Journal of Geomatics i Vol.7 No.2 October 2013
Journal of Geomatics (A publication of the Indian Society of Geomatics)
Editorial Board
Chief Editor: Dr. Ajai (Address for Correspondence: Group Director, Marine, Geo & Planetary Sciences Group, Space Applications Centre, ISRO, Ahmedabad 380 015) Phone: +91-79-26914141 (O), 91-02717-235441 (R), Email: [email protected]
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V. Balaji ICRISAT, Patancheru, A.P., Email: [email protected]
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A.K. Gosain Indian Institute of Technology, New Delhi, Email: [email protected]
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Paul J. Curran Vice-Chancellor, Bournemouth University, Poole, UK.
V. Jayaraman Bangalore, India. R. Krishnan Thiruvananthpuram, India. Sugata Mitra NIIT GIS Ltd, New Delhi, India. P. Nag Varanasi, India.
M.P. Narayanan President, CSDMS, NOIDA, U.P., India.
R.R. Navalgund ISRO H.Q., Bangalore - 560 094, India
Y.S. Rajan ISRO H.Q., Bangalore - 560 094, India
R. Siva Kumar Head, NRDMS & NSDI, DST, New Delhi, India.
Josef Strobl Dept. of Geography, Salzburg University, Salzburg, Austria. Journal of Geomatics ii Vol.7 No.2 October 2013
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Headquarters (Office of Secretary) 39, Basant Bahar II, Bopal, Ahmedabad - 380 058, India Email: [email protected] or [email protected] Journal of Geomatics iii Vol.7 No.2 October 2013
Journal of Geomatics (A publication of the Indian Society of Geomatics) Vo. 7 No. 2 October 2013
Research articles
1. Land suitability analysis for industrial development using GIS 101 Amita Johar, S.S. Jain and P.K Garg 2. Power distribution information system using GIS – A case study for SAC , ISRO, Ahmedabad 107 Rajeshkumar J. Ajwaliya and P.M. Udani 3. Applications of geo-informatics technology for Surkha lignite mining area in Bhavnagar district, Gujarat 112 Khalid Mehmood, Ajay Patel, Jose Joy and Manik H. Kalubarme 4. Material of interest based sub pixel classification of remote sensing images 120 R.D. Garg and M.D. Sarat Chandra 5. GIS for mapping updates of spatial spread and the ecological reasoning of JE transmission in India 126 (1956 -2012) M. Palaniyandi
6. Morphometric and morphologic analysis of Lunar impact craters 134 Disha Lal, Prakash Chauhan, A. S. Arya and Ajai 7. Crowdsourcing geographic information using field based mobile GIS developed on open source for 138 biodiversity conservation- An Indian Bioresource Information Network (IBIN) spatial data node initiative Sameer Saran, Hariom Singh, S.P.S. Kushwaha, K.N. Ganeshaiah, P.L.N. Raju and Y.V.N Krishnamurthy 8. Prioritisation of sub-watersheds: A case study of Dohan and Krishnawati rivers in Mahendergarh, Haryana 145 Gulshan Mehra and Rajeshwari 9. Effect analysis of GPS observation type and duration on convergence behavior of static PPP 153 Ashraf Farah 10. Hydrological modelling to estimate rainfall based runoff in the lower Tapi basin 158 N. Goswami, P. K. Gupta and Ajai
11. FFT geoid models for Egypt using different modified kernels 163 Raaed Mohamed Kamel Hassouna
12. A web based solution for online application processing for mining information system- A pilot study 169 for Kadapa district, Andhra Pradesh, India V.Raghu and K. Mruthyunjaya Reddy
13. Reservoir impact assessment on land use/land cover in the catchment of upper Tunga reservoir in 175 Shimoga taluk and district, Karanataka, India, using remote sensing and GIS P. D. Jayakumar, Govindaraju and D. C. Lingadevaru
14. Energy balance modelling for ablation estimation of Gangotri glacier 178 Gunjan Rastogi and Ajai
15. Flood simulation for ungauged basin: A case study of lower Tapi basin, India 186 Sudhakar Sharma, Anupam K. Singh and Akshay O. Jain
16. Site suitability analysis for a central wastewater treatment plant in Accra metropolitan area using 191 geographic information system Alex Barimah Owusu and Paulina Ansaa Asante
17. Forest fire risk and degradation assessment using remote sensing and GIS 198 R. Nambi Manavalan and S. Jayalakshmi
Reviewers for Journal of Geomatics, Volume-7 v Author Index, Volume-7 vi ISG Annual Awards viii National Geomatics Awards ix Format for nomination for National Geomatics Awards and Prof. Kakani Nageswara Rao Endowment Young Achiever Award x Fellows and Patron Members xi Instructions for Authors xii Journal of Geomatics: Advertisement Rates xiv Indian Society of Geomatics - ISG Membership Form xv
Published biannually by the Secretary, Indian Society of Geomatics on Behalf of the Society Copyright Indian Society of Geomatics ISG Website: www.isgindia.org Distributed free to Members of the Society (other than annual members and student members) Design: Printed at Chandrika Corporation Ahmedabad Journal of Geomatics iv Vol.7 No.2 October 2013
Journal of Geomatics 101 Vol.7 No.2 October 2013
Land suitability analysis for industrial development using GIS
AmitaJohar,S.SJainand P.K Garg Centre for Transportation System (CTRANS), IIT Roorkee, Roorkee 247667, Uttrakhand, India Email: [email protected] ; [email protected] ; [email protected]
(Received: September 14, 2012; in final form August 14, 2013)
Abstract: Site selection is a critical decision made by private and public owners that affects a wide variety of activities ranging from land use planning to sitting of industrial facilities. The selection of an industrial site involves a complex array of critical factors involving economic, social, technical, and environmental issues. Industrialization is a dynamic phenomenon that requires a lot of data to support the decision, and it should be carried out to satisfy the human needs. Thus, it is important to plan and monitor the industrial process in a systematic manner, and carry out the suitability analysis for industrial sites. For proper planning, accurate and timely data are required. Geographic Information System (GIS) has opened a great avenue for analyzing the data generated from remote sensing and other sources. GIS and remote sensing provides a broad range of tools for industrial area mapping, monitoring and management. The present study has been carried out for a part of Uttar Pradesh state (i.e. Banda and its surrounding), for identifying suitable sites for industrial development. The proposed approach has been developed in GIS environment to find out the suitable sites for industries, and thus suitability map is prepared showing different suitability classes for industrial development.
Keywords: GIS, Remote sensing, Industrial development, Land-use suitability analysis, Multi-criteria decision making
1. Introduction In the present study, a decision needs to be made about the areas that are most suitable for Building a new capital improvement facility is a industrial settlement. The concept consists of major, long-term investment for owners and investors. inserting interactive effects of several contributing Site selection of a capital project is a critical decision factors and constraints that may contribute in made by owners/investors that significantly affects enhancing or decreasing industrial susceptibility. their profit and loss. As such, industrial site location The constraints are taken into account to create analysis is a big business, whether measured in terms suitable areas. Areas included are those that satisfy of amounts invested, decision-makers involved, a given criteria, like proximity to main railway employees affected or the economy of the area stations, away from main residential areas and influenced. The process of selection depends upon proximity to main roads. Certain areas are excluded large number of parameters. A number of tools may be from consideration, like water bodies, forests, strong used to determine the proper site for capital erosion areas, areas endangered with floods etc. The improvement facilities. The mapping tools include objective of this research is to find suitable sites for Geographic Information System (GIS), image setting up industries in Banda district of Uttar Pradesh processing system and remote sensing techniques. GIS state using GIS. This study will be helpful for and remote sensing provide a broad range of industrial planners and development authorities to plan capabilities for industrial area mapping, monitoring the development of region and surroundings in proper and management to achieve optimization in their direction using available land fulfilling the scientific utilization and conservation. GIS has opened a great criteria. avenue for analyzing the data generated from remote sensing and other sources. Presently, industrial 2. Literature review planners have started to use this powerful tool to find the best locations to develop industries. For preparing the methodology to find the suitable sites for setting up industries using GIS, literature Remote sensing data with its unique characteristics of review was carried out. Since land suitability analysis synoptic view, repetitive coverage and reliability have for industrial development is a complex process opened immense possibility for industrial area involving several parameters and also has constraint mapping and change detection. Spatial data stored in conditions, literature review is essential to provide a digital database of GIS, such as Digital Elevation better understanding. Model (DEM) and capability of GIS to integrate different datasets can be effectively used to evaluate Brans and Vincke (1986) proposed PROMETHEE the suitability of land for industrialization. Satellite approach to be as easily understood by the decision- images along with other terrain information can be maker. Six possible extensions were considered. used within the GIS, which will provide useful data Valued outranking graph was constructed by using required by industrial planners and developers. preference index. Ranking problem was solved by two
© Indian Society of Geomatics Journal of Geomatics 102 Vol.7 No.2 October 2013
possibilities using this valued graph. PROMETHEE Svoray et al. (2005) incorporated the use of a multi- I provides a partial preorder and PROMETHEE II criteria mechanism in a GIS for the evaluation of the a total preorder on the set of the possible actions. suitability of ecologically sensitive areas for four possible land-uses namely, nature reserves, forest Saaty (1990) provided an analytic hierarchy process- a plantations, residential areas and industrial areas. The multi-criteria decision-making approach, in which evaluation procedure pronounces the effect of existing factors are arranged in hierarchy structure. Principles land-uses, soil characteristics, topographic attributes, and philosophy of the theory are summarized giving vegetation cover and landscape heterogeneity. information of measurement utilized, its properties and application. 3. The study area
Jun (2000) developed a framework for integrating the The study was carried out on Banda and its strengths of GIS, expert systems (ES) and the analytic surrounding, a part of Jhansi uplands, spread over an hierarchy process to incorporate the decision maker’s area of 7.424 km2 (Fig 1). It is bounded on the north preferences on a range of factors used in finding by Fatehpur district, on east by Allahabad district, on optimally suitable sites. This study also illustrated how its west by Hamirpur district and on the south by the integrated system may be applied to industrial site Rewa, Satna, Panna and Chhatarpur districts of selection. Madhya Pradesh state. It consists of irregular upland with outcrops of rocks intermingled with nearly low Ascough et al. (2002) presented a general overview of land often under water in rainy seasons. It is divided multi-criteria spatial decision support systems (MC- into four tehsils, Banda, Naraini, Baberu and Atarra. SDSS) and reviewed its applications to a broad range The Baghein River passes through the Banda district of decision problems while providing direction for from south-west to north-east. Other important rivers future trends and research in this area. are the Ken River in the east and the Yamuna to the north. According to Eldrandaly et al., (2003), industrial site selection is a complex process for owners and analysts. The study area is geographically located between Therefore simultaneous use of several decision support 24º53' to 25º55' North latitudes and 80º07' to 81º 34' tools, such as ES, GIS and multi-criteria decision East longitudes. As per the Census of India 2001, making (MCDM) methods is required. This poses the Banda is home to 15,00,253 people. Banda is well challenge of integrating these decision support tools. connected by rails and roads transport. The internal To alleviate these limitations, this study used road linkage is also adequate. Chitrakut Dham, a place Component Object Model (COM) technology in of tourist interest and celebrated place of pilgrimage designing a decision support system for industrial site for Hindus, lies among the northern spur of Vindhyan selection. The presented system was illustrated using range at a distance of about 70 km from Banda town. real regional data that is maintained by a state agency. The Banda district and its surroundings are mainly Phua and Minowa (2005) used a GIS-based multi- dependent on agriculture, with the important crops criteria decision making approach for forest being rice, wheat and vegetables. The terrain and conservation planning at a landscape scale. It enables uneven distribution of soil sometimes result in decision makers to evaluate the relative priorities of uncertainty of turn out. The irrigational network conserving forest areas based on a set of preferences, includes canals, tube wells and wells etc. Banda criteria and indicators for the area. Compromise district is known for its Shajar stone generally used for programming techniques are used to integrate the making jewellery. It has four kind of soil, two of forest conservation priority maps of decision groups which are agriculturally difficult to manage. They are where a separation distance is calculated. A clustering black cotton soil. Rainfall is scanty and erratic and analysis was applied to identify potential conservation water-resources are fright. Practically dry farming is areas as the basis of delineating potential new done on a larger scale. protected areas.
Figure 1: Location map of study area Journal of Geomatics 103 Vol.7 No.2 October 2013
4. Data used Table 1: Coverages prepared for Banda and its surroundings Following data were used: S. Coverage Description Feature i. Survey of India (SOI) Toposheets No. Name Type ii. Cartosat data 1 ROAD Road network Line iii. IRS LISS III Data 2 RAILWAY s Line 5. Software used 3 RIVER River network Line 4 URBAN Urban map Poly The present work used following software: 5 POND Pond map Poly i. ARC/INFO GIS (version 9.3), designed and 6 CANAL Canal map Poly developed by Environmental Systems Research 7 CONTOUR Contour map Poly Institute (ESRI), Redlands, California, USA 9 BASE MAP Administrative Poly was used for GIS-based database creation and boundary of analysis. Banda and its ii. ERDAS IMAGINE (Version 9.1), designed and surrounding developed by Earth Resource Data Analysis 10 LUSE 08 Land use/land Poly System (ERDAS), Atlanta, Georgia, USA was cover map used for digital image processing work. 6. Methodology used Table 2: Features and their buffer zones Feature Distance Category The methodology followed in present study can be classified into following steps: 0-2000 Very good Railway Line 2000-5000 Good 1. IRS LISS III satellite data (acquired in 2004- 5000-10000 Moderately 2008) was interpreted in consultation with the good SOI toposheets for extracting information and 10000-20000 Poor preparation of base map. >20000 Poor 2. About 10 easily recognizable Ground Control 0-500 Very good Points (GCPs) were used to apply first order 500-1000 Good polynomial and nearest neighborhood 1000-2000 Moderately sampling method. Satellite images were Road good geometrically corrected. The root mean square 2000-4000 Poor error (rmse) was less than 0.5 pixels. >4000 Poor 3. The study area was extracted as shown in Fig Distance 0-2000 High 2. Various features of different themes were From River 2000-5000 Medium digitized using the ADS (ARC Digitizing 5000-10000 Low System) module of GIS package. For this, a copy of the reference coverage was used for 10000-20000 Very Low the digitization of new theme. Thus, a >20000 Nil coverage corresponding to each thematic map 0-100 Poor was created in GIS. Table 1 gives the details of 100-200 Poor all the coverage’s prepared for Banda district Proximity to and its surrounding, as well as their spatial Urban Area 200-300 Moderately features. good 4. Created buffers for various feature that were 300-500 Good digitized. Table 2 gives the detail of features >500 Very good for which buffer zone was created.
1. Created data for slope map.
a. DEM was derived from contour features with the spacing interval of 20. b. The slope map was derived using DEM which was extracted from Cartosat data.
2. Preparation of land use/ land covers maps.
Land use classification is the process of categorising the data based on their data file Figure 2: The study area values. If pixels fulfil a certain set of criteria, Journal of Geomatics 104 Vol.7 No.2 October 2013
then the pixel is assigned to the class that Table 3: Suitability scoring corresponds to that criterion. There are two Parameters Category Scoring ways to classify the pixel in different Waste land 5 categories that are supervised and Land Use Agriculture 4 unsupervised. Finally the classification image Forest 0 is created as shown in Fig 3. River 0 Ponds 0 0-2000 m 5 Railway 2000-5000 m 4 Accessibility 5000-10000 m 3 10000-20000 m 2 >20000 1 0-200 m 5 Road 200-500 m 4 Accessibility 500-1000 m 3 1000-2000 m 2 >2000 m 1 >2000 m 5 Distance From 1000-2000 m 4 River 500-1000 m 3 250-500 m 2 0-250 m 1 >2000 m 5 Proximity to 1000-2000 m 4 Urban Areas 500-1000 m 3 250-500 m 2 Figure 3: Land-use and land-covers image 0-250 m 1
3. Selection of different parameters for land 9. Suitability map with weighting system suitability. Following parameters were considered for the site suitability analysis for All the five thematic maps were converted in raster industrial development. format, so that for each pixel, a score can be determined. These maps were combined into i. Existing land use composite suitability map by simple addition of ii. Railway accessibility rescored map with weight system. The main iii. Road accessibility requirement for any kind of development is the iv. Distance from river availability of wasteland or suitable site for v. Proximity to urban area construction. Moreover, for development of industrial area, mainly due to industrial hazards, 8. Suitability scoring and ranking the main requirement is to have such sites away from the settlements. This will not have much For suitability analysis, it is necessary to give some impact on the health of the people residing away score to each category as per its importance for from the industrial sites. Therefore in this study, industrial development since each category will not the agriculture land, rocky area, mining area and have the same weight or usefulness for industrial undulating land with or without scrub were development. The suitability scoring used in this considered for analysis. Therefore a higher weight study for each of the maps and its category at 10 was assigned to the land use. Next important point scale are given in Table 3. Forest and water parameter was the accessibility to the particular bodies were assigned zero weights, hence these vacant sites defined by its distance to the main road sites are not considered for industrial development. and other roads from the sites. This may not affect Distance from river was taken as one of the strongly to site selection, but large distance may parameters as industrial site should be as far as involve some extra costs and increase in possible from flood zone of a river. Since the area transportation costs, and also for development was moderately flat therefore slope was not taken purpose accessibility is another important into account for the analysis. In undulating area requirement. Moreover, new road might be slope should be considered as an important factor. required to change the situation. Journal of Geomatics 105 Vol.7 No.2 October 2013
As per descending order of importance less weight very low weightage in order to find suitable sites are assigned to distance from river. It should be for further development of non urban areas. The realized, that the choice of a weight is most final map is shown in Fig 4. important as it has a great effect through multiplication of the scores. The weighting system 6. Conclusion and recommendations in this case was designed to allow a maximum score of 500. The five scored maps were added On the basis of above study, following conclusions while applying the following weighting system. could be drawn-
i. Land use - 5 1. Remote sensing data acquired by IRS ii. Railway accessibility - 4 (Satellite) LISS-III sensor were found to be iii. Road accessibility - 3 very helpful in mapping current status of iv. Distance from river - 2 land use and land cover classes. v. Proximity to urban areas - 1 Conventional methods are expensive and time consuming to provide accurate and fast Finally, a suitability map was prepared by applying information of land use and land cover in a the above scoring using ‘Spatial Analyst’ module short time span. of Arc GIS software. The suitable sites are 2. GIS is useful in generating various thematic identified using criteria developed on the basis of layers (input data) from topographic maps literature review and consultation with various and remote sensing data, as required for site experts. The final output is in the form of a raster suitability analysis. layer having particular suitability score which is 3. Multi-criteria decision making in GIS based on following relationship: environment by integrating various thematic layers, is found to be helpful in determining Suitability score = (Land use score)*5 + (Railway sites suitable for industrial development. accessibility)*4 + (Road accessibility)*3 + 4. Without much effort, GIS can be used to (Distance from River)*2 + (Proximity to Urban modify weights and more number of input Areas)*1 layers can be included for such study. 5. The suitable sites are identified using criteria Table 4: Suitability classes for industrial developed on the basis of literature review development and consultation with various experts. Sr.No. Class Area (km2 ) 1. Suitable 844.2 The following are the recommendations for future work. 2. Moderately suitable 44.1 3. Unsuitable 28.0 1. The technique developed can also be extended for other applications dealing with site suitability, after careful selection of input parameters and their respective weights. 2. Weights can be changed depending upon area as there are no well defined guidelines to allocate weights to each input parameter. A sensitivity analysis for this purpose can be performed.
References
Ascough II J.C, H.D. Rector, D.L. Hoag, G.S. McMaster, B.C. Vandenberg, M.J. Shaffer and L.R. Ahuja (2002). Multicriteria spatial decision support systems: Overview, applications and future research directions. Proc. IEMSS Conference – Figure 4: Suitability map for industrialization Integrated Assessment and Decision Support, Lugano, Switzerland, June 175-180, 2002 The range of suitability score was equally divided into three parts and classified into very suitable for Brans J.B. and P.H. Vincke (1986). A preference higher range value to unsuitable for lower range ranking organization method: The PROMETHEE values, as given in Table 4. For suitability method for multiple criteria decision-making. mapping, existing urban area and river were given Management Science, 31(6), pp.647-656. Journal of Geomatics 106 Vol.7 No.2 October 2013
Eldrandaly K., N. Eldin and D. Sui (2003). A Saaty T.L. (1990). How to make a decision: The COM-based spatial decision support system for analytic hierarchy process. European Journal of industrial site selection. Journal of Geographic Operational Research, 48(1), 9-26. Information and Decision Analysis, 7(2) 72 – 92. Svoray T., P. Bar (Kutiel) and T. Bannet (2005). en.wikipedia.org/wiki/Banda,_Uttar_Pradesh Urban land-use allocation in a Mediterranean ecotone: Habitat heterogeneity model incorporated Jun C.(2000). Design of an intelligent geographic in a GIS using a multicriteria mechanism. information system for multicriteria site analysis. Landscape and Urban Planning, 72 , 337-351. URISA Journal, 12(3), 5-17.
Phua M. H and M. Minowa (2005). A GIS-based multi-criteria decision making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu Area. Sabah, Malaysia, Landscape and Urban Planning,71, pp. 207-222.
Journal of Geomatics 107 Vol.7 No.2 October 2013
Power distribution information system using GIS -A case study for SAC-ISRO, Ahmedabad
Rajeshkumar J. Ajwaliya and P.M. Udani Space Applications Centre (ISRO), Ahmedabad – 380 015, India Email: [email protected] ; [email protected]
(Received: June 11, 2013; in final form August 26, 2013)
Abstract: Power distribution system comprises of spatially distributed and connected elements like substation, HT ( High Tension) & LT (Low Tension) cables, transformers, electric poles, switching equipments etc. and information about the location of each elements along with electrical properties and length of HT & LT cables connecting various sub-systems is essential for network operations and maintenance planning. Geographic Information System (GIS) capable of handling location and attributes data is very useful for power distribution system for spatial visualization of network elements as well as for query, decision support and report generation. GPS enabled mobile GIS system is useful for mapping and site specific data collection. At Space Applications Centre (SAC) Power Distribution Information System (PDIS) is built using available analogue network diagrams, specification sheets, observation manuals, mobile GIS field observations and Map Objects library. ArcInfo GIS software is used for GIS data preparation and integration of GIS, MIS and In-situ data. Tasks specific modules are prepared pertaining to theme, maintenance schedule, single line diagram, substation, LT panels and map / report generation. Generic GIS functions like pan, zoom, overlay, identify, query, theme table are provided in developed GIS based PDIS. This paper provides details about the power distribution network of SAC, GIS database design, user interface design and salient feature of PDIS.
Keywords : Power distribution system, GIS, GPS, Mobile RS, Q-Pad, Substation, Electric pole, HT & LT Cable
1. Introduction 2. Existing System
Power distribution systems are facing operational Power distribution network of Space Applications problems related to power losses, illegal operations of Centre (SAC) comprises of main receiving substation consumers, poor maintenance of the installed facilities, (MRS) which is receiving three phase HT( High lack of proper planned network, lack of monitoring Tension) 11kV AC power supply of 5.25 MW from mechanism and updating of consumer records etc. It is 132 kV substation of M/s Torrent power Limited. MRS also required to reduce and monitor power substation is feeding three phase HT 11kV AC power transmission and distribution losses (Olaniyi and supply to RSA (Remote sensing Area) substation, CSL Usman, 2006). This calls for modernization of power (Communications System Laboratory) substation, transmission and distribution network using spatial RISAT (Radar Imaging Satellite) substation and RSA technology. The Geographic Information System substation is further feeding power to TVS ( TV (GIS), Geographic Position System (GPS) and Remote Studio) substation. Three phase LT 415 volts; 50 Hz Sensing (RS), technologies that have evolved over the AC power is distributed to various buildings of SAC, last two decades, are the three most relevant spatial having campus area of 83 acres, from above database technologies for the developmental planning, substations. During interruption of power supply asset management and decision support (Smith, 2005). power failure, break down of feeder and shut down for RS is used for real time and accurate data capture. GIS maintenance purpose, it is very difficult to trace out is used as the most effective and efficient tool for exact location of the fault of substations, HT & LT storing, integrating, manipulating and presenting distribution, street lighting etc. are maintained by spatial and non-spatial information. GPS is used for analogue methods. obtaining precise coordinates of important geographical features, emergency mapping and The data of power distribution systems is incident reporting. GIS is useful in fault analysis, maintained through separate map sheets with optimization of networks, load forecasting and cost facilities data printed in text form. These maps are estimation. GIS enables utility engineer to analyze rarely updated and there is a lack of linkage power system networks in less time, more between spatial and non-spatial data. economically and more accurately (Hassan and Any decision-making regarding maintenance of Faheem, 2012). Hence there is a need to modernize substation equipments, enhancement of load in the existing power distribution system using RS, GIS transformers, LT panels and other feeders, and GPS technologies inputs economically and more performance of equipments, fault logging etc are accurately (Hassan and Faheem, 2012). made on a rough basis as data are available in
© Indian Society of Geomatics
Journal of Geomatics 108 Vol.7 No.2 October 2013
reading sheets and referring old data is also difficult. The substation has very little or no information regarding performance status of transformers and the feeders. Substations are located at different places in SAC campus as per load requirement of that area. Hence data are not centralized and data are available in file forms.
Thus, designing and implementing GIS based power distribution system for the purpose of efficient network management and providing instant information access to all concerned engineers is essential to minimize time for power restoration, efficient planning and for Figure 1: Entity-Relationship diagram preventive maintenance. (Nawaz-ul-Huda et al., 2012)
3. Study area The selected study area of SAC is housing one main receiving substation and four distribution substations viz. RSA, CSL, TVS and RISAT substation. There are more than 95 buildings spread over 83 acres lush green campus of SAC.
4. Objective The main objective is to develop an Electrical Power Distribution System using GIS and GPS observations and develop an application for data visualization, query and analysis.
The detailed objectives are:
Study of the existing electrical power distribution Figure 2: Methodology of database development network Design and develop GIS data model and GIS databases for three phase HT 11kV AC power distribution, three phase LT 415 volts AC power distribution, feeder wise power distribution. Understand GIS software, GIS database design, Mobile GIS system. In-Situ data collection for power distribution system of SAC using Mobile GIS System Data customization and integration of GIS, Management Information System (MIS) and in- situ data. Develop GIS based software for effective functioning of power distribution network for various query, analysis and generation of information products. Figure 3: Procedure for data integration Customization of the software to fulfill the application needs. 5.2 Development of layers and desktop GIS with mobile GPS 5. Database design and development Various GIS database layers were created using 5.1 E-R diagram ArcGIS 9.0 following steps described in figure-2. Shape files data model was adopted. Stack of Entity – Relationship (ER) Diagram has been prepared developed GIS database layers is shown in figure-4 to develop Entity Relationship of all substations and Q-Pad based mobile GIS system used is shown in attributes as shown in figure-1. figure-5.
Methodology of database development is shown in figure-2 and procedure adopted for database integration is shown in figure-3.
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Table 1: Menu and sub menus of developed software
Main Menu Sub Menu Z File Manu Open, close, exit etc. Z Theme Add theme, Remove layer, Remove all layer Z Maintenance Z Preventive Maintenance Schedule 11 kV VCB Panel Transformer LT Circuit breaker Diesel Engine Z Actual Preventive Maintenance done Figure 4: Stack of GIS layers MRS Substation RSA Substation CSL Substation TVS Substation RISAT substation. Z Single Line Main HT distribution Diagram MRS Substation RSA Substation CSL Substation TVS Substation RISAT Substation Z Substations RSA HT INCOMER-1 (volts & Parameters Amps) Figure 5: Q-Pad based mobile GIS system RSA HT INCOMER-2 (volts & Amps) The procedure followed in the development of GIS RSA HT Transformers (Load & database and GIS software is briefly described below. Temp.) RSA LT panel-1(volts & Amps) Z All analogue data in form of CAD drawings were RSA LT panel-2(volts & Amps) collected, scanned and geo-referenced. RSA LT panel-3(volts & Amps) Z Non-spatial data in form of text records were RSA Power factor (LT-1,2,3) converted in to tables and attached to relevant Z Photographs MRS spatial data elements. SS RSA Z GIS point, line and polygon databases were CSL created as per the Shape File data model and TVS integrated under GIS environment. RISAT Z Z Field verification and additional database LT Panels MRS RSA collection was done using Q-Pad based mobile CSL GIS system developed at SAC. Z TVS GIS functions and modules were defined and RISAT developed using Visual Basic development environment and proprietary GIS Map Objects library Table 2:Tool buttons and functions of developed Z software Prepared an installation kit for the distribution of developed GIS software Toolbar Function Z Zoom In Feature zoom in by drawing rectangle Menu and sub menus of developed software are given Z Zoom Out Feature zoom out by drawing in Table 1 and tool buttons and functions of developed rectangle software are as given in Table 2. Z Pan Feature navigation in map window using directional move Developed software provides facility for viewing of Z Full Map zoom to show all layers in map maintenance schedule, HT distribution and substation Extent window layout and query response. Some views/snapshots are Z Identify Provides information about selected shown in Figure 6 to 8. feature
Z Attribute Opens attribute table of active layer Monitoring of attributes of substations, LT panels, Table APFC panels and DG sets, electric poles is essential for power distribution network and attributes query response of developed software is provided in figure 9-12.
Journal of Geomatics 110 Vol.7 No.2 October 2013
Figure 6: Maintenance schedule Figure 10: Attributes of APFC panels
Figure 7: HT distribution within SAC Figure 11 Attributes of DG sets
Figure 8: Equipments of RSA substation
Figure 12: Attributes of electric poles
Figure 9: Layout of RSA substation equipments Figure 13: HT R phase maximum current
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6. Query, results and reports Z Development of modules for preparation of analysis charts for HT & LT distribution system Analysis of different parameters of electrical system Development of module for alerts generation at like maximum and minimum HT/LT volts, HT/LT the time of preventive maintenance currents, temperature of transformer, power factor of Z Supervisory Control and Data Acquisition LT panels etc. were carried out and few outputs are (SCADA) system is used by many power shown below in figure 13-14. distribution companies for collecting online information of critical parameters. The SCADA inputs can be integrated with GIS database for online monitoring of power network. It is planned to develop SCADA and GIS integrated system for SAC Z It is planned to migrate to IGiS platform and demonstrate GIS application for other center's of ISRO.
8. Conclusions
From this case study undertaken, it can be concluded that PDIS can be used in effectively to improve planning, maintenance and management standards. Figure 14: LT B phase maximum current
Acknowledgement This software is installed at eight locations for internal usage of engineers of SAC. The software is useful for We are sincere gratitude to Shri A. S. Kiran Kumar, decision making during shut down. In case of power Director, SAC and Shri J. Ravisankar, Head of break down it is possible to identify the fault location HRDD, Space Applications Centre (ISRO), and switching OFF power supply to affected area/ Ahmedabad for giving us an opportunity to carry out building for avoiding further damage to area / building. study work at SAC-ISRO, Ahmedabad. We would like
to express our thanks to Shri Rajesh Ranjan, Head 7. Discussions and future scope CMD, Shri P.D. Murthy, Sci/Engr. SF and Smt. P. B. Shah, Head, DWD and their team for support and This study shows that PDIS can be used in many ways encouragement provided during design and to improve the planning, maintenance and management implementation of project. We are thankful to Shri standards. Santanu Chowdhury Deputy Director, SIPA and Shri
DRM Samudraiah, Deputy Director, SEDA of Space It can be used to achieve following things efficiently. Applications Centre- ISRO, Ahmedabad and their
team those who helped us for reviewing paper. Z Entire network with all elements can be viewed together References Z User can select individual layer like transformer, cable, pole, substation etc. for navigation, Hassan, H.T. and M. Faheem Akhtar (2012). Mapping browsing and query of power distribution network using geographical Z Preventive maintenance schedule of each information system (GIS). International Journal of equipment and records for actual maintenance Emerging Technology and Advanced Engineering done can be accessed (ISSN 2250-2459, 2(6) , June 2012). Z Analysis can be performed for HT & LT voltage,
HT & LT current variation, power factor of LT Nawaz-ul-Huda, S., F. Burke, M, Azam and S. Naz panels and temperature of transformers etc. (2012). GIS for power distribution network: A case Z Affected area can be identified during power study of Karachi, Pakistan. Online TM Malaysia break down and preventive maintenance Journal of Society and Space, 8(1), (74 - 82), ISSN Z Substation wise single line diagram and 2180-2491. photographs can be visualized
Z Cable route can be traced out for rerouting and Smith, P.H. (2005). Electrical distribution modeling: fault finding An integration of engineering analysis and geographic Z Provides user friendly interface for records information system, Dec 15, 2005. maintenance and updating
Olaniyi, S.S. and USMAN, R. (2006). Electricity However, further improvements as listed below are distribution engineering and geographic information being contemplated to enhance the performance of system (DeGIS). Shape the Change, XXIII FIG power distribution system. Congress, Munich Germany, October 8-13, 2006.
Journal of Geomatics 112 Vol.7 No.2 October 2013
Applications of geo-informatics technology for Surkha lignite mining area in Bhavnagar district, Gujarat
Khalid Mehmood, Ajay Patel, Jose Joy and Manik H. Kalubarme BhaskarcharyaInstitutefor Space Applications and Geo-informatics, Department of Science & Technology, Government of Gujarat Gandhinagar - 382 007 E mail: [email protected]
(Received: August 7, 2012; in final form March 26, 2013)
Abstract: In the present study, detailed investigations have been carried out in Surkha North Lignite Mining Lease areas in Bhavnagar and Ghoga Talukas in Bhavnagar district of Gujarat State using latest high resolution data from Indian Remote Sensing satellite (IRS-P6). GIS database for various thematic layers have been generated using satellite and ground based information. Large scale land use /cover mapping coupled with Digital Elevation Model (DEM) generation and derivation of contour pattern with limited ground truth and Differential Global Positioning System (DGPS) survey have been used in preparing mine and environmental management plan. The topographic analysis using DEM data indicate that, the area is flat terrain (RL ranging between 48 to 52m) and tends to decrease in South-Eastern and North-West. Contour mapping shows that the area represents topography and the South & North pit areas shows an elevation difference of around 40-45 meter. A number of surface water bodies have been observed at the intersection of 2nd order drainage. The IRS P-6 LISS-IV digital data analysis for land use/cover mapping shows broadly five categories viz. built-up area, agriculture land, wasteland, water bodies and mine area. Agricultural land is the major land use category; total agriculture area is 893.2 ha (53.1%).Total area under wasteland comprising of major open scrub has been 566.1 ha (33.7%). Mining activities and dump area including small quarrying covers this category and is spread in 138.3 ha (8.2%). Water bodies have marginal area of 13.9 ha (0.8%). The major environmental changes observed by analyzing multi-temporal satellite data covering Surkha North Mining Lease area as well as in the 10 km buffer area were also monitored. It was observed that during the mining period, social forestry was undertaken in mining lease area as well as in the buffer zone, which showed increase in forest cover during 2011 as compared to initial period of mining. Using geo-informatics technology, the Decision Support System (DSS) can be developed for monitoring the progress of mining activity along with assessment of impacts of mining activities on the surrounding areas. Keywords: Indian Remote Sensing satellite (IRS-P6), Digital Elevation Model (DEM), Differential Global Positioning System (DGPS), Decision Support System (DSS), Lignite Mining Lease area, Environmental Impact Assessment (EIA)
1. Introduction protective measures during and after commissioning of mining projects (Prets, 1999, Morris and Riki, 1995). 1.1 Background Implementation of EMP can be simplified and made Mineral resources and allied industries play an user friendly by generating geo-informatics based important role in the socio-economic status and Decision Support System (DSS). industrial growth in the country. Water, soil, minerals 1.2 Study area and biota constitute most significant natural resources endowment of a community. Energy is also an The study area comprises of Surkha North Lignite important component that serves as a back-bone of all Mining Lease (ML) area, Bhavanagar and Ghogha Talukas in Bhavnagar district, Gujarat State. This industrial activity (Aswathanarayana, 2001). O Environmental impact due to mineral development mining area is bounded by Longitude 72 10’47.788’’E to 72O16’27.214’’E and Latitude 21O40’55.892’’N to commences with the exploration phase, extends O through mining, extraction and processing of minerals 21 36’57.176’’N. The location map of the study area and continues even after the cessation of mining in the Bhavnagar district of Gujarat State is given in activity. The magnitude of this impact depends upon Figure -1. The mining area of Surkha North lignite various factors such as mining methodology and size mining blocks which is about 11 km South-East from of operation, geomorphology, surface and sub-surface the Bhavnagar township. The Surkha North Lignite water regime, climate, potential of the surrounding mines ML total area is about 37.2 sq km and its extent environment to absorb the negative effects of mining is about 9 km W to E and 9.3 km N to S. and other interrelated factors (Chandrasekhar, et.al, 1.3 Objectives 1991). Environmental Impact Assessment (EIA) is essentially an exercise to evaluate the effects of mining The major objectives of this study on Environmental activity, whether beneficial or adverse. Environmental Planning in Surkha North Lignite Mining Lease area Management Plans (EMP), deal with the formulation, are as follows: implementation and monitoring of environmental
© Indian Society of Geomatics Journal of Geomatics 113 Vol.7 No.2 October 2013 i) Delineation of mining lease area boundary iii) Generation of Digital Elevation Model and creation of 10 km buffer zone (DEM) using stereo data of CARTOSAT and ii) Preparation of data-base of various thematic preparation of elevation contour map of the layers like, land use / land cover, geology, core mining lease area along with the buffer geomorphology, drainage, surface water zone. bodies, settlements, road network and iv) Environmental Impact Assessment (EIA) of administrative boundaries in GIS the mining area using various thematic layers environment. prepared from multi-temporal Remote Sensing Satellite data in GIS environment.
Figure 1: Location map of the study area, Surkha North, Bhavnagar district
1.4 Data used data products are given in Table-1. The IRS LISS-III image covering Surkha North mining area along with In the present study, two scenes of IRS-P6 LISS-IV 10 km buffer is given in Figure 2. (5.8 m resolution) data of Kharif & Rabi season (January 2011 to March 2011) have been used for In the study area, ortho-rectified satellite data of various thematic interpretations. For most of the Landsat Thematic Mapper were used for geo- thematic interpretations such as land use / land cover, referencing of satellite images and base map drainage, water bodies mining related features, high preparation. Preliminary information from GMDC resolution satellite data (Cartosat-1 Pan of 18-March website and report of GMDC Ltd. Were also used. For 2007) of 2.5m resolution has been used. Since the mining related information and other planning, existing Cartosat data is in panchromatic mode, this data is report (1999) on ‘Environmental management Plan of merged with the LISS-IV (MX) data to achieve high Surkha North lignite Mines’ provided by GMDC was resolution (2.5m resolution) multi-spectral image. This used. merged data product is extensively used for base map updation of settlement, road, drainage, water bodies, Limited ground verification was carried out for land mine area mapping, change detection due to mining use/land cover and other mining features during 9-12th activity and environmental management. Stereo Sept., 2008. During these period DGPS (Sokkia product of Cartosat-1 Pan-Aft and Pan-Fore of 13- Radium IS) survey were also carried out to collect February 2010 were exclusively used for DEM and various location / elevation data and around the ML contour generation. Satellite characteristics of all these area, which is also necessary for DEM registration. Journal of Geomatics 114 Vol.7 No.2 October 2013
Table 1: Satellite data used for Surkha North mine area Satellite Spectral Resolution Swath Path Date of (Sensor) Bands (m) (km) (Orbit) Pass (µm) / Row IRS P6 LISS III 0.52 – 0.59 24 141 98 – 65 10.01.2011 0.62 – 0.68 0.77 – 0.86 IRS P6 LISS- IV 0.52 – 0.59 201/104 12.03.2007 (Multi-spectral 0.62 – 0.68 5.8 23 Mode) 0.77 – 0.86 202/086 18.04.2009 IRS-P5 Pan Aft and Fore 0.5 – 0.85 2.5 27 507/296 13.02.2010 (Cartosat-1) (Panchromatic Mode) 507/297 13.02.2010
vii) The methodology flow chart of the general methodology adopted is given in figure-3.
Figure 2: LISS III image of Surkha mining area with Figure 3: Flow chart of the methodology adopted 10 km buffer 2.1 Topographical analysis and settlement location: The major habitations and their extent in the buffer 2. Methodology zone of this project has been marked using the IRS The general methodology adopted for satellite data LISS-III and CARTOSAT-I digital data. The urban analysis and generation preparation of various thematic area of Bhavnagar Municipal Corporation (BMC) is layers in GIS environment is as follows: about 11.1 km away from the project area. The National Highway No.80 (NH-80) Bhavnagar – i) Digitization of mining lease area base maps Somnath is about 3.2 km away from the project area. and geo-referencing with satellite digital data The features of religious importance like temples etc. ii) Creation of GIS database of various thematic were delineated using satellite data. layers using open source GIS software iii) Generation of GIS database for base maps, The location of habitat areas delineated using satellite drainage, surface water bodies and land use / data indicate that village boundaries of nine villages land cover etc. namely, Bhumbhal, Bhuteswar, Malpar, Rampar, iv) Mapping of land use / land cover, mining Surka, Thordi, Gundi, Hoida and Nava and Juna areas and impact assessment using multi-date Ratanpar are within the project area. However, only 3- satellite digital data of IRS-P6 LISS-III village settlements of Thordi, Rampar and Hoida fall /LISS-IV and CARTOSAT-1. within the project area. The total project area as well as v) Generation of DEM using stereo data of the area within the buffer zone is relatively very flat CARTOSAT and preparation of elevation with gentle slope towards the eastern part leading to contour map of the core mining lease area the Gulf of Khambat. The majority of the project area along with the buffer zone along with the buffer zone comes under 0 to 1 % slope vi) Development of simplified and user friendly category. The slope map of the study area is given in geo-informatics based DSS for analysis and Figure 4. impact monitoring. Journal of Geomatics 115 Vol.7 No.2 October 2013
Figure-4: Slope Map of the study area
Figure 4: Slope map of the study area
2.2 Drainage pattern and surface water bodies was performed to enhance the linear features and edges delineation: The major river Maleswari which flows required for ease of interpretation of lineaments and from West to East and draining into the Arabian Sea is delineation of boundary. within the Lignite project lease area. There are two other tributaries, one in the north and other in the south 2.4 Ortho image generation: Ortho image is image part, in the buffer zone of the project area. There are that is fully corrected for relief displacement, internal two major reservoirs namely Lakhanka, Badi padri and scale differences and other displacements. The Fore several check dams in the buffer zone of the project and Aft scenes of cartosat-1 were provided with the area. The lignite project area along with 10 km buffer Rational Polynomial Coefficients (RPCs). These zone comes under the watershed of Maleswari River in coefficients represent the orbital parameters. Ground the Southern Kathiawar basin. There are several Control Points (GCP) for this area were collected using irrigation schemes and check dams to provide DGPS survey of the study area. Tie points, which are irrigation to the agricultural land in the project area. the ground points appearing on overlapping images The majority area comes under moderate to poor were generated and corrected, depicts the distribution potential of ground water. The good potential of of the control points. Using these GCPs and the tie ground water is along the sea coast however, ground points the triangulation was performed. Block water is saline. The ground water potential map of the triangulation is the process of establishing a project area is given in Figure 5. mathematical relationship between the images 2.3 Digital image processing: Indian Remote Sensing contained in a sensor model and the ground. The Satellite (IRS-P6) LISS-IV Digital data were used for procedure for ortho-image preparation is preceded by extraction of different thematic features using image DEM generation. Terrain differences are modeled by a processing software such as ArcGIS 10.0, ENVI, DEM and the computer calculates the position of a Geomatica, etc. The satellite data (raster data) have pixel in the original image for each new output pixel in been used for delineating existing drainages (dry/ the correct position, using a resampling procedure. The perennial) as well as water-inundated areas. Band ortho-image was generated using bilinear convolution combinations were used to enhance particular feature resampling method. The ortho-image of the study area e.g. Normalized difference vegetation index (NDVI) is given in Figure 6. The contours at 5 m interval were was used to highlights vegetation features. Filtering also generated using DEM of the study area. Journal of Geomatics 116 Vol.7 No.2 October 2013
Figure-5: Ground water potential map of the study area
Figure 6: Cartosat–1 ortho-image of Surkha mining area
2.5 DEM Generation: DEM generated through the ML is mining pit where it is as deep as -6 meter below stereo satellite data (Cartosat – P5 with RPC) has ground level due to coastal region. The DEM image of enabled to visualize the lease area in three dimensions. the study area is given in Figure 7. DEM of the mining lease area shows that the range of 2.6 GIS database for thematic map generation: elevation is from -6 meters to 60 meters. Contours Various database such as base map (road, rail, were generated using DEM at an interval of 5 meters settlements, administrative boundaries), with the help of DGPS points. For Surkha North ML drainage/watershed, land use/land cover, have been area among the distinct saddle shape passing through prepared using open source GIS software. These the center ridges there is tableland ache sparsely thematic layers can be delineated through on-line located contours speaks of the relatively rugged digitization on the satellite image and coverage of topography. Aspect is from SW to NE the divide for point, line and polygon features may be created. The drainage directly confluence with Maleshri. Highest attribute table can be filled and spatial analysis can be peak of dump where RL is 60 meters raising it 34 done for thematic layers. Finally all the thematic meters above ground, lowest point in the Surkha North information may be integrated in the Arc/Info Journal of Geomatics 117 Vol.7 No.2 October 2013 environment. DEM of the study generated using base map details, drainage, surface water bodies, Cartosat stereo pair data, DGPS survey and elevation and land use/ Land cover layers using, open contours have been derived from it. The methodology source GIS software. These maps are prepared on for GIS database creation for mine and environmental 1:5,000 scales in buffer & core Zone. management adopted for thematic map preparation is ii) Creation of DEM and elevation contour within as follows: mining lease area using stereo pair of Cartosat i) Image interpretation for Land use / Land cover, data. status of mining and creation of GIS database for
Figure 7: DEM image generated using IRS LISS IV and Cartosat data of Surkha mining area
In the present study, two scenes of IRS-P6 LISS-IV classification scheme validated with the preliminary (5.8 m resolution) data of Kharif & Rabi season pre-field classification and ground truth data (January 2011 to March 2011) have been used for collection. One of the detailed land use/cover map for various thematic interpretations. These images are 2011 is given in Figure 8. properly geo-referenced pertaining to the study area. For most of the thematic interpretations such as land 3.2 Mine plan for Surkha North area: After analysis use / land cover, drainage, water bodies mining related of terrain conditions and existing mining activities features, high resolution satellite data (Cartosat-1 Pan following actions are suggested: of 18-March 2007) of 2.5m resolution was used. Since a) Top soil/Waste dump site, b) Mine face, c) Bench the cartosat data is in panchromatic mode, this data is failure and d) Plantations merged with the LISS-IV (MX) data to achieve high resolution (2.5 m resolution) multi-spectral image. This merged data product is extensively used for base map updation of settlement, road, drainage, water bodies, mine area mapping, change detection due to mining activity and environmental management. Stereo product of cartosat-1 Pan-Aft and Pan-Fore of 13-February 2010 are exclusively used for DEM and Contour generation.
3. Results and discussion
3.1 Land use/cover and forest cover/density maps: The land use / cover and forest cover / density maps Figure 8: Land use map of the study area were prepared from the digitally enhanced and geo- referenced satellite imagery using digital classification Prime application of DEM is to find the nearest path technique. Other themes were visually interpreted and between two locations based on constrains defined by digitization of the units was carried out. Digital the user, incidentally same is the requirement for any classification has been performed based on the Journal of Geomatics 118 Vol.7 No.2 October 2013 mining activity. Top soil/ waste dump site is chosen so Decision Support System (DSS) have been prepared as to reduce the cost of transportation. The using data from GMDC as well as remote sensing aesthetically good waste dumpsite is such which is not data. Using this geo-spatial database, environmental visible from common road and disturb the horizon. suitability for mining areas can be visualized and Visual analysis of DEM has brought out areas which impact of mining can be analyzed. Digital terrain are concealed from the line of site from two vintage model was also generated to visualize terrain/slope of points of existing road. Besides wasteland area have the area. Environmental suitability study can also be been chosen which has less potential for growing carried out using this DSS. Some of the components of vegetation. Fresh plantation along 50m strip along the DSS are given in Figure 9. North boundary of lease area has been done, which can be monitored using latest satellite data. The areas for plantation were suggested through integrated analysis remote sensing data in GIS environment. The power of spatial analysis was used to derive data for mine planning tasks, for example, the classification of post mine land use. A spatial analysis was conducted to determine the slope angles of the proposed final landform. Classification of slope analysis into categories appropriate for post mine land-uses such as grazing, forestry and natural re-vegetation gives a tangible and spatially accurate output that can be used to assist with native vegetation decision making.
3.3 Present status of Surkha North Lignite mining areas: The present status of Surkha North Lignite Mining Areas has been monitored using the multi- temporal Indian Remote Sensing Satellite data of various years during the mining period. Basically, environmental impact due to mineral development commences with the exploration phase itself, therefore satellite data of pre-mining phase as well as other periods was analyzed for monitoring changes. The impact extends through mining, extraction and Fig 9: Components of decision support system processing of minerals and continues even after the developed for Surkha mining area cessation of mining activity. 4. Conclusions The magnitude of this impact depends upon various factors such as mining methodology and size of In the present study, detailed investigations were operations, geomorphology, surface and sub-surface carried out in - Surkhna North Lignite Mining Lease water regime, climate, potential of the surrounding areas in Bhavnagar and Ghoga Talukas in Bhavnagar environment to absorb the negative effects of mining, district of Gujarat State using most recent high and other interrelated factors. The intensity of impact, resolution data from Indian Remote Sensing satellite however, varies with the stage of mineral development (IRS-P6). GIS database for various thematic layers as for example the exploration phase has considerably have been generated using satellite and ground based less impact than the mining and processing phases. The information. Large scale land use /cover mapping scenario of mine area changing frequently as mining coupled with DEM generation and derivation of progress, remote sensing change monitoring can be contour pattern with limited ground truth and DGPS used for dynamic environmental modeling. Finally, survey have been used in preparing mine and remote sensing data analysis, 3D visualization and the environmental management plan. The major entire GIS database helped in formulating mining plan conclusions of this study area as follows: of the Surkha North Mining Lease area. Environmental • Impact Assessment (EIA) is a tool used to identify the Contour mapping using DEM shows that the area environmental, social and economic impacts of a represents topography of lease area and the South project prior to decision-making. It aims to predict & North pit areas shows an elevation difference of environmental impacts at an early stage in project around 40-45 meter. planning and design, find ways and means to reduce • Land use/cover mapping shows broadly five adverse impacts, shape projects to suit the local categories viz. built-up area, agriculture land, environment. wasteland, water bodies and mine area. Agricultural land is the major land use category; total agriculture area is 893.2 ha (53.1%).Total 3.4 Development of DSS: In order to make the process of monitoring impact assessment of mining area under wasteland comprising of major open scrub has been 566.1 ha (33.7%). Mining activities more rational and transparent, geo-informatics based Journal of Geomatics 119 Vol.7 No.2 October 2013
and dump area including small quarrying covers Geo-informatics (BISAG), Department of Science & this category and is spread in 138.3 ha (8.2%). Technology, Government of Gujarat, Gandhinagar 382 • The scenario of mine area changes frequently as 007, for their contribution in successfully executing mining progresses and the remote sensing change this project. The authors are thankful to Mr. D. U. monitoring can be used for dynamic Vyas (GM-Geo) and Mr.Piyush Shah,GMDC, for their environmental modeling. technical guidance and support. • The major environmental changes observed by analyzing multi-temporal Satellite data covering References Surkha North Mining Lease area as well as in the 10 km buffer area were also monitored. It was Aswathanarayana, U. (2001). Natural Resources and observed that during the mining period, social Environment. Geol. Soc. India, Bangalore, 69p. forestry was undertaken in mining lease area as well as in the buffer zone, which showed increase Chandrasekhar, M.G., A.K. Gupta and K. Ganesh Raj in forest cover during 2011 as compared to initial (1991). Baseline information needs for environmental period of mining. appraisal and role of remote sensing. Proc. National • Using Geo-informatics technology, the Decision Symposium on ‘Remote Sensing of Environment’, Support System (DSS) has been developed for held at Anna University, Madras from Des.1012, 1991. monitoring the progress of mining activity along with assessment of impacts of mining activities on Gupta, R.P. (1991). Remote sensing geology. Springer- the surrounding areas. This will be very useful in Verlag, Germany, 356p. environmental monitoring activities which can be implemented to other mining areas in the state. Morris, P. and T. Riki (Eds.) (1995). Method of environmental impact assessment. UCL Press, London, Acknowledgements ISBN 1-85728-214-0.
The authors like to express their sincere thanks to Shri Prets, J (ed.) (1999). Handbook of environmental T. P. Singh, Director and Team Members of impact assessment – Process, methods and potential. Bhaskarcharya Institute for Space Applications and Vol.1, Blackwell science Ltd., London. Journal of Geomatics 120 Vol.7 No.2 October 2013 Material of interest based sub pixel classification of remote sensing images
R.D. Garg and M.D. Sarat Chandra GeomaticsEngineeringGroup,Department of Civil Engineering, IIT Roorkee, Roorkee - 247667, India Email: [email protected] ; [email protected] (Received: June 18, 2012; in final form April 12, 2013)
Abstract: The urbanization level is a significant parameter to measure a country’s extent of civilization, social progress and economy. It is very important to carry out reasonable and effective urban planning, which requires updated and accurate land use land cover information. Remote sensing techniques have been used as a means to get the spatial information and to generate land use land cover maps. This paper presents a methodology to classify the urban land cover classes by deriving the signature based on the component that is common to the training set pixels called material of interest. Conventional classification techniques simply form a signature by combining the spectra of all training set pixels for a given feature. The fuzzy logic concept was applied to quantify the uncertainty or imprecision in the boundaries between natural geographic features. The images include moderate resolution (Landsat ETM, 30m) used as the source data and high resolution (Quick Bird, 2.88m) used as reference data. Both the data sets are classified using a soft classification. Fuzzy error matrix is used to summarize the accuracy assessment information. Because of the high degree of heterogeneity, the overall accuracy value in the range 80-85% was achieved. Material of interest based sub pixel classification has provided better results for deriving land cover information from lower resolution data sets. Keywords: Fuzzy membership, Fuzzy classification, Spectral signature, Material of interest, Overall accuracy
1. Introduction mixed pixels in remotely sensed imagery is soft classification technology (Foody, 1996; Liu and Wu, The urban system is a geographical synthesis of 2005). A mixed pixel has digital number representing population, resources, environment, social, economic the average of several spectral classes within the area and so on. As one sign of civilization and social that it covers on the ground, each emitted or reflected progress, the effects of urbanization on national by a different type of material. In contrast to hard politics, economics and culture become prominent. In classification technologies, soft classification other words, the urbanization’s level is a significant approaches do not assign mixed pixels as a single land parameter to measure a country’s extent of civilization, cover class but instead predict the proportional area of social progress and economy. Therefore it is very each land cover classes within each mixed pixel. Soft important to carry out reasonable and effective urban classification approaches that have been proposed planning and management (Wenbing, 2006). Remote include linear spectral mixture modeling (Holben and sensing data can provide a timely and synoptic view of Shimabukuro, 1993; Garciaharo et al., 1996), fuzzy c- urban land cover, as well as means to monitor change means classifiers (Atkinson et al., 1997; Bastin, 1997), in urban landscapes and to compare urban artificial neural networks (Carpenter et al., 1999, environments globally. Image classification techniques Foody, 2002; Liu et al., 2004; Lee and Lathrop, 2006), play key role in information extraction from remote regression trees (Liu and Wu, 2005), expert system sensing data. However, this approach can be rules (Hung and Ridd, 2002) and support vector problematic for several reasons. First, most urban land machines (Brown et al., 2000). The highly use classes are not spectrally distinct, resulting in heterogeneous nature of urban surface materials is considerable confusion between classes. Second, the problematic at multiple spatial scales, resulting in a physical composition of land use classes may vary high percentage of mixed pixels in low resolution dramatically from region to region due to different imagery and even limiting the utility of high spatial building materials and different construction practices, resolution imagery. The problem of mixed pixel can be and therefore cross-regional comparisons between solved to reasonable extent by using the fuzzy logic. urban areas are limited (Small, 2005). In rapidly growing cities, particularly in the developing world, Fuzzy logic is not based on usual Boolean logic having multiple forms of land use may occur within the same only "true or false" (1 or 0), i.e. indicating just the geographic space, limiting the usefulness of traditional presence or absence of a land use class. The boundaries land use categories (Ridd, 1995; Arora and Varshney, between natural geographic features have the 2011). Deriving accurate, quantitative measures from imprecision or uncertainty, therefore in order to remote sensing imagery over urban areas remains a quantify them, fuzzy logic can be applied. It consists of fundamental research challenge due to the great computing based on “degrees of truth”, so instead of spectral and spatial variability of the urban land cover expressing the boundary by exactly ‘true=1’ or (Forster, 1985; Lu and Weng, 2004; Xian and Crane, ‘false=0’, it can have a value in between 0 and 1. 2005). Multiple classes are assigned to a single pixel in soft An efficient method for addressing the problem of classification, thus a mixed pixel can be represented in © Indian Society of Geomatics Journal of Geomatics 121 Vol.7 No.2 October 2013 a better manner (Salman et al., 2008). In this study a 4. Methodology supervised approach is adopted for classifying urban features. The main objective of this study is to use the Initially prior to the classification, logical consideration fuzzy logic in deriving a signature for the component has been worked out to design a classification scheme which is common to the training set pixels for urban associated with the local environment of the study area. and other land use classes, sub pixel classification of The US Geological Survey land use land cover the given data and finding the accuracy of the (LULC) classification scheme for use with remotely classification. sensed data for level-1 classification has been used. Four major LULC classes present in the study area, 2. Study site namely vegetation, barren Land, built up area and water, have been considered. The material of interest The study area is situated between 88° 20´ E to 88° 41´ (MOI) classification of satellite imagery includes E longitudes and 22° 31´ N to 22° 37´ N latitudes in different stages starting from geo-referencing to MOI the vicinity of Kolkata city in West Bengal state. The classification as shown in figure 3. The output of every area is mainly covered with agricultural lands, built up stage is the input of the next stage. Initially the area, water, barren land etc. There are several other reference image, QuickBird image of 2.88m spatial classes, but due to their smaller spatial extent, these resolution is resampled to 3m. Then this image is used have been neglected. to georeference Landsat ETM image of 30m resolution. This has provided a better geometric matching between 3. Satellite data used in the experiment Landsat ETM and QuickBird images, since 10 x 10 pixels of QuickBird correspond to one pixel of Landsat Data obtained from Landsat ETM (Enhanced Thematic ETM. From the georeferenced Landsat ETM image, Mapper) dated 7 November, 2009 in four spectral the subset of the study area was created. bands of 30m resolution has been used for classification. QuickBird multispectral data of 2 After that an artifacts removal tool is used to remove November, 2009 in four spectral bands of 2.88m several types of artifacts in the remote sensing resolution is used as reference data in this study. From imagery. Artifacts are characterised for different the Landsat image a subset of 373 rows x 373 columns, similarity measures. The different types of artifacts are shown in figure 1, is taken. Figure 2 represents edge artifacts, saturated pixel, duplicate line artifacts. QuickBird image of the study area, which is used as a The output is an image without these artifacts. Pixel reference data. spectra judged to represent artifacts are replaced with all zeros. MOI sub-pixel classifier ignores pixel spectra with values that are all zero. For the artifacts free image, preprocessing is performed for finding out the list of potential backgrounds which will be removed during the signature extraction and MOI classification. To derive a sub pixel signature, it is necessary to remove other materials, leaving a candidate MOI spectrum. The backgrounds identified by preprocessing are retained in a separate file for this purpose. The environmental correction is applied to the image which is free from the artifacts to compensate for variations in atmospheric and environmental conditions including the cloud pixels during image acquisition. This process automatically searches the entire image for bright and Figure 1: Landsat ETM image of the study area dark areas within the scene. This feature generates a set of environmental correction factors based on the spectral data from these areas which makes the image ready for signature derivation and MOI classification. These correction factors are necessary for scene-to- scene transferability of MOI signatures as well as for development of in-scene signatures. The final output is a file containing environmental correction factors that are used as input to the signature derivation and MOI classification functions. In-scene files are used for signature derivation and classification within the same scene. Scene-to-scene files are used when classifying an image using a signature developed from another image.
Figure 2: QuickBird image of the study area
Journal of Geomatics 122 Vol.7 No.2 October 2013 4.1 Signature derivation used to classify materials that exhibit variability in their spectral signature, either in-scene or scene-to- Signature derivation step is to develop a signature for a scene. particular material of interest. This is developed using a training set defined by either an AOI/ROI (area of Signature evaluation and refinement have been used to interest / region of interest) or a classification tool, further improve the performance of derived signatures, together with a source image, an environmental especially in case of scene-to-scene. This process correction file and the material pixel fraction in the evaluates the existing signature files. The multiple training set. The training set is either a whole pixel or signature files can be combined using the signature sub pixel training set. The signature development is an combiner and the performance comparison can be done iterative process and is continued until the signature either between two signatures for same class within an with high confidence is obtained. The signature image or from different images. This process generates derivation process derives a signature file from the a performance metric based on classification results training data set, environmental correction file and the within selected AOIs. The refinement step adjusts the image. The different signatures of different images input signature(s) and creates a new signature which is could be combined. To find the confidence and used as an input to MOI classification. This new improving the signature, signature evaluation and signature is called a “child” signature and is said to be refinement is applied. The output report indicates the derived from a “parent” signature. It is useful to confidence of the signature and the fractions of the evaluate the performance of child signatures in pixels of the training data set in the MOI. If the user is comparison with parent signatures. confident about the AOI that consists of only MOI pixels or non-MOI pixels, then it can be provided as an 4.2 MOI classification input to prepare the signature with high confidence. The MOI classification is used to apply a spectral The signature combiner has been applied to combine signature to an image for locating pixels containing the existing signatures and environmental correction MOI or MOI is associated with the signature. The factors for input into the MOI classification process. output of classification is an overlay image that The signatures are combined to form a signature contains the locations of MOI. The tolerance value can family, i.e., a collection of signatures representing be increased to include more pixels into the detection variations in a single material of interest. Multiple set or decreased to reduce unwanted false detections. signature files containing signature families have been
Geo referencing
Artifacts removal
Pre processing
Environmental Correction
Signature Automatic Signature derivation Derivation
Manual signature derivation
Signature Evaluation and MOI Classification Refinement
Fractional images of the Classified Data
Figure 3: MOI Classification workflow
Journal of Geomatics 123 Vol.7 No.2 October 2013 5. Results and discussion 5.2 Soft reference data
5.1 Soft classified data The QuickBird image of the study site is also classified for classes present in classification scheme. The The four bands of Landsat ETM data were classified reference image is resampled using the nearest simultaneously. The experiment was made in order to neighbour algorithm into 13912900 pixels (3730 x test potentiality in terms of the classification accuracy 3730), so that each pixel in the source image is equal to when using the signature derived from the material of 10 x 10 pixels in the reference image. Now from the interest. Due to large size of the image, the subset resampled reference data, the soft reference data is image of 139129 pixels (373 x 373) is taken for each obtained using MOI classification and then the band. In general after classification, for each fraction proportions of each class in the fractional images is image (corresponding to class I, say) the pixel value obtained by relating each 10 x 10 pixels as a single indicates the fraction of the pixel that contains the end unit. The training set of 88.8% quality with 80% member material corresponding to the class I. The confidence was taken to derive the signature and soft results are completely dependent on input signature reference fractional image of each class in the selected. The training set of 90.8% quality with 80% classification scheme was produced. The figure 5 confidence was taken to derive the signature and soft shows the soft reference fractional images of the each classified fractional image of each class in the class present in the classification scheme. classification scheme was produced. Figure 4 shows the soft classified fractional images of the each class present in the classification scheme.
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Figure 4: Soft classified fractional images of (a) Vegetation (b) Built-up area (c) Water (d) Barren land
Journal of Geomatics 124 Vol.7 No.2 October 2013 6. Accuracy assessment less separable. Table 1 shows the overall fuzzy error matrix by considering all pixels in the accuracy In this experiment accuracy measures were computed assessment. The overall user's and producer's accuracy using the fuzzy error matrix. MATLAB software was were 87.03% and 88.59% respectively. An overall used to compute the classification accuracy and it was accuracy value 86.1% and the Kappa coefficient (K) observed that the fraction of barren land which is value 0.8159 were achieved. The value of K=0.8159 classified as water is somewhat less when compared to means the classification achieved an accuracy that is 80% others, it means the water and barren land classes are better than could be expected from random assignment of more separable, while the vegetation and built up area are pixels to classes.
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Figure 5: Soft reference fractional images of (a) Vegetation (b) Built-up area (c) Water (d) Barren land Table 1 Fuzzy error matrix
Soft Classified Data Built Up Area Vegetation Barren Land Water Total Grades Built up Area 0.264 0.08 0.0069 0.098 0.29 Vegetation 0.25 0.22 0.1906 0.055 0.3 Barren Land 0.203 0.083 0.1456 0.049 0.2236 Water 0.0341 0.191 0.016 0.1854 0.1902 Reference Total Grades 0.2971 0.2546 0.2395 0.1988 1
Journal of Geomatics 125 Vol.7 No.2 October 2013 7. Conclusion Garciaharo, L.L., J.D. Guang, J.Z. Chao and S. Yong This paper described a fuzzy classification concept by (1996). Linear spectral mixing modeling to estimate developing the signature without finding the average of vegetation amount from optical spectral data. all the training dataset, developing the best signature International Journal of Remote Sensing, 17, 3373- for the material common to training data set using the 3400. membership function. The methodology was developed for producing effective MOI based sub pixel Holben, B.N. and Y.E. Shimabukuro (1993). Linear classification for multispectral data from remote mixing model applied to coarse spatial resolution data sensing sensors. from multispectral satellite sensors. International Experimental results show that classification accuracy Journal of Remote Sensing, 14, 2231–2240. of the order of 80% can be achieved for multispectral image classification by appropriately selecting the MOI Hung, M.C. and M.K. Ridd (2002). A sub pixel method, the signature development method. classifier for urban land-cover mapping based on a maximum-likelihood approach and expert system rules. Based on this experiment, it is clear that MOI based Photogrammetric Engineering and Remote Sensing, sub pixel classification is an excellent method for 68, pp. 1173–1180. deriving land cover information from low resolution data sets. This approach can be successfully utilized for Lee, S. and R.G. Lathrop (2006). Sub pixel analysis of soft classification. Landsat ETM using Self-Organizing Map (SOM) neural networks for urban land cover characterization. References IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 1642–1654. Arora, M.K. and P.K. Varshney (2011). Improving sub-pixel classification by incorporating prior Liu, W. and E.Y. Wu (2005). Comparison of non- information in Linear Mixture Models. IEEE linear mixture models: sub-pixel classification. Remote transactions on Geosciences and Remote Sensing, 49, sensing of Environment, 94, pp. 145- 154. pp. 1001-1013. Atkinson, P.M., P. Aplin, R.A. Hill, D. Wulf and M.S. Liu, W., K.C. Seto, E.Y. Wu, S. Gopal and C.E. Nixon (1997). Mapping sub-pixel proportional land Woodcock (2004). ART-MMAP: A neural network cover with AVHRR imagery. International Journal of approach to subpixel classification. IEEE Transactions Remote Sensing, 18, 917–935. on Geoscience and Remote Sensing, 42(9), 1976-1983.
Bastin, L. (1997). Comparison of fuzzy c-means Lu, D. and Q. Weng (2004). Spectral mixture analysis classification, linear mixture modelling and MLC of the urban landscape in Indianapolis with Landsat probabilities as tools for un-mixing coarse pixels. ETM+ imagery. Photogrammetric Engineering and International Journal of Remote Sensing, 18, 3629– Remote Sensing, 70, pp.1053−1062. 3648. Ridd, M.K. (1995). Exploring a V–I–S (vegetation– Brown, M., H.G. Lewis and S.R. Gunn (2000). Linear impervious surface–soil) model for urban ecosystem spectral mixture models and support vector machines analysis through remote sensing: comparative anatomy for remote sensing. IEEE Transactions on Geoscience for cities. International Journal of Remote Sensing, 16, and Remote Sensing, 38, pp. 2346–2360. 2165−2185.
Carpenter, G.A., S. Grossberg, N. Markuzon, H. John Salman, A.A., A.E. Ali and H.E. Mattar (2008). and D.B. Rosen (1999). A neural network method for Mapping land use land cover of Khartoum using Fuzzy mixture estimation for vegetation mapping. Remote classification. Emirates Journal for Engineering Sensing of Environment, 70, pp. 138–152. Research, 13, 27-43. Foody, G.M. (2002). Hard and soft classifications by a neural network with a non- exhaustively defined set of Small, C. (2005). A global analysis of urban classes. International Journal of Remote Sensing, 23, reflectance. International Journal of Remote Sensing, 3853–3864. 26, 661−681.
Foody, G.M. (1996). Approaches for the production Wenbing, F. (2006). Application of “3S” technique to and evaluation of fuzzy land cover classification from the city planning and building. Anhui Construction, 1, remotely-sensed data. International Journal of Remote pp. 1-16. Sensing, 17, 1317–1340. Xian, G. and M. Crane (2005). Assessments of urban Forster, B.C. (1985). An examination of some growth in the Tampa Baywatershed using remote problems and solutions in monitoring urban areas from sensing data. Remote Sensing of Environment, 97, satellite platforms. International Journal of Remote pp.203−215 Sensing, 6, 139−151.
Journal of Geomatics 126 Vol.7 No.2 October 2013 GIS for mapping updates of spatial spread and the ecological reasoning of JE transmission in India (1956 -2012)
M. Palaniyandi Remote Sensing and GIS Laboratory, Vector Control Research Centre, (ICMR), Indira Nagar, Pondicherry – 605006, India Email: [email protected]
(Received: March 29, 2012; in final form August 22, 2013)
Abstract: The mapping of spatial extent of district level Japanese Encephalitis (JE) epidemics in different parts of the country was updated for the past 56 years. The JE epidemics in the country have been mostly associated with the environmental transition of mega water resource irrigation projects induced land use / land cover changes and the regional micro climate changes. The changes of land use / land cover are most probably causing the conducive environments for survival of JE vector mosquitoes i.e. Culline mosquitoes mainly Cx. vishnui group (Cx tritaeniorhynchus, Cx. Vishnui, Cx. pseudovishnui, Cx. whitmorei, Cx.epidesmus, Cx.fuscocephala, Cx.gelidus, and Cx. bitaeniorhynchus) which are directly influencing the changing nature of epidemiology of the disease transmission in the country. Regions of JE epidemics across the country (1956–2012) were analysed using GIS technology. The IRS WiFS data was used for land use analysis. The information on water resource development projects, land use / land cover changes, climate variables (temperature, relative humidity, rainfall, floods) and JE epidemics data were captured for geo-statistical analysis. The spatial analysis was also performed. The result showed that the geographical distributions of JE epidemics in the country were mostly associated with the introduction of intensive wet irrigation rice cultivation and the land use / land cover changes, and the regional micro climatic changes in the coastal districts and regions along river belt of India.
Keywords: GIS mapping, remote sensing, land use / land cover changes, climate variables, JE epidemics, Cx. Vishnui group, and vector mosquito breeding habitats
1. Introduction and are determining the sporadic occurrences of JE epidemics across the country (Palaniyandi, 2004). The present study was designed for updating the The irrigated regions of rice cultivation are disease prevalence of Japanese encephalitis (JE) supporting breeding sites for Cx. genus of JE vector epidemics in the country for the past 56 years (1956 mosquitoes as rice plants mature and form a dense to 2012). Totally, 152 districts of 22 States / Union canopy over the water (Palaniyandi, 2004, Wood et Territories in the country were affected with JE al., 1991 and 1992). The vegetation types of wet epidemics from 1956 to 2012. The JE epidemics in cultivation (e.g. irrigated rice area) with 2.5km buffer the country have been mostly associated with the zones of surrounding areas provide the potential environmental transition of mega water resource breeding sites for JE vector mosquitoes larvae, adult irrigation projects induced land use / land cover mosquitoes resting sites, sugar-feeding supplies for changes and the regional micro climate changes. The JE adult mosquitoes and safe protection for survival changes of land use / land cover are most probably and longevity (Palaniyandi, 2004, Wood et al., 1991 creating the conducive environments for survival of and 1992). Japanese encephalitis was first reported in JE vector mosquitoes i.e. Culline mosquitoes mainly Vellore district of Tamil Nadu the year 1956 (Webb Cx. vishnui group (Cx. tritaeniorhynchus, Cx. and Pereira, 1956, Lapevssonnie and Gobalakichenin, Vishnui, Cx. pseudovishnui, Cx. whitmorei, 1957) and it has become a major public health Cx.epidesmus, Cx.fuscocephala, Cx.gelidus, and Cx. problem in India since 1973 (NIV, Pune 1980, bitaeniorhynchus) and are directly influencing the NVBDCP, 2006, and 2007). changing nature of epidemiology of the disease in the country. The geo-spatial analysis was carried out for 2. Objectives studying the relationship between the JE epidemics 1. To map geographical occurrences of JE and the geo-climate variables. The environment epidemic transmission in India (1956-2012) induced transitions of land use / land cover changes 2. To study the environmental transition of and the climate variables have been supporting key water resource projects on land use / land factors for JE vector mosquito habitats, vector cover changes and JE epidemics in India survival and the adult vector abundance (Palaniyandi, (1956-2012) 2004). The land use changes (dry land agriculture to 3. To study the spatial agreements between the wet cultivation), vegetation types and growth stages geo-environmental variables and JE of land cover are creating the conducive transmission in India (1956-2012) environments for vector survival, vector abundance Journal of Geomatics 127 Vol.7 No.2 October 2013
Table 1: The Outbreaks of JE occurrence in India (1956-2012)*
Sl. Name of States No. of No. of No. of JE epidemic’s Year No. Districts Cases Deaths 1970,1971,1972,1973,1974,1979,1980,1981,1982,1983, 1 Andhra Pradesh 17 11068 3119 1984,1985,1986,1987,2001,2002- 2005,2007-2011 1978,1979,1980,1981,1982,1983,1984,1985, 2 Assam 2 7848 2113 1986,1987,1992, 2001, 2002-2005, 2007-2011 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 3 Bihar 2 6173 2136 1987, 2001, 2003-2005, 2007-2011 4 Chandigarh 1 2002 4 0 5 Delhi 1 2002, 2003, 2004, 2005 39 5 4 Goa 1 1978, 2001, 2002, 2005, 2007-2011 424 31 6 Haryana 6 1990, 1994, 1990, 1994, 2001, 2002-2005, 2007-2011 528 292 7 Jharkhand 2 2010, 2011 12603 2506 1978,1979,1980,1981,1982,1983, 1984,1985,1986, 8 Karnataka 22 321 21 1987, 1993, 2001-2005, 2007-2011 9 Kerala 2 1996, 2001, 2003, 2004,2007-2011 2116 19 10 Madhya Pradesh 3 1980 66 36 11 Maharashtra 9 1979, 2001, 2002, 2003, 2004, 2005, 2007-2011 754 154 1982,1983,1984,1985,19861987, 2002, 2003, 2005, 12 Manipur 1 725 204 2007-2011 13 Nagaland 2 1985,1986,2007, 2009-2011 250 126 14 Punjab 2 2002,2010,2011 12 2 15 Pondicherry 1 1978,1979,1980,1981,1982,1983,1984,1985,1986 1956 832 1955,1956,1958,1965,1981,1982,1983,1984,1985,1986, 16 Tamil Nadu 13 7963 1522 1987,1988, 1989,1990, 2003-2005, 2007-2011 17 Tripura 1 1978, 1980, 1981,1982,1983,1984 199 178 18 Uttaranchal 2 2006-2008, 2010, 2012 220 1 1978,1979,1980,1981,1982,1983,1984,1985,1986, 19 Uttar Pradesh 44 216663 8555 1987,1988, 2001-2008 1971,1972,1973,1975,1976,1977,1978,1979,1980, 20 West Bengal 18 18378 7443 1981,1982,1983, 1984,1985,1986,1987, 2001-2011 Note: The number of districts and the number of states are based on as on 2012
* Data Sources of JE epidemics in India Proceedings of the National Conference on JE, 1984, PP1-9, 10-15, 22, 24 Guidelines for prevention and control of JE, NVBDC-WHO 2006, PP1-18 NVBDCP, New Delhi Report 2007, – the reports of JE outbreaks, October 2012, (the no. of cases and deaths of JE records received from the state governments) Journal of Communicable Disease, Vol.13 (4) PP257-265, 1981 Journal of Communicable Disease, Vol.18 (2) PP103-108, 1986 Journal of Communicable Diseases Vol.20 (1) PP18-21, 1988, Journal of Communicable Disease, Vol20 (4), PP263-275, 1988 Journal of Communicable Disease, Vol.21 (2) PP87-95, 1989 Journal of Communicable Disease, Vol.24 (3) PP145-149, 1992 Journal of Communicable Diseases, Vol.25 (2) P83-85, 1993 Journal of Communicable Disease, Vol.30 (2) PP129-131 Annual Report, NIV, Pune, 1986-87, PP3, 8, 9, 18-19 JE Epidemics in India, NIV, Pune, JE Report 1980 IJMR, Vol. 63(8), PP 1164-1177, 1975 IJMR, Vol. 106, PP4-6, July1997 IJMR, Vol.72. PP 471-474, Feb1980 IJMR, May 1988. PP417-421 VCRC Annual Report 1975 WHO Bulletin Vol.73, Nov2. PP-237-244 Journal of Geomatics 128 Vol.7 No.2 October 2013
3. Materials and methods Mohan Rao et al., 1988, Narasimham et al., 1988, Badrinath and Rao, 1989, George et al., 1990, The JE epidemics data was collected from the various Sharma et al, 1991, Vajpayee et al., 1991). available sources of published records from 1956 to 2012 (Table1). The database was developed in the The third major spatial extend of JE outbreaks Dbase format using the MS Excel software and later occurred in many of the virgin areas and the JE on imported to the MapInfo Professional 4.5 GIS epidemics was reported from Kerala during 1996 software platform and Arc View 3.2 Spatial analyst (Kar and Saxena, 1998, Gajanana, 1998, NVBDCP, for mapping the JE epidemics from 1956 to 2012. 2006 and 2007) and consequently, the irregularity of The JE epidemics data and the geo-climate variables JE epidemics was reported from different parts of the were captured for geo-statistical analysis using SPSS country (Table - 1) from 1978 to 2007. During his 10.0. The IRS WiFS data of indigenous remote period, 103,389 cases and 33,729 deaths were caused sensing satellite was used for land use / land cover by the JE outbreaks in different parts of the country. analysis using ERDAS Imagine 8.5. The land use Major epidemics of JE death were recorded in the land covers was constructively classified for states of Assam, Andhra Pradesh, Tamil Nadu, identifying the JE epidemic risk zones in India. The Karnataka, Kerala, West Bengal, Goa, Uttar Pradesh, monsoon seasonal temperature, relative humidity Manipur, Haryana and Bihar during the period of (RH), saturation deficiency (SD), the amount of 2007 to 2012 and less significantly from other parts seasonal monsoon rainfall and the occurrence of of the country in every subsequent year from 2007 flood over the period of studies were analyzed for the where the areas under the construction of irrigation spatial agreements and the spatial auto correlations canals and construction of water resource between the climate variables and the JE epidemics development projects, the geographical extension of across the country. irrigation rice cultivation agricultural practices and, the land use/ land cover changes have occurred. As a JE epidemics in India (1956-2012) result of increasing availability of irrigation facility, agricultural practices changed leading to land use / The serological survey of JE virus activity in India land cover changes in different part of the country. was first conducted by the National Institute of The first JE outbreak was reported from 6 districts Virology (NIV), Pune, India during the year 1952. In during 1956 to 1965, and followed by 6 districts from the year 1956, the JE virus was clinically first 1966 to 1975, the major epidemics of JE from 80 identified and recognitions was made at Vellore, districts during 1976 to 1985 and 43 districts during Tamil Nadu and the JE virus was also isolated from the period of 1986 to 1995, and it reduced to 17 the mosquitoes during the year 1956 and from human districts during 1996 to 2012. The experience of brain tissues in 1958 (Webb and Pereira, 1956, repeated occurrence of JE epidemics occurred 1 to 3 Lapevssonnie and Gobalakichenin, 1957). The first times in 93 districts, 3 to 6 times occurred in 43 epidemics of JE in India, 52 cases were identified and districts, 6-9 times occurred in 12 districts and more it was clinically reported from Vellore district than 9 times occurred in 4 district. In all, 152 (previously North Arcot) of Tamil Nadu during the districts were affected in different parts of the period of 1956 to 1965 (Carey et al., 1969) and, country (Fig.1 and 2) over the period of past 56 followed by these cases, during the late sixties and years. the early seventies, the ecological changes and the geographical extension of sporadic JE epidemics 4. Results and discussion occurred in different parts of the country (Dandawate et al., 1969) The second major JE outbreaks India has the land area of approximately 3.29 million occurred in the states of West Bengal during the year km2. The land occupied for agricultural activities is 1973–76 (Chakravarty et al., 1975 and 1980) and about 54.7 % of the total area. Changes in land use / subsequently JE outbreaks happened in the states of land cover especially in agricultural, industrial, Andhra Pradesh, Bihar, Tamil Nadu and Uttar urbanization sectors are bringing the huge changes in Pradesh during the year 1977-79 (Mathur et al., 1982, the environment. Bringing water resource NIV Pune, 1980). Soon after, the first report of JE development projects was the prime goal of the was extended to the newer areas of Assam, Karnataka nation during the Five year Plan period. During the and Pondicherry during the period of early eighties First Five Year Plan in 1951, the number of dams and it was subsequently reported in the states of Uttar was nearly 300 which steadily increased to 4000 by Pradesh and Haryana in the late 1980 and the the year 2000. Consequently, a great number of beginning of 1990 (Prasad et al., 1982, Prasada Rao water resource development projects including the et al., 1982, Rodrigues, 1984, Mall and Khanna 1986, check dams were constructed across the country, and Journal of Geomatics 129 Vol.7 No.2 October 2013 hence the 54.7 % of the area has been converted in to the intensive wet cultivations irrigation rice practice which was steadily increased during the period of 1956-2012. The construction of lakes and check dams projects of flood control activities for flood management in the flood prone river basins has also increased during study period.
Figure 3: The relationship between the number of DAMs and the JE epidemics in India (1956 – 2012)
Fig. 4: The relationship between the heavy rainfall, Figure 1: The first report of JE epidemics across the flood occurrences and the JE epidemics country and the spatial diffusion of JE epidemics in the newer areas, where the intensive wet cultivation However, this has witnessed growing incidence of JE is being practiced (1956-2012) outbreaks more frequently in the districts where the intensive irrigated rice cultivation is practiced (Fig.3) These land use transition have provided the suitable sites for JE vector mosquito breeding, the adult mosquitoes abundance in the virgin areas and JE outbreaks in and around of the buffer zone (1 km to 2.5 km) of water resource development projects and wetland cultivation areas. It was also supported by the incidence of JE epidemics in the districts where the area received heavy rainfall and experienced flood (Fig.4). The spatial relationship between land use / land cover changes (dry land to wet cultivation) and JE epidemics has spatially significant (r=0.625, p value <0.05) relation.
The environmental aspects of JE Epidemics in India, using remote sensing and GIS
The land use / land cover changes and the climate risk variables are fueling JE vector mosquito breeding, vector survival, JE adult vector mosquito Figure 2: The repeated occurrence of JE epidemics abundance and the JE epidemics in different parts of in the country and the spatial diffusion of JE the country for the past 58 years. One of the epidemics in the newer areas where the intensive wet difficulties associated with achieving a desired cultivation was practiced (1956-2012) control of disease was that the combinations of many Journal of Geomatics 130 Vol.7 No.2 October 2013 diverse and complex risk factors are contributing to the disease infection and spatial diffusion of the JE transmission. , JE vector abundance, density and the vector survival are being associated with climate variables (temperature, relative humidity, rainfall, floods) and the environmental variables including the landscape environment (altitude) and land use / land cover categories the number of larval breeding sites, soil alkalinity, water temperature, water turbidity and water hardness of breading sources (Dale et al., 1998). The environmental transition and climate variables are principally fueling JE vector mosquito abundance and the disease outbreak in the places Figure 5: Land use / Land cover categories of with year round regular irrigation and intensive rice Karnataka state, the image classes derived from IRS cultivation (Palaniyandi, 2004, Wood et al., 1991 and WiFS data 1992). A close association was found between the JE epidemic and the occurrence of heavy flood. The spatial overlay analysis provides the high correlation between the intensive irrigation rice cultivation, land use / land cover categories and JE epidemics (Palaniyandi, 2004 and 2013). The intensive irrigation rice cultivation areas are potential for mosquitoes breeding sites (and thereby provide potential resting sites, sugar-feeding supplies for adult mosquitoes and protection from climatic conditions) and these are directly playing important role in determining the abundance of mosquitoes (Wood et al., 1991 and 1992). These variables have important role in creating the conducive environments for survival of JE vector mosquitoes mainly Cx. vishnui group (Cx tritaeniorhynchus, Cx. Vishnui, Cx. pseudovishnui, Cx. whitmorei, Cx.epidesmus, Cx.fuscocephala, Cx.gelidus, and Cx. bitaeniorhynchus). It was found that 4-6 weeks after rice transplantation,the extensive epidemic of encephalitis occurred in most part of Southern India, between the months of August to December. The Figure 6: The spatial association between the land regional scale climate change (temperature, rainfall, use categories of satellite remote sensing IRS WiFS and humidity) and the environmental disturbances data of Karnataka state in India (land use / land cover changes, ecological changes) are the key factors in promoting the breeding site and The NDVI values derived from IRS WiFS data the JE epidemics in the virgin newer areas of the provides the value of < 0.0 – 0.22 for wet cultivation country. The spatial relationship between land use / /rice cultivation areas with breeding habitats positives land cover changes (dry land to Wetland), the wet for Cx. genus immature JE vector mosquitoes species cultivation agriculture practice as derived from (Cx. tritaeniorhynchus, Cx. Vishnui, Cx. remote sensing of IRS WiFS data shows very good pseudovishnui, Cx. whitmorei, Cx.epidesmus, and statistically significant spatial agreement with JE Cx.fuscocephala, Cx.gelidus, and Cx. epidemics in different parts of India (r=0.625, p value bitaeniorhynchus) positives, and the NDVI value > <0.05). It was constructively classified for identifying 0.2 and < 0.4 vegetation indicates the actively the JE epidemic risk zones in Karnataka State with photosynthesizing vegetation, which isvulnerable to 93.4% accuracy with 100 % specificity (Fig.5 and the high risk of JE transmission, and followed by the Fig. 6), and further it shows close association with >0.4 to <0.6 and the < 0.022 and > 0.013, having the land use categories of satellite remote sensing of moderate risk of JE transmission during the Kharif Kharif and Rabi crop season land use categories of and Rabi crop seasons. India.
Journal of Geomatics 131 Vol.7 No.2 October 2013
The spatial association between the JE breeding sites (and thereby provides potential resting Transmission and major soil types in India sites, sugar-feeding supplies for adult mosquitoes and protection from climatic conditions) may also be The properties of soil types, soil porosity, soil depth, important in determining the abundance of water holding capacity and chemical compounds mosquitoes associated with the breeding site (Hassan present in the soils have considerable contribution to and Onsi 2004). The irrigation rice cultivation the immature JE vector mosquitoes with 95% provide breeding sites for JE vectors early in the significance of confidence and have influence over growth cycle of the plants, this changes as the rice the profusion of JE vector mosquito breeding and it plants mature and form a dense canopy over the has also control over the longevity and survival of the water (Wood et al., 1991 and 1992). The regional mosquitoes.. The alluvial soil type land has 44.9% climate change (temperature, rainfall, and humidity) contribution to the occurrences of JE epidemics with and land use / land cover changes are fueled to confidential interval of (CI = 0.364 to 0.520), and promoting a new emerging vector borne diseases in followed by red soil 23.97 %, with confidential many newer areas (Hassan and Onsi 2004, interval of (CI = 0.182 to 0.317), and black cotton Palaniyandi, 2013 and 2004). soil 23.16%, with confidential interval of (CI = 0.171 to 0.0.303), and the red sandy soil 7.94%, has no significance role in the JE epidemics with confidential interval of (CI = 0.0534 to 0.164), and hence, it is concluded that the soil types are spatially correlated to the occurrences of JE in India (Fig.7).
Figure 7: The spatial association between the major soil types and JE transmission in India
Climate and the landscape environmental changes and JE epidemics in India
Figure 8 : Wet cultivation irrigation crop land in The geographical distribution of JE epidemics from association with JE epidemics in Tamil Nadu state 1956 to 2012 are highly associated with the developments of mega water resource irrigation The recent years have witnessed growing incidence projects in the country especially during the period of of vector borne diseases in different parts of our 1970 to 1990. Consequently, the increase of irrigation country, and more frequently in the districts where rice wet cultivation areas and the sustainable the water resource / irrigation projects (Palaniyandi, agricultural developments were achieved in the 2004) (Fig.8) are related to, as shown in the country. Subsequently, the results of the landscape environment of elevation of less than 220 developments projects have brought out the land use / m MSL (Fig.9) the mosquito abundance and disease land cover changes, regional micro climatic changes outbreak in and around of the buffer zone of 2.5 km and, added with the incidence of sudden heavy radius from the water resource projects (Irrigation rainfall and flood in the areas fueled to the spatial canals, lake, perennial or Semi-perennial River / extension of experience of sporadic occurrences of JE stream, water pools) and irrigation wet cultivation epidemics in different part of the country from 2007 areas (sugarcane, rice and plantain). to 2012. The type of vegetation which surrounds the
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IRS WiFS data had very good spatial agreement with JE epidemics in different parts of India and produced statistically significant (r=0.625, p value <0.05) results. Thus, integrated use of GIS, RS and geo- spatial analysis can be used for tracking the JE epidemic risk zones. It was also observed that the frequent epidemics occurred in the coastal districts and the river belt.
References
Badrinath, S. and S.R. Rao (1989). A serological study of Japanese Encephalitis and related flaviviruses in and around Pondicherry, South India. The Natl Med J India, 2 (3), 122-125.
Bouma, M. and H.J. Van Der Kaay (1995). Epidemic malaria in India’s Thar Desert. Lancet 346: 1232– 1233.
Carey, D.E., R.M. Myers, R. Reuben and J.K.G. Webb (1969). Japanese encephalitis in South India. Figure 9 : The landscape (elevation) environment in A summary of recent knowledge. J. Indian Med. association with JE epidemics in Tamil Nadu state Assoc. 52, 10.
The type of vegetation which surrounds the breeding Chakraborty, M.S., S.K. Chakraborty, K.K. sites (and thereby provides potential resting sites, Mukherjee, A.C. Mitra, K.K. Mitra, B. Gupta and sugar-feeding supplies for adult mosquitoes and J.K. Sarkar (1980). Recurrent Japanese Encephalitis protection from climatic conditions) may also be in West Bengal. Indian J Med Res 72, 1. important in determining the abundance of JE vectors associated with the breeding site. The changes of Chakravarty. S.K., J.K. Sarkar, M.S. Chakravarty, agricultural practices, increasing the intensive M.K. Mukherjee, B.C. Das and A.K. Hati (1975). irrigation rice cultivation areas, retention of high The first epidemic of Japanese encephalitis in India – surface moisture, mismanagement of irrigation water, virological studies. Indian J Med. Res. 63, 77. poor maintenance of irrigation canals etc., have been contributing to the profusion of Cx. vishnui group of Dale P.E.R., S.A. Ritchie, B.M. Territo, C.D. Morris, JE vector mosquitoes which were earlier unknown to A. Muhar and B.H. Kay (1998). An overview of the new areas (Palaniyandi, 2013, 2012, and 2004, remote sensing and GIS for surveillance of mosquito Tyagi et al, 1995, and Bouma and Van Der Kaay vector habitats and risk assessment. J Vector Ecol. 2, (1995). It was also observed that the frequent major 54-61 epidemics occurred in the districts of coastal areas and the river belt where the climatic condition were Dandawate, C.N., P.K. Rajagopalan, K.M. Pavri and suitable with high relative humidity and the intensive T.H. Work (1969). Virus isolations from mosquitoes wet irrigation rice cultivation, sugarcane and collected in North Arcot district, Madras state and plantains agriculture in the country. Chittoor district, Andhra Pradesh between November 1955 and October 1957. Indian J Med. Res. 57, 1420 5. Conclusion Gajanana, A. (1998). Epidemiology and surveillance The remote sensing of JE epidemic landscape regions of Japanese Encephalitis in Tamil Nadu. ICMR. Bull. provides guideline to identify areas under risk of JE 28 (4), 1-5 transmission. It was observed that duration of 4 - 6 weeks after rice transplantation is critical period for George, S, P.N. Yergolkar, Hanumaiah and C.S. spread of JE epidemic. The spatial relationship Kamala (1990). Outbreak of encephalitis in Bellary between land use / land cover changes (dry land to district of Karnataka and adjoining areas of Andhra Wetland), the wet cultivation agriculture practice Pradesh. Indian Journal of Medical Research. land use categories derived from remote sensing of Section A, Infectious Diseases, 91, 328-330. Journal of Geomatics 133 Vol.7 No.2 October 2013
NVBCD-WHO reports, (2006). Guidelines for Palaniyandi, M. (2004). The Impact of National prevention and control of Japanese Encephalitis, pp1- River Water Projects on Regional Climatic Changes 18. and Vector Borne Disease Outbreaks in India. National Conference on Climate Change and its Hassan, A.N. and H.M. Onsi (2004). Remote sensing Impact on Water Resources in India, Dec. 15-17, as a tool for mapping mosquito breeding habitats and School of Earth and Atmospheric Sciences, Madurai associated health risk to assist control efforts and Kamaraj University, Madurai – 21, Tamil Nadu, development plans: a case study in Wadi El Natroun, India. Egypt. J Egypt Soc Parasitol., 34 (2):367-82. Prasad, S.R., S. George and N.P. Gupta (1982). National Institute of Virology (NIV), Pune, Studies on an outbreak of Japanese Encephalitis in Information document, (1980). Japanese Encephalitis Kolar district, Karnataka in 1977-78. Indian J Med (JE) in India. p. 25. Res 75, 1.
Kar, N.J. and V.K. Saxena (1998). Some Prasada Rao, G.L.N., F.M. Rodrigues, M. epidemiological characteristics of Japanese Nambiapan, G.R. Ghalsasi, J.J. Rodrigues, B.D. Encephalitis in Haryana state of Northern India. J Pinto, C.V. Mohan Rao and N.P. Gupta (1982). Com Dis., 30 (2): 129-131Kitron U, (1998). Aetiology of the 1978 outbreak of encephalitis in Landscape ecology and epidemiology of vector borne Trinulveli and other districts of Tamil Nadu. Indian J diseases. J Med Entomol 35, 433-445. Med Res., 76, 36.
Mall, M.P. and P.N. Khanna (1986). An epidemic of Rodrigues, F.M. (1984). Epidemiology of Japanese Japanese encephalitis in Pilibhit district. Indian J of Encephalitis in India: A brief overview. In: Comparative Microbiology, Immunology and Proceedings of the National Conference on Japanese Infectious Diseases, 7 (4), 179-180. Encephalitis. Indian Council of Medical Research, New Delhi, 1-9. Mathur, A, U.C. Chaturvedi, H.O. Tandon, A.K. Agarwal, G.P. Mathur, D. Nag, A. Prasad and V.P. Sharma, R.C., V.K. Saxena, M. Bharadwaj, R.S. Mittal (1982). Japanese Encephalitis epidemic in Sharma, T. Verghese and K.K. Datta (1991). An Uttar Pradesh, India during 1978. Indian J Med Res. outbreak of Japanese encephalitis in Haryana - 1990. J. of Com Dis., 23 (2), 168-169. 75, 161.
Mohan Rao, C.V., A.R. Risbud, F.M. Rodrigues, Tyagi, B.K., R.C. Choudhary, S.P. Yadav (1995). B.D. Pinto and G.D. Joshi (1988). The 1981 epidemic Epidemic malaria in Thar Desert. Lancet 346: 634– of Japanese encephalitis in Tamil Nadu and 35. Pondicherry. Indian Journal of Medical Research, Vajpayee, A., M.K. Mukherjee, A.K. Chakraborty 111, 417-421. and M.S. Chakraborty (1991). Investigation of an Narasimham, M.V.V.L., K.C. Rao, M.S. Bendle, outbreak of Japanese encephalitis in Rourkela City R.L. Yadava, Y.C. Joshi and R.S. Pandey (1988). (Orissa) during 1989. J Com Dis., 23 (1), 18-21 Epidemiological investigation on Japanese Encephalitis outbreak in Uttar Pradesh during 1988. J Webb, J.K.G. and S.M. Pereira (1956). Clinical Comm Dis., 20 (4), 263-265. diagnosis of an arthropod borne type of virus encephalitis in children in North Arcot district, Palaniyandi, M. (2013). GIS mapping of vector Madras State, India. Indian J Med. Sci. 10, 572. breeding habitats”, Geospatial World Weekly, (GIS- e-news letter), 14th January, 2013, Vol.9, Issue.2, Wood BL, L.R. Beck, R.K. Washino, K. A. Hibbard pp.1-4. and J. S. Salute et al., (1992). Estimating high mosquito-producing rice fields using spectral and Palaniyandi, M. (2013). GIS for epidemic control in spatial data. Int. J. Remote Sensing, 13, 2813–2826. India, Geospatial World Weekly, (GIS-e-news letter), 22nd July, 2013, Vol.9, Issue.28, pp.1-4. Wood, BL, L.R. Beck, R.K. Washino, S.M. Palchick and P.D. Sebesta et al., (1991). Spectral and spatial Palaniyandi, M. (2012). The role of Remote Sensing characterization of rice field mosquito habitat. Int. J. and GIS for Spatial Prediction of Vector Borne Remote Sensing, 12, 621–626. Disease Transmission - A systematic review, Journal of Vector Borne Diseases, 49 (4), 197-204 Journal of Geomatics 134 Vol.7 No.2 October 2013
Morphometric and morphologic analysis of lunar impact craters
DishaLal1,PrakashChauhan2,A.S.Arya2 andAjai2 1Gujarat University, Ahmedabad 2Space Applications Centre (ISRO), Ahmedabad - 380 015 Email: [email protected] (Received: July 10, 2013; in final form October 4, 2013) Abstract: Topographic study was carried out about complex lunar craters of copenican geologic period. Digital Elevation Model (DEM) acquired from new age sensor data sets was used to extract spatial profile of lunar craters and different morphometric parameters such as crater diameter, crater depth and central peak height were obtained. These data sets consist of lunar craters in the range of 30-104 km of diameter with prominent central peaks. Derived parameters were used to compile scaling trends. These scaling trends as obtained from new high definition topographic data sets were found to be quite comparable with previous relations given by Pike (1977, 1985). Morphological study of crater central peaks suggests that the geometry of peaks appear to be sensitive to geologic setting of the crater whereas there is no relativity in crater complexity and its increasing size.
Keywords : Lunar surface, Complex crater, DEM, Central peak, morphology
1. Introduction made available, with the aim of obtaining newer Impact cratering process is one of the most important insights in the field of topographic study of lunar geologic processes leading towards understanding the surface. development and evolution of planetary surfaces. 3. Topographic study of lunar craters Craters are very common features which are resulted from impacts of large bodies onto the planetary A DEM offers most common method for extracting the surfaces. Previous researches (Wood and Anderson, vital topographic information. Morphological 1978; Cintala et al., 1977) have shown that the lunar parameters, which are useful for identifying and craters change morphologically with increasing size, describing topographic forms and processes, were from simple bowl shaped structure to complex craters extracted using DEM. Parameters viz; crater diameter, with prominent central peaks. The study of scaling crater depth, central peak height, crater wall slope were trends and variation in central peak morphology can extracted from topographic profiles of the craters. provide information of crater formation mechanism. These data with high resolution bestow crater depth and other parameter information with appreciable 2. Data accuracy. An annoted topographic cross section of a crater is shown in Figure 1. Craters selected for current studies represent young copernican complex craters with diameters ranging from 30-104 km. These craters are geologically situated on the NearSide of the moon. The topographic data acquired from the LISM (Lunar Imager / SpectroMeter), including the Terrain Camera (TC), Multi-band Imager (MI), and Spectral Profiler (SP) onboard SELENE (Kaguya). The TC 10-bit image data with a spatial resolution of about 10 m provide global Digital Terrain Model (DTM)s with a relative height resolution of a few tens of meters or better, ultimately a Digital Elevation Model (DEM) with absolute height Figure 1: Geometry of crater with labeled dimensions information, and global or local high-contrast (adopted from Bray et al. (2008)) mosaicked maps for examination of detailed morphologies of the lunar surface. These DTMs having 4. Spatial profiles of lunar craters capability of providing higher details for accurate morphometric measurements, were utilised. Due to Craters of various sizes as well as with different limited data availability, the study included 10 young morphological characteristics were selected. Software complex craters with prominent central peaks. These that was used to generate topographic profiles of crater collections of craters are combination of both, highland was Erdas Imagine 9.1. Crater diameters were as well as mare regions locales. Previous studies (Pike, determined by taking an average value of three to four 1977; Head, 1975; Cintala et al., 1977) have shown rim-to-rim distances. Similarly other crater dimensions the importance of such studies in characterising crater and internal features, as shown in Figure 1, were groups and establishing intergroup relationships. These measured from topographic image. Series of lunar studies were carried forward with advanced data sets crater spatial profiles are presented in Figure 2.
© Indian Society of Geomatics Journal of Geomatics 135 Vol.7 No.2 October 2013
Diversity in topography of various craters can also be Their studies showed that simple craters are deeper identified. Profiles of following types of crators were relative to the crater diameter than larger complex studied. craters, resulting in a shallower d/D trendline. Pike (a) Crater with a simple central uplift, (1977) calculated that there occurs a change in the d/D (b) Crater with more than one prominent uplifts, ratio at around 11km. A summary of scaling trends (c) Crater with many small uplifts which are possibly presented by Pike (1977, 1985) for the moon is shown the remnants of a bigger central peak, in Table 1. (d) Crater surrounding fallen rims, and (e) Crater with a central pit. Table 1: Summary of scaling relations presented in previous studies. The spatial profiles of different sized craters indicate Dependence D Sr variable height of central peak. Large central peaks are Properties Symbol on crater range No. seen to have two tiered morphology with a wider base diameter, D (km) as can be observed in case of crater Theophilus shown 1. Crater depth D 0.196D1.01 <11 in figure 3. Further, correlation among different 11 measured morphometric parameters of selected craters 2. Crater depth D 1.044D0.301 were computed which are discussed later in this article. to 400 3. Rim height Hr 0.036D1.014 < 21 21 4. Rim height Hr 0.236D0.3999 to 400 Peak 20 5. Dcp 0.22D diameter to 140 6. Peak height Hcp 0.0006 D1.28* >35
7. Peak height Hcp 0.06 D1.969** 17 to 51 *relation given by Wood 1973; **relation given by Hale and Grieve 1982
The d/D ratio were calculated for lunar craters with varying diameters using DEMs as acquired from Selene data sets and tabulated the parameters for different large complex craters. The relation derived is depicted by plot shown in figure 4. Figure 2: DEMs and spatial profile of different craters with distinctive morphologies.
Figure 4: The depth-diameter relation for selected Figure 3: Moon Mineralogy Mapper (M3) image complex craters. generated FCC of crater Theophilus draped on DEM. The measurement various parameters viz; depth 5. Morphometric evaluation diameter, central peak height for complex craters is given in Table 2. Previous studies by Pike (1977, 1985) on lunar craters have revealed a linear relationship between depth (d) Figure 4 shows the depth-diameter plot for complex and diameter (D) of impact craters given by equation 1 craters as obtained by DEM datasets. Depth-diameter relation of complex craters provides evidence for the d= D …………… (1) fact that depth increases with increasing diameter of the crater. Depth-diameter relation as acquired by the where is constant of proportionality approximately topographic parameter study of our data for complex equal to 0.2 for simple craters (Pike, 1977). crater is given by equation (2):
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Table 2: Summary of morphometric parameters for all can be divided into three groups: Linear peaks the craters (elongate single ridge), Arcuate peaks (arcuate single Central ridges or clusters) and Symmetrical peaks (single Depth/ Sr Diameter Depth peak central peak or centrally oriented clusters). Examples Crater Diameter N (km) (m) height of each of these types are shown in Figure 5. The three Name ratio o D d (m) dimensional perspective provided by the DEMs helps d/D to distinguish the crater regions clearly. The crater Hcp walls, rims, central peak, crater floor, secondary craters 1 Trieskener 27 3507 276.52 0.130 on the crater floor are identified clearly (see fig. 6).
2 Autolycus 41 3736 526.00 0.091 3 Burg 41 3669 542.09 0.089 4 Herschel 43 3688 585.33 0.086 5 Aristillus 56 3736 1104.50 0.067 6 Taruntius 58 1210 736.00 0.021 7 Hercule 71 3835 892.00 0.054 8 Stevinus 77 2189 1168.42 0.028 9 Tycho 88 3986 1855.96 0.045 10 Theophilus 104 4123 2726.00 0.040
d= 1.15* D0.1088 …………… (2)
Figure 6(a): Reflectance image draped on DEM of where d= depth; and D = diameter Taruntius crater
Another such parameter that was measured is central peak height, Hcp. A plot (Figure 5) of central peak height with crater diameter was attained to understand their relationship. It was observed that there is a positive correlation between crater peak height and crater diameter. The relation is given by equation 3:
Hcp=0.02*D1.6114 …………… (3)
where D = diameter of the crater; and Hcp = height of the central peak
Figure 6(b): Reflectance image draped on DEM of Stevinus crater
Figure 5: Crater diameter Vs central peak height relation as obtained for young lunar impact craters.
6. Crater peak morphology
3 Crater central peaks are considered to be formed by Figure 6(c): Reflectance FCC image obtained from M uplift of the transient crater floor during the draped on DEM of Aristillus crater modification phase of impact crater formation (Melosh, 1989). Therefore it is apparent that larger the impact, Figure 6: Different types of morphology of central greater will be the amount of central uplift. The peaks classified by geometry. (a) Linear peak, (b) variability in the morphology of central peaks was Arcuate peak and (c) Symmetric peak. esamined. On the basis peak geometry these craters Journal of Geomatics 137 Vol.7 No.2 October 2013
The depth-diameter differences in the images are quite References apparent in all three craters (Fig. 6). Crater peaks morphology as classified on the basis of its geometry Bray, V.J, Gareth S. Collins, Joanna V Morgan and are given in table 3. Paul M. Schenk (2008). The effect of target properties on crater morphology: Comparision of cenyral peak Table 3: Summary of topographic parameters craters on the Moon and Ganymede. Meteoritics and Planetary Science 43:1979 Sr.No Crater Name Crater peak morphology 1. Triesnecker Linear Cintala, M. J., C. A. Wood and J. W. Head (1977). 2. Burg Linear The effects of target characteristics on fresh crater 3. Herschel Linear morphology: Preliminary results for the moon and th 4. Aristillus Symmetric Mercury. Proc. 8 Lunar Sci. Conf., 3409-3425. 5. Taruntius Linear Hale, W. and R. A. F. Grieve (1982). Volumetric 6. Stevinus Arcuate analysis of complex lunar craters: Implications for 7. Tycho Symmetric basin ring formation. Proceedings, 13th Lunar Planetary 8. Theophilus Symmetric Science Conference. Journal of Geophysical Research 87:A65 A76. 7. Discussion and conclusions Head, J. W. (1975). Processes of lunar crater Morphometric parameters like depth, diameter and degradation: Changes in style with geologic time. The Central peak height of lunar craters were analysed and Moon 12, 299-329. their scaling trends were presented using DEMs. These scaling trends are compared with already existing Melosh H.J. 1989. Impact Cratering : A geological relations given in previous studies. process. Newyork: Oxford University Press. 265p.
Craters with single, massive peak or more complicated Pike, R. J. (1977). Size-dependence in the shape of forms of massif-like ridges which may be elongated or fresh impact craters on the moon. In Impact and symmetric and arcuate were seen. In addition to these, explosion cratering, edited by Roddy D. J., Pepin R. a crater with a central pit was also seen. Further the O., and Merill R. B. New York: Pergamon Press, 489- morphological study of central peaks has shown that 509. there does not exist any relationship between the crater size and crater peak complexity. However, there Pike, R. J. (1985). Some morphologic systematics of exists a relationship between central peak height and complex impact structures. Meteoritics 20:49 68. crater diameter which is in accordance with the previous studies (Wood, 1973). Wood, C. A. and L. Anderson (1978). New morphometric data for fresh lunar craters. Proc. 9th The study of geologic setting of crater and peak Lunar planetary Sci. Conf., 3669-3689. morphology provides information of variability in morphology related to the target material. However, Wood, C. A. (1973). Central peak heights and crater the relation is weak, but it is observed that linear origins. Icarus 20, 503-506. central peaks are observed in highland areas while symmetric peaks are observed in mare regions. Journal of Geomatics 138 Vol.7 No.2 October 2013
Crowdsourcing geographic information using field based mobile GIS developed on open source for biodiversity conservation- An Indian Bioresource Information Network (IBIN) spatial data node initiative
Sameer Saran1, Hariom Singh1, S.P.S. Kushwaha1, K.N. Ganeshaiah2, P.L.N Raju1 and Y.V.N. Krishnamurthy1 1Indian Institute of Remote Sensing (ISRO), Dehradun 2University of Agricultural Sciences, Bangalore Email: [email protected]
(Received: May 15, 2013; in final form August 21, 2013)
Abstract: Crowdsourcing has been used quite frequently among the GIS community for field data integration. It basically allows the members of public domain to create and contribute geospatial facts of the field which specify both spatial and non spatial properties of that location. This was only feasible with the help of field based mobile GIS which uses mobile communication networks and the internet as the main communication medium for mobile spatial information service framework. In order to integrate end user data along with other databases, a national initiative has been undertaken under the umbrella of Indian Bioresource Information Network (IBIN) with a joint collaboration of Indian Institute of Remote Sensing, Dehradun and University of Agricultural Sciences, Bangalore for dissemination of bioresouce database. It aims at developing a single window gateway to access distributed bioresource database available in the country to offer spatial (IBIN Spatial Data Node) and non-spatial (IBIN Species Data Node) services on diverse domains of bio-resources and biodiversity. It networks and promotes an open ended, co-evolutionary growth among all the loosely coupled digital databases related to biological resources of the country and to add value to the databases by integration and sharing. IBIN spatial data node is one of the core data nodes providing access to distributed spatial databases through web services. An attempt has been made to integrate end user database directly from the field or on the spot locations through the mechanism of crowdsourcing or voluntary geographic information (VGI). The end user field database is either integrated through desktop GIS or mobile GIS (smart phones). Therefor both the internet GIS and mobile GIS application have been developed for integrating end user data through crowdsourcing or VGI using open source tools and technology. The application developed uses OGC WFS-T specification and Open layer & Geolocation APIs, Geoext, ExtJS etc. as open source tools. Keywords: Crowdsourcing, Voluntary Geographic Information (VGI), Mobile GIS, Open source, IBIN
1. Introduction India, various initiatives are being under taken by the government to share and disseminate the bio-resource The exponential growth of todays computing, data and information available with knowledge together with information and network institutions, departments, universities, NGO’s and communication technologies, are providing new individuals. The web based tools and technologies dimensions to engage the public to participate in and are very important and effective to enhance usability contribute to a myriad of scientific, business and and application of these data and information for technical challenges (Kelling et al., 2011). This decision making, planning and scientific studies. crowdsourced information is related to open Indian Bio-resources Information Network (IBIN) is innovation, co-creation or user-generated content a distributed national database infrastructure offering which should undergo through an expert curation information on diverse aspects of bio-resources of the group for validation and further integration to an country (Saran et al., 2012). IBIN has successfully authentic database. Many scientific volunteers have networked the otherwise independent databases and been observing and reporting information about information into a single window delivery system to various environmental events and real world serve research scientists, bio-resource managers, phenomenon for a long time. These volunteers play a policy makers, entrepreneurs and common man. One pivital role especially for biodiversity conservation of the core objectives of IBIN is to integrate the end strategies. There are many projects like Galaxy Zoo, user data into the IBIN database. Similar effort has eBird etc. who have demonstrated the power of been made through IBIN core spatial data node of crowdsourcing for investigating large-scale scientific IIRS, by populating with the end user data through problems on biodiversity (Kelling et al., 2011). In process of crowdsourcing or voluntary geographic © Indian Society of Geomatics Journal of Geomatics 139 Vol.7 No.2 October 2013 information (VGI). This can be achieved by using technology has taken a quantum jump from open source tools and technology with smart mainframe to the desktop personal computer (PC) to phones/mobile devices. the browser and now to the portable device. Moreover, the advantage of the internet, i.e. global 1.1 Crowdsourcing or Voluntary Geographic and real-time accessibility, ensured its potential as an Information (VGI) important medium for the dissemination of GIS functions and data (Su et al., 2000). It promoted In the recent years the concept of VGI has taken a participation of the public and customers, and quantum leap among the world community for field resulted in the increased scale and profitability of data integration (Goodchild, 2007) and it has been many GIS projects as Carver (2001). With recent considered as an alternative mechanism for advances in broadband and wireless communication acquistion and compilation of geographic information technologies as well as the dramatic increase in (Goodchild and Li, 2012). A synonym of VGI is internet technology it is promising to extend further crowdsourcing wherein the members of the public the reach and range of GIS user working in offices domain are able to create and contribute and laboratories in the field or at home would lead to georeferenced facts which specifies both spatial and the development of internet GIS or mobile GIS (Peng non spatial properties of that location. The properties and Tsou, 2003). The internet technology as a digital of that location includes “Where”, “What”, “When” communication medium enhances the capability of and “How” characteristics. The locational GIS data and software application by making them information could be either in the form of a point, a more accessible and reachable to wider range of line, an area, or a volume (Longley et al., 2011). The users, planners and decision makers. biggest advantage of using VGI/crowdsourcing is timely data integration at a very low cost but may 1.3 Open Geospatial Consortium (OGC) suffer in data quality (Goodchild and Li, 2012). specification Many efforts have been made to study the five fundamental elements of spatial data quality like Today the GIS based web portals provide a positional accuracy, attribute accuracy, logical centralized and uniform interface to access the consistency, completeness and lineage (Sidda et al., distributed and heterogeneous resources and data 2010). In addition to these five elements of spatial services (Roy et al., 2012). Most of the web GIS data quality, two more elements of temporal and based portals available on internet are designed for semantic accuracies have been added to cover all the specific theme and are targeted to specific class of aspects (Brassel et al., 1995; Goodchild and Li, users. A single GIS service may not be sufficient to 2012). Henceforth, there is an additional effort address the requirement of all kind of target users. required to improve the data quality or curation with The positive development in this emerging area is respect to both the geospatial location and its adoptions of common international standards description to maintain data integrity. published by Open Geospatial Consortium (OGC) for GIS data and services. GIS services defined by the 1.2 GIS and internet GIS OGC are part of a larger effort to build distributed systems around the principles of Service Oriented Geographic information is collected for Architectures (SOA). Such systems unify distributed geographically dispersed locations and archived, services through a message-oriented architecture. processed and maintained by numerous organizations Web service standards are a common implementation spanning multiple application objectives (Goodchild of SOA ideals (Roy et al., 2012). The most common et al., 1999). With the advent of new technologies for services for the spatial data dissemination provided spatial data access and disseminations, the entire under OGC specifications are OGC WMS (Web globe being digital and every part of earth surfaces Mapping Service) and OGC WFS (Web Feature being integrated through digital data products where Service). The OGC WMS service generates the maps the distribution and dissemination of spatial data which are available in the form of projected images assumes greater significance. Integration of geo- while OGC WFS service provides geographical spatial technologies with information and entities (or features) in the form of GML communication technology provides tremendous (geographical markup language) format which can be opportunities as well as challenges in spatial data edited and spatially analysed. The spatial data editing storage, access, analysis and dissemination. The operations and transactions on the fly are provided evolutions of Geographic Information Systems are through OGC WFS-T in addition to optional now common place within many sectors where the transaction and lockfeature operations. Journal of Geomatics 140 Vol.7 No.2 October 2013
1.4 Free and open source solutions incidents reporting and for directly mapping on the field (Brovelli and Magni, 2004). Some mobile One of the significant contributions towards the applicatons are simply read-only mode while real development of web based geospatial application is time event based mobile applications are under the umbrella of free and open source solutions transactional functionalities (create, modify and for geoinformatics (FOSS4G) and OSGeo (Open delete) which can be in synchronous or asynchronous Source Geospatial) foundation. There are many tools mode (Brovelli and Magni, 2004). available to build spatially-enabled web applications like for map serving are UMN Map server, Geoserver 2. IBIN background etc., for database management system are PostgreSQL, Post GIS etc., for web server are The Indian sub-continent is known for its diverse apache, apache tomcat etc., for data abstraction like bioclimatic regions and harbours rich flora and fauna. GDAL, OGR, PROJ4 etc. for specific mobile According to an estimate, about 30 percent of plant application MOSS4G (Mobile Open Source Software species are endemic to India. In order to inventory, for Geoinformatics) like GPS enabled application analyze, prospect and conserve the vast Indian bio- with data exchange and remote services (WFS-T) and resources, a large number of organizations are raster support. working towards generating enormous datasets. Thus, it was realized that these datasets from diverse 1.5 Mobile GIS: Field based GIS thematic specialties and from different geographic areas need to be networked in such a manner that the Mobile phones and the internet have revolutionized large variety of databases can be seamlessly made the communication and with it the lifestyle of people. accessible and at the same time will be available for Nowadays mobile GIS systems are being commonly any rational query. The developments in the used by people compared to internet GIS systems information and communication technology have since the mobile applications can run on all the made it possible to bring such information systems in mobile platforms ranging from laptops and tablet PCs one portal. Thus Indian Bio-resource Information to PDAs (Personal Digital Assistants), tabs, pocket Network (IBIN) was conceived as the single portal computers and cell phones. An increasing number of on Indian bio-resources where all the distributed mobile phones and PDA allow people to access the databases and information systems on the bio- internet where ever they are and when ever they resources and biodiversity elements are brought want. together in an easily compatible and accessible format. Field based GIS is one of the components of location based services (LBS) which are interesection of three 2.1 IBIN portal in public domain technologies i.e. Web GIS, Mobile GIS and Mobile (www.ibin.gov.in) Internet (Fig.1). Indian Bio-resource Information Network (IBIN) main portal (Fig.2) was released by Prof. M.S Swaminathan during 11th Conference of Parties to Convention of Biological Diversity (COP11-CBD) at Hyderbad on 11th Oct., 2012. IBIN is a single window gateway to access distributed bioresource database available in the country to offer spatial and non-spatial services on diverse domains of bio- resources and biodiversity. It was being developed as a distributed bioresurce national data infrastructure to serve relevant information on diverse range of issues of bioresources of the country to a range of end users. Figure 1: New information and telecommunication Its major goal is to network and promote an open technologies (Modified Brimicombe2002) ended, co-evolutionary growth among all the digital databases related to biological resources of the Presently there are many mobile applications country and to add value to the databases by available for various purposes like navigation integration. IBIN is designed to serve relevant (routing and tracking), recreation (visiting parks or information on bioresources of the country to the areas of natural, cultural interest), inventories professionals involved in bio-prospecting, marketing, characterised by a geographic component, events and protecting bio-piracy and conservation of Journal of Geomatics 141 Vol.7 No.2 October 2013 bioresources. IBIN is proposed to be uniquely placed • Core data – database that has already been as a single portal data provider on India's bioresource created by the existing major IBIN nodes - plant, animal, marine, spatial distribution and • Distributed data- the datasets that are microbial resources. contributed by its major partners (BRICs) and, • Captured data - the data contributed by the end There are two core data nodes of the IBIN portal, viz. users through crowdsourcing in the public the spatial data (Jeeva Manchitra) maintained at IIRS domain. (ISRO), Dehradun, and the species data (Jeeva Sampada) maintained at UAS, Bangalore; while the The captured data recognizes two kinds of knowledge partners of IBIN, called as Bio-resource Information base: Centres (BRICs), serve India's bio-resources data on plant, animal, microbial resources etc. along with • Curetted data - the information that is processed their spatial distribution from their respective and filtered periodically. The data is filtered and institutes/centers located in different parts of the evaluated periodically by a national curating country. IBIN facilitates value-addition to the diverse team of IBIN and uploaded on the portal. and distributed datasets by bringing them together • Raw data which is captured from the public under one platform. sources but yet to be curated.
3. Objective
IBIN spatial data node is one of the core data nodes providing access to spatial databases through web services (http://ibin.iirs.gov.in). One of the core objectives of IBIN is to integrate end user database directly from the field or on the spot locations through the mechanism of crowdsourcing or VGI. The end user field database is either integrated through desktop GIS or mobile GIS (smart phones). Therefore both the internet GIS and mobile GIS applications are being developed for integrating end user data as crowdsourcing or VGI using open source tools and technology. The application developed uses Figure 2a: IBIN Main Portal (www.ibin.gov.in) OGC specifications and Open layer APIs, Geoext, ExtJS etc. as open source tools.
4. Implementation of crowdsourcing /VGI application The VGI application has been implemented in both Internet GIS and Field Based mobile GIS (http://115.113.55.24:8081/wfst/index.html) 4.1 Process flow of field based mobile GIS The conceptual flow elaborates the processes involved in field based mobile GIS application using open source tools and technology (Fig. 3). The spatial database is stored into PostgreSQL using PostGIS for better spatial database management and performance. Figure 2b: IIRS Spatial Data Node The linkage of spatial database with application (http:/115.113.55.24:8085/ibin) server is established as application server is used either as a software framework that provides a Apart from the identified BRICs, there is a provision generalized approach to create an application-server to capture the information provided by the end users implementation environment, without regard to what as crowdsourcing/ VGI. Therefore IBIN data are the application functions are, or the server portion of served under the given three categories: a specific implementation instance. The Geoserver Journal of Geomatics 142 Vol.7 No.2 October 2013 deployed in Apache Tomcat (Web Server) is provide adding the services as Web Feature Service designed for interoperability and further PostgreSQL using OpenLayers APIs. open-source Object-Relational DBMS and PostGIS is also used for spatial database transaction using Table 1: Open source tools and technologies used associated open source technology viz Geoserver, S. Tools Purpose OpenLayers, ExtJS, GeoExt and Servlet etc. No Moreover the Mobile Position System is also customized using location based APIs. HTTP and 1. PostgreSQL Database Server for storage and WAP protocols are incorporated in the Mobile GIS 9.1 transaction management environment for transaction processing using OGC 2. PostGIS 2.0 Spatial Database Types and based Web Feature Service-Transaction (WFS-T) Gateway specification. The overall methodology is shown in Figure 3. 3. Geoserver GIS Server for interoperability 2.3.0 and publishing OGC Web Services (Web Feature Service- Transaction etc) 4. JSP 2.2 Programming environment for application server and customized database GUI for Administrator.
5. Servlet 2.5 Transaction (Image ,Video etc) on server side
6. OpenLayers Development of Geoweb 2.0 2.10 application 7. GeoLocation Mobile user positioning Figure 3: Process flow of field based mobile GIS APIs
4.2 Tools and technologies 8. GeoExt 1.0 Rich Web GIS GUI
The entire application has been developed using open 9. ExtJS 3.4 JavaScript application framework source tools and technologies. Table 1 provides the for building interactive web tools used with their respective usage. applications using techniques such as Ajax, DHTML and DOM 4.3 Implementation process scripting. 10. Web Includes tools for configuration, Geoserver is configured with Apache Tomcat (Web Server(Apach execution and management Server) for map rendering. The customization of e Tomcat 7.0) OGC based WFS-T (Web Feature Service- Transactional) has been deployed using Geoserver The entire customized application of Web feature and Apache Tomcat. A transactional Web Feature Service- Transaction were performed on 3-tier Service (WFS-T) allowed creation, deletion, architecture of client server computing environment modification and updation of features. The spatial where Geoserver was working as middleware and schema has been created using spatial database JavaScript APIs was working as thick client while (PostgreSQL/PostGIS). The spatial schema spatial database were used for data creation, storage, comprises of common data types and spatial (SDTs). transaction (atomicity, consistency, isolation, and Since the application allows the inputing of durability) and management. geolocation using mobile device, a GeoLocation based APIs were used for locating mobile user The conversion of web GIS to mobile GIS was made position in web browser environment. Apart from possible by adding OpenLayers functionalities for Geolocation API, additional available APIs of mobile device (iPhone, iPAD etc.) and performed OpenLayers, GeoExt and ExtJS were used for few alterations in Cascading Style Sheet (CSS) of developing rich Web GIS and Mobile GIS Graphical OpenLayers APIs. The inputing of User Interface (GUI). In order to publish the spatial crowdsourced/VGI raw species data was made schema as a service using Geoserver and further possible through a customized GUI developed by Journal of Geomatics 143 Vol.7 No.2 October 2013 using OpenLayers, GeoExt, ExtJS and GeoLocation The curation team will further access the raw species APIs and the input data in the format of string, input data through GUI of database administrator on number, geographic location (in place of geometry web browser environment. Presently the user can because geometry can be interpreted as point, line, input the following species information i.e. species polygon database creation and uploading to server category, species name, local name, geographic and present application is probably not envisaging coordinates (inputing manually Latitude and this aspect), image and video. The GUI captures Longitude or automatically through mobile with GPS location of both Latitude (DMS) and Longitude enabling), photograph and field video as shown in (DMS) automatically or provision of inputing Figure 5. through GPS system was also made possible. The Geoserver provided the transaction functionality only The administrator GUI is customized using JSP for string and geometry, however it did not provided programming. The centralized spatial database the facility for image (JPEG, IMG etc) transaction repository of the end user raw data stored in the and video transaction. Nevertheless it can be made server is automatically updated whenever mobile possible by converting Geoserver to Image Server users update it. However the curation team has rights and do some needful customization in Java and priveliges of database administrator to validate, environment. Henceforth for image transaction the update and even delete the raw input species data as entire application was customized in Java (Servlet) per decision of the curation team (Fig. 6). environment for both uploading image and video on server side form as client application and enabled to add only the image name as a string in spatial database management system using one to one relationship (Fig. 4).
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1 !( Figure 6: Web enabled remote adminsitration for data '( '( curation 10 10 . / * , ,0 / . / * , ,0 / 5. Conclusions The field based mobile GIS application developed has been tested on Windows, ipad, iphone etc. platforms and devices. The mobile application is 1 1 1 _ 1 _ operational from IBIN spatial data node of IIRS. This Figure 4: Implementation methodology flow crowdsourced voluntary geographic information utility will also be available under IBIN main Portal for integrating species field data under various categories. The mobile application provides inputing, updation and deletion of field data transaction through the mobile devices by using OGC WFS-T specification.
This mobile application is dynamic in nature and is compatible with Web 2.0. It is also capable to integrate and consume freely available OGC WMS Input Field Data Uploading Photos Playing Video services or KML which can be directly overlaid on & Video on Mobile top of the existing web application. The user also has Figure 5: Field based Mobile GIS for inputting field a provision to input the field data through desktop raw data using Web GIS application. Journal of Geomatics 144 Vol.7 No.2 October 2013
Both internet and mobile GIS applications have been Goodchild, M.F. and L. Li (2012). Assuring the developed using open source tools and technologies. quality of volunteered geographic information. Apart from the open source tools, various APIs like Spatial Statistics, 1(2012) 110-120. Open Layer APIs, Geolocation APIs etc. which are freely available are consumed inorder to consume the Kelling, S., J. Gerbracht, D. Fink, C. Lagoze, W.K. services which have been integrated into web Wong, J. Yu, T. Damoulas and C. Gomes (2011). application. The rich Web GIS GUI were developed eBird: A Human/Computer Learning Network for using GeoEXT and ExtJS for building interaction Biodiversity Conservation and Research. Proceedings web applications using techniques such as Ajax, of the Twenty-Fourth Innovative Appications of DHTML and DOM scripting. The interoperability Artificial Intelligence ConferenceSponsored by the between the spatial datasets was achieved through Association for the Advancement of Artificial OGC WMS/WFS/WFS-T services. In future this Intelligence (Daniel Shapiro and Markus Fromherz), mobile based field GIS application will be further San Francisco, California, USA. Published by The enhanced by integrating basic spatial analysis tools in AAAI Press, Menlo Park, California, August 9 – 11, the form of OGC WPS (Web Processing Services) 2011, pp. 2229-2236. for the decision makers and planners to develop planning and conservation at the field level. Longley, P.A., M.F. Goodchild, D.J. Maguire and D.W. Rhind (2011). Geographic information systems References and science. 3rd Ed. Wiley, Hoboken, NJ. Brassel, K.F., E. Bucher, E. Stephan and A. Vckovski (1995). Completeness. In: Elements of Spatial Data Peng, Z.R., and M.H. Tsou (2003). Internet GIS: Quality by Guptill, S.C., Morrison, J.L. (Eds.). Distributed geographic information services for the Elsevier, pp. 81–108. internet and wireless networks. March 2003 (ISBN: 0-471-35923-8m). Brimicombe, A.J. (2002). GIS - Where are the frontiers now?. In: Proceedings GIS 2002, Bahrain, Roy, P.S., H. Karnatak and S. Saran (2012). pp. 33-45. Geospatial data processing in distributed computing environment. CSI Communication, 6, 7-9. Brovelli, M.A. and D. Magni (2004). Open source mobile GIS solutions for different application fields. Saran, S., S.P.S Kushwaha, K.N Ganeshaiah, P.S. Proceeding of the ISPRS International Archives of Roy and Y.V.N. Krishna Murthy (2012). Indian the Photogrammetry, Remote Sensing and Spatial Bioresource Information Network (IBIN): A Information Sciences, Vol. 34, Part XXX distributed national bioresource portal. ISG (www.isprs.org - /proceedings/XXXVI/5- Newsletter, 18(3), 14-19. C55/papers/magni_diego-1.pdf). Sidda, N., S.Saran, I. Ivanova, P.L.N. Raju and V.K. Carver, S. (2001). Public participation using web- Dadhwal (2010). A framework for the management based GIS. Environmental and Planning B: Planning of the spatial data quality information. Journal of and Design, 28 803–804. Geomatics, 4(2), 69-76.
Goodchild, M.F., M.J. Egenhofer, R. Fegeas and C. Su, Y., J. Slottow and A. Mozes (2000). Distributing Kottman (1999). Interoperating geographic proprietary geographic data on the World Wide information system (Norwell, MA: Springer). Web—UCLA GIS database and map server. Computers & Geosciences, 26, 741–749. Goodchild, M.F. (2007). Citizens as sensors: the world of volunteerd geography, Geo. Journal 69(4), 211-221. Journal of Geomatics 145 Vol.7 No.2 October 2013
Prioritisation of sub-watersheds: A case study of Dohan and Krishnawati rivers in Mahendergarh, Haryana
GulshanMehraandRajeshwari Department of Geography, Kurukshetra University, Kurukshetra, Haryana, India Email: [email protected]
(Received: January 24, 2013; in final form August 18, 2013)
Abstract: Delineation of watersheds within a large drainage basin and their prioritisation within administrative boundary is required for proper planning and execution of plan for management of natural resources and sustainable development. In the present paper, detailed characteristics of two sub watersheds of Krishnawati and Dohan river falling in Mahendergarh district in Haryana are studied and their mini-watersheds are prioritised for sustainable development. The parameters for prioritisation were taken from the theme layers of hydrogeomorphology, landuse/landcover, slope, soil, underground water prospects, drainage density and rainfall distribution. The terrain information for these layers was obtained from geocoded satellite data and their corresponponding toposheet maps on 1:25,000 scale. Groundwater prospects and rainfall data were obtained from secondary sources. In order to measure the high priority areas, weightages have been assigned to 7 parameters, using Saaty’s analytical hierarchy process in both the sub-watersheds of Dohan and Krishnawati rivers. A composite picture of priority areas of both the sub-watersheds are presented for conservation and better management of natural resources in the area.
Keywords: Micro-watershed, prioritisation, Remote sensing, Geographical information system, Analytical hierarchy process, Sub-watershed
1. Introduction levels of hierarchy using empirical formula on the basis of sediment yield index and annual erosion losses (GOI, A watershed is a natural hydrogeological entity which 1990). In case of arid and semi areas, however, it is allows surface runoff to a defined channel, drain, stream necessary to conserve and develop resources in the or river at a particular point (Chopra et al., 2005). watersheds of the size ranging between 500 ha and 1000 Watershed is considered as an ideal unit for ha (NWDPRA, 1991; IMSD, 1995; Joshi et al., 2008). management and sustainable development of its natural It has been documented that the size of 500 ha of micro- resources. Watershed management is the process of watershed is a functional watershed development unit carrying out a course of action to achieve specified (Rajora, 1998; GOI, 2003). Literature suggests that the objectives. It is also a well-known fact that regional development of micro-watersheds which are lying planning and its implementation in our country is within a district boundary may be suitable unit of largely based on administrative divisions rather than on development due to easy and effective implementation natural divisions. Though the importance of natural within a reasonably short period and which may have divisions based watershed approach cannot be denied demonstration effect. and land resource development programmes are applied on a watershed basis, even then the implementation is In this context, in the present paper an attempt was always carried out by individual administrative units. made to study the major watershed characteristics and Hence, delineation of watersheds within a large its prioritisation for further augmentation of resources drainage basin and overlapping district/administration for sustainable development in one administrative unit boundaries is equally important (Mehra and Rajeshwari, of Haryana comprising two sub-watersheds (sws) of 2012). Further their prioritisation is also required for Dohan and Krishnawati rivers. proper planning and management of natural resources for sustainable development. The prioritisation of 2. Objectives watersheds is generally for proper management in the most vulnerable parts of the watershed, which has been The objectives of the present study were attempted by number of scientists in various areas (NWDPRA, 1991; Adinarayana et al., 1995; Rajora, i) to study a detailed watershed characteristics of 1998; Kumar and Kumar, 2011). Krishnawati and Dohan sub-watersheds of In high altitude areas, watersheds are prioritised Mahendergarh district in terms size, drainage, soil, applying sediment yield models. Literature reveals that terrain, hydrogeomorphology, landuse and ground a number of sediment yield models, both empirical and water prospects. conceptual, are in practice to address wide ranging soil and water management problems (Shinde et al., 2012). ii) to study and highlight the high priority micro- It may be noted that All India Soil and Land Use Survey watersheds (based on 7 major themes) in both sub- provides large scale watershed boundaries at various watersheds of the region for better management. © Indian Society of Geomatics Journal of Geomatics 146 Vol.7 No.2 October 2013
3. Study area and its characteristics been classified into 25 micro-watersheds (Mehra and Rajeshwari, 2012). These rivers after entering the Haryana state enjoys two river basins namely Ghaghar district gradually shrink and loose water at high rate of and Yamuna basins. Mahendergarh district, situated in evaporation and excessive percolation in sandy south-western part of Haryana, has a geographical area material. The rivers are active only during the rainy of 1927.72 km2. The district is part of Yamuna basin, as season which raise the fresh quality sub soil water. The reported in Soil and Landuse Survey (Watershed atlas seasonal flow in Dohan and Krishnawati periodically of India, 1988) and this district comprises two sws of raises the level of fresh quality subsoil water. Besides, it two seasonal rivers i.e. Krishnawati and Dohan Rivers. also helps base flow during early part of the dry season. Figure 1 presents the shape of these two sws falling in Now since Rajasthan has made a number of dams in the Mahendergarh district. The climatic condition in the upstream of the river, as a result there is no appreciable district varies from arid to semi-arid. The summer water flow in these rivers even in rainy season (District months are very hot whereas, winter season is fairly Gazetteer, Mahendergarh, 1988). cool and dry. The average annual rainfall of the district is 592.5mm. About 75 percent of annual rainfall is received during the south west monsoon in the months of June, July, August and September. The land tract of the district dealt with an Indo Gangetic alluvial plain marked vast stretch of flat land. Upland tract areas are situated between Mahendergarh, Narnaul and Nangal Chaudhary hills in Mahendergarh and Narnaul tehsils. The highly dissected upland situated between 284m and 302m above mean sea level belong to Aravalli system. Rocky outcrops traverse through most part of the district in roughly southwest to northeast direction. The hills are longer than its width, forming roughly parallel series of ridges. Mobile sand dunes also occur at a few places in south and southwest of Mahendergarh town. Stabilized sand dunes are most significant and largely confined to Mahendergarh and Satnali area. It is dominated by dry lands with presence of inland streams, sandy plains, shifting sand dunes, stabilized sand dunes, dissected upland tracks and often barren, denuded, rocky hill ranges and their outcrops. Overall relief is undulating with a regional slope (Chaudhary and Sinha, 2003). The soil depth and texture varies from place to place. In the plains, the soil is deep but shallow on hill slops. The soils texture varies from sandy loam to clay loam in plains and sandy loam to sandy on the hills (District Gazetteer, Mahendergarh, 1988).
Dohan and Krishnawati streams of the district make Figure 1: Location map of study area irregular flood plains, which are ephemeral. The flood plain occurs in association with sandy terrain and dunes 4. Data sources and methodology to variable morphology. Dohan orginates from Jaipur hills about 6 km short of Nim ka thana (Rajasthan) and Realizing the importance of a comprehensive and flows 29 km in Rajasthan territory before entering the integrated approach to the problem, various data from Mahendergarh Tehsil. Dohan is an important source of different sources were obtained. Broadly two types of drinking water for the areas of the Namaul and data were used. The first is Survey of India (SOI) Mahendragarh tehsils. It runs a length of about 50 km in topographical maps and another is satellite data. In the district (District Gazetteer, Mahendergarh, 1988). addition to this, ground water level data obtained from The total area of Dohan seasonal river which is draining Ground water Cell of Mahendergarh district for 83 Mahendergarh district is 732.31 km2. This has been wells were used. The drainage was derived from SOI classified into 15 micro-watersheds (Mehra and toposheet and later masking it with IRS-LISS IV FCC Rajeshwari, 2012). Krishnawati originates about 1.6 km of 2008. The slope map was prepared by creating digital south east of Nim ka thana in Jaipur hills (Rajasthan). elevation model (DEM) from contours. The micro- Flowing in northerly direction it enters Narnaul Tehsil watershed boundaries were demarcated on the basis of near Bhadanti and Dostpur, about 25 km south of contour values, slope, relief and drainage flow. The Narnaul town. The stream has a course of about 49 km geomorphological features were obtained from IRS- which terminates near Dahina village at the northern LISS IV FCC of 2008. The geology map of the district boundary of the Rewari Tehsil of Rewari district. The along with SOI toposheet were used for corroboration total area of Krishnawati seasonal river which is of Google imageries. The soil map was taken from draining Mahendergarh district is 1195.49 km2. This has Haryana Space Applications Centre, Hisar and the same Journal of Geomatics 147 Vol.7 No.2 October 2013 classification of soil types is being used as presented by HARSAC, Hisar. Landuse/landcover categories were identified using LISS IV and PAN merged data for the year 2008. The 5 year average from 2005 to 2009 of rainfall data for 16 stations were mapped.
In order to prioritise areas for conservation and better resource management, seven themes/layers namely hydrogeomorphology, landuse/cover, slope, soil, underground water table, drainage density and rainfall distribution were taken into account. For ranking of areas with high and low priority, Saaty’s analytic hierarchy process was used (Saaty, 1980).
Table 1: A comprehensive detail of its methodology Parameters Data sources Factor/Priority
Hydrogeomorphology Satellite data of IRS 1D-LISS The more vulnerable terrain, IV and PAN merged (Year more the priority. 2008). Slope 23 topographic sheets of More the sloppiness, more the Survey of India on the scale of priority. 1:25,000. Landuse/cover Satellite data of IRS 1D-LISS Parameter of wasteland has IV and PAN merged (Year been considered. More the 2008). wasteland, more the priority. Soil Derived from Haryana space More the soil depth, less the applications centre priority. (HARSAC). Drainage density 23 topographic sheets of Higher the drainage density, Survey of India (1:25,000). more the priority. Underground water 83 hydrograph station’s data More the water depth, higher depth are used. the priority. Rainfall 16 location’s data has been Less the rainfall, higher the used. priority. Figure 2: Hydrogeomorphology map of study area
5. Results and discussion Landuse/Land cover: The landuse affects rates of runoff, infiltration and types and quality of vegetation. Suitable 5.1 Characteristics of Dohan and Krishnawati sws landuse minimizes the soil erosion and reduces the runoff. The landuse/landcover mapping of the Hydrogeomorphology : Hydrogeomorphology generally watershed was carried out by standard visual describes the subsurface hydrological characteristics of interpretation techniques. Satellite data of IRS 1D-LISS a region based on its geological and geomorphological IV and PAN (merged) for year 2008 was used for aspects (NRDMS, 2004). The terrain influences the studying the landuse/landcover pattern. Eight broad surface water hydrology by modelling the movement of land use categories were identified in both the sws water over the land surface. In the present paper, (Figure 3). These are (i) agricultural land, (ii) different hydrogeomorphological units of both sws were barren/rocky, (iii) forest, (iv) open scrub, (v) river categorized into 9 classes. For this, IRS LISS IV FCC course/channel (vi) sand dunes, (vii) settlements or image was visually interpreted for delineation of built-up area and (viii) other water bodies i.e. dry and physiographic units of the sws. Different image fill ponds. elements such as colour, texture, pattern, association were considered to identify and delineate both Since the study area represents a typical rain-fed physiographic units and hydrogeomorphology. These characteristic, agriculture is the primary landuse activity were corroborated with the hydrogeomorphological for livelihood. Table 2 reveals that agriculture land categories as delineated by NRDMS (NRDMS, 2004). occupies a total of 1547.62 km2 area accounting for Ancillary data from SOI toposheets and other secondary 81.51 and 80.36 percent of Dohan and Krishnawati sources were utilised to delineate these features. The river sws respectively. The uncultivated area, which detailed features of both the sws are presented as Figure largely consists of barren or rocky land is quite 2. Majority of the area in the sample study region is considerable i.e. 6.3 and 4.4 percent of the Dohan and under eolian plain in both the sws i.e. 85.05 percent in Krishnawati sws respectively (Table 2). In both sws, Dohan and 87.74 percent in Krishnawati sws. Table 2 settlements or built-up area accounts for about 3 to 3.5 presents a detail account of these units. It shows that percent of total geographical area. It may also be noted sand dunes account for around 6 percent area in that sand dunes occupy 6 percent area in Krishnawati Krishnawati sub-watershed and for about 3.2 percent in sub-watershed, while it is 2 percent in Dohan sub- Dohan. Similarly pediment and rocky outcrops (i.e. watershed. Forest cover is relatively high in Dohan sub- structural, denudational and residual hills) account for 5 watershed (4 percent), as compared to Krishnawati sub- percent and 7 percent of total area in respective sws. watershed, where it is 1.16 percent.
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total geographical area is under agriculture. Remaining are habitation, road, hillocks, degraded land, water bodied, forest land, etc. The soil type is sandy loam and it covers a 73.38 and 89.98 percent of sample study area, accounting for Dohan and Krishnawati sws respectively (Table 2). The underdeveloped soils (Lithic Ustorthents and loamy skeletal) occur on the hilly region with steep slopes and on undulating lands. Fine- grained eolian sand (Typic torripsamments) is found in west of Krishnawati sub-watershed. Dohan river sub- watershed have much more sandy soil as compare to Krishnawati river sub-watershed. The well developed very deep coarse loamy soils (Typic Udipsamment and typic haplustepts) are situated generally on nearly level sloping areas of both the sws (Figure 4).
Slope: Slope and aspect of a region are vital parameters in deciding suitable land use, as the degree and direction of the slope decide the land use that it can support. Slope is also very important while determining the land irrigability and land capability classification and has direct bearing on runoff (GOI, 2008). The degree of slope sets limits on land use for annual crops, plantation and even on land reclamation, depending on soil depth, stoniness etc (Tideman, 1996).
Figure 3: Landuse/landcover map of study area
Figure 5: Slope map of study area
Figure 4: Soil map of study area In the present paper, slope analysis was carried out with the help of 1:25,000 scale SOI toposheet (10 m contour Soil: The soil of an area determines the infiltration of interval). After following the standard procedure for water, percolation of water, runoff and soil erosion. Soil calculating the slope degree, slope has been categorized types affect the productivity and production of as gently sloping (less than5 degree), moderately agricultural, horticultural, forest lands and grasslands. sloping (5-10 degree), strongly sloping (10-15 degree) The soil of the watershed also determines the amount of and moderately steep to steep sloping (more than 15 water percolation and correction measures needed. As degree). The detail slope map of study area depicting revealed by landuse pattern, more than 80 percent of slope of the micro-watersheds is presented as Figure 5. Journal of Geomatics 149 Vol.7 No.2 October 2013
Overall about 93.28 percent area is of gentle slope density is 5.34 and 1.74 percent in Dohan and accounting 91.93 and 94.64 percent in Dohan and Krishnawati sws respectively. Krishnawati sws respectively. The category strongly sloping and moderately steep to steep sloping covers 4.21 percent area of the total geographical area accounting 5.21 and 3.22 percent in Dohan and Krishnawati sws respectively. It also reveals a highly dissected upland situated between 284 m and 650 m above mean sea level belong to Aravalli system and spread from south to north direction (District Gazetteer, Mahendergarh, 1988).
Figure 7: Underground water table depth map of study area
Underground water depth: The amount of water quality, quantity and regime of the underground water determines the behavior of watershed. The declining rate of ground water Table affects recharge adversely. In Mahendergarh district, there is acute shortage of water. The district has experienced prolonged period of Figure 6: Drainage density map of study area aridity. The area under water bodies have been Drainage density: Drainage density has direct declining drastically in the district (Chaudhary and relationship with erodibility. High drainage density Sinha, 2003). A detail characteristic of underground watershed drains runoff water rapidly. The coarser the water Table is presented in Figure 7. Total 83 drainage texture, the higher the conductivity (Tideman, hydrograph stations data with their geographic 1996). Drainage density, which is characterized by the coordinates were obtained to prepare well location map. average length of streams per unit area (Adinarayana et For underground water depth map, underground depth al., 1995). In the term of watershed, Mahendergarh values at these locations were attached with well district is unique in the sense that it is a dry land which location map in form of attribute Table. After that has presence of inland seasonal streams. Both the spatial distribution maps were generated by carrying out Dohan and Krishnawati rivers, and their tributaries, are point interpolations using moving average (inverse non- perennial in nature (Figure 6). Drainage density distance) method. The spatial variation of groundwater was calculated in the sample study. Figure 6 shows depth were classified into 25-45, 45-65, 65-85 and drainage density ranging from nil to low, medium and below 85 m ranges. The underground water table of high drainage density categories. Generally, the Krishnawati sub-watershed is deeper than Dohan river drainage density were higher in the hilly terrain, sub-watershed. In 2010, the underground depth in followed by pediments in both the watersheds. In Dohan sub-watershed was in the range of 65-85 meters. Dohan sub-watershed, many streams in north of sub- It occupies a 271.66 km² which is 37.10 percent of its watershed lose their water in dry land without joining total geographical area. It may also be noted that 28.45 Dohan river and in Krishnawati sub-watershed most of km² area is having worst underground depth. The water the tributaries flow towards north-east and finally join table tends to be close to the surface (25 to 45 meters) the Krishnawati river in several parts. Overall about in Krishnawati sub-watershed and covered 72.19 589.37 km² area is of less (0.000001-0.002336) to high percent of the total area (Table 2). (0.004547-0.009099) category accounting 27.90 and 29.95 percent in Dohan and Krishnawati sws Rainfall distribution: The 5 year average of rainfall respectively (Table 2). Area under high drainage data for 16 stations has been mapped, with the Journal of Geomatics 150 Vol.7 No.2 October 2013 interpolated isohytes, spatial distribution of rainfall was slope parameter, the areas with gentle slope were given generated. It was overlaid with boundaries of both sws low priority and with very steep slope were considered and resultant distribution is shown in Figure 8. It for prioritisation. Soil mapping was carried out using reveals that the average annual rainfall varies from 350 HARSAC map as discussed in earlier section. The map to 710 mm. Rainfall distribution has been classified into area depicting deep soils with sandy clay and which are four zones: (i) less than 350 mm, (ii) 350-450 mm, (iii) moderately deep and well drained are considered for 450-550 mm, and (iv) more than 550 mm. The spatial prioritisation. In case of landuse/landcover, high forest pattern shows that it decreases from south to northward cover was given less values. While high waste land and in both of sws. The highest rainfall occurs in south-west sand dunes were ranked high and considered for of Krishnawati sub-watershed and covers almost 26.5 priority. Similarly ground water prospects was ranked, percent of the total area (Table 2). where high rank was assigned to areas where its prospects were low in term of deep water table. The The low rainfall area of this sws accounts for 24.26 drainage pattern of any terrain reflects the percent of its total geographical area. In case of Dohan characteristics of surface as well as subsurface sub-watershed, only 2.79 percent area receives scanty information. Its density (in terms of km/km²) indicates amount of rainfall. the closeness of spacing of channels. It characteristics the run-off in the area. Hence, lesser the drainage density, higher is the probability of recharge or potential groundwater zone (Vittala et al, 2008). In case of drainage density layer, higher density (4.547-9.099) was given highest value. Similarly, the highest rainfall region was given low priority. Applying the above said ranking values, a composite picture was obtained which is presented in Figure 9. This shows composite picture of vulnerable area in the district comprising of both sws. A segregated picture of both river sws is presented in Table 3.
Table 2: Watershed Characteristics of Dohan and Krishnawati rivers. Dohan sub-watershed Krishnawati sub-watershed Hydrogeomorphology Total area in Percent Total area in Percent Km² area Km² area Alluvial Plane (Younger) 21.66 2.96 10.17 0.85 Sand Dune 23.43 3.20 68.65 5.74 Eolian Plain 622.84 85.05 1048.94 87.74 Gullies 2.93 0.40 1.68 0.14 Pediment 29.94 4.09 30.91 2.58 Valley Fill 8.66 1.18 4.14 0.35 Residual Hill 0.40 0.05 6.44 0.54 Denudational Hill 2.11 0.29 7.86 0.66 Structural Hill 20.67 2.82 16.77 1.40 Total Geographical area 732.31 100.00 1195.49 100.00 Drainage density in Km/Km² Nil 527.97 72.10 837.46 70.05 Less (0.000001-0.002336) 132.63 18.11 279.36 23.37 Moderate (0.002336-0.004547) 32.61 4.45 57.86 4.84 High (0.004547-0.009099) 39.10 5.34 20.81 1.74 Total Geographical area 732.31 100.00 1195.49 100.00 Slope in degree <5 Gently sloping 673.19 91.93 1131.43 94.64 5-10 Moderately sloping 20.91 2.85 25.29 2.12 10-15 Strongly sloping 12.02 1.64 15.23 1.27 >15 Moderately steep to steep sloping 26.16 3.57 23.38 1.95 Figure 8: Rainfall distribution map of study area Total Geographical area 732.31 100.00 1195.49 100.00 Landuse/cover Agriculture Land 596.89 81.51 960.73 80.36 Barren/Rocky 45.71 6.24 52.25 4.37 5.2 Prioritisation of area for conservation of Forest 28.92 3.95 13.92 1.16 Open Scrub 14.83 2.03 42.31 3.54 resources and sustainable development: River Course/channel 11.71 1.60 9.22 0.77 Sand Dune 13.21 1.80 71.91 6.01 Settlement/builtup 20.68 2.82 43.33 3.62 Other water Bodies 0.36 0.05 1.82 0.15 In this section, a composite picture of prioritised area of Total Geographical area 732.31 100.00 1195.49 100.00 Soil types both Dohan and Krishnawati rivers sws is presented. Loamy 16.37 2.24 318.53 26.64 Fine loamy 88.91 12.14 475.17 39.75 The basic premise is to identify and give priority to Coarse loamy 432.05 59.00 282.04 23.59 Sandy soil 140.56 19.19 27.23 2.28 those areas which are vulnerable and where intervention Rock out crop soil 25.97 3.55 33.47 2.80 Habitation mask 13.80 1.88 54.90 4.59 is needed on urgent basis for management of natural Water body mask 14.65 2.00 4.15 0.35 Total Geographical area 732.31 100.00 1195.49 100.00 resources and development. In order to prioritise such Underground water depth in mts). areas, seven theme based layers were used. These are 25-45 175.18 23.92 863.01 72.19 45-65 257.01 35.10 309.89 25.92 hydrogeomorphology, landuse/landcover, slope, soil, 65-85 271.66 37.10 22.55 1.89 Below 85 28.45 3.89 0.00 0.00 underground water table, drainage density and rainfall. Total 732.31 100.00 1195.49 100.00 Rainfall distribution in mm. Saaty’s analytic hierarchy classification was used for Below 350 20.42 2.79 290.08 24.26 350-450 395.59 54.02 215.81 18.05 weights and to measure the area (Saaty, 1980). The 450-550 158.88 21.69 373.10 31.21 Above 550 157.43 21.51 316.49 26.47 more vulnerable themes were given higher weightage or Total 732.31 100.00 1195.49 100.00 rank and are considered as higher priority. In case of Journal of Geomatics 151 Vol.7 No.2 October 2013
Table 3: Composite profile of prioritised area. References Priority categories Dohan sub-watershed Krishnawati sub-watershed Total area in Percent Total area in Percent Adinarayana, J., N. Rama Krishna and K. Gopal Rao Km² area Km² area Low priority 665.07 90.82 1130.96 94.60 (1995). An integrated approach for prioritisation of Moderate priority 28.05 3.83 33.43 2.80 watersheds. Journal of Environmental Management, 44, High priority 22.14 3.02 22.02 1.84 375-384. Very high priority 17.06 2.33 9.074 0.76 Total 732.32 100.00 1195.49 100.00 All India Soil and Land Use Survey (1990). Watershed atlas of India. Department of Agriculture and Both Figure 9 and Table 3 show that in Dohan sws, Corporation, Government of India, IARI Campus, New about 40 km2 area accounting for about 5.4 percent of Delhi, Plate Number 10, 14. its total area falls in high priority. In case of Krishnawati sws, about 31 km2 area accounting for 2.5 Chaudhary, B. S. and A. K. Sinha (2003). Study on land percent of total has come in the category of urgent use/ land cover evolution in southern part of Haryana, attention. Overall it account for 70 km2 of the study India using remote sensing and GIS. XII World Forestry area. Hence the study highlights that this area can be Conference, Quebec City, Canada. taken up for sustainable development and management of resources with immediate effect. Chopra, R., R. Dhiman and P.K. Sharma (2005). Morphometric analysis of sws in Gurdaspur district, Punjab using remote sensing and GIS technique. Journal of the Indian Society of Remote Sensing, 33(4), 531-539.
Department of Land Resources (DOLR) (2003). Guidelines for hariyali. Ministry of Rural Development, Government of India, New Delhi, India.
Government of India (1991). District gazetteer of Mahendergarh, Haryana. Government of Haryana, pp. 20-66.
Government of India (1991). WARASA guidelines- National Watershed Development Project for Rainfed Area (NWDPRA). Ministry of Agriculture, New Delhi. pp. 1-119.
Government of India (2008). Common guidelines for watershed development project, GOI, New Delhi. Detailed project report of all micro-watershed of IWMP-III, J.P. Nagar. pp 1-124.
Haryana Space Applications Centre (HARSAC), CCS HAU Campus, Hisar, Haryana.
Joshi, P.K., A.K. Jha, P. Wani Suhas, T.K. Sreedevi and Figure 9: Prioritisation of sub-watersheds F.A. Shaheen (2008). Impact of watershed program and conditions for success: A meta-analysis approach. 6. Conclusion Global Theme on Agroecosystems, Report no. 46, Patancheru 502-324, Andhra Pradesh, India, Characterization and analysis of watershed is International Crops Research Institute for the Semi-Arid prerequisite for management of natural resources in any Tropics, pp. 1-18. area. The present paper studied the characteristics of Dohan and Krishnawati sws falling in one Kumar, B. and U. Kumar (2011). Micro watershed administrative unit of Haryana i.e. Mahendergarh characterization and prioritization using geomatics district. It presented a detailed analysis of technology for natural resources management. hydrogeomorphology, soil, drainage density, landuse, International Journal of Geomatics and Geosciences, 1 groundwater prospects and rainfall distribution of both (4), 789-802. sws. In order to highlight the area for planned action and implement, it prioritised the two sws taking seven Mehra, G. and Rajeshwari (2012). GIS based parameters of above said themes. Overall the paper delineation of micro-watershed and its applications: suggests 70 km2 area as high priority area. The high 2 Mahendergarh District, Haryana. International Journal priority area for Dohan sws is 39.20 km and for of Human Ecology, 38(2), 155-164. Krishnawati sws is 31.76 km2. Journal of Geomatics 152 Vol.7 No.2 October 2013
Shinde, V., K.N. Tiwari and M. Singh (2012). National Remote Sensing Agency (1995). Integrated Prioritisation of micro-watersheds on the basis of soil Mission for Sustainable Development (IMSD) technical erosion hazard using remote sensing and geographic guidelines. Dept. of Space, Govt. of India, Balanagar, information system. International Journal of Water Hyderabad, pp. 1-127. Resources and Environmental Engineering, 2(5), 130– 136. National Watershed Development Programme for Rain fed Areas (NWDPRA) (1990-91). Guidelines for Tideman, E.M. (1996). Watershed management watershed development projects issued by the National guidelines for Indian conditions. Omega scientific Rainfed Area Authority (NRAA). Department of publishers, New Delhi, pp. 11. Agriculture and Cooperation, Government of India. Vittala, S. S., S. Govindaiah and H. H. Gowda (2008). Rajora, R. (1998). Integrated watershed management. Prioritization of sub-watersheds for sustainable Rawat Publications, Jaipur, pp. 27-43. development and management of natural resources: An integrated approach using remote sensing, GIS and Saaty, T. L. (1980). The analytic hierarchy process. socio-economic data. Current Science, Volume 95, No. McGraw Hill International, New York. 3, 345-354. Journal of Geomatics 153 Vol.7 No.2 October 2013
Effect analysis of GPS observation type and duration on convergence behavior of static PPP
AshrafFarah College of Engineering, Aswan University, Egypt Email: [email protected]
(Received: July 30, 2013; in final form September 17, 2013)
Abstract: Precise Point Positioning (PPP) has been used for the last decade as a cost-effective alternative for the ordinary Differential GPS with an estimated precision sufficient for many applications. PPP requires collecting observations at the unknown station and correcting them for different types of errors using proper models. PPP precision varies based on observation type (single or dual frequency) and the duration of observations among other factors. This research presents an evaluation study for the variability of Static PPP precision based on different GPS observation types and duration.
Keywords: Static PPP, Observation type, Observation duration
1. Introduction offered by different organizations such as the IGS (International GNSS Service). IGS has been providing Global Positioning System (GPS) is a satellite-based the most precise satellite ephemeries and clock system for navigation and positioning. It has become corrections currently available (IGS, 2013). To the backbone of many aspects of our lives through compensate for ionospheric effect (the largest source applications, such as vehicle navigation, recreation, of error for GPS observations), dual frequency marine navigation, airborne navigation, time transfer, measurements are used. In the case of single frequency rescues, mapping, and missile guidance. observations, some kind of ionosphere modeling has to be applied. For better accuracy, PPP users are advised GPS technology offers different positioning techniques with dual frequency measurements as it is the most varying in cost and accuracy. The simple technique of efficient way of mitigating ionospheric delay. positioning is called “autonomous positioning” which is the most flexible positioning form and is the original The PPP convergence period defined as the duration of positioning technique that GPS was designed for. time required from a cold start to a decimeter-level However, because of the errors caused by satellite positional solution is typically about 30 minutes under ephemeries, satellite clock, ionosphere, troposphere, normal conditions and significantly longer for multipath and noise, the autonomous point positioning converging to the few centimeter level (Bisnath and provides the user with a horizontal accuracy 13 m Gao, 2008). PPP accuracy improves with the length of and a vertical accuracy 22 m (GPS-SPS, 2008). the data collection period. A minimum period of good quality GPS data (no loss-of-lock) is required to permit To obtain higher accuracy down to the centimeter level convergence and/or resolving ambiguities, which in , the user needs to mitigate the aforementioned errors turn can improve the accuracy of the entire dataset. by using the spatial correlation between one or more The minimum period and the accuracy attainable will reference stations with known coordinates and the depend on the type of GPS equipment, the site nearby rover GPS receiver station whose coordinates (multipath, obstructions) and atmospheric conditions. are to be determined. The concept behind this spatial Extending the data collection past this minimum period correlation is that nearby GPS receivers observe all should further improve accuracy, but more so with errors equally except multipath errors. The GPS dual-frequency receivers than with single frequency positioning technique that uses the concept of spatial receivers (Yves Mireault et al., 2008). The duration of correlation is known as Differential GPS (DGPS) collected observations should be decided according to (Abdel Salam, 2005). The limitations for DGPS are; the accuracy required. the need for a reference station, the distance limitation ( 20 km) between the rover and reference station, and This research presents an evaluation study for Static the need for simultaneous observations between the PPP precision variation based on different types of reference and rover stations, which increases the cost observations (single and dual frequency) as well as of DGPS over autonomous positioning. different lengths of observation duration. An observation set of 3h 52 min. was collected on one The PPP technique (Zumberge et. al., 1997) aims at station (GPS day 17191) with Topcon GR-3 dual correcting the observations errors and overcoming the frequency receiver (Topcon GR-3, 2013) using 15 sec DGPS limitations. PPP is an enhanced single point observation interval and 10o cut-off elevation angle. positioning technique for code or phase measurements PPP-solutions were estimated, using two types of using precise orbits and clocks instead of broadcast observations namely single frequency L1 and dual data. PPP became viable with the existence of the frequencyL1/L2 observations. Each solution contains extremely precise ephemeries and clock corrections, different lengths of observation duration. The two sets
© Indian Society of Geomatics Journal of Geomatics 154 Vol.7 No.2 October 2013 of observations were processed and the PPP solutions not require input of an external source of ionospheric were estimated through Canadian Spatial Reference information. System (CSRS) Precise Point Positioning (PPP) service (CSRS-PPP, 2013). 3. Test study
2. CSRS- PPP (CSRS, 2013) To assess PPP precision variation based on different types of observations (single and dual frequency) as The Canadian Spatial Reference System (CSRS) well as different lengths of observation duration. An Precise Point Positioning (PPP) service provides post- observation set of 3h 52 min. was collected on one processed position estimates over the Internet from station (GPS day 17191) with Topcon GR-3 dual GPS observation files submitted by the user. Precise frequency receiver. PPP-solutions were estimated position estimates are referred to the CSRS standard using two types of observations namely single North American Datum of 1983 (NAD83) as well as frequency L1 and dual frequency L1/L2 observations. the International Terrestrial Reference Frame (ITRF). Each solution contains different lengths of observation Single station position estimates are computed for duration (10 min., 20 min., 30 min, 45 min., 1 hr, 1.5 users operating in static or kinematic modes using hr, 2 hr, 2.5 hr, 3 hr, 3.5 hr and 3.875 hr). Management precise GPS orbits and clocks. The online PPP of observations files was done using the software positioning service is designed to minimize user TEQC "translate, edit, quality check" GNSS data tool interaction while providing the best possible solution (TEQC, 2013). The different sets of observations were for the given observation availability. Currently, users processed and the PPP solutions were estimated need only specify the mode of processing (static or through Canadian Spatial Reference System (CSRS) kinematic) and the reference frame for position output Precