected Papers. Berlin: CODATA-Germany, 89–102.

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Dushkova, D.O., and A.V. Evseev (2011) Ekologija i zdorov’e cheloveka: regional‘nyie issledovanija na Evropejskom Severe Rossii (Ecology and human health: regional studies at the European North of Russia). Moscow: Geographical Faculty of Moscow State university (in Russian). ESRI (2011) Geographic Information Systems and Environmental Health: Incorporating Esri Technology and Services. An Esri White Paper. New York. Evseev, A.V., A.P. Belousova, V.V. Ivanov, T.M. Krasovskaya, T.G. Sazykina and N.P. Solntseva (2000) Environmental hot spots and impact zones of the Russian Arctic. Moscow. Kistemann T., F. Dangendorf and J. Schweikart (2002) New perspectives on the use of Geographical Information Systems (GIS) in environmental health sciences. International Journal of Hygiene and Environmental Health. Vol. 205, Issue 3, PP. 169–181. Kosenkova (Dushkova) D., M. Zierdt and A. Evseev (2005) Humanökologische Probleme im Norden Russlands. Norden. Beiträge zur geographischen Nordeuropaforschung. Vol. 17, PP. 19–28. Prokhorov, B. (2007) Ekologija cheloveka (human ecology). Textbook. M.: Academia (in Russian). Wade, T., and S. Somer (2006) A to Z GIS: An Illustrated Dictionary of Geographic Information Systems. Redlands, CA: Esri Press. WHO (1997) Health and environment in sustainable development. Five years after the Earth Summit. Geneva: WHO. WHO (2014) World Health Organization. Environmental Health. Retrieved April 8, 2014, http://www.who.int/topics/environmental_health/en/.

THE STUDY OF THE SPATIAL DISTRIBUTION OF SOIL AREAS USING GIS TECHNOLOGIES IN SMOLENSK REGION

______A.F. Varfolomeev Mordovia State University, Saransk, Russia S.P. Evdokimov Smolensk State University, Smolensk, Russia. [email protected]

Abstract In order to analyze and evaluate the relationship between the spatial distribution of soil and relief digital models of soil and topographic maps were created. On the basis of the digital model of the soil map of Smolensk region we built the cartogram of soil uniformity, which is based on the entropy function and the cartogram of quantitative distribution of soil areas. The largest number of soil areas is observed in the central part of the region, and the lowest – in the north-east and partly in the west and south of the territory.

Keywords: soil; area; landscape; relief; map; geoinformatics; entropy; model

The relief is one of the determining factors of the spatial distribution of soil and its type diversity. It controls the groundwater level and distribution of solar radiation, and those in turn control the nature of geochemical processes and vegetation. When choosing the software for the study of the spread of soil areas by the example of Smolensk region the general-purpose GIS tool (ArcView version 3.1.) was used. At the initial stage we created a digital model of the soil map of Smolensk region. The soil map of Smolensk region with scale 1:200000 served as a source material (Soil map…, 1989). It was originally presented as a bitmap array in *.jpg format and its size was 300 megabytes, which hampered further work with it. In this connection the original bitmap array has been processed in the Adobe Photoshop CS2 graphic editor. Binding and digitizing of the original cartographic material were carried out in

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Easy Trace 7.3 vector editor. There is no grid on the original map, so when binding the pattern of hydrographic system was used as reference points. We obtained geographic coordinates of reference points in WGS-84 coordinate system in the Google Earth 6.0.3 software, which subsequently with help of the cartographic calculator in ERDAS IMAGINE 8.4 software have been translated into flat rectangular coordinates of CS-42 coordinate system of Gauss Kruger projection. Thus, 20 reference points were chosen and 5 of them were used as reference. The next step was digitizing of the soil map. As a result we obtained the following layers: soil areas, rivers and settlements. For the soil areas layer we created topologically correct structure in ERDAS IMAGINE 8.4 software using Vector module. 16 518 polygonal objects were obtained as the result. All received layers were exported to ArcView 3.1 GIS, where the identification of soil contours was carried out and legends to all layers were appropriately configured. In order to analyze and evaluate the relationship between the spatial distribution of soil and relief the digital model of the topographic map was created (Evdokimov, Kovalev, 2011). Relief digital model was created on the basis of topographic maps with the scale of 1:200 000 with contour interval of 10 meters. Also the hydrography, elevation marks, and water edge were taken into account. The raster cell size is 100x100 meters. Accuracy of the relief digital model is sufficient for a regional study. According to the study elevations in the region occupy 59%, lowlands - 41%, the average absolute altitude mark for Smolensk region is 206 m, and the standard deviation is 23 m. Considering the variogram of altitude values we can note two peak elevations - 200-202 m and 219 -221 m. Each of these groups corresponds to 6% of the region territory. When analyzing the digital model of the soil map of Smolensk region we obtained the following statistical characteristics: the total area of the territory is 49.700 thousand km², the number of soil areas is 16 518 and the maximum area of the soil area is 705 km²; the minimum area of the soil area is 0,016 km2; an average area of the soil area is 3 km²; sod-podzol eroded soils (4 976 areas) and eroded and drift soils of ravines, gullies, floodplains of small rivers and adjacent slopes (4 540 areas) are the most common soil areas by the number; bog transition peaty soils on small and medium turfs (24 areas) and alluvial sod soil (26 areas) are the least spread soil areas; by area size sod-podzolic low gleyic soil dominates (total area of soil areas is 1455 km²); sod-podzolic soils with the second humus horizon (total area of soil area is 2 km²) are the least common by area size. Based on a digital model of the soil map of Smolensk region the cartograms of soil uniformity (Fig. 1) and quantitative distribution of soil areas were built (Fig. 2). Building of cartogram data was performed using the functions available in Avenue language in ArcView 3.1 package. Using these functions a range of modules was written. The grid of squares was created with the help of “Grid generation” module. The value of the sliding window of 10 by 10 km that comprises 100 km2 was accepted as an averaging cell. Grid cells of this magnitude (for practical reasons) provide sufficient statistical sampling and visual presentation of the evaluation. By using the “Analysis of the territory” module a relative entropy and quantitative distribution of soil areas were calculated in each grid cell.

Entropy E(A) of some system (A) is the sum of products of probabilities ω i - of the various states of this system multiply by the logarithms of probabilities taken with the opposite sign: n i log 2 i Е(А) = Е(ω 1 , ω 2 , …, ω n ) = - i1 (1) E(A) function vanishes when the system has only one state (n=1). It is assumed that if n=1 E(A) is also zero. For a given number of states function is maximized if all states are equally probable, while increasing the number of states E(A) has been steadily growing. Finally, and it is the most important, the function is additive, i.e. when several systems are combined into one, their entropy can be summarized as:

Е(А)+Е(В) + ...+E(N) = Е(А+В+ ...+N) (2)

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Figure 1. Cartogram of soil uniformity

These properties allow using entropy as a measure of uniformity (heterogeneity) of cartographic representation, and also as a measure of phenomena relation shown on different maps (Berlyant, 1986). Entropy depends both on the number of areas on the map, and the area size per each of them. In this case however the entropy index does not take into account the irregularity, dispersion of areas and other features of dissection: everything is determined by the ratio of areas. For entropy calculation it is necessary only to determine on the map the proportion of each area ωi, which is a ratio of the area of this soil area to the area of all soil areas on the map. Studying the cartogram of soil uniformity and on the basis of entropy values received, we can say that the greatest uniformity of soil areas is observed in the central part of the region, and the lowest one in the north-west and north-east. This is due to the fact that the north-western and north-eastern parts of the region are in the lowland areas. North-west region is located in the interfluve of Western Dvina and the Dnieper, and a network of reservoirs is located in the north-east of the region. Groundwater depth preferably varies from 1 to 3 m. This causes the development on this territory at the same time the sod, podzolic and bog soil formation processes and thus explains the great heterogeneity of the soil cover. It may be noted that the entropy coefficient value for the selected grid cell does not exceed 0.5 and an average value is 0.2.

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Figure 2. Cartogram of quantitative distribution of soil areas

During the study the quantitative characteristics of soil areas distribution was estimated. Based on the name itself, we can say that the quantitative distribution of soil areas examines the quantitative aspect of the phenomenon. In this case, the number of soil areas is determined in each square of the given grid, and the qualitative component is not accounted for. Based on this cartogram and using the standard Spatial Analyst module of ArcView 3.1 GIS software, a Grid model was built (thematic raster model) on the territory of Smolensk region, which clearly characterizes the quantitative distribution of soil areas (Fig. 3). According to the cartogram and grid model we can say that the greatest number of soil areas is observed in the central part of the region, and the lowest one in the north-east and partially in the west and south of the territory. Parts with the least number of soil areas are confined to the river network, reservoirs and lakes, that’s why small variety of sediments and alluvial soil types dominate in this territory. Maximum number of soil areas per one cell is 13, average amount is 8.

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Figure 3. Grid model of quantitative distribution of soil areas

At the present stage of science development it is possible to evaluate the spatial distribution of soil using GIS technologies, which allow creating digital models of reality. On the basis of these correct digital models it is possible to carry out the evaluation and comparison of vast areas, promptly obtain various statistical and descriptive characteristics, select the most relevant results and present them both numerically and graphically (Varfolomeev, 2003).

References Soil map of Smolensk region of scale 1:200 000 / A.A. Maymusov, N.I. Antonov. Minsk: GUGK USSR, 1989. Evdokimov S.P, Kovalev D.V. Landscape differentiation in Smolensk region // Proceedings of the Smolensk State University. 2011. № 3. P. 324-331. Berlyant A.M. Image space: map and information, 1986. 240 p. Varfolomeev A.F. GIS for the assessment of natural and anthropogenic factors in territorial natural resources // Intercarto 9: GIS for sustainable development. Proceedings of the international conference, Novorossiysk, 25-29 June 2003. P. 173-179.

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GEOMORPHOLOGICAL FEATURES OF ANCIENT VOLCANIC TERRAIN OF MARS

______S.G. Pugacheva, V.V. Shevchenko Sternberg State Astronomical Institute MSU, Moscow, Russia. [email protected]

Abstract The article considers geological and morphological features of a volcanic relief of a surface of a planet Mars. Effusive magmatism processes at early stages of planet crust forming come out in structure of solidified relief forms of Mars. The volcanic relief of a planet represents relic ancient line oriented forms and central lava flooding. Linear forms generate shield volcanoes, chains of volcanic mountains and radial-concentric faults. Hypsometric high-rise profiles of volcanoes and average slopes surface of the relief defined on the materials of Mars Orbiter Laser Altimeter (MOLA) of the spacecraft Mars Global Surveyor (NASA). The relative age of volcanoes and volcanic plains is estimated on density of shock craters. The system of the nomenclature of names of volcanic and extended forms of relief Mars, according International Astronomical Union (IAU), is presented in paper. The results of studies give an opportunity to compare scientific and technical information about the planet and make considerably easier the usage of space survey material in studying the surface geomorphology of Mars in view of outlooks of the planet learning.

Introduction The article presents basic features of volcanic surface relief of earth-type planets. Effusive magmatism processes at early stages of planet crust forming come out in structure of solidified relief forms of Mars, Venus, Mercury and Moon. Volcanic relief of planets represents relict ancient line oriented forms, areal and central lava flooding. Linear forms generate shield volcanoes, chains of volcanic mountains and radial-concentric faults. Major volcanic edifices you can see on Mars. Shield volcanoes located in the northern hemisphere of Mars, huge shield volcanoes Olympus, Arsia, Pavonis and Alba, represent them.

Figure 1. The map of geological types of a relief of Mars is submitted. The regions of location of volcanic structures and volcanic plains painted on the map in red color and marked legend of the map digits of 8 - volcanic structure, 9 - volcanic plains

Detailed description of the geologic types of Martian terrain gives in articles (Brian et al, 2003; Tanaka et al, 1992; Сиротин, 2006).

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Volcanoes and volcanic plains The largest volcanic constructions volcanoes and volcanic plains are located in the northern hemisphere of Mars in Tharsis Montes (12°S–16°N; 101°W-125°W), Elysium Planum (15°N-35°N; 200°W-220°W) and in the southern hemisphere in northern part of the Hellas Planitia (30°S-55°S; 275°W-310°W). Volcanic Tharsis Planum and Elysium Planum stretch for huge territories. So the size volcanic Thasis Planum makes 4000 km on latitude and 3000 km on longitude. Some volcanoes are located in areas of the Acidalia Planitia (35°N-55°N; 10°W-50°W) and Hesperia Planum (10°S-35°S; 240°W-280°W) (Carr; Справочник …; Neukum et al, 2010). On a surface of Mars about 70 ancient volcanoes, from them the hugest shield volcanoes Olympus Mons (18°N; 133°W), Arsia Mons (9°S; 121°W), Pavonis Mons ((1°N; 113°W), Ascraeus Mons (12°N; 104°W) and Alba Patera (40°N; 110°W) are found. These shield volcanoes are shown on the hypsometric map of Mars NASA 1:5 million-scale (Fig. 2) (Hypsometric …). Olympus is the highest in comparison with the volcanoes on other planets. The basal diameter of Olympus Mons is 700 km, its height is 27 km. As a comparison, the largest ancient shield volcano on Earth (Mauna Loa, Hawaiian Islands) has the diameter of 200 km, the height of the volcano above the Pacific Ocean bed is 9 km. Martian volcanoes have peculiar characteristic features: volcano shield, calderas at tops, arched grabens and chains of craters round the calderas (Fig. 3).

Figure 2. Approximate boundaries for Mars regional feature names

The age of more ancient Martian volcanoes, like Arsia, Ascraens, Pavonis and others, partly buried under lavas of later overflow, comes to 4*108 – 0.9*108 years. Martian volcanic plains are limited to mountainous areas of Tharsis and Elysium, and run vast domain. So, the dimensions of Tharsis volcanic field come to 4000 km northing and 3000 km easting. Huge canyons, flooded with lava, reside on Mars surface. One of them, Marineris Valles (0°-17°N; 32°W-95°W) , is 4.5 thousands kilometers by length, canyon width exceeds 100 kilometers, and depth is about 2-3 kilometers (Whitford-Stark, 1982; Masursky, 1973).

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Figure 3. High-rise profiles of a volcano the Olympus on Mars and volcanoes on the Earth (on left). The figure placed on the right picture of ancient extinct volcano Olympus Mons (photo NASA)

Great low volcano Alba is located in the Northern part of the Tharsis Planum. The maximum height of the volcano is relatively small 6.8 km, but the area lava emission huge 1350 km from the peak of the volcano. The slopes of the volcano are very shallow, the average slope of the steep slope are 0.5°, which is five times less inclination of the slopes of other volcanoes of the Tharsis Planum. Central part of the volcano surrounded incomplete ring grabens and cracks. The length of some lava flows is over 300 km. Valleys between mountain ranges in the Northern part of the form of small ravines and channels formed by flowing lava or water. On the Pater Alba discovered the most ancient volcanic deposits formed from 3600 to 3.200 million years ago. Montes Tharsis formed by stress related to internal pressure and movements of the crust of the planet (Whitford-Stark, 1982; Wilson et al, 1999).

Figure 4. Alba unique volcanic structure has no analogues on Earth or Mars. On the image spacecraft “Mars Express” shows the caldera of the volcano Alba. Diameter caldera is 10 km, a depth of about 600 m

Volcanoes Hecates Tholus (32°N; 210°W), Elysium Mons (25°N; 213°W) and Albor Tholus (19°N; 210°W) are located on the Elysium Planum. Elysium Planum is elevated part of the plain Elysium Planitia, the height of the Planum above the average level is 6 km. Figure 5 shows the hypsometric map Elysium Planum and pictures of volcanoes Elysium Mons and Hekates Tholue. On the map (left) visible at the top of the volcano Hecates Tholus, located in the center of the volcano Elysium Mons, bottom right – Albor Tholus.

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Figure 6. Hypsometric map Elysium Planum. On the map in the center – Elysium Mons, at the top of the Hecates Tholus, at the bottom right of the Albor Tholus. In the centre of the figure shows pictures of the volcano Elysium Planum and Hecates Tholus (right). Photo Mountains Elysium Planum received spacecraft by Mariner 9” October 16, 1972. Spatial resolution – 75 meters per pixel. The Hecates Tholus and Albor Tholus less volcanic Elysium Planum have a convex top and steeper slopes

The height of the mountain Elysium Mons 14 km above the surrounding plains, diameter Caldera 14 km, base diameter of about 500 km. Mountain Elysium Mons the largest volcano on the planum, base diameter of the Hecates Tholus 170 km, and Caldera 10 km. Albor Tholus has a base diameter 160 km, Caldera 30 km (Хохлов, http://www.shvedun.ru/stsol/htm).

Canyons and ancient rivers On the surface of Mars are huge canyons, flooded by lava. One of them is a giant canyon in Mariner valley outdoor spacecraft “Mariner-9” in 1971 (Fig. 7). The length of the canyon 4.5 thousand km, width 600 km, the depth of 7-10 km. Apparently, the canyons of Mars were never filled with water, their origin is connected with the ancient tectonics of Mars with the movement of huge slabs of the Martian crust. However, the images of the surface spacecraft show traces of water erosion formations in the channels of the ancient rivers of Mars. In particular, this is seen on the example of the ancient riverbed of the river Nergal (25°S-30°S; 36°W-48°W). Winding riverbed Nergal has many tributaries, the length of the riverbed 400 km (Fig.6). Valley Nergal dry, there have long been no water (Ксанфомалити, 1997; Митрофанов, 2005; Basilevsky et al, 2006).

Figure 8. Above: the ancient riverbed Nergal with tributaries. Bottom: the Valley Moadim length is about 700 km (longitude 187°, latitude from 29°N to 14°S)

Many details of the topography of Mars suggest the presence of water in the distant past of the planet. Perhaps winding traces of dry rivers, coming from the Marineris Valles, appeared in the result of sudden floods. Pictures of areas heavily pitted with craters show that the river flowed not their failures, and ran from the hills rounded craters, collecting water, as the rivers of the Earth.

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Figure 9. The plot of the Mariner valley (length more than 3000 km), crosses the area of Coprates Chasma. The depth of the abyss Mariner reaches 10 km. On the Left – wide system of cracks of the Labyrinthus Noctis. The picture obtained OLA “Viking 1” (NASA), 8 June 1998

Sensational discoveries made interplanetary stations “Mars Reconnaissance Orbiter” and “Mars Pathfinder” and the Rover “Opportunity”, Rover “Curiosity” when exploring the surface of Mars. With higher resolution were examined, and the form and structure of stones, some of them were sediments, on the other traces of fractures. Also found a large number of clay, as a result of a long finding material under water. The Rover “Opportunity” has found a large number of sulfur salts (hematite), which are formed in the slow evaporation of the concentrated solutions. The peculiarity of the Martian rivers was their relationship with symptoms similar to karst, to disappear under the surface at some point. On images of the Martian surface everywhere traces of “activity” to strong currents, dry river- beds of the rivers, ravines, gullies, etc. Currently, the water in the Martian rivers, lakes, or seas is not detected. The reason is the highly rarefied atmosphere of Mars, which excludes the presence of water on the surface. Under pressure 6o mbar water boils at a temperature of 2oC. The atmosphere of Mars is composed of carbon dioxide 95%, water vapor 0.05%, oxygen 0.1-0.4%, nitrogen 2.5%.

Conclusions There are several hypotheses for the great catastrophe on Mars. One of them is the formation of canyons on Mars is connected with the cooling of the planet, reduction of geological activity, on the other, the fall of a giant meteorite or a neutron star was held near the solar system. Perhaps Mars gradually lost the atmosphere due to its low mass. Perhaps Mars lost its atmosphere around 5 million years ago. Some facts confirm this hypothesis. On Mars, not all water sources have been subjected to wind erosion. The ancient river beds are not covered with sand, despite the fact that the planet is constantly raging sand storms, the wind speed reaches 50 to 90 m/sec. Relative age of the pack-ice plateau Elysium Planum in the area of the ancient sea is also about 5 million years. In General, the exact age of fresh basalt deposits will only be possible when studying rock samples. The main volcanism on Mars presents drenched basalt plains, similar to the “lunar seas”, formed about 2-3 .5 billion years ago. Separate huge volcanoes, located in the Equatorial zone Mars was formed later, around 1-2 billion years ago. Gradually, this process was stopped, and now on Mars is not observed any active volcanic and tectonic activity. Most likely, it is caused by gradual cooling of subsoil and surface in General.

References Brian, M. H., Roger, J. P., and Raymond, E.A. Explosive volcanism in the Thasis region: Global evidence in the Martian geologic record // J. Geophys. Res. 2003. V.108, No. E9, 5111, p. 15-1 – 15-16. Tanaka, K.D., Scott, D., and Greeley, R. Global stratigraphy,in Mars // Univ. of Ariz. Press Tucson. 1992. P. 345-382. Сиротин В.И. Сравнительная планетология. Труды НИИ геологии ВГУ. Вып. 36, стр. 82-164, 2006. Carr, Michael, H. Volcanism on Mars // Journal of Geophysical Research.1973.V. 78 (20) P.4049– 4062.

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Справочник по планетной номенклатуре (Gazetteer of Planetary Nomenclature) http://planetarynames.wr.usgs.gov. Hypsometric map of the Mars 1:5 million-scale MOLA. USGS NASA http://planetarynames.wr.usgs.gov. Whitford-Stark, J.L. Tharsis Volcanoes: Separation Distances, Relative Ages, Sizes, Morphologies, and Depths of Burial // J. Geophys. Res.1982. 87: 9829–9838. Masursky, H.; Masursky, Harold; Saunders, R. S. An Overview of Geological Results from Mariner 9 // J. Geophys. Res. 1973. 78 (20): 4009–4030. Wilson, L., J, Head W., and Michell K.,L. Tharsis Montes as composite volcanoes: Lines of evidence for explosive volcanism in far-field deposits // Lunar Planet. Sci., 1999. XXIX, abstract 1125. Хохлов С. Вулканы Марса. Элизий. По материалам NASA и Malin Space Science Systems. http://www.shvedun.ru/stsol/htm. Ксанфомалити Л.В. Парад планет. 1997. М: Наука, стр.92-137. Neukum G., Basilevsky A.T. and the HRSC Team. Episodicity of volcanic and fluvial processes on Mars. // The first Moscow Solar System Symposium, Space Research Institute, 2010. Moscow, 1MS3-1- 2. Митрофанов И.Г. Поиски воды на Марсе. 2005. Природа, № 9, стр.34-43. Basilevsky, A. T.; Rodin, A. V.; Raitala, J. et al. (2006). Search for causes of the low epithermal neutron flux anomaly in the Arabia Terra region (Mars). Solar System Research // 2006. Volume 40, Issue 5, pp.355-374.

USING MULTISPECTRAL SATELLITE IMAGES FOR MONITORING OF THE FOREST ECOSYSTEMS STATE

______Iu.F. Rozhkov State Natur Reserve "Olekminsky", Olekminsk, Russia. [email protected] M.Y. Kondakova Hydrochemical Institute, Rostov-on-Don, Russia. [email protected]

Keywords: Satellite image decoding, monitoring of boreal forests, NDVI, Image Difference

Introduction Аreas of forest's fires, violation of landscapes that have arisen as a result of natural disasters and anthropogenic influences, are determined successfully with help of interpretation of satellite images. Monitoring of terrestrial ecosystems, tracking of long-term processes can be carried out with availability of series of pictures, which are covering the long-term observation period. For example, monitoring of forest in Australia has successfully carried out by analysis of satellite images Landsat (Lehmann et al, 2013). In this region, the forest condition, the use of agricultural land (Prishchepov et al, 2012) and the disturbance of landscapes due to industrial development (Linke, McDermid, 2012) and urbanization (Vila , 2012) are evaluated in this way. With help of series of images covering the long-term observation period it can be traced back such process as invasion of alien plant species (Gavier-Pizarro et al, 2012). Using multispectral images of boreal forests, which made during the growing season, it can track the vegetation progress. It is possible to trace the course of long-term processes such as forest restoration after fires, swamping, desertification using images of selected area, which made for 10 - 20 years. The perspective of joint use of NDVI and Image Difference in the boreal forest monitoring of South-Western Yakutia is shown in this paper.

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The aim of research was to evaluate the possibility of using satellite image interpretation in monitoring of the state of the boreal forest on three levels: 1. Assessment of forest at the time of the shooting or mapping; 2. Determination of seasonal changes in forests, mainly during the growing season; 3. Calculation of long-term changes in the studied parameters for the example of forest restoration after the fire.

Methodology The software packages of ArcView-3.3 with modules Image Analisis, Spatual Analist and ENVI- 4.0 was used in this investigation. The binding between satellite images and topographic base was carried out. Then the decoding these images with segregation of predominating types of forests based on their coloring in different regions of the spectrum was held. To carry out continuous monitoring the condition of the boreal forest the high-resolution satellite, such as Landsat TM/ЕТМ+, Aster, Spot, was accomplished. There were 15 Landsat images, which made of the period from 1995 to 2010, 3 Aster images and 9 Spot images. All of them were radiometrically and geometrically corrected. The investigations were carried out in the Southwestern Yakutia, over the area of 1 million hectares. In addition, the tool "classification" (with training and no training) was used (ArcView Image Analisis…, 1998), which based on the method "isodata". Recalculation of a pixel in the square was made. It based on the technical characteristics of the images Landsat, Aster (values of the spatial resolution of the images). Since one pixel is 30 m long side, the pixel area is 900 m².

Results 1. First level of monitoring or mapping at the time of shooting. In this investigation mapping of the forest vegetation by index NDVI, which reflects the current status at the time of shooting, is held. NDVI index gives an estimate of the state of plant biomass and productivity, and the rate of Image Difference gives the characteristic optical density and allows us to estimate the density of the stand. NDVI changes range from -0.3 to + 0.65, and Image Difference from -35 to +350. 2. Second level of monitoring or evaluation of seasonal changes. Definition of the vegetation index and calculation of changes in forest productivity over time were used to assess changes in the state of boreal forests during the growing season. Curves showing changes of productivity of different forest types during the whole growing season were built. Images taken during the period from June to October have been used for these purposes. It is shown that larch forests have the highest productivity in July, while pine and cedar forests have a smoother curve of productivity with a maximum in September (Fig. 1).

Figure1. The curve of vegetation in forests of different types during the growing season

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There is a strong correlation between vegetation index and productivity for different types of ecosystems. The equation of dependence between these two indicators: Y = 1490 X, where Y is the productivity of the ecosystem in g/m² per year; X - is the index of vegetation (NDVI). Authors have calculated the total productivity of the main forest types, which is the total productivity of the four months of the active growing season from June to September. In the other months the vegetation index values are negative because of the biomass growth is absent. Calculations showed that the highest productivity was characteristic of birch forests and larch mixed grass forests - 348.0 g/m2 per year and 283.4 g/m2 per year, respectively (Table 1). The lowest values of productivity were characteristic of dwarf-pine and spruce forest - 168.0 g/m2 per year and 183.8 g/m2 per year, respectively.

Table 1. Productivity of the basic types of boreal forest Type of forest Productivity g/m² per month (P) Forest productivity for the June July August September entire growing season (g/m² per year) Larch forests 27.4 134.2 64.6 57.2 283.4 Cedar forests 37.2 109.2 38.8 82.0 267.2 Cedar elfin wood 2.4 99.4 38.8 27.4 168.0 Pine forests 2.4 94.4 36.2 57.2 190.2 Birch forests 37.2 156.4 89.8 64.6 348.0 Spruce forests 2.4 99.4 34.8 47.2 183.8

The Image Difference definition, made for summer and autumn images allows to divide forest areas composing of deciduous, conifers species and larch, which shedded needles at the end of the vegetation period. Therefore, it is possible to differentiate the pine, cedar and spruce forests on one side and birch, larch, alder forests on the other side. The optical density of the forest after a litter of leaves and needles decreases. The using of tools or subtract the difference between channels (Image Difference) is another way to differentiate between forest types and to assess their condition. The difference between pictures taken in July, September and October was calculated. The difference was calculated in pair: July- September, July - October, September-October on all three channels (R, G, B, or channels 1, 2, 3). The calculation of the ratio between the first channel of one image and the first channel of the second shot is produced, then between the second and third channels of the same pair. From the values of the differences between channels, it becomes possible to estimate the proportions of pine and larch, crown density in mixed forests. Than the thick stands, the lower the value of the difference. In the dense forest with well-developed understory values of the difference channels often have negative values. Conversely, the less stands, with large edges, the higher the value of the difference between channels. Compare the difference between the channels for pine forests and loaches is in the Table 2. In the case of mixed pine – larch forests, the values of the difference between the channels in July-September and September-October columns decreases as the proportion of pine increase.

Table 2. Opportunity of differentiation between larch woods and pine woods An Image Difference for channels over a period of: Type of forests July-September September – October B G R B G R Light pine forest on a slope 72 50 3 56 36 25 Dense pine forest on a slope 50 12 -25 30 10 -8

Light larch forest 180 130 86 150 120 120

Dense larch forest 120 90 50 100 95 90

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An Image Difference for channels over a period of: Type of forests July-September September – October B G R B G R Larch forests with a pine 95 42 2 73 28 22 Pure larch forests 173 122 79 152 118 116

3. Third level monitoring - evaluation of long-term changes in the studied parameters for the example of forest restoration after the fire. NDVI is used in long-term monitoring of the condition of the boreal forests. An analysis of the process of forest restoration after the fire showed how the overgrown wasteland formed on the location of the fire in 1985 (Fig.2).

Figure 2. The border of overgrown fire in 1985 year

Increase of vegetation index is due to shoots which grown in the place of fire. Thus, in the period from 1995 to 2004, overgrown smoother, and for the period from 2004 to 2009 rapid overgrowth (Fig. 3).

Figure 3. Changing the speed of burning overgrown by vegetation index change

In addition, the defined area in which there was a change of index NDVI. During the period from 1995 to 2004 on the area affected by fires (40773 ha), it was registered an increase in productivity on 75% of the area.

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At that, the magnitudes of increase in productivity were different: 50% - 117130 pixels or 1.054 ha; 40% - 480825 pixels or 4.350 ha; 30% - 1.311.670 pixels or 11.805 hectare; 20% - 2.573.325 pixels or 23.160 ha; 10% - 3.468.645 pixels or 31.218 hectares. 24 years later it was happened an increase in the optical density on the images of the overgrowing place of fire. Herewith using the tool «Image Difference» it was defined as far as the optical density has changed when the place of fire was overgrown. Fig. 4 shows how the overgrowing of the place of fire occurs.

Figure 4. The increasing in area, which planted forest in the place of fire in 1985 at the period from 1995 to 2009 yy.

In this case, it is possible to determine the change of areas which occupied by different tree species. If in the image of 1995 the heathland and woodland area occupied 20514 hectares, that in the image of 2004 the wasteland and sparse forest areas decreased to 17,833 hectares, and in 2008 to 13,835 hectares - due to overgrown undergrowth larch, birch and pine. And the area occupied by the larch forest increased from 15,900 hectares in 1995 to 22,774 hectares in 2008. The birch forest area increased from 4,467 ha in 1995 to 7,083 hectares in 2008.

References Lehmann E.A, Wallace J.F., Caccetta P.A., Furby S.A. and Zdunic K., “Forest cover trends from time series Landsat data for the Australian continent,” Int. Journ.of Appl.Earth Observation and Geoinformation, 21,453-462(2013). Prishchepov A.V., Radeloff V.C., Dubinin M. and Alcantara C., “The effect of Landsat ETM/ETM+ image acquision dates on the detection of agricultural land abandonment in Eastern Europe,” Remote Sensing of Environment, 126, 195-209 (2012). Linke J., McDermid G. J. “Monitoring landscape change in multi-use west-central Alberta,Canada using the disturbance-inventory framework,” Remote Sensinge of Environment, 125,112-124 (2012). Vila P., “Mapping urban growth using Soil and Vegetation Index and Landsat data: The Milan (Italy) city area case study,” Lanscape and Urban Planning, 107(3), 245-254 (2012). Gavier-Pizarro G.I., Kuemmerle T., Hoyos L.E., Stewart S. I., Huebner C.D., Keuler, N.S. and Radeloff, V.C., “Monitoring the invasion of an exotic tree (Ligustrum lucidum) from 1983 to 2006 with Landsat TM/ETM+ satellite data and Support Vector Machines in Córdoba, Argentina,” Remote Sensing of Environment, 122, 134-145 (2012) ArcView Image Analisis.User Guide.- М : Data+, 1998.- 214 p.

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ASSESSMENT OF A CURRENT STATE OF RESERVOIRS ON THE BASIS OF REMOTE SENSING OF THE EARTH

______A.V. Skripchinsky Institute of mathematics and natural sciences of the North-Caucasian federal university, Stavropol, Russia. [email protected]

Keywords: reservoir, remote sensing of Earth, Landsat, geoinformation model

The reservoirs represent water objects of land of the artificial origin, demanding control from the person. Similar objects demand system of the supervision, allowing to make modeling for the purpose of the forecast of their development. As the tool for an assessment of a condition of objects of environment most effectively to use data of remote sensing at the solution of problems of management of similar systems and the analysis of their state. These data are documentary confirmation defined time-section at the expense of what they often act as the only objective source of information. Materials of space shooting allow to define a condition of objects several decades ago and to serve as a starting point for their assessment and identification of extent of the occurred changes. In spite of the fact that space images act as carriers of the various information which analysis promotes identification of existential changes and establishment of certain regularities, integrated management of natural and anthropogenous objects not possibly without use of the geographical information systems, acting as a powerful tool of spatial modeling. The assessment of a current state of artificial water objects, is represented to us very perspective, by means of modeling of tendencies of their development as allows to make it remotely. Similar researches are very important now when the considerable part of reservoirs of Stavropol Krai is in operation within 40-50 years. Otkaznensky and Chograysky reservoirs served as objects of research, owing to high rates of changes of the area of a mirror of water. The Otkaznensky reservoir is created in 1965 by control of a drain of the Kuma River on the southern suburb of page. Negative Sovetsky district of Stavropol Krai. The full volume of a reservoir according to the project makes 131 million км³, but for years of operation there was its siltation for 50% (Lurye, Panov, Solomatin, 2001). The reservoir provides seasonal regulation of a drain of water for agricultural grounds and as prevents floods and floodings of lands and settlements during high waters. Chograysky reservoir it was created in 1969-1973 for an irrigation. Now this reservoir needs large volume of works on its cleaning (Shebalkov, 2008). Water of the Chograysky reservoir use for an irrigation of lands and drink. As the first investigation phase definition time-section for research of changes of reservoirs which depended on time of establishment of the area of a mirror of water defined during the period low water level, falling on an early autumn (Lurye, Panov, Salomatin, 2001) acted. Space pictures from satellites Landsat 5 TM, Landast 7 ETM + and Landast 8 received from USGS archive for a narrow time interval (1 month) were the main source of information. A time number of researches covers 28 summer period from 1985 to 2013, mainly cloudless scenes were selected. In work the contour of the coastline of the reservoirs, received from tablets of the topographic maps published in 1985, the researches which have served as a starting point was used. The second investigation phase was geometrical correction of space pictures as separate from them had geographical inaccuracies - a divergence with the card and other pictures to 200 m. Geometrical transformation of space pictures was made in the Erdas Imagine program. For identification of the area of mirrors of reservoirs classification with training, with use of the parametrical rule – the minimum distance was used. This rule allowed to receive the most exact results of a decryption. For identification of overgrown sites and a zone of shoal combinations 3-2-1, 4-5-1 and 4-5-3, and also the NDVI index were used. On the basis of settlement data of the area of a mirror of

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water, we created the model describing change of the Otkaznensky reservoir. Contours of the coastline of a reservoir were the basis for developed model for the 28-year period from 1985 to 2013. In ArcMap the grid with the resolution of 1 hectare was constructed, and with selection use on an arrangement and the calculator of a field one of fields of the table (grid) was filled with attributes. The field of the table received values from 0 to 10 (in the analysis 10 temporary cuts were considered). I acted as the third analysis stage identification of sites of the different depth, the similar technique was developed on the basis of Landsat pictures (Jupp, 1988, Bullard, 1983). Using 1-4 channels of pictures, we allocated areas with depths less than 0.5 meters and more which, were respectively carried to shallow and deep-water sites. For the analysis of change of the coastline of the Otkaznensky reservoir we created digital model of a relief of its territory, as of 1956, that is before flooding and dam construction. The topographic map of scale 1:25 000 was a source of information. This card was transformed, on the basis of a topographic map of 1985, in the Erdas Imagine program. Vectorization of isolines of a relief was made by means of the EasyTrace program, by use of the automatic vectorizer. Further we created TIN model in the ArcGIS program which was draped subsequently with space pictures for different dates from 1987 to 2013. Analyzing the developed model of the Otkaznensky reservoir, it is possible to note that the central region of a reservoir near a dam and certain formed "lakes" along east coast appeared the most static. The most dynamic appeared the territory which at the moment time is occupied with agricultural fields, but by us was considered. In fact, the maximum extent of change corresponds to a temporary interval from the moment of creation of a reservoir of 1966 for 1987. Separately it is worth allocating area of a current of the Kuma River at a confluence of a reservoir. For the 13-year period from 1987 to 2000 there was a considerable change of the mouth of the Kuma River (Fig. 2). During this period, the tendency gained development and arose now. The created model of changes of the water area of a reservoir allows to reveal not only a trend of change in reservoir limits, but also coastline variations. Variability of the coastline is very important for adoption of investment decisions for placement of recreation facilities, fishing lodges, etc.

Figure 1. Extent of change of the water area of a reservoir from 1985 for 2013

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1987 2000 2002

2007 2010 2013 Figure 2. Change of the area of a mirror of the Otkaznensky reservoir in pictures of Landat 5, 7 and 8 given to the 30th meter permission

The Otkaznensky reservoir was created for adjustment of a drain of the Kuma River in this connection the factor of migration of its mouth is important. The river Kuma very muddy river of Stavropol Krai, an average annual turbidity reaches 2-2.8 kg/m³ (Sokolov, 1952). Significant amount of the weighed particles brought by the river has intensive impact on a reservoir. During the period from 2006 to 2013 active migration of the mouth of the Kuma River is observed. The variation of the deepest zone of a reservoir turns out to be consequence of its instability. On the presented scheme (Fig. 3), dependence of a place of a confluence of the Kuma River and change of depth of a reservoir, owing to a considerable share of deposits is distinctly noted. The increase in deposits is dated for places of a confluence of the river in a reservoir. The border of the most deep-water zone of a reservoir moves all farther from a flood spillway. It is important to note that east coast of a reservoir is steep and the river washed away it. Are frequent along this coast and soil collapses. All this promoted formation here to area of deposits which tends to increase. The area of deposits is distinctly allocated in space pictures owing to emergence here shoal areas. The increase in shallow area in the field of a flood spillway water outlet allows to say about possible change of speed and volume of water let out from a reservoir that can constitute a certain danger in the conditions of emergency situations.

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Figure 3. Migration of the bed of the Kuma River at a confluence of the Otkaznensky reservoir and change of deep-water zones

The analysis of changes of the Chograysky reservoir was made at the level of the contours of the coastline received from space pictures from 5 summer temporary logs. The comparative analysis of contours of coastlines for 28 summer period allowed to carry out division into districts of the territory of a reservoir on extent of changes. It is possible to allocate three zones, being characterized with various dynamics (Fig. 4). The central zone stretches from a confluence of river. Pigeon to islands. This zone is characterized by the minimum extent of changes. Here insignificant fluctuations of the coastline of a reservoir are noted. Other two areas have high extent of changes. The first of them is the western zone in "tail" part of the Chograysky reservoir from a place of a confluence of river. Pigeon to the westernmost tip of a reservoir. The western part of a reservoir is subject to a overgrowing and a shallowing. The greatest rates of reduction spare in this part, answer the period from 1985 to 2006 when there was a coastline retreat practically on 5 km. The period from 1985 to 1991 is characterized an increased by the speed of change of the coastline - about 300 m/year, still the high speed of changes is peculiar to the temporary period from 2000 to 2006 when the coastline receded almost on 3 km. In this part of a reservoir the place of a confluence of river is allocated. The pigeon which alluvial deposits press the cape in the reservoir, advanced for the studied 28 summer period more than on 500 m.

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Figure 4. Division into districts of the territory of the Chograysky reservoir on the basis of coastline changes during the period from 1985 to 2013

The most dynamic part of the Chograysky reservoir is the third zone stretching from islands and to a dam of a reservoir. In 1985 in this part of a reservoir there were no islands which started appearing by 1991, and by 2013 created the uniform massif (Fig. 5). The similar situation develops and in northern part of a dam where there is the most active zarastaniye of a reservoir. The southern coast of a reservoir in this zone, in places of a confluence of the rivers and channels is characterized by the greatest changes. In a place of a confluence of river Chogray Square of a zarastaniye and deposits the allyuviya made about 2 sq.km. Kuma-Manych Canal brings in the Chograysky reservoir of water of the Kuma River which form deposits, total area for the studied period made a little more than 1 sq.km. On satellite images the area of deposits an allyuviya and zarastaniye areas, and as mutyevy streams is distinctly allocated. Especially intensively process of formation of the cape answers time from 1985 to 2006, with the periods of the maximum distribution with 1985 on 1991 and from 2000 to 2006. During these periods advance of deposits made to 350 meters for the specified periods. The general tendency characterizing the western zone - a zarastaniye and reduction of the area of the water area. There is an increase in the area of islands and in the future their association with land that considerably will reduce in this part reservoir width to 1.5 km.

1991 1994 2000

2006 2010 2013 Figure 5. Chograysky reservoir. Fragments of pictures of Landat 5, 7 and 8 given to the 30th meter permission

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Thus, on the basis of space pictures and the geoinformation analysis the spatial model describing changes, occurring in the Otkaznesky reservoir was created. Division into districts of the territory of the Chograysky reservoir on extent of changes is carried out, and speeds of changes of its separate sites are determined. Dynamic mapping on the basis of space information occurring at different times is represented to us very perspective as allows to develop the cartographical models illustrating trends of change of reservoirs and to make an assessment of their state.

References Lurye P. M., Panov V.D., Solomatin A.M. Reka Manych: hydrography and drain. - SPb. 2001. - 160с. Sokolov A.A. Gidrografiya of the USSR. - L. 1952. - 287с. Shebalkov N. Dry rest [Electronic resource]: // The Russian newspaper - the North Caucasus. - No. 4626. - 2008 . URL: http://www.rg.ru/2008/04/01/reg-kavkaz/poliv-iznoshennost- stavkray.html#comments (address date: 25.03.2014). Bullard, R.K. Detection of marine contours from Landsat film and tape. In Remote Sensing Applications in Marine Science and Technology, edited by A.P. Cracknell (Dordrecht: D.Reidel). 1983. pp. 373-381. Jupp, D.L.B. Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. Proceedings of the Symposium on Remote Sensing of the Coastal Zone, Gold Coast, Queensland, September 1988, IV.2.1-IV.2.19.

TOTAL SUSPENDED SOLIDS, PARTICULATE ORGANIC MATTER AND SECCHI DEPTH IN THE SEA OF AZOV

______V.V. Sorokina, V.V. Kulygin Institute of Arid Zones of the Southern Scientific Center of RAS, Rostov-on-Don, Russia. [email protected] S.V. Berdnikov Southern Scientific Center of RAS, Rostov-on-Don, Russia

Abstract The results of field studies of total suspended solids (TSS), particulate organic matter (POM) and Secchi depth in the Sea of Azov in 2006-2010 are given in this paper. The seasonal dynamic of TSS and POM concentration in Taganrog Bay is presented. Compare of these parameters for 1970s and 2000s demonstrate the decreasing of TSS concentration and increasing of percentage of organics in Taganrog Bay waters. Long-term variations of Secchi depth depending on hydrological, hydrochemical regimes, organic life and terrigenous sediment inputs are shown.

Keywords: Total suspended solids, particulate organic matter, transparency (Secchi depth), the Sea of Azov

Introduction Suspended solids concentration and water transparency data are necessary for an understanding of production and sedimentation processes in the sea. The Sea of Azov is well studied water body, but despite this, the materials on water turbidity are poor. Fundamental regularities of suspended solids formation and distribution in the Sea of Azov were established on the basis of regular observations in the 1970s (Khrustalev et al., 1982; Khrustalev, 1989). Meanwhile, the Sea of Azov ecosystem has operated under conditions different from those in the 1970s, it concerns hydrometeorological processes, changes in the terrigenous inputs and organic life.

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Field observations Complex expeditions of the Southern Scientific Centre of RAS in recent years are aimed at establishing the relationship between water transparency (Secchi Depth), concentration of total suspended solids (TSS), particulate organic matter (POM) and biomass of planktonic organisms. It allows expanding the existing database, and using historical data of transparency for the assessment of particulate matter in the Sea for long period (since 1920). Field works are performed by conventional methods: determination of transparency - by using Secchi disk, total suspended solids - by filtration, suspended organic matter - by using wet combustion method.

Results The dependency between the transparency (Secchi Depth) and TSS concentration in the water was determined on the basis of the expedition research and can be described by the formula:

y = 13.31x -1.20 where х – Secchi Depth, m; у – the TSS in the surface water (0-0,3 m) of the Sea of Azov, mg/L. It seems interesting to compare the data of TSS concentration and its organic component in different periods of the Sea of Azov ecosystem functioning (during the XX – early XXI centuries). Comparison of these data (the methods of sampling and analysis were the same) for the 1970s and 2000s shows the decreasing of TSS concentration in a half to two times in Taganrog Bay waters in all seasons (Figure 1, Table). Absolute values of particulate organic matter are similar, but its percentage in the total suspended solids is increased.

Figure 1. Total suspended solids (mg/L) in Taganrog Bay in 1971–1977 (Khrustalev et al., 1982; Khrustalev, 1989), and in 2006, 2008-2010 (SSC RAS Database). 1 – Winter, 2 – Spring, 3 – Summer, 4 – Autumn

Thus, we can note the changing balance between the organic and mineral parts of the suspended matter in the indicated periods; the 2000s are characterized by a higher part of organic matter in the TSS in Taganrog Bay. An insufficiency of field observation data in the inner Sea not yet allows confirmation this phenomenon for the entire Sea. Analysis of suspended matter concentration in the Sea of Azov can be supplemented with data of transparency, as they are more numerous and cover a century-long period. Additionally, we can

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analyze the long-term changes in the hydrological and hydrochemical regimes, organic life and sediment inputs of main sources (rivers, the atmosphere, the coast).

Table. Total suspended solids and particulate organic matter concentrations in Taganrog Bay Winter Spring Summer Autumn Layer 1 2 3 1 2 3 1 2 3 1 2 3 mg/L % mg/L % mg/L % mg/L % 1971-1977 (Khrustalev et al., 1982; Khrustalev, 1989) 0–0.5 m deep - - - 27.1 3.2 12 39 6.3 16 27.7 3.6 13 (surface) 0.5–6 m deep - - - 38.2 4.2 11 50.4 6.1 12 34.3 4.1 12 2006, 2008-2010 (SSC RAS Database, this work) 0–0.5 m deep 4.8 1.2 25 12.5 2 16 23 5.8 25 20 4.2 21 (surface) 0.5–6 m deep 11 1.9 17 18 2.7 15 24 4.7 20 23.3 3.3 14 Comments: 1 – total suspended solids; 2 – particulate organic matter (POM concentration is determined by wet combustion method), 3 – percentage POM in total suspended solids.

To date, the Sea of Azov database of SSC RAS contain information about the Secchi depth in the period from 1922 to 2012. Total number of observations is 16501. These data have passed quality control procedure: check of spatial-temporal location, check of measerments’ values, and search for duplicates. Interannual fluctuations of transparency and sediment inputs of the rivers Don and Kuban are in antiphase to 1963 (Fig. 2). This fact proves the role of the solid runoff as one of the main factors in formation of water transparency and total suspended solids concentration in the first half of the twentieth century. In the subsequent time delivery of sedimentary material of the rivers has decreased significantly due to the construction of reservoirs. The river Don solid runoff was reduced 10 times in comparison with the conventionally natural conditions, the river Kuban - 4 times. The overall input of terrigenous matter decreased by 2.5 times between 1940 and 2000 (Sorokina, Berdnikov, 2008). The water transparency was relatively high in 1970-1980-ies, due to reducing the solid river runoff, and relatively low phytoplankton biomass under increased salinity of the Sea. The continued decrease of salinity in the period of 1990-2006 contributed to the gradual growth of phytoplankton biomass in this time. This fact could be the main reason for the increase of the organic component of the suspended matter in the Sea. Among the major sources of particulate matter in the Sea also note resuspension of bottom sediments as a result of storms. General trend of decreasing wind activity of the Sea of Azov basin, reducing the amount of dust storms and sea storms, and reducing of coastal erosion material input were observed since 1975. It contributed to the reduction of the suspended solids in water, increasing transparency and the photic layer depth and increasing of phytoplankton production in the subsequent time. Currently (since 2006), while maintaining the trend of increasing salinity, wind speed in the region and hydrodynamics of the Sea, reverse processes can occur. The decreasing of the organic component of the suspended solids and reduction organic fluxes and stocks in the sediments of the Sea can happen.

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Figure 2. Long-term variations: 1 – Secchi depth of the Sea of Azov, m; 2 - solid runoff, thousand tons (Sorokina et al, 2006), 3 - phytoplankton biomass, mg/m3 (Kovaleva, 2011) and 4 - salinity, ‰ (Ecological Atlas..., 2011)

The research is carried out at the Institute of Arid Zones SSC RAS and at the Southern Scientific Centre of the Russian Academy of Sciences in the frame of the Grant of the Russian Foundation for Basic Research N 14-05-31322 and RAS Presidium program “Fundamental problems of Oceanology: physics, geology, biology, ecology”.

References Kovaleva G. V. Long-term dynamics of phytoplankton biomass in the Sea of Azov // in Proc. Int. Sci. Conf. “Study and Exploration of Marine and Land Ecosystems in Arctic and Antarctic”, Rostov-on-Don, July 6–11, 2011 (Yuzhn. Nauchn. Tsentra Ross. Akad. Nauk, Rostov-on-Don, 2011), pp. 164–166. Khrustalev Yu. P. Zakonomernosti osadkonakopleniya vo vnutrikontinental’nykh moryakh aridnoi zony (Regularities of the Sedimentation in the Inland Seas of the Arid Zone), Leningrad: Nauka, 1989. 261 pp. Khrustalev Yu. P., et al. Quantitative distribution and principal types of sedimentary matter in the Sea of Azov // Lavinnaya sedimentatsiya v okeane (Avalanche-like sedimentation in the ocean), Rostov-on-Don: Rostov State Univ., 1982, pp. 95-118. Sorokina V. V. and Berdnikov S. V. (2008) Mathematical modeling of the terrigenous sedimentation in the Sea of Azov. Oceanology (Engl. Transl.) 48 (3), 418–427. Sorokina V. V., Ivlieva O. V., and Lurie P. M. (2006) Dynamics of inlet in estuary region of Don and Kuban Rivers in the second half of XX century. Vestn. Yuzhn. Nauchn. Tsentra Ross. Akad. Nauk 2 (2), 58–67. Ecological Atlas of the Sea of Azov, Ed. by G. G. Matishov, et al. (2011) (Yuzhn. Nauchn. Tsentr, Rostov- on-Don [in Russian].

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AN EVALUATION OF SEASONAL SNOW ACCUMULATION PROCESSES IN FORESTED AND OPEN AREAS

______O.V. Sukhova Perm State University, Perm, Russia. [email protected]

Abstract It is generally assumed that the forest vegetation is mainly considered in the snowmelt processes. Field observations had been conducted on the area of Perm region revealed some regularity. These regularities are used in seasonal snow accumulation geoinformatic model. Suggested descriptions of model elements (components) are: a) cartographic and remote sensing typization of different forest association; b) the features of spatial variability snow depth and snow water equivalent (SWE); c) estimate of redistribution snow accumulation from open to forested areas. 2013 SWEmap for the period of maximum snow depth was build based on this geoinformation model. Preliminary estimates indicate an increase in the accuracy from 2 to 20 percent.

Keywords: snow accumulation; map of the forest vegetation; SWEmap

Introduction The spatial distribution of the snow cover has a significant influence on hydrological, biological, and ecological processes in boreal ecosystems. His high spatial variability is expressed differently in different scales observations. At the same time the spot character of snow cover field measurements complicate its description (Pomeroy et al., 2002). The main difficulty in selecting factors deciding the distribution of snow cover is a choice of scale levels. Evidently, environment factors of snow accumulation may effect differently at different scales. It is well accepted that, one of the most significant factors affecting the snowpack at all scale levels is forest vegetation (Pomeroy et al., 1998). There are many researches devoted snow retention ability forest strips, papers devoted to processes of interception, sublimation and wind redistribution snow, the numerical hydrologic models, their representation in land surface schemes used in numerical weather and climate prediction models (Pomeroy et al., 1998; Lundberg, Koivusalo, 2003; Rutter et al., 2009). All these models, if they are spatial distribution model, conditionally consider vegetation as factor (Pomeroy et al., 1998; Rutter et al., 2009). Otherwise some of the models are based on spot, local characteristics of vegetation at single site or small catchment (Lundberg, Koivusalo, 2003). In the issue make an attempt the seasonal snow accumulation geoinformatic model creation. This model should takes in account the basic macroscale and mesoscale spatial snow drift factors.

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Model Representations of Snow Accumulation

Figure 1. Chart of the model structure — local points data — scale levels of modeling — full coverage of area data

Chart of the model structure is presented on fig. 1. It is illustrated the possibility of interpreting the data of low spatial resolution to the information base relevant to the current scale of the study. The main elements of the model are signed below.

Spatial distribution of winter precipitation The data were received from 19 meteorological stations Perm region and were presented winter precipitation during the winter of 2012—2013. Winter precipitation was taken due to their high univocal interpretation in comparison to snow depth and density surveys on meteorological stations. Total precipitation for the period of steady snowpack accumulation set equal to snow accumulation in the deciduous forest. Observations of snow depth and density were made on meteorological stations in different forest types, situated in different geomorfological positions; therefore they cannot be properly accounted for. Observations of snow depth and density were used to count the SWE during the period of unstable snow accumulation.

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The annual maximum snow water equivalent is equal the sum of winter precipitation for the period of steady snowpack accumulation and observed SWE during the period of unstable snow accumulation. Orography Effect of mid-mountain terrain on the spatial distribution of precipitation caused an average height of the entire mountain range. Impact of terrain elevation starts from a height of each 100 m and at a distance of 100 km, before the mountain barrier. Ratio of precipitation to the height varies according with parabolic curve. It is reflected in a snowfall increase by 20% for each 100 meters thereafter (Швер Ц.А., 1976).

Map of the forest vegetation Vegetation has a significant impact on the snow accumulation. Mostly it take place in forested areas, were snow accumulation is depended of species mix, canopy density, age, tier structure and meteorological conditions for the period of snow accumulation. Snow accumulation of forest vegetation varies due to three major factors: − snow accumulation rates in the transition zone from open to forested areas; − snow interception and sublimation in the forest canopies; − local weather patterns and under forest canopies temperature. Existing digital forests maps contain outdated information or are based on classifications unusable for modeling of snow accumulation. Map of the forest vegetation was build on the principles of typization forest association, which have an influence on the spatial distribution of the snow cover (Сухова, 2013). The initial data for the mapping in the software product ScanEx Image Processor were two season satellite images. In the processing were combined channels 1, 2 two multitemporal MODIS Terra images for 26.07.2010 and 28.02.2011. The classification was performed on the base of method Kohonen's Self-Organizing Maps (Dobrynin, Saveliev, 1999). As the results of classification seven types of forested and open areas were obtained.

Snow accumulation rates in different forest association

Table. Coefficients of snow accumulation Width of the transition zone from open to forested Coefficients of snow accumulation Coefficients areas, m of snow Type of vegetation In the accumulatio In the transition By open By forested transition n zone by forested area, Vfi area, Vwi zone by open area, kbw area, kbf 1 Coniferous forests 0.63 100 50 1.42 1.05 2 Pine forests 0.78 100 150 1.23 0.86 3 Mixed forests 0.93 100 100 1.29 1.07 4 Deciduous forests 1 100 50 1.29 1.07 Open stand low bonitet 5 1.2 100 100 1.43 1.31 forests 6 Shrubs 1.42 100 — 1.42 1.42 7 Open areas 0.66 — — — —

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Snow accumulation in the transition zone from open to forested areas

Figure 2. SWE heterogeneity in the transition zone from open to forested areas: kw1 и kw4 — coefficients of snow accumulation 1- coniferous forest, 4 – deciduous forest; kbw1 и kbw4 — coefficients of snow accumulation in the transition zone of relevant forests; kbf1 и kbf4 — coefficients of snow accumulation in the transition zone by open area of relevant forests; kf — coefficients of snow accumulation on the open area; Vw1, Vf1, Vw4, Vf4 — width of the transition zone from open to forested areas

On the base of coefficients (Tab., Fig. 2) SWE value for any area of each 7-th map classes can be count: SWEwi = H0 (Sw0i kwi+ Sbwikbwi - Sbwi kwi) and SWEf = H0 (Sf0 kf+ Sbfikbfi - Sbfi kf), where SWEwi — snow water equivalent of i-type forest vegetation; SWEf — snow water equivalent of open area; Н0 — amount of winter precipitation, mm; kwi — coefficients of snow accumulation of i-type forests vegetation; kbwi — coefficients of snow accumulation in the transition zone of i-type forests vegetation; kbfi — coefficients of snow accumulation in the transition zone by open area of i-type forests vegetation; kf — coefficients of snow accumulation on the open area; Sbwi, Sbfi — areas of increased snow accumulation by open and by forested areas: Sbwi = VwiLi, Sbfi = VfiLi, where Li —border length of the current forest type area and open area; Vwi, Vfi — width of the transition zone from open to forested areas.

General directions wind coefficients Wind direction takes in account for modelling only in open areas more than 3 km in diameter. If the square is less then 3 km in diameter the influence of regional winds ignored and border buffer coefficient is count alone, as in tab. 1. For all others events additional wind-direction accounting coefficients were calculated: 1.5 — for the windward position, sub parallel to wind direction 1, for the other cases — 1.2.

Geomorphology factors In SWE modelling some geomorphological elements can be taken into account. 1. Traditionally Digital Elevation Model (DEM) data involves in snow accumulation modelling. This way is preferable in detailed map analysis. 2. Statistical approach based on digital vector model of erosion elements. Each of them supports rise or low coefficients. This way is better for regional modelling. 3. Statistic-geomorphological approach based on vertical and horizontal partition coefficients. It well use in most regional and global modelling scales

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For current model the statistic-geomorphological approach was used. It will be change on statistical approach on the step of model detailed.

Results and discussion

Figure 3. Fragments of maps with results of redistribution SWE, with and without the influence of the underlying surface

In Fig. 3 comparison of results of redistribution SWE, with and without the influence of the underlying surface. The model, interpolates rare enough points meteorological observations to local points for environmental observations and experiments, using the detailed characteristics of the factors redistribution and modification of the terms of snow cover accumulation. The obtained data can serve as an additional source of information for research of the quality and success of the animals and plants, fodder availability and the possibility of moving animals; the influence of soil moisture stock on productivity, droughts and freshets. Additional local dimension SWE, in their analysis included in the model, more appropriately interpreted and compared with observations for various geomorphological positions. Remote sensing data used in the model building, with its high visibility to allow spatial assessment of the allocation of factors of snow accumulation in the study area, to identify a number of important, but difficult analyzed point data factors as spatio-temporal dynamics of the development of plant complexes. It is for this in the model is built on the results obtained by analysis of satellite images, allowing you to make the model reproduced in the time series and keep records of the dynamics of nature complexes for the long-term model is used to identify long-term trends of ecosystems in conditions of global climatic changes.

Conclusions The presented model of snow accumulation allows to consider mesoscale features of snow accumulation depending on local factors. The data that have low spatial detail successfully integrate with the local elements of landscapes and ecosystems at the level of their characteristics and descriptions for the various geo-ecological studies.

References Pomeroy J. W., Gray D. M., Hedstrom N. R. and Janowicz J. R. Prediction of seasonal snow accumulation in cold climate forests // Hydrological Processes, 2002, vol. 16, p. 3543—3558. Pomeroy, J. W., Parviainen J., Hedstrom N. and Gray D. M. Coupled modeling of forest snow interception and sublimation// Hydrological Processes, 1998, vol. 12, p. 2317—2337. Lundberg A., Koivusalo H. Estimating winter evaporation in boreal forests with operational snow course data // Hydrological Processes, 2003, vol. 17, p. 1479—1493.

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Rutter N., Essery R., Pomeroy J. et al. Evaluation of forest snow processes models (SnowMIP2) // Journal of Geophysical Research, 2009, vol. 114, D06111. Швер Ц. А. Атмосферные осадки на территории СССР. Л.: Гидрометеоиздат, 1976, 302 с. (Schwer Ts.A. Precipitation in the USSR). Сухова О. В. Создание карты лесной растительности для моделирования снегонакопления на территории Пермского края // Вестник Удмуртского университета. Биология. Науки о Земле. 2013. Вып. 4. С. 132-139. (Sukhova O. V. Mapping of forest vegetation for snow accumulation modeling in the Perm region). Dobrynin D., Saveliev A., Hierarchical Multispectral Image Classification Based on Self Organized Maps.- Hamburg, IGARSS, 28 june- 02 july 1999. P. 2510-2513.

APPLICATION OF GIS-TECHNOLOGIES FOR IDENTIFYING THE THERMOHALINE VARIABILITY AT THE CENTURY SECTIONS OF THE BARENTS SEA

______A.E. Tsygankova Southern Federal University, Rostov-on-Don, Russia. [email protected] S.V. Berdnikov Southern Scientific Center RAS, Rostov-on-Don, Russia I.V. Sheverdyaev Southern Federal University, Rostov-on-Don, Russia.

Abstract In article there are the results of initial oceanographic data processing for 1870-2013 period to finding climatic changes of hydrological characteristics at the century sections of the Barents Sea: Kola section (from Murmansk to Bely Island along 33°30'E, includes VI century section – “Kola meridian” – and its continuation to 80°N) and Bear Island section (along 74°30'N from Bear Island to Novaya Zemlya, it merges XXIX and XVIII century oceanographic sections). Climatologically distributions of water salinity and temperature and its anomalies of every month every year are built, season vibrations of water temperature and salinity are estimated.

Keywords: water temperature, salinity, century section, climatic norm, anomaly, Barents Sea

Researches environment and ecosystems of the Barents sea on the methodological and technical level, meets modern requirements, have been held for more than a century. Deep surveys system are oriented on the network of the standard and century oceanographic sections, a special place among which is occupied VI section among the Kola meridian (Fig. 1). It’s maintained regularity and ensured the highest density of observations in all months of the year. Therefore conclusions about the variability of oceanographic processes often made on the basis this section data (Matishov et al., 2009; Levitus et al., 2009; Karsakov, 2010), as processes in Atlantic water mass, which is crossed by this section, define trends in climatic system of this part of Arctic and Northern Europe. There are considered two century oceanographic sections for analysis climatic changes of hydrologic characteristics of the Barents sea: - Kola section, it passes along 33°30'E from Murmansk to Bely Island and merges VI century section (“Kola meridian”) and its continuation to 80°N (Fig. 2); - Bear Island section, it passes along 74°30'N from Bear Island to Novaya Zemlya and merges XXIX and XVIII century oceanographic sections (Fig. 3);

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Each section is correlated by certain number of stations, which is situated in the 80-km buffer and used for calculation climatic norms and anomalies. Total number of this stations is 36 748 for Kola section and 19 257 for Bear Island section.

Figure 1. Stations distribution in 80-km buffer (A) and bottom relief (B) along Kola section. Position of main and century oceanographic sections in the Barents Sea (C)

Vertical distributions of water temperature and salinity averaged over the period 1870-2013 (climatic norms) are built for Kola and Bear Island sections. For its building computational nodes are estimated. Its vertical distribution correlated to standard horizons 0, 10, 20, 30, 50, 75, 100, 150, 200, 250 and 300 m. For Kola section longitude uniform step is 55.5 km (0.5), for Bear Island section latitude uniform step is 27.75 km – half as much. For constructing of the time series of water temperature and water salinity anomalies standard deviations (σ) of the average values have been calculated, values of the anomalies were estimated in

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degrees of σ. There are maps examples of climate norm of water temperature at September (Fig. 4) and water temperature anomaly at September 2003 (Fig. 2C). Thermohaline water mode of the Barents Sea south is formed by several factors: advection of the heat and salinity by Norwegian, North Cape and Murmansk currents, heat exchange between ocean and atmosphere, as well as river discharge, rainfalls and transpiration. Heat exchange between ocean and atmosphere defined the rate of the spring-summer warming-up and autumn-winter cooling of the water. Predominance of the heat losses from the sea surface over the atmospheric inflow is compensating by the advective inflow of the heat. All of these processes are recognized by the thermahaline water characters changes at the “Kola meridian” section.

Figure 2. Example of the Kola section (A) and the climate vertical distribution of the water temperature at September (B). Example of the calculated water temperature anomaly at the Kola section at September 2003 (C) Note: Anomaly is calculated for every node as a rate of standard deviation (σ) of the average values

Obtained average annual thermohaline characters enable the analysis of standard horizons. Maximum amplitude of the water temperature changes is observed at the top 50-meters layer, where

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more intense processes of the interaction with the atmosphere occur, and water temperature at the summer because of more solar radiation inflow versus winter. At the first two stations significant season changes of water temperature stored throughout the stratum. At all other stations season changes amplitude decays and less than 1.5°С from the 150-meters depth. This is due to the fact of weak spread of the solar radiation to the underlying layers. In autumn water stratification decreases and at the same time solar radiation inflow sharply decreases too. Season change amplitude of water salinity significantly less expressed than of water temperature, there are some similarities yet. So, maximum season salinity and temperature amplitude is observed at the top 50-meters layer. At the depth of 50 m and more, a seasonal swing in salinity decrease, not only with depth, but also in the direction from north and south to the central part, and constitutes on the first five stations and the northern areas no more than 0.1‰. Seasonal minimum water temperature at the first two stations and in the upper 150-meter layer practically throughout the entire thickness of the cut comes in April. In the deep layers the onset of the seasonal minimum is somewhat delayed. Time of onset of seasonal maximum water temperature unlike seasonal minimum lags in proportion to depth. Time the temperature maximum depth determined by the rate of formation of mixed layer water temperature and its vertical measure. In the upper 20 m layer it comes in August and is further delayed as depth increases. As a result, the bottom layer of the onset of seasonal multi-year high water temperature ranges from October to January on the first ten stations. The upper 50 m of the salinity changes are characterized by a minimum - August-September - and one maximum - January-May. The exception is the central station (73.5°-74° N) section where the depth of 50 m to the seasonal maximum is observed in December. Salinity in the upper layers of the water is almost exclusively dependent on local factors, so the summer salinity minimum is due largely to increased river flow and precipitation, while winter maximum - mainly to the weakening of coastal runoff on the one hand and an increase in revenues from other Atlantic waters on the other (Sedykh , 1958). In the deep and bottom layers to determine the onset of seasonal maxima and minima is not possible, because long-term data on the magnitude of salinity here throughout the year virtually unchanged (swings in less than 0.1‰). Thermohaline water mode of the central part of the Barents Sea (the Bear section) is formed to a greater extent under the influence of heat and salt advection of warm - the central branch of the North Cape , Western Novaya Zemlya - and cold currents - Medvezhinsky and flow "Perseus" , as well as heat exchange between ocean and atmosphere. The processes described above are typical for the Kola section in general and for Medvezhinsky, only there changes occur not in the direction from north to south and from west to east. In winter, the water temperature is 0.5- +3.50°C, in summer 0.5 -7°C in the upper 50 m layer , with maximum values observed in the vicinity of Bear Island trough (300v.d.), the minimum on the central hill (400v.d.) . Bottom rugged terrain significantly affects the hydrological conditions of the sea, are proof of this are claims made by N.N. Zubov , who believed in the Barents Sea classic example of the influence of bottom topography on the hydrological processes in the sea. Vertical temperature distribution largely depends on spreading the warm Atlantic waters of winter cooling, extending to a considerable depth, and from the bottom topography. In this regard, the change in water temperature with depth occurs differently in different parts of the cross-section. Atlantic waters spread eastward dredging, so they lower the temperature of the water from the surface to the horizon 100-150 m, and then rise again to the bottom: it is typical for the first five stations. In the eastern districts cut deeper in the layer 50-100 m, not affected winter vertical circulation, the temperature increases slightly and is about -10°C. In the lower horizons are Atlantic waters and the temperature rises to 10 here. Thus, between 50-100 m a cold intermediate layer is observed. In trenches where water cannot penetrate the warm and strong cooling occurs, for example, West Novaya Zemlya trough and Central Basin, the water temperature is fairly uniform over the entire thickness of the winter, and in summer from positive on the surface (50°C) , it is reduced to about - 1°C at the bottom.

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Underwater uplands are the natural barriers to the movement deep Atlantic water, so it flows around them. Thereby over the bottom mountains low water temperature is observed at the horizons near the sea surface (20-50m). Then over the uplands and over their slopes longer and more intense cooling of the waters is occurred than in the deep regions. As a result hats of cool water are occurred typical for the Barents sea banks (Dovrovolskij, Zalogin, 1982). Near the Central upland very low water temperature in winter -1.5°C distributes from top to bottom. In summer (August-September) it decreases with depth and reaches minimal values at the 50- 100 m layer with the little increasing deeper. Therefore, at this season cold intermediate layer is observed, which low border is made by local Barents Sea waters. Because of good connection with the ocean, values of water salinity at the Bear Island section of The Barents Sea little difference from the average water salinity on the ocean, although in some areas of the section there are significant differences. Water salinity distribution dues to Atlantic water inflow, the currents system, bottom relief, ice formation and ice melting processes and water mixing. The highest salinity on the sea surface (35‰) is observed at the south-western part in the Bear Island trough area (25°E - 30°E), where salt Atlantic waters pass as well as sea ice doesn’t form. At the west and east water salinity decreases to 34.5‰ due to the ice melt. Water salinity changes at the sea surface occur season to season. Winter, at the Bear Island Trough area water salinity is enough high (near 35‰), but at the eastern and western areas of the Bear Islandsection (Novosemskaya upland, Zapadno-Novezemel’sky trough) salinity decreases to 34.5‰, because of Atlantic water inflow increases at this time and intense ice forming occurs. In spring, almost universally high values of water salinity are reserved. In summer Atlantic water inflow decreases, ices melt, river water distributes along Novaya Zemlya Islands to the far north, therefore water salinity at the eastern stations of the section decrease to 33.8‰. At the second half of the season it everywhere decreases below 35‰. In autumn salinity stays low at the whole section, but because of decreasing of continental runoff to the November and the starting of the ice forming, it increases and reaches winter values. Season changes of the vertical distribution of water salinity at the most of Bear Island section are rather weakly. In summer, sea surface layer is desalinated, but from the horizons 20-30 m sharp increase of salinity starts. In winter salinity jump at this horizons is smoothed, but continues. Difference between surface and bottom salinity may reaches 1.5‰. Season changes of vertical salinity distribution are well manifested here. In winter water salinity almost aligned through the whole water depth. In spring, river waters and ice melting start desalinating surface layer. In summer, its desalinating increases on account of melted ice, therefore between 10 and 20 m horizons sharp jump of salinity creates. In autumn runoff decreasing and ice formation entails increasing of salinity and its alignment through the depth.

References Dobrovolsky A.D., Zalogin B.S. Morya SSSR [Seas of USSR]. Moscow: publ. MGU, 1982. P.69-81. Karsakov A.L. Zakonomernosti I osobennosti rejima vod Barentseva morya (po nablyudeniyam na vekovom razreze “Kol’sky meridian”) [Regularities and features of mode of the Barents sea waters (observations at the “Kola meridian” section)] // Murmansk: publ. «Poligrafist». 2007. 18 p. Sedykh K.A. O sezonnykh I mnogoletnikh izmeneniyakh solyonosti vod yuzhnoy chaste Barentseva morya [About the seasonal and long-term variations in salinity waters in the southern Barents sea] / Otchot o NIR. 1958. 29 p. Levitus S., Matishov G., Seidov D., Smolyar I. Barents Sea multidecadal variability // Geophys. Res. Lett. 2009. 36. P. L19604. DOI:10.1029/2009GL039847. Matishov G.G., Matishov D.G., Moiseev D.V. Inflow of Atlantic-origin waters to the Barents Sea along glacial troughs // Oceanologia. 2009. 51(3). P. 293–312.

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TO A PROBLEM OF INFORMATION SYSTEM CREATION OF ENVIRONMENTAL MONITORING OF KRASNOYARSK REGION ON THE BASIS OF MODERN GIS- TECHNOLOGIES AND EARTH REMOTE SENSING DATA

______Yu.P. Yuronen, E.A. Yuronen, V.V. Ivanov Siberian state aerospace university, Krasnoyarsk, Russia

Abstract In this article the creation concept of the center of expeditious supervision and reaction for the solution of problems of environmental monitoring and support of adoption of administrative decisions is con-sidered. The justification of need of creation of the similar center in the territory of Krasnoyarsk region as the object consolidating existing and planned systems of land supervision and system of remote sensing is given.

Keywords: GIS-technologies, Earth remote sensing, regional information system

Now in the territory of Krasnoyarsk region there is no integrated formalized system of expeditious environmental monitoring of natural and anthropogenic complexes. There are only separated for various reasons (lack of interdepartmental interaction, inconsistence or ignorance of opportunities of the existing centers) systems of conditionally expeditious monitoring of an environment's state in some parameters, such as air and water pollution, a radiation situation, a condition of a forest cover, etc. Thus there is a necessity of consolidation of the existing centers of reception, storage and data processing, both the Earth remote sensing, and these land supervision, for creation of united available system of environmental monitoring of the territory capable in real time quickly to reflect the existing situation on all square of supervision. This problem can be solved by creation of the integrated center of collecting, storage and information processing from sources of land supervision (contamination control stations) and materials of Earth remote sensing which allow extrapolate and compare data of land supervision with space spectrozonal pictures. In the subsequent, with introduction of these data in the geographic information systems (GIS) supporting modern cartographical informational and WEB technologies, there is an opportunity to make a natural environment status evaluation in the territory of Krasnoyarsk region in real time. Eventually it allows to react quickly to arising negative impacts and to develop optimum administrative and managerial decisions at various levels of executive power of the region.

The ideology of creation of system of environmental monitoring on the basis of a small, separate and uneven network of land stations and materials of space shootings allows trace and control an ecological situation. The advantage of this ideology is that there is an opportunity to trace and control an ecological situation in the territory with a big area as small local sites with big environmental risks, and ecology of a region “an masse”. Thus, it is possible to say about scalability of this system what to allow essentially reduce the costs of creation of a uniform network of stations of the land supervision which number if necessary can be increased.

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Figure 1. Scheme of creation of system of environmental monitoring

In general the space pictures of Earth possess the following properties doing them by unique at expeditious environmental monitoring of territories:  single multi-scale supervision of all territory;  high degree of efficiency of obtaining information  existence of continuous archives of space shooting;  objectivity and reliability of received information;  possibility of verification according to land supervision with the subsequent extrapolation on big squares;  the automated generation of information products for the end user;  various level of generalization and spectral characteristics;  distributed access to databases. These properties make space pictures by the irreplaceable tool at different solution of problems of an assessment of an ecological condition of all region or its local areas on which the risk of change of an ecological state is great. The generalized function creation chart of such system can look as follows (Fig. 1). Having assumed as a basis the general scheme of creation of system of environmental monitoring on the basis of data of Earth remote sensing it is necessary to carry out the analysis of an existing state in this area in the territory of the region and to estimate needs for expeditious environmental monitoring. Further it is necessary to reveal a demanded data set and to develop the technological model of system allowing in an operational mode to carry out a complex assessment of an ecological condition of the region with the subsequent forecasting. As a result of this work the following generalized scheme (Fig. 2) which reflects as existing segments of planned system (the blocks allocated with green color), and those elements of system which demand creation or completion (the blocks allocated with blue color) will be received. In general, the successful functioning of system first of all requires existence of data of Earth remote sensing. It is necessary to carry out retrofitting of existing stations of reception of satellite information to provide possibility of reception of data from satellites, both Russian, and foreign space segments (on the scheme are italicized the name of the satellites which are minimum demanded for functioning of system which aren't accepted now by any of the existing centers of reception of space information in the territory of Krasnoyarsk region).

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The following step for the solution of a problem of creation of uniform system of environmental monitoring of edge is coordinating of activities of the existing centers of space monitoring of the Siberian federal university, the Siberian state aerospace university and the Ministry of Emergency Situations. It will allow to organize reserve systems of reception of space information and to relieve the centers of space monitoring of transmitted data duplication, and also to organize the coordinated uniform system of reception of given Earth remote sensing from numerous space group of satellites as being in operation, and only planned to a conclusion to an orbit.

Mid‐scale and Small‐scale shootings Large‐scale shootings Meteor Spot (5,6,7) TERRA, AQUA DMC 2 Suomi NPP Landsat 8 Radarsat‐2 Ресурс ‐ П

Space segment

Reception station of the Reception station of the Ministry of Emergency Siberian federal Siberian state aerospace Situations reception university university station

Center of space Center of space Federal system of the monitoring of the monitoring of the prevention and Siberian federal Siberian state aerospace emergency elimination

The scientific and educational center "Institute of Space Researches and High Technologies" training according to the program 120100 "Geodesy and remote sensing"

Center of monitoring of an ecological Land network of stations condition of Krasnoyarsk region of supervision over a condition of components

of environment Consumers of information on an ecological state

Land segment

Figure 2. The generalized scheme of system of environmental monitoring

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Besides, productivity of functioning of the center of ecological monitoring and the organizations consumers of space information will depend in many respects on existence in the region of the specialized training center, capable to realize programs, both higher education, and short-term advanced training courses for specialists of various branch enterprises and executive authorities. The center of environmental monitoring of the region with functions of coordinating, collecting, storage, data processing of space shooting and results of land supervision over a state of environment will be result of association of all these components. All obtained as a result of work of this center data can be informed to the consumer of information (both state, and private structures) by means of modern GIS in an operational mode.

Center of monitoring of an ecological condition of Krasnoyarsk region

Coordination center of expeditious reception of space information and information of land posts of supervision

System of access to Preliminary processing of Data processing of land operational and archival space‐shooting data supervision data of space shooting

Thematic processing of Earth remote sensing data Department of Assessment of an ecological situation of a natural technical support complex (forest, water, mineral, resources and conditions and system of the atmosphere) administration

Anthropogenous loading (influence of city agglomerations, monitoring of the industrial enterprises, large technogenic objects, etc.)

Geographic information Assessment of Interpretation of Earth systems environmental risks and remote sensing data and damage land supervision

WEB cartography Work with the customer

Figure 3. The integrated structure of the center of monitoring of an ecological condition of Krasnoyarsk region on carried-out functions of divisions

Now it is necessary to consider in more detail structure of the center of monitoring of an ecological condition of Krasnoyarsk region (Fig. 3). Apparently from this scheme the center can have branched system of the divisions having various functions and united by the general GIS and information system of collecting storage and processing of various data.

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All this allows system to function as a unit and to aggregate all production cycles in one program environment that minimizes temporary delays at the organization of operational system of monitoring of an ecological condition of the territory and to accelerate process of development of the administrative and managerial decision. In addition in system the system of public informing of the population and feedback for rapid response to public statements can be realized. In summary it is possible to tell there is an urgent need in the organization of such system in the territory of the considered region. It is dictated both a geographical arrangement, and the area on which it is required to conduct expeditious monitoring. Besides, active social and economic development of the region and, as a result, probability of emergence of the environmental risks connected with development of industrial infrastructure, active mining and carrying out exploration in all territory of Krasnoyarsk region is of great importance. Also offered scheme in the basis assumes high extent of adaptation to arising tasks not only ecological character, it can include the regional monitoring systems founded on results of land supervision. Considering that fact that all system initially has to use modern GIS-technologies, there is a possibility of realization on this platform of information and analytical system for support of adoption of administratively administrative decisions at the level of the region.

WAVE AND STORM SURGE MODELLING FOR SEA OF AZOV WITH USE OF ADCIRC+SWAN

______V.V. Fomin Marine Hydrophysical Institute, Sevastopol, Ukraine. [email protected] A.A. Polozok Marine Branch of Ukrainian Hydrometeorological Institute, Sevastopol, Ukraine R.V. Kamyshnikov Sevastopol National Technical University, Sevastopol, Ukraine

Abstract Wave and storm surge modelling technology in the Sea of Azov on unstructured grid with the use of tightly-coupled model ADCIRC+SWAN (Dietrich et al., 2012) was implemented. Results of the fusion of calculation data and observations showed that the model adequately described the sea level variations that had been measured. Good reproduction of the phases of ascents and abatements took place. Thus, the model reproduced extremely dangerous sea level ascent in the town of Berdyansk on November 11, 2007 satisfactorily. It was ascertained that the main contribution to the formation of sea level fluctuations was brought by the surface stresses. The importance of the selection of the bottom friction coefficient optimal value to calculate the maximum sea-level ascent was shown.

Keywords: Sea of Azov, Waves, Storm Surge, SWAN, ADCIRC, Parallel Computing

Introduction Storm surges and wind waves are the most important characteristics of the dynamics of waters of the Sea of Azov (SA). The combination of extreme sea level and high wind waves can cause catastrophic consequences. Considerable part of extreme storm situations in the basin occurs in autumn and in winter, with strong stable eastern and north-eastern winds as well as with the deep cyclones movement from the west, south-west and north-west over the sea surface. To adequately simulate extreme storm situations in the SA it is always necessary to use coupling models that take the account of the interaction between waves and currents. Usually, coupling models are based on a combination of the model of water circulation and wind-wave spectral model.

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The coupling model SICOM, which is based on the water circulation model POM and spectral wave model SWAN, is offered for the SA in (Booij et al, 1999). The SICOM has such an advantage as the relatively full parameterization of the wave and currents interactions. The SICOM disadvantage is the fact that it is implemented on a rectangular grid for single-processor systems. An important step in the growth of the adequacy of the SA dynamics modeling can be the use of unstructured meshes and parallel computing technologies. Unstructured meshes can be easily adapted to the changes of the coastline and depths; therefore, they give more precise description of the coastal regions, increasing the spatial resolution, if necessary. The usage of unstructured meshes needs more computations compared to rectangular meshes. However, by the optimal triangulation of the computational domain, one can reduce the total number of calculated nodes significantly. Furthermore, the usage of unstructured meshes minimizes the need of nesting and the interpolation on liquid boundaries. The aim of this work is to implement the technology of the numerical simulations of storm surges and wind waves for the SA. The above mentioned technology uses unstructured meshes and high- performance computing environments.

Model Description This paper deals with the SWAN+ADCIRC coupling model, which was recently proposed in (Dietrich et al, 2011). It combines two well-known and well-tested models – ADCIRC (Ivanov, Fomin, 2010) and SWAN (Luettich, Westerink, 2004) – in a single software package. Both models are used to calculate the storm surge and wind waves. ADCIRC is a shallow-water model that calculates the magnitudes of water levels and currents. The currents are obtained from the vertically-integrated momentum equations:

U U U   Pa   sx  bx M x  Dx  U  V  fV  g      t x y x  g 0   0 H H (1)

V V V   Pa   sy  by M y  Dy U V  fU  g      t x y y  g0  0 H H (2) and water levels are obtained by solving the general wave continuity equation:

2    J x J y 2  0    0  t t x y (3) U U g  2 H P   J  Q  Q  fQ   a  sx bx x x x y y y 2 x  x  0 0    (M  D )  Q U  gH x x 0 x t x (4) V V g  2 H P   J  Q  Q  fQ   a  sy by y x x y y x 2 y  y  0 0    (M y  Dy )  0Qy V  gH t y (5)  sx  sx,wind  sx,wave ,  sy  sy,wind  sy,wave (6) The terms in equations (1)–(6) are the following – x, y and t are horizontal grid points and time; H  h  is total water depth;  is surface elevation; h is bathymetric depth; U and V are depth- integration currents in the x - and y -directions, respectively; Qx  UH and Qy VH are fluxes per unit width; f is the Coriolis parameter; g is gravitational acceleration; Pa is atmospheric pressure at the surface;  0 is the reference density of water; ( sx,wind , sy,wind ) and ( sx,wave , sy,wave ) are surface stresses due to winds and waves, respectively;  bx and by are bottom stresses; M x and M y are horizontal eddy

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viscosity; Dx and Dy are momentum dispersion terms;  0 is a numerical parameter that optimizes the phase propagation properties. SWAN represents the wave field as a phase-averaged spectrum (Ivanov, Fomin, 2010). SWAN predicts the evolution in geographical space and time of the wave action density spectrum N (x, y,t, , ) , with relative frequencies  and directions , as governed by the action balance equation:

     Stot  (cx U )  (cy V )  c   c   t x y    (7) where ( cx , cy ) is the group velocity, (U ,V ) is the ambient current, c and c are the propagation velocities in the  - and  -spaces. The source term Stot represents wave growth by wind, action lost due to white-capping, surf breaking and bottom friction, and action exchanged between spectral components due to nonlinear effects in deep and shallow water. SWAN and ADCIRC run on the same mesh and core. SWAN is driven by wind speeds, sea levels and currents from ADCIRC. In its turn, ADCIRC is driven partially by wind speeds, that are computed in SWAN through the radiation stress gradients (Dietrich et al, 2011):

S xx S xy S xy S yy  sx,waves    ,  sy,waves    x y x y (8) 2 S  g (ncos   n 1 2)Ndd , S  g (nsin cos)Ndd xx 0  xy 0  (9) 2 S  g (nsin   n 1 2)Ndd yy 0  (10) where S xx , S xy and S yy are the wave radiation stresses; n is the ratio of group velocity to phase velocity.

Model Validation The calculations were performed on a mesh with 10,835 finite elements and 6,730 nodes (Fig. 1). Surface wind field and surface pressure over the SA in November 2007 based upon the numerical atmospheric model SKIRON (http://forecast.uoa.gr) were used as external forces. The spatial resolution of atmospheric fields made 10 km, and the discrete time made 2 hours. Time steps in the

ADCIRC and SWAN were accepted as follows: t A = 1 s; tS = 600 s. The effect of changes in model parameters on the sea level variations was analyzed. In ADCIRC, to compute bottom stress, a quadratic dependence on the flow rate with a hybrid approximation of the friction coefficient was applied:

a b a Cd  Cd 0 1 Hb H (11) where Cd 0 is minimum friction coefficient; H b = 1 m is wave break depth; a = 10 and b = 1/3 are dimensionless parameters defining the rate of bottom friction change

It is well-known (Blain et al., 2012) that value of the parameter  0 can be calculated from the following inequality in the general wave continuity equation (3):

2 2 1  0  max 10 ,  max  max Cd U  V H (12)

Preliminary calculations showed that the range of the acceptable value  0 is 0.0025–0.005. The sea level simulation results were compared with the data of field observations at coastal meteorological stations. These data represented the hourly level measurements at the Henichesk and Mariupol stations and every-6-hours measurements at the Berdyansk and Mysovoye stations. Results of the comparison showed that the model adequately describes the measured sea level variations. There

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is a good reproduction of the phases of ups and downs. The model reproduces satisfactorily the particularly dangerous climb of November 11, 2007 at the Berdyansk station (Fig. 2). Equations (1)–(6) include horizontal surface stress and atmospheric pressure gradients. It is of interest to determine, which of these components is the main one. The calculations show that the surface stresses make a major contribution to the formation of sea-level fluctuations. In its turn, calculation of the atmospheric pressure gradient leads to 2-3 cm changes in the level only. Thus, this forcing component can be neglected in the first approximation. As an example, Fig. 3 shows the level variation at the Berdyansk station at different types of forcing.

. Figure 1: Unstructured finite element mesh domain for the Sea of Azov

Coefficient Cd 0 ranged from 0.0025 to 0.01. As the results of the calculations showed, the increase of this coefficient within the above mentioned limits led to a decrease in the maximum sea level values by 0.3 m. Furthermore, the magnitude has a lesser impact on the maximum reduction of the sea level. Fig. 4 shows the effect of Cd 0 on the level variations at the Mariupol station.

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L, m

0.8

0.4

-0.4 0 5 10 15 20 25 30 t, day Figure 2: Sea level at the Berdyansk station (red - measuring, black - modeling) L, m

0.8

0.4

-0.4 0 5 10 15 20 25 30 t, day Figure 3: Sea level (m) at the Berdyansk station for different forcing types (red - shear stresses and pressure gradient, blue - only the shear stresses, black - only the pressure gradient)

L, m

0.8 Cd0 = 0,010

Cd0 = 0,005

Cd0 = 0,0025

0.4

-0.4 0 5 10 15 20 25 30 t, day Figure 4: Sea level at the Mariupol station for different values of the Cd 0

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Conclusion Based on a parallel version of the ADCIRC+SWAN model, the technology of numerical modeling of storm surges and wind waves in the Sea of Azov is realized on the unstructured mesh. Comparison of simulation results with the observed data showed that the model adequately described the measured sea level variations. Good reproduction of the phases of ups and downs takes place. The model reproduces satisfactorily dangerous sea level rise on November 11, 2007 at the Berdyansk station. It was investigated that the surface stresses made a major contribution to the formation of sea level variations. It is shown that the choice of the bottom friction coefficient is important for the correct calculation of the sea level rise maximum. In the future, it is planned to construct a more detailed computational mesh for the Black Sea and the Sea ofAzov. This mesh will be used to create a real-time waves and storm surges forecasting system.

Acknowledgements The authors thank Crystal Fulcher from UNC-Institute of Marine Sciences for providing the source codes of SWAN+ADCIRC. Calculations were based on a computing cluster of Marine Hydrophysical Institute and "Lomonosov" supercomputer of Moscow State University. Some ADCIRC visualizations were produced with FigureGen (Dietrich et al., 2013).

References Booij. N., Ris. R.C., Holthuijsen L.H., A third-generation wave model for coastal regions. Model description and validation. // J. Geophys. Res., 1999, 104(C4), p. 7649–7666. Dietrich J.C., Zijlema M., Westerink J.J., Holthuijsen L.H., Dawson C., Luettich R.A., Jensen Jr., R., Smith J.M., Stelling G.S., Stone G.W. Modeling Hurricane Waves and Storm Surge using Integrally-Coupled, Scalable Computations. – Coastal Engineering, Volume 58, Issue 1, 2011, p. 45–65. Ivanov V.A., Fomin V.V. Mathematical Modelling of Dynamical Processes in the Sea-Land Area. – K: Akademperiodyka, 2010. – 286 p. Luettich R.A., Westerink J.J. Formulation and Numerical Implementation of the 2D/3D ADCIRC; 2004. http://adcirc.org/adcirc_theory_2004_12_08.pdf.

DEVELOPMENT OF MARINE ECOSYSTEM DYNAMIC MODEL OF THE SEA OF AZOV

______V.V. Kulygin, V.V. Sorokina Institute of Arid Zones of the Southern Scientific Center of RAS, Rostov-on-Don, Russia. [email protected]

Abstract Ecosystem models, coupled with ocean circulation models, are being widely used to quantify fluxes of nutrients and carbon in the ocean at regional and basin scales. In this study, the development of ecosystem dynamic model for the Sea of Azov considered.

Keywords: NPZD-model, the Sea of Azov

The production and transformation of organic matter in the Sea of Azov are the most important issues needed to be studied. It should be noted that the shallow Sea ecosystem is sensitive and quickly responds to changing external conditions such as river flow, meteorological parameters, etc. and it may result to the changes of the carbon balance components.

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Marine ecosystem dynamic model was developed to explore the mechanisms and assess the impact of climate change and anthropogenic factors on the production and transformation of organic matter in the Sea of Azov. It consists of two coupled modules: the compartmental model of the hydrological regime (Matishov et al., 2006) and the biogeochemical model (NPZD-model). Compared to the relatively better known physical process, the biogeochemical processes are more complex due to the biological diversity and the food chain variations with various time and spatial scales. In this study, it was taken into account that the complexity (dimension, spatial resolution, nonlinearity, and so on) must ensure understanding how the model works, possibility of identification and validation. The carbon cycle in the Sea of Azov is simulated using two environmental groups of phytoplankton (summer and spring species). Dissolved and suspended organic matter is divided into autochthonous and allochthonous components due to the influence of estuarine zones. Zooplankton aggregated into a single state variable. The model includes equations describing the dynamics of potentially limiting nutrient (nitrogen and phosphorus) to account the dependence of primary production from the concentration of nutrients. In the first approximation the kinetics of interaction "water-sediment" described by the equation of organic carbon balance in the active layer of bottom sediments, which is measured by the number of labile carbon per one square meter of the surface of the Sea. Processes simulated by the model of the biochemical dynamics include: - phytoplankton production (primary production) and assimilation of mineral nitrogen and phosphorus; - non-predatory phytoplankton and zooplankton death; - excretion of dissolved organic carbon by phytoplankton; - zooplankton grazing; - mineralization of organic matter in water and sediments; - zooplankton respiration; - sedimentation of organic matter; - inputs allochthonous dissolved and suspended organic matter and nutrients with river runoff; - mixing with other compartments. Input parameters in the proposed model are the river runoff, water exchange with the Black Sea, precipitation, solar radiation and concentration of allochthonous dissolved and suspended organic matter and nutrients in river waters. Depending on time step, the developed NPZD model is used to examine seasonal and interannual variability of the primary production and nutrient balance, associated with variations of river runoff and meteorological characteristics. The research is carried out at the Institute of Arid Zones SSC RAS and at the Southern Scientific Centre of the Russian Academy of Sciences in the frame of the Grant of the Russian Foundation for Basic Research N 14-05-31322 and RAS Presidium program “Fundamental problems of Oceanology: physics, geology, biology, ecology”.

References G.G. Matishov, Yu.M. Gargopa, S.V. Berdnikov, S.L. Dzhenyuk Regularities of Ecosystem Processes in the Sea of Azov. Nauka, Moscow, 2006. 304 pp.

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NEW TECHNOLOGIES AND APPROACHES TO WORK WITH REMOTE SENSING DATA

METHODICAL SOFTWARE FOR ADJUSTMENT OF THE ”RESOURCE-P” SPACECRAFT HYPERSPECTRAL SHOOTING EQUIPMENT AND FOR HYPERSPECTRAL DATA PREPROCESSING

______S.A. Arkhipov JSC «S.A. Zverev Krasnogorsky zavod» Krasnogorsk, Russia. [email protected]

Introduction The hyperspectral shooting apparatus GSA-RP intended for onboard operation at the «Resurs-P» spacecraft was designed and manufactured by JSC «Krasnogorsky Zavod» under the contract with Space Rocket Center TsSKB-Progress. The "Resource-P" satellite was launched on July 25, 2013, successfully passed a flight test and since October 01, 2013 until the current time is in regular operation.

Instrument characteristics The GSA-RP apparatus is designed on the basis of a dual-channel autocollimating prismatic spectrometer and high-frequency frame-transfer CCD matrices. The main technical characteristics of the instrument GSA-RP are presented in Table 1.

Table 1. Technical characteristics Characteristic Value Spectral range, m 0.42-0.96 Number of spectral channels 77-130 Spectral resolution, nm 7.0 Span (in Nadir), km 30 Resolution (GSD), m 30 Signal to noise ratio >230 Wavelength scale accuracy, nm 1.0 Number of digits of data, bit/pixel 14 Non-linearity of channels’ calibration curve, % <1.0 Signal irregularity at observation of uniform surface, % <0.5 Accuracy of photogrammetric parameters,  5.0

GSA-RP is controlled by a control unit with commands generated by «Resurs-P» spacecraft controlling system. The main operation modes of GSA-RP are: 1) «Shooting»; 2) «Calibration». In the «Calibration» mode, the instrument measures spectral radiance of the check unit. This mode is used for in-flight monitoring of GSA-RP parameters stability (sensivity and wavelength scale (WLS)) and for making data files intended for computation of corresponding correction factors.

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GSA-RP is an adaptive device allowing one to configure its settings and ensure maximum signal- to-noise ratio for specific targets. The methodical software (MSW) of the GSA-RP settings contains three segments:  MSW supporting GSA-RP in the process of exploitation;  MSW adapting the GSA-RP settings to shooting conditions;  MSW for hyperspectral data preprocessing.

MSW supporting GSA-RP in the process OF exploitation MSW is intended for: • stability control and, if necessary, correction of GSA-RP radiometric parameters (RMP): -sensitivity - wavelength scale (WLS) - gain according to the onboard calibrations data during the GSA-RP operation. • correction of the photogrammetric parameters (PGP) for an actual current value of temperature during the GSA-RP operation.

Figure 1. Represents the structure of MSW supporting GSA-RP in the process of exploitation

MSW adapting the GSA-RP settings to shooting conditions MSW implements: • exposure time and spectral resolution parameters optimization, taking into account the weather forecast and the expected range of coefficients of spectral brightness (CSB) (by choosing gain coefficients, rows/columns binning coefficients) • the ability to change the spectral range and spectral resolution of shooting depending on a shooting task (by channel selection and choosing the boundaries of the binning zones); • the ability to change the spatial resolution depending on a shooting task (by choosing the frame rate division coefficients and columns binning coefficients).

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Figure 2. Represents the structure of MSW adapting the GSA-RP settings to the conditions of shooting

MSW for hyperspectral data preprocessing MSW is intended for: • relative radiometric correction of the heterogeneity of the GSA-RP sensitivity • calculation of spectral density of radiance (SDR) at the top of the atmosphere (at the entrance pupil of the GSA-RP) • spectral and spatial joining data in the channels of the GSA-RP • atmosphere optical properties calculation based on the forecast obtained at the time of the shooting • SDR at the level of the underlying surface calculation (in absolute and relative units); • atmospheric model (DB) replenishment • the CSB database replenishment.

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Figure 3. Presents the structure MSW for hyperspectral data preprocessing

Developed MSW for tuning GSA-RP and hyperspectral data preprocessing is deployed and functioning at Research Center for Earth Operative Monitoring (NTs OMZ). MSW GSA-RP provides accumulation of the spatially coordinated spectral information, provides the ability to compare the pieces of information received at different times, by various space systems, allows one to solve a wide range of remote sensing tasks and to get a new knowledge about the Earth.

TOTAL METHANE MIXING RATIOS IN WEST SIBERIA FOR 2003-2013: RESULTS FROM AIRS/AMSU-AQUA AND CHEMISTRY TRANSPORT MODELS

______A.A. Lagutin Altai State University, Barnaul, Russia. [email protected]

Atmospheric methane (CH4) is an important greenhouse gas. It accounts for 18% of the anthropogenically produced greenhouse gas radiative forcing and also plays an important role in atmospheric chemistry. After a nearly stable period of about one decade, in 2007 the methane rose again. Global-scale modeling suggests that the increase has mostly been driven by the tropics and northern mid-latitudes. The purpose of this study is to investigate the seasonal cycle and interannual variability of the total CH4 mixing ratios (CH4-Tot) in West Siberia for 2003-2013 using the

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AIRS/AMSU-Aqua observations and the results from chemistry transport models MOZART4 and ACTM-CCSR/NIES/FRCGC. The key feature of the proposed approach is chemistry transport model- based regression equation linking CH4-Tot with mid-upper tropospheric CH4, the tropopause height and the surface temperature. The observational information in our approach comes from the AIRS/AMSU measurements. The combined AIRS-MOZART4/ACTM CH4 products are validated against TCCON ( the Total Carbon Column Observing Network) in situ CH4 measurement at Bremen, Garmisch, Karlsruhe and Orleans. Comparison of the retrieved CH4-Tot with the TCCON data has shown that the model captures observed seasonal cycles and interannual variability at mid-latitude sites. The spatial and temporal distributions of CH4-Tot in West Siberia for 2003-2013 are presented. Analysis of deseasonalized time-series indicates that the total CH4 increases about 4 ppbv/yr from 2007. The retrieved total CH4 mixing ratios can be used as an additional constraint for inverse modeling. This work was supported in part by the Ministry of Education and Science of the Russian Federation (state assignment for the fundamental and applied research performed at the Altai State University).

SATELLITE ALTIMETRY FOR MONITORING SEA LEVEL CHANGES IN THE EASTERN MEDITERRANEAN

______D. Papazachariou, G. Zodiatis, A. Nikolaidis, S. Stylianou Oceanography Center, University of Cyprus, Nicosia, Cyprus and D. Arabelos Department of Geodesy and Surveying, Aristotle University of Thessaloniki, Thessaloniki, Greece

Sea level variations in the Eastern Mediterranean Sea are examined through the use of altimetric data (SSALTO/DUACS gridded DT MSLA (Mediterranean Sea), PISTACH products). Annual Sea Level Anomalies for the period between 1992 and 2013 were obtained by averaging weekly values of SSALTO/DUACS gridded Delayed-Time Map of Sea Level Anomalies for the Mediterranean Sea. Annual Sea Level Anomalies were also computed at locations near tide gauge stations in Cyprus. The results showed a small increase of Sea Level Anomaly. Sea Level Anomalies for passes 7, 68, 83, 159, 170 and 246 of Jason-2 mission were obtained by averaging the values for each cycle. Data from 199 cycles were used, covering the period from 2008 to 2013. The investigation resulted in a large increase of Sea Level Anomaly of about 20-30 cm in the period between 31 July 2012 and 7 August 2012. PISTACH products, due to their nature, cannot be considered as reliable.

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STEREOSCOPIC TERRAIN MODEL CREATION BASED ON A SINGLE SATELLITE (SPACE) IMAGERY AND VISUALIZATION OF GEOSPATIAL INFORMATION BY MEANS OF LENS RASTER

______A.V. Molochko, T.V Pyatnizyna., A.V. Fedorov, D.P. Khvorostukhin Saratov State University named after N.G. Chernyshevskiy, Saratov, Russia. [email protected]

Abstract Methodic of stereoscopic terrain model creation based on single high resolution space photo with using digital elevation model and following visualization this image by means of lens raster is described in the article. The main idea of the methodic is consist on construction of consecutive frame set imitating stereo pair series by the use of initial space photo transformation. When lens raster applied, derived image optical transformation is occurred and stereo effect arises, that approximates to real perception of terrain bird`s-eye view, due to using absolute heights with regards to buildings altitude for objects depth calculating.

Keywords: remote sensing data, 3D spatial information, digital elevation model, stereoscopic terrain model

Stereoscopic terrain models generated by a pair of orthophotos, have been successfully used to solve the basic photogrammetry problem - definition plane coordinates and elevation terrain points, more than 100 years. Theory, technology and methods of obtaining and processing aero- and space photos were improved, as well as the presentation of the final product. Schematic diagram of stereoscopic terrain model using within the geographical research is as follows: a pair of conjugate orthophotos → geometric stereoscopic model → orthoplan, DTM, DSM → final cartographic product. With the development of new technologies, traditional topographic maps with relief depicting isolinear were replaced and new GIS products, including created on the basis of DTM and satellite imagery with three-dimensional terrain videomodels with opportunity 3D visualization were come (Akermann, 2011; Tyuflin, 2002; Tyuflin, 2011). It should be noted, that we are talking about the video sequence, i.e. series of flat images observed from different points and at different angles. For creation of the actual stereo, based on the features of human visual perception of the world, i.e. the stereoscopic terrain model, perceived by the observer as a psychological reality is necessary to observe a number of conditions (Knizhnikov, 2004). Prerequisite is the existence of at least two overlapping images - stereo pair for left and right eye, as well as a special device (optical, anaglyph, polarization) for image registration in accordance with the natural eye convergence and accommodation. Obviously, using such a stereo model has a number of disadvantages, the first of which is associated with the complexities and costs of buying of color high-resolution satellite stereo pairs for the interested area; the second disadvantage is associated with the obligatory presence of viewing equipment. The proposed project methodology solves both of these problems, because for stereoscopic terrain model creating is enough a single satellite imagery, and to view the final stereoscopic space map is no need additional devices. The general scheme of this methodic is shown in figure 1.

Figure 1. Scheme of stereoscopic space map creation, based on single satellite imagery and DEM

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The project consisted of several stages: - preparation of satellite (space) imagery; - DTM creation; - creation of depth maps and parallax calculation; - creation of stereoscopic model; - stereoscopic space map creation and alignment with the lens raster images. As a result, stereoscopic space map of Saratov city central part was created. The map’s horizontal scale was 1: 6000. As a basis for stereoscopic models creation can be used satellite (space) imagery or space map with any spatial resolution. For methodology testing, multispectral satellite fragment of Saratov city central part with a spatial resolution of 2 meters was taken. Space imagery obtained under filling SREC "GIS-Center" (Saratov State University) geoportal. Then the transformation of spatial reference image in the projection of DTM using the program ArcGIS was carried out. For a more convenient and natural perception of the human eye structure of the territory, as well as the partial light scattering lens raster the colors and image brightness have been adjusted. Methods of DTM creation could vary depending on the scale and structure of the territory. Thus, for larger the earth's surface areas can be used ready- made digital models such as SRTM or GTOPO. In our case, the area of the modeled territory is small, so SRTM or GTOPO will not give the necessary precision. That is why, we constructed a detailed digital elevation model with a cell size of 2 m (Fig. 2). Considered Saratov city central part area of approximately 31 km2 GRID- model size was 3458x2246 pixels.

Figure 2. DTM of Saratov city central part

The feature of stereoscopic terrain model in the city is needed to consider the presence of high- rise buildings, which are often comparable to the height of major landforms. That is why using DTM without further processing could not create a stereoscopic terrain model in fully reflecting in the real countryside views. To avoid these inaccuracies, authors have created a DTM, that taking into account the height of buildings. To do this, namely layer "Structures" was used in digital map of the Saratov city. Using ArcGIS software, all layer’s objects were converted to raster image (Fig. 3). In order to avoid empty values in a raster, the polygonal object with carved buildings, covering the entire area of modeling was added to the model. Heights of buildings calculated according to floors’ number were recorded in raster. Thus created DSM was the basis of the depth map.

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Figure 3. The Map of the Saratov city central part, combined with DTM

The depth map is a gray mask that using gradations of brightness reflects the depth of the original image points in the stereoscopic terrain model space and determines the position of the plan focus (stereo window). In this, the points of foreground are depicted in white, background points - black, intermediate points - gray color of varying intensity depending on the depth of their location. Thus all image points have luminance characteristic which values can vary from 0 (black) to 255 (white) in a palette of RGB, or from 0 to 100% black color in CMYK palette. Stereo window position between the foreground and background simulates converged survey focusing on the average level of relief, which in our case corresponds to approximate level of ten-floor buildings in the central plain part of the city. The relief of the territory is not too significant height difference from 15 m (storage pound’ mirror) to 250 m (Lisogorskoe plateau), so when we created a depth map we have used average luminance range - from 40 to 60% black in the CMYK palette. In fact, the depth map is a raster DSM constructed taking into floor account and executed by black-and-white color scheme in the specified gradations of brightness (Fig. 4, 5). On the basis of the depth map is calculated parallax of each point of source space (satellite) imagery in constructing frames pseudo stereo pair.

Figure 4. The part of space (satellite) imagery Figure 5.The part of depth map

Obligatory condition of pseudo stereo pair correct creation by transforming the original image is an exact fit to the size of the depth map of the image. To comply with this condition, both raster combined in one map window in the MapInfo program. Means of the program, depth map can be slightly corrected in order to better display in the space of created stereoscopic terrain model of the engineering infrastructure (bridges, road junctions) by digitizing them with additional polygons

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obtained by assigning the desired brightness value. Subsequently, the original image and the depth map with overlaid layer of the adjustment of digitizing alternately exported in raster format directly from the map window, which allowed for an exact match of pixel sizes. Parallax calculation and pseudo stereo pair’ frames creation were made in the Deep Matrix software complex "Variography" developed by Siberian Innovation and Technology Center "Progress", for stereo and variograph images creation, and intended for the manufacture of individual stereo and variofotography and advertising production. When calculating parallax and subsequent construction (morphing) of a number of frames the program takes into account depth map (reflecting the range of variation of parallax within one frame) and the maximum parallax set. Maximum parallax between stereo pair frames, allowing to keep a stable stereo image is associated with the physiological characteristics of the eye (including the exigencies of stereoscopic vision and interocular distance) and depends on the viewing distance. In general, its modular value have not exceed the value of 0,03 R, where R - distance from the viewer's eye to the plane of the screen (in this case - lens raster) (Valyus, 1962). With the final image width of 910 mm (the central part of the city on a scale of 1: 6000) and the viewing distance of 2 m, maximum parallax will be 60 mm, i.e. about 6% of the width of the image. Empirically, it was found that for the maintenance of stable stereo effect without depth loss is enough to set the parallax equal to 4%. It is enough only two frames for the stereoscopic terrain model creation, but in this case the stereo effect retained only when viewing the image at right angles. The lens raster allows using multiple stereo pairs into each other along the chain, which significantly extends the stereovision. The maximum stereo pair amount depends on the angle of convergence, defined by the distance and angle of view of a raster, which is calculated on the basis of the raster pitch and thickness. Assuming, that α = 2 arctg (b/2r), where r - viewing distance, and b - interocular distance (65 mm), we find that while viewing the image from a distance of 2 m the ocular convergence angle (α) is approximately 2º. The lens raster angle (β) can be calculated using the formula β = 2 arctg (d / 2(t-d/2)), where t - the thickness of the raster, and d – it’s pitch (i.e., the width of a single lens). Thus, the raster of 2.08 mm thickness and 0.635 mm raster pitch (40 LPI in the International Classification) has a viewing angle of 20º. Given these two values (angle of eye convergence and angle raster) we get that from a distance of 2 m observer can perceive 20º/2º = 10 different angles images created by lens raster with the characteristics, i.e. 10 stereo pairs made up from 20 frames. However, the number of stereo pairs encoded in a single image, limited resolution of the printing device, in other words, in which the number of pixels in the image can be reproduced under each lens raster. Thus, the calculation is adjusted by the amount of stereo pairs: N dpi/n lpi, where N - the printer resolution, n – lineature. With a maximum printer resolution of 720 dpi we will have 720/40 = 18 frames. Series of stereo pair creation by transforming the original space (satellite) imagery occurs at the stage of morphing, in which each pixel of the input orthophoto imagery during the transition from one frame to the next moves on the calculated value of the parallax. During the movies, gaps in the image being completed by trail pixels of the same color as the original. As a result of a single image morphing we get 19 frames (9 left, 9 right + original center), constituting a series pseudo stereo pair. Thus obtained frames in "Variography" program were combined and encoded into a single image that can be exported into one of the raster formats. Making final space photo map was produced in the Adobe Photoshop program, where the encoded raster imagery was imposed signature streets, the storage pond, the map title and scale. When imposing of the lens raster, the optical transformation occurs and the resulting image appears stereo effect, which, through the use of the absolute values of the heights (including the buildings) to calculate the depth of the objects and such stereo effect is close to real perception of types of terrain. Knowing the pseudo stereo pair frames parallax, elevation terrain and viewing distance, we can calculate the resulting depth of the image and the vertical scale of the stereo model. Depth Images (Δl), parallax (pl) and the viewing distance (r) are related by: Δl / (r-Δl) = pl / B, where B – interocular distance (Valyus, 1962). By simple transformations: ∆l=plR/(B+pl). In this case, stereo pair frames parallax was 4 mm (0.04 910/9). When viewing distance of 2000 mm and 65 mm interocular distance, we obtain the depth of the image relative to stereo window

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is about 115 mm. Taking into account the central position of the stereo window, elevation terrain was (250-15) / 2 = 117.5 m. Thus we obtain the model vertical scale is approximately equal to 1: 1000. It should be noted, however, that these calculations are approximate because the stereo model perception largely depends on the individual viewer. The developed method can be used to create and visualization of stereoscopic terrain models in order to: - visual estimation of the current state and the territory use; - preliminary territory estimation during development; - as a visual aid in the educational institutions.

References Akermann F. Sovremennaya tekhnika i universitetskoe obrazovanie // Izvestiya vuzov. Geodeziya i aerofotosemka. – 2011. - № 2. – P. 8-13. Valyus N.A. Stereoskopiya. M. : AN SSSR, 1962. – 380 p. Knizhnikov Yu.F., Kravtsova V.I., Baldina E.A. and others. Tsifrovaya stereoskopicheskaya model mestnosti: eksperimentalnie issledovaniya– M. : Nauchniy mir, 2004. – 244 p. Tyuflin Yu.S. Informatsionnie tekhnologii s primeneniem fotorrammetrii // Geodeziya i kartografiya. – 2002. – № 2. – P. 39-45. Tyuflin Yu.S. Fotogrammetriya - vchera, segodnya, zavtra // Izvestiya vuzov. Geodeziya i aerofotosemka. – 2011. - № 2. – P. 3-8.

SPATIAL MODELLING OF FOREST REGENERATION DYNAMICS AND BIODIVERSITY USING REMOTE SENSING DATA AND GIS-TEHNOLOGY

______V.A. Ryzhkova, I.V. Danilova V.N. Sukachev Institute of Forest, Siberian Branch, Russian Academy of Science, Krasnoyarsk, Russia. [email protected]

Abstract An algorithm of spatial modelling of forest regeneration dynamics based on the analysis of a digital elevation model (Shuttle Radar Topography Mission (SRTM)), Landsat 5-TM images and ground observation data was developed. Firstly, preliminary classification of forest vegetation taking into account its regeneration dynamics in different growing conditions was developed and based to the map legend. An automated classification and mapping of potential forest growing conditions of a test area were performed. The forest regeneration dynamics mapping was based on this classification. Landsat 5-TM multi-band multitemporal images were classified by the method of maximum likelihood. The obtained classes were interpreted as an age regeneration stages in the range of growing conditions. The forest vegetation chronosequences were formed using the preliminary classification. On the final step, decision tree expert system for classification and mapping of forest regeneration dynamics was developed. As a result, raster and vector polygonal layers were built.

Keywords: Central Siberia, digital elevation model (DEM), Geographical Information System (GIS), forest regeneration dynamics map, remote sensing data

Introduction Problems related to monitoring of biodiversity and dynamics of natural landscapes are becoming more challenging as human impact on the environment grows and global climate change is getting more pronounced. Siberian boreal forests are a unique biome of forest ecosystems situated along the northern tree line of Asia and also a unique sourсe of genetic, species and ecosystem diversity.

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Monitoring of forest ecosystem diversity and sustainability under ever increasing human-caused environmental stresses is a major forest protection challenge. Mapping is an important tool in studying and monitoring forest cover, particularly its spatial inventory, dynamics and biodiversity estimation. Vegetation maps are actually spatial vegetation cover models. However, maps built using traditional methods become out of date very soon. Accurate vegetation maps are missing for the most part of Siberia. Nowadays, vegetation maps are successfully built and effectively updated using GIS technologies and remote sensing data (Burnett and Blaschke, 2003; Hill et al, 2010; Wondie et al, 2010; Ohmann et al, 2011; Higgins et al, 2012). A technique of automated mapping of forest regeneration dynamics was developed and applied to south central Siberia. This technique is based on GIS-technology involving spatial analysis of multi-band satellite data, digital elevation model (DEM) and ground observation data.

Materials and methods 1. Study Area The southern part of near-Yenisei Siberia was chosen to be a test site (56º - 58º , 92º - 96ºE). This area is heterogeneous topographically and consists of two distinct parts: West Siberian Plain west of and Central Siberian Plateau east of Yenisei including the low mountains of Yenisei Mountain Ridge and Usol Hollow. The forests of the area are markedly diverse in composition, structure and regeneration dynamics of vegetation communities. They are heavily disturbed by both natural and human factors. The major vegetation age stages occur in big areas covering a range of forest growing conditions. 2. Methods Classification of forest cover was performed using a topogenetic approach of Russian forest scientist Kolesnikov (1956). According to this approach, forest ecosystem diversity is formed by forest development or regeneration stages present at the same time in a given area. The entire diversity of vegetation communities is classified based on growing condition similarity, and not based on continuously changing outward characteristics (e.g., species composition). Forest type as a main unit of this classification represents a series of genetically linked and sequentially replaced communities developing within a certain type of growing conditions. Growing conditions type presents a key element of the forest type concept. It is identified based on the climate, geological, geomorphological, and soil parameters of a given area. To carry out automatized classification of a DEM-composite (elevation above sea level (a.s.l.), slope) and satellite images, we used standard methodologies, such as ISODATA and MAXLIKE (Richards and Xiuping, 2005) as well as ERDAS IMAGINE 9.2 product.

Results and Discussion A technique of spatial modelling of forest regeneration dynamics based on GIS-technology was developed and applied to south central Siberia. Automated mapping of forest cover, particularly its regeneration dynamics, is a challenging problem covering a number of tasks, which can be accomplished only through interdisciplinary research efforts. In this work, we have solved several key tasks in a stepwise manner to resolve this problem. The first step was to develop an appropriate vegetation classification, upon which the map legend would be based. A preliminary classification of forest regeneration series (chronosequences) with an account of forest growing conditions was developed by analyzing thematic maps, literature, and field data. The second step was an automated classification and mapping of potential forest growing conditions of the test area. The obtained topographic profiles and landscape maps (Sochava, 1977, Gudilin, 1987) were used to analyze the test site geomorphological conditions and to identify sites relatively similar in topography (ratio between mesorelief forms, range of elevations a.s.l., and surface roughness) and corresponding to certain landscape types and their combinations. A preliminary number of classes for an unsupervised DEM classification was determined. To establish the boundaries of these classes, two-layer DEM-composite (elevation above sea level and slopes) was classified using method ISODATA (Richards and Xiuping, 2005). It enabled to identify terrain roughness classes

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relatively similar in morphometric relief parameters and interpret them thematically as a geomorphological complex of growing conditions (GMC) with respect to geomorphology, zonal soil types, and vegetation (table1). Forest growing conditions types were identified for each GMC based on the ranges of slope and elevation above sea level. As a result, a two-level map of potential growing conditions was built, which shows sites similar in topographic location, soil, and hydrological conditions, i.e. in ecological regimes, which determine the vegetation cover structure. This map provides the basis of forest regeneration dynamics mapping. The next step was an automated classification of multi-band satellite images. Landsat 5-TM images were classified by the method of maximum likelihood to identify land cover classes based on spectral characteristics. We used four summer (June-July) and four autumn (September-October) cloud free Landsat 5-TM scenes (1989-1990 years) received as close as possible to the periods of field studies and ground forest inventory. To classify multitemporal images, the training samples for major woody species and age were made. The information classes were formed through the elementary forest inventory site database query. As a result, the information classes most common in a given part of the test site and the associated image were developed. Then parametric signatures were calculated for each information class and a supervised classification of the images was done based upon these signatures. A generalized raster layer of surface spectrum-based elementary land cover classes was obtained using a fuzzy composition approach (ERDAS, 1999). Accuracy assessment of image classification was performed using Kappa statistic. Kappa coefficient = 0.74, therefore the satellite imagery classification quality corresponds to the excellent object recognition accuracy (Monserud and Leemans, 1992). The classes obtained were then visually assigned to the different forest regeneration stages by expert judgment with the help of all the available reference data. To build a forest vegetation series layer in a stepwise manner, we made the expert classification using the Knowledge Engineering Module (ERDAS, 1999). The elementary land cover classes obtained from remote sensing data classification were distributed among regeneration series using a classification of growing conditions types and the associated forest vegetation (Ryzhkova et al., 2011). On the final step, decision tree expert system for classification and mapping of forest regeneration dynamics was developed using Knowledge Engineer Module. As a result, raster and vector polygonal layers were built (Fig. 1). The forest regeneration dynamics map we developed through application of ground and satellite data for the southern part of Yenisei Siberia within Krasnoyarsk Region, is of a scale of 1: 200 000. On the one hand, this map reflects a current state of forest cover. On the other hand, it shows spatial distribution of forest regeneration series in a range of growing conditions since it is based on the dynamic classification.

Forest growing conditions Predominant vegetation Geomorphological complex of forest Type of forest Native forest types Secondary growing conditions (GMC) growing vegetation Number and Description conditions mean parameters II. Elevation Elongate-elevated or Watersheds and 1. Scots pine/ - grass canopy above sea level, rolling, elevated and flat adjacent very soft tall grass/forb (initial forest m: denudation, and slopes stands regeneration 257.15 ± 16,25 denudation-erosional Soft and 2. Scots pine/forb stage), Slope, deg.: plains made up by moderately steep stands - young and 1.29 ± 0.83 eluvial-deluvial and slopes of river 3. Scotspine/forb/ middle-aged birch alluvial-deluvial brown terraces whortleberry and aspen tall heavy loams, brown (Vaccinium vitis- grass/forb stands, loessed clays and loamy idaea) stands - mixed sands on river terraces. Flat-topped 4. Fir/spruce/tall conifer/deciduous The soils are dark- elevations and the grass/forb stands stands,

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colored sod-meadow, adjacent soft - ripening conifer sod-gley, forest grey and slopes of high stands, double-humus-horizon plain (0-1º) - mature and old dark-grey and sod- Soft slopes (1-3º) 5. Mixed fir/ conifer stands podzol. spruce/grass stands IV. Elevation Hilly-ridged or rolling Flat watersheds 6. Mixed above sea level, soft sloping, low slightly and soft slopes of fir/spruce/ m: dissected plateau made low-elevated part feather moss/short 213.6 ± 55.72 up by eluvial-deluvial of the mountane grass stands Slope, deg.: and alluvial-deluvial range (0-3º) 2.19 ± 2.51 brown heavy loams The Soft and 7. Mixed soils are grass sod- moderately steep fir/spruce/ podzolis (grass sod-deep slopes (3-5º) feather moss/forb podzolic), forest grey. stands

Watersheds and 1. Scots pine/ various slopes of tall grass/forb river terraces stands 2. Scots pine/forb stands Table 1. Forest growing conditions and the corresponding forest vegetation (fragment of conjugate classification of forest growing conditions and vegetation)

Therefore, this map enables to assess current state of forest vegetation, and to predict vegetation succession directions and rates in different growing conditions. A fragment of conjugate classification of forest growing conditions and vegetation is presented in table 1. It is a base of map legend. It summarizes the characteristics of geomorphological complexes of forest growing conditions (with an account of geomorphology and zonal soil), growing conditions types, forest types, and forest regeneration stages. The methodology proposed enables to achieve a relatively high accuracy of recognition of forest communities found in a range of environmental conditions. The automated satellite image classification combined with spatial analysis procedures allows us to minimize human involvement in the process of thematic map development. It is very important because the satellite imagery-based vegetation cover map for Krasnoyarsk Region does not exist.

Conclusions GIS technologies are very useful to compare and analyze various information layers and thus to ensure more objective identification of the links between vegetation and eco-geographic factors. A methodology of automated mapping of potential forest growing conditions and forest regeneration dynamics involving a spatial analysis of multi-band satellite data, a digital elevation model, and ground measurements were developed and applied to south central Siberia. The combined use of automated methods and expert interpretation of the classes obtained allowed us to identify certain characteristics, such as forest growing conditions types, forest types, and age stages of regenerating vegetation, directly unrecognizable in space images, but important regarding thematic mapping. The maps incorporated in the GIS database reflected the distribution of forest vegetation series and regeneration stages in the range of growing conditions. They can be used to model forest succession in similar landscape types within central Siberia.

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Figure 1. Fragment of two layers of the study area forest dynamics map

Regeneration stages: 1- initial regeneration stages on burned and logging sites; 2-young (up to 40 years old) deciduous stands; 3 – middle-aged deciduous stands (40-60); 4-mixed conifer-deciduous stands aging 40-80; 5- conifer stands 80- 120 years old approaching cutting age; 6- mature and old conifer stand over 120 years of age. Regeneration series: see table 1.

Acknowledgments This study was supported by project ZAPAS (Assessment and Monitoring of Forest Resources in the Framework of the EU-Russia Space Dialogue).

References Bock M, Xofis P., Mitchley J., Rossner G., Wissen M., 2005, Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK. Journal for Nature Conservation, Issue 13:75-89. Burnett C., Blaschke T., 2003, A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, Issue 168: 233–249. ERDAS Field Guide. Fifth edition [Text]. - Atlanta, Georgia. USA: ERDAS Inc., 1999. – 672 р. Gudilin I.S., 1987, The USSR Landscape Map (1:2 500 000). Moscow, Russia. Hay G.J., Guillermo C., Wulder M., Ruiz J., 2005, An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation. Issue 7: 339–359. Higgins M., Asner G., Eneas P., Elespuru N., Tuomisto H., Ruokolainen K. and Alonso A., 2012, Use of Landsat and SRTM Data to Detect Broad-Scale Biodiversity Patterns in Northwestern Amazonia. Remote Sens., doi:10.3390/rs4082401 Hill R, Wilson A., George M., Hinsley S., 2010. Mapping tree species in temperate deciduous woodland using time-series multi-spectral data. Applied Vegetation Science, doi: 10.1111/j.1654- 109X.2009.01053.

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Kolesnikov B.P., 1956, Siberian pine forests of the Russian FarEast. Nauka Press. Moscow- Leningrad, Russia. Monserud R., Leemans R., 1992, Comparing global vegetation maps with the Kappa statistic. Ecological Modeling, Issue 62:275-293. Ohmann J., Gregory M., Henderson E., Roberts H., 2011, Mapping gradients of community composition with nearest-neighbour imputation: extending plot data for landscape analysis. Journal of Vegetation Science Issue 22: 660–676. Platt R. and Rapoza L., 2008, An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification. The Professional Geographer, Volume 60, Issue 1: 87–100. Ryzhkova V., Danilova I., Korets M., 2011, GIS-based mapping and estimation the current forest landscape state and dynamics. Journal of Landscape Ecology, Volume 4, Issue 1: 42-55. Sochava V.B., 1977, Landscapes of Southeastern Siberia (1:1 500 000 map). Moscow, Russia. SRTM-3-DEM (Shuttle Radar Topography Mission, Digital Elevation Model). 2010. Date of Use: May 25, Available from http://www2.jpl.nasa.gov/srtm/russia.htm Richards J., Xiuping J., 2005, Remote sensing digital image analysis: an introduction. Birkhäuser, Basel, Switzerland. Wondie M., Schneider W., Melesse A., Teketay D., 2010, Spatial and Temporal Land Cover Changes in the Simen Mountains National Park, a World Heritage Site in Northwestern Ethiopia. Remote Sensing, Issue 3: 752-766.

MONITORING THE LEVANTINE BASIN THROUGH THE USE OF MULTIPLE SATELLITE REMOTE SENSING PRODUCTS: WITH A FOCUS ON AN INTERCOMPARISION OF THE NEW SMOS GLOBAL SALINITY DATA WITH MYOCEAN MODEL DATA

______Y. Samuel-Rhoads*, G. Zodiatis, A. Nikolaidis, D. Solovyov Oceanography Centre, University of Cyprus, Nicosia, Cyprus. *[email protected]

The Oceanography Centre, of the University of Cyprus (OC-UCY) has been using a variety of satellite remote sensing products to monitor the Eastern Mediterranean Levantine Basin. The OC-UCY operates the CYCOFOS - CYprus Coastal Ocean Forecasting and Observing System at the OC-UCY, a satellite remote sensing module that daily processes satellite data for sea surface temperature (SST), Chlorophyll-a, light attenuation, tidal forecasts, detection for oil slicks and recently sea surface zooplankton and suspended matter. The recent availability of satellite remote sensing data of ocean salinity from SMOS, has promoted the oceanographic exploitation of these results and their comparison with other available ocean salinity databases, in order to determine the validity and accuracy of the SMOS measurements. Here, we examine monthly global SMOS salinity data from 2010 and 2011, and compare them with monthly global data from the project MyOcean during the above timeframe. The MyOcean project aims to deliver and operate a rigorous, robust and sustainable Ocean Monitoring and Forecasting system of the GMES Marine Service (OMF/GMS) to users for all marine applications. SMOS salinity data were obtained from the CP34 SMOS Barcelona Expert Centre (SMOS-BEC) site. Ten-day average satellite ocean salinity reprocessed data were obtained for 2010 and 2011, at 1 degree resolution from the ascending orbit, and averaged into monthly mean values. MyOcean global salinity data were obtained from the L4 reprocessing for 2010 and 2011, at 1/3 degree resolution. These data were then interpolated to 1x1 degree resolution and compared with the SMOS global monthly salinity data. Over the examined months, the SMOS satellite underestimates global salinity values, except for three occasions. In January 2010, SMOS overestimates salinity values (+0.112 psu), while for April 2010

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(+0.02 psu) as well as for April 2011 (-0.017 psu) the SMOS satellite is recording similar salinity values as the MyOcean data. During September for both years, the highest discrepancy is observed between the two datasets, with the SMOS satellite recording salinities much lower than the MyOcean data (0.43 psu and 0.516 psu difference respectively). On a monthly basis, SMOS satellite records higher than MyOcean salinity values in the Gulf of Mexico. The results of this project will provide insights and input not only for the accuracy and validity of the SMOS ocean salinity product, but also their possible offset and pattern detection shown in the MyOcean datasets. All satellite remote sensing products are available after processing through the Center’s website, providing a broad overview of the conditions that exist in the Levantine Basin, as well as a tool for long-term time series monitoring of climate variability and change in the area.

VEGETATION MAP FOR THE SELENGA RIVER BASIN ON THE BASE OF LANDSAT TM IMAGERY

______E.Zh. Garmaev, B.Z. Tsydypov, A.A. Ayurzhanaev, Zh.B. Alymbaeva, B.V. Sodnomov Baikal Institute of Nature Management, Siberian Branch of the Russian Academy of Sciences, Ulan-Ude, Russia. [email protected]

Abstract The vegetation map for the Selenga river basin on the base of Landsat TM imagery is created. The interpretation of 37 classes of plant communities on the base of automated classification (first method is supervised algorithm of maximum likelihood and second algorithm is ISODATA – Iterative Self- Organizing Data Analysis Technique) is carried out. The verification of obtained areas with vegetation maps of southern East Siberia and Mongolia is carried out.

Keywords: vegetation, mapping, Landsat, automated classification, maximum likelihood, ISODATA

Introduction Actuality of the study is due with necessity of realization the most important research areas of biological diversity, such as the execution of major cartographical projects for different regions using satellite-based information and GIS technology to settle processes of nature management. Land cover maps (geobotanic, forest management, land use, etc.) often are the basis for monitoring of biological diversity of large regions. Mostly thematic maps which showing vegetation were separately created for areas: 1) vegetation map of southern East Siberia in 1972 edited by Belov (M 1:1 500 000) (Maps…, 1972); 2) vegetation map of the MPR in 1990, part of the National Atlas of the Mongolian People's Republic (The National …, 1990). Satellite images are a convenient material for compiling and updating the land-cover maps. Promptness, accuracy, availability and variety of source data of remote sensing allow creating a map of the area of interest with the desired accuracy within a short time. Currently, the greatest need is notes in theoretical and applied geographical researches for medium-scale maps, which contains system information about the spatial and temporal patterns of organization of regions geosystems the direction of their conversion to natural and anthropogenic conditions (Vegetation …, 1989). The basin approach is the highest priority in the study and assessment of modern vegetation for developed mesoscale maps, allowing trace the spatial structure of vegetation, cross-border relationships and approach to validating the necessary of environmental measures.

Objects and methods We attempted to create a modern mesoscale vegetation map of the Selenga River basin on remote sensing data by comparing their spectral brightness in the image with the data taken from maps of vegetation in southern East Siberia (Maps…, 1972) and Mongolia The National …, 1990). Processing

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satellite images is performed using the software package ENVI 4.7, which includes the most comprehensive set of functions for remote sensing data processing and integration with GIS. The sequence of work of satellite images processing as follows: 1) downloading from the Internet pseudocolor mosaics Landsat TM (spatial resolution of 28.5 m/pixel) in the combination of channels 7:4:2 using the search system GloVis of the U.S. Geological Survey (USGS); 2) cross-linking fragments – getting whole mosaic for the whole swimming pool; 3) the allocation of the basin mask (vector layer of the basin); 4) interpretation of objects (classification); 5) creation of vector layers, editing; 6) receiving the final map.

Discussion of results When studying the landscape structure of geosystems one of the most important tasks is to analyze the morphometric parameters. For morphometric analysis of the topography of the study area altitude data of the digital elevation model SRTM v. 4 is involved. DEM downloaded from the FTP- server of the USGS. Topographical modeling of three-dimensional images were taken. In contrast to the two-dimensional maps, three-dimensional terrain model draped by satellite imagery allow distinctly see the form and «plastic» of relief, borders of geomorphological units and natural objects. Decoding of the vegetation was carried out on the basis of automated classification in two ways: controlled (i.e., with training) classification method of maximum likelihood and ISODATA algorithm (Iterative Self-Organizing Data Analysis Technique). After the unsupervised classification resulting classification map objectively combines close meaning signs group of objects than in supervised classification because clusters are detected automatically, and the algorithm used when interpreting objects hardly distinguishable on the spectral brightness. However, obtained classification map required further decomposition classes and associations because the same objects were combined in different clusters (e.g., due to the lighting conditions), and different objects were combined in the same cluster (due to the same brightness). In the first case, the clusters were combined into a single class, and in the second case additional interpretive features for discriminating objects were involved. In the Belov`s map (Maps…, 1972) in the Russian part of the Selenga river basin includes 42 categories of vegetation, and 5 of them are marked in the status of recovery series, which we were excluded. We also excluded separation by phratries and groups of formations. Consequently, from the legend of the map we used 23 categories. In the vegetation map of Mongolia (Vegetation …, 1989) basin of the Selenga river includes 31 category, of which we used 28, and 10 categories had a match with the Belov’s map, i.e. have the same numbers in both maps. In the Mongolian vegetation map for the basin classes were combined twice for 3 classes in one, and fourthly 2 classes in one, and in Belov’s map association made by 2 classes twice. Together with these two maps, which are the main reference materials, we also used the vegetation map of the Lake Hovsgol (Vegetation …, 1989) as one of the common areas. Here are 33 categories, of which 19 classes merged with each other, and 19 classes have a comparison with other maps. The model site «Urochitsche Podnaran» was taken as the reference test site which placed in the Uda valley, 4.5 km west of Udinsk, Khorinsk region of the Buryatia Republic. The area was mapped in a scale of 1:25 000, identified 36 categories. At generalization of map these 36 categories are grouped into three categories. As a result, we have allocated 37 categories (Fig. 1, 2): 5 classes of goltsy vegetation; 18 classes of taiga vegetation, of which 16 classes of mountain taiga forests (4 classes of cedar forests formation, 9 classes of larch forests, 3 classes of pine forests) and 2 classes of piedmont and basins associations (shrub and wetlands with combination with shrubs); 9 classes of steppe vegetation (3 classes of middle and low mountain steppes; 6 classes of piedmont, hummocky and steppes); 4 classes of floodplain vegetation; 1 class of arable land and sloping grasslands.

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Figure 1. Vegetation map of the Selenga river basin

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Figure 2. Descriptive data of vegetation map of the Selenga river basin

This work was partially supported by RFBR project 13-05-41378-RGO_a «The transformation of natural environment of Transbaikalia and adjacent territories: retrospective analysis and current state».

References Maps of nature, population and economy of southern East Siberia of scale 1:1 500 000. Vegetation. – M.; Irkutsk: GUGK, 1972. The National Atlas of the Mongolian People's Republic. – Ulan Bator – Moscow, 1990. – 144 p. Vegetation map of the scale 1:1 000 000. Atlas of the Lake Hovsgol. – M., 1989. – pp. 44-45. Konovalova T.I. Geosystem mapping / T.I. Konovalova; edited by A.K. Cherkashin; RAS, Sib. Branch, The Sochava Institute of Geography. – Novosibirsk Academic Publishing House «Geo», 2010. – 186 p.

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VERIFICATION OF REMOTE SENSING BASIC PRODUCTS WITH IN SITU DATA FOR THE KARA SEA

______D.V. Moiseev, G.N. Duhno, Murmansk Marine Biological Institute KSC RAS, Murmansk, Russia. [email protected] Yu.V. Fedorkova Research Center for Earth Operative Monitoring (NTs OMZ) «Russian Space Systems» Ltd, Moscow, Russia

Abstract Procedure of MODIS Terra and in situ data verification is described. Results obtained in the Kara Sea demonstrate that it is possible to use satellite SST data for common analysis of climate changes in the Arctic Seas.

Keywords: satellite, in situ, verification, Arctic

In conditions unending debate about the nature and vector of contemporary climate change one of the most important sources of information are data of sea surface temperature (SST) (Belkin, 2009), that is acquired from infrared (IR) radiometers installed on satellites. Particularly it is important for difficult to access waters of the Arctic Seas, where observations on shipboard carry out sporadically. In this case, it is vital to estimate the reliability of satellite data in comparison with the information obtained in situ (Zhang et al, 2009). For this purpose, it is necessary to carry out the verification of remote sensing data with the ground-truth SST marine expeditionary observations on regular basis (Kennedy et al, 2011). According to the agreement between MMBI and NTs OMZ «Russian Space Systems» Ltd the work on verification of remote sensing basic products (BP) with the in situ data carried out in 2013. To conduct verification MMBI was provided with MODIS Terra satellite data in HDF format. During the processing of satellite and in situ information were used the programs SeaDAS (http://seadas.gsfc.nasa.gov), ArcGIS (http://www.arcgis.com) and Ms Access, etc. First of all it is necessary on the basis of the original data in HDF format create a spatial arrays of information with the ability to import into ArcGIS. It can be done in two ways. The simplest method is a pixels geocoding in SeaDAS followed by raster export in to common GeoTIFF format. However, for a more precise characterization of the parameters that measured by remote sensing we need use another requiring long processing time algorithm. Using this algorithm firstly by instrument of SeaDAS program HDF data exported to a text file ASCII, which imported into MS Access at once. Where data are filtered with a series of requests and then a personal geodatabase is created. For more efficient using of ArcGIS, the personal geodatabase is converted to a file geodatabase. As a result, we have in ArcGIS a complete digital copy of satellite imagery with filtered data without any "noise" (Fig. 1).

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Figure 1. ArcGIS project view of a satellite image digital copy with filtered data

For verification we used sea temperature in situ data obtained during the MMBI expedition on board R/V "Dalnie Zelentsy" in the Kara Sea in August 2012. Vertical profiling of sea from surface to bottom carried out by CTD-profiler SEACAT SBE 19plus. Temperature data converted in ASCII text format. Information necessary for verification of SST extracted and converted to shape-files by using ArcCatalog. Verification of data from satellite radiometer MODIS Terra and in situ made in ArcMap. Previously prepared data as (1) GeoTIFF, (2) file geodatabase and (3) water temperature in situ data shapefile added to a new ArcGIS project. The desired station activated with the tool «Identification» and as a result data in layers at this point were displayed in the identification table. Information of all visible layers was showed with all three types of data. It is necessary exactly to keep up the arrangement of the layers in the table of contents. Upper layer should be with data in situ, under it - layer file geodatabase and the lowest - GeoTIFF (Fig. 2).

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Figure 2. Fragment of the ArcGIS project view with three types of data and "identification" instrument panel in ArcMap

Visualization of verification results is graphically presented in Figure 3. Additionally Figure 4 illustrates data comparison from 3 SST sources: in situ, GeoTIFF file and database. Despite of the significant difference in time between the remote sensing and in situ SST, it is possible conclude that data agreement in most cases is quite good. Thus, for an overall assessment of the temperature conditions of the Arctic Seas and to identify the direction of the climate changes vector is quite possible to use SST satellite data. The next stages of this project will focus on verification of Russian satellites and new in situ SST and chlorophyll data.

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Figure 3. Results of verification in situ data and remote sensing GeoTIFF images of the Kara Sea in August 2012

Figure 4. Data comparison from 3 SST sources: in situ, GeoTIFF and file database

This work was partially funded by the project "Creating databases and development of space- based information dissemination technologies" (in the period during 2011-2013) under the aegis of the Russian Federal Space Program for 2006-2015.

References Belkin I.M. Rapid warming of Large Marine Ecosystems. Progress in Oceanography 81 (2009) 207– 213. Kennedy J.J., R.O. Smith, and N.A. Rayner, 2011: Using AATSR data to assess the quality of in situ SST observations for climate studies. Remote Sensing of Environment, 116 (special issue): 79–92 doi:10.1016/j.rse.2010.11.021. Zhang H.-M., Reynolds, R.W., Lumpkin, R., Molinari, R., Arzayus, K., Johnson, M. & Smith, T.M. (2009). An integrated global observing system for sea surface temperature using satellites and in situ data. Bull. Amer. Meteor. Soc., 90, 31–38, doi:10.1175/2008BAMS2577.1

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WEEDINESS ASSESSMENT OF ANTHROPOGENIC PHYTOCENOSES ON THE BASIS OF SATELLITE REMOTE SENSING DATA

______O.E. Arkhipova Institute of Arid Zones SSC RAS, Faculty of High Technogies Southern Federal University, Institute of Mechanics and Applied Mathematics Vorovich I.I. Southern Federal University, Rostov-on-Don, Russia. [email protected] N.A. Kachalina Institute of Arid Zones SSC RAS, Faculty of High Technogies Southern Federal University, Rostov-on-Don, Russia Yu.V. Tyutyunov Institute of Arid Zones SSC RAS, Faculty of High Technogies Southern Federal University, Institute of Mechanics and Applied Mathematics Vorovich I.I. Southern Federal University, Rostov-on-Don, Russia

Abstract We present results of the decoding of Landsat satellite images that allows identifying patches of common ragweed in agrophytocenoses of the South of Russia, basing on the normalized difference vegetation index (NDVI). The fact of the absence of extensive areas heavily infested by ragweed confirms the conclusions of the field studies about the long-term efficiency of the introduction from the North America of the ragweed leaf beetle in the period 1978–1990. Retrospective analysis of the archival satellite images of Stavropol region that were taken in the second half of the 1980’s shows the reduction of weed-covered areas during this period. Most weed patches detected are systematically located on the roadsides and field boundaries.

Keywords: remote sensing, vegetation indices, NDVI, common ragweed

Introduction Currently, the increase of natural and anthropogenic weediness phytocenoses is one of the most pressing problems. Special form of human impact is the process of introducing invasive species in plant communities. One of them is aggressive North American species -ragweed (Fig. 1). This quarantine weed infests almost all agrophytocenoses, especially field occupied vegetable and row crops, as well as meadows, pastures, shelter belts. Ragweed perfectly adapts to any environment and has the ability to mass invasive fouling. Aggressiveness and harmfulness of ragweed caused by its biological features. This adventive plant has full features of the "ideal" weed. Now weed spread over vast territories around the world is seen as a biological contamination. Currently ragweed distributed in Europe, Asia, North and South America, Africa, Australia, found in the territories of the 35 states. Spread it in Russia has the character of environmental explosion.

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Figure 1. Ragweed in the flowering period

Harmfulness ragweed is manifested not only in the mass contamination of natural and anthropogenic phytocenoses but also in its ability to be a strong allergen. To date, ragweed pollen is one of the leading factors in development of seasonal allergies (hay fever, allergic rhinitis) (Matishov et al., 2011). On the other hand, it does not take into account the economic damage caused by ragweed when clogged agrophytocenosis that entails significant yield losses of cultivated crops and cultivated crops.

Formulation of the problem Significant ragweed-covered areas annually celebrated in the Krasnodar, Stavropol and Rostov region and some republics of the North Caucasus. In order to timely detect pockets of growth of ragweed and its destruction, quarantine phytosanitary inspection of the territory is held by state inspectors of Rosselkhoznadzor. However, field works require large economic cost and time. It is quite difficult to compose a comprehensive picture about the size of the contaminated area, even if a large number of local samples and regular route inspections were made. As a result quarantine inspection data about the size, position and degree of infestation foci areas usually are not only deprived of spatial detail, but may be too high , or subjectively , or incomplete. This fact significantly reduces the effectiveness of the decision of choosing the most appropriate method of weed control (mechanical, chemical, biological, or a combination there of in each case .In addition, the lack of complete spatial pattern does not allow to assess the effectiveness of measures to combat weeds. Methods of remote sensing (RS) have significant advantages over traditional terrestrial and aviation methods by their ability to instant overview of large areas, including remote and inaccessible, as well as the regularity of the filming of the objects and territories. The unique capabilities of space imagery in different regions of the electromagnetic spectrum allow to provide digital multispectral shooting, thermal infrared shooting, as well as all-weather shooting , great potential lies in the possibility of using satellite data for agriculture. However, the task of using remote sensing data to determine pestholes ragweed is more complicated than the traditional problem of determining the types of crops and their yields. Cultivated plants occupy sufficiently large and homogeneous areas and have strong spectral characteristics within a single species. In contrast, ragweed grows heterogeneously within one field, and its spectral characteristics are strongly dependent on both the type of the main crop , and on the degree of contamination of the study field. The idea of using remote sensing data to detect ragweed thickets was launched Asselin and Mops (Asselin, Maupin, 1998). They tested a hyperspectral sensor, but due to technical problems have not achieved their goal. Later, using a spectroradiometer, Mops and Boyvin (Maupin, Boivin, 2001) have compiled a database of spectra of plants and mineral rocks. Today, remote ragweed detection methods actively used by researchers in France and Austria (Auda et al., 2011). Since 2004, using the decryption

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of satellite images of high and medium resolution are monitored ragweed in Hungary (Csornai et al., 2011). The purpose of this study is to assess ragweed weediness of the Southern Russia. Subject of research is development of methods of interpretation satellite images to study the dynamics of different vegetation types (for example, ragweed). Due to the fact that at the current stage uniquely identify ambrosia on pictures is not possible, in future work the term "risk of weediness" will use. The basis of the used methodology is the use of remote sensing and geoinformation systems (GIS) in conjunction with the field studies for the creating of risk maps of ragweed weediness.

Methods The study was divided into the following stages to achieve this purpose. - Creating an archive of satellite images Landsat 4-5 TM and Landsat 8; - Calculation of vegetation index NDVI in the software package ArcGis 10.1 based on satellite imagery; - Obtaining values of the coefficient of spectral brightness in field studies using 4 -channel spectrometer SkyeInstruments SpectroSense 2 +; - Mapping the risk of ragweed weediness based on remote sensing data ; - Verification of results; - Preparation of the final risk maps. Figure 2 shows the steps of the methodological chain.

Figure 2. Scheme of creating of risk maps

A characteristic feature of the vegetation and its condition is the spectral reflectance, expressed in terms of the albedo and the luminance factor. Spectral brightness coefficient (SBC) is the ratio of the brightness of the reflected and received by the surface electromagnetic wave a certain range. The highest values for SBC plant communities are achieved in the near infrared (0.6-0.7 µm), while minimum is achieved in the red portion of the visible range (0.7-1.0 µm). Knowledge about the connection between the structure and condition of vegetation with its spectral reflectivity allow using

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of aerospace imagery for mapping and identification of vegetation types. Calculating vegetation indexes was made for work with the spectral characteristics of ragweed. The paper was used normalized difference vegetation index (normalized difference vegetation index - NDVI). NDVI values calculated by the formula: NDVI= (NIR - RED)/(NIR + RED), Where RED and NIR stand for the spectral reflectance measurements acquired in the visible (red) and near-infrared regions, respectively. These spectral reflectance are themselves ratios of the reflected over the incoming radiation in each spectral band individually; hence they take on values between 0.0 and 1.0. NDVI values were calculated on the basis of medium-resolution images of the Landsat satellite (Table 1).

Table 1. Combination of channels of satellite cameras Landsat, used to calculate the NDVI The sensors of satellites The channels TM Landsat (4–5) 3 (0.63–0.69 µm), 4 (0.76–0.90 µm) ETM+ Landsat 8 3 (0.63–0.69 µm), 4 (0.75–0.90 µm)

Calculation NDVI algorithm is built nearly into all common software packages related to the processing of remote sensing data (Arc View Image Analysis, ERDAS Imagine, ENVI, Ermapper, Scanex MODIS Processor, ScanView etc.). In addition, NDVI was calculated on the basis of data obtained during ground surveys. To do this, we measured SBC of vegetation types using ground spectrometers which have a set of channels in the red and near-infrared regions of the spectrum. For the experiment were chosen covered ragweed agrophytocenoses near Kagalnik Azov district of Rostov region, was produced froze coordinates of the boundary points using equipment GPS, and the test areas were identified on the territory of Adygea and Lake Manych (Fig. 3).

Figure 3. Field studies

Calculation of NDVI Software ArcGis Desktop 10.1 Esri was used for index calculation. Index NDVI was calculated after an analysis of images; the values of the vegetation index for ragweed were defined after comparisons with field studies (Fig. 4) (Arkhipova, Kachalina, 2013). Analysis of the results showed the presence of ragweed at the edges of the survey area and on side roads, which was confirmed by the results of field studies. Based on the results obtained during

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the experiment were constructed risk maps for Azov district of Rostov region. Data were used during the growing season of ragweed from May to September 2011 when constructing maps.

Figure 4. Results of NDVI calculation

It should be noted that information about vegetation peaks of both the ragweed and related plants is the most important in the creation of risk maps. With a high degree of confidence we can speak only of the weediness of grain fields in late August - early September as the vegetation peaks of ragweed and grain in this case does not match. Is rather complicated definition of ambrosia on sunflower fields, as tall plants oppress ragweed whose height in these areas does not exceed 60 cm. Important results have been obtained in a retrospective analysis of archival images taken American spacecraft during the execution of works on the introduction and acclimatization of ragweed phytophagous. These data provide insight into the dynamics of the spatial distribution of ragweed which was observed in the second half of the 1980s on the site of the primary issue striped ambrosia beetle in the Stavropol region. The obtained results decryption of satellite images show no expanse of ragweed areas characteristic of the 1970s (Kovalev, 1989), and a reduction weedy areas in this period.

Conclusion Analysis of the results showed the need to combine the data of remote sensing methods with field studies. It should be noted that the method of satellite data used in the current phase of work , allow us to estimate only the degree of weediness fields development of algorithms for a clearer definition of weed species and its application to decrypt images urbanized areas - challenges of the next phase of research . Unlike crops, whose spectra are fairly well understood and allow very accurately localize them using satellite images to assert unequivocally that the spots identified on images of weeds occurs field is ambrosia , is not yet possible . In other words developed at the present stage of research method for identifying vegetation provides only the top (i.e. inflated) assessment of the weediness fields of ragweed which should be regarded as provisional. However, results obtained at this stage, indicate the absence of currently extended ragweed regions in the study area as in the Rostov region, the Stavropol region. While a strong weediness systematically observed at the edges of fields and along roadsides. Retrospective analysis of satellite images taken by satellites Landsat 4-5 TM in the second half of the 1980s shows a decrease in these period areas of fields covered with weeds.

References Arkhipova O.E., Kachalina N.A. Evaluation of natural and anthropogenic weediness of phytocenoses based on remote sensing data (for example, ragweed) / / Ecology, economics, computer science. V.2: Geoinformation sciences and environmental development: new approaches, methods and

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technologies. GIS technology and space monitoring Abrau Durso (8-13 September 2013). Rostov-on- Don: 2013. C. 298-301. Asselin S., Maupin P. Apport du capteur hyperspectral AISA à la cartographie des populations d'Ambrosia artemisiifolia de l'Île de Montréal. Québec, Canada: Direction de la santé publique de Montréal. 1998, 25 p. Csornai G., Mikus G., Nádor G., Hubik I., László I., Suba Z. The first seven years of the remote sensing based ragweed monitoring and control system // EARSeL eProceedings, 2011. N 2. P. 110–118. Kovalev O.V. Settling alien plant of ambrosia tribe in Eurasia and the development of biological control of weeds genus Ambrosia L. (Ambrosieae, Asteraceae) / / Theoretical bases of biological control of ambrosia / Ed. OV Kovalev, SA Belokobylsky Leningrad: Nauka, 1989. Tr. Zoological Institute; T. 189. P. 7-23. Matishov G.G.,Esipenko L.P.,Ilina L.P., Agaseva I.S. Biological ways of dealing with ambrosia in anthropogenic phytocenoses of southern Russia. Rostov n / D Univ SSC RAS, 2011. 144 p. Maupin P., Boivin M. C. Reconnaissance des populations d’Ambrosia artemisiifolia sur l’Île de Montréal à l’aide d’un capteur hyperspectral. Étude des propriétés spectrales et de l’écologie végétale. Québec, Canada: Direction de la santé publique de Montréal. 2001. 22 p.

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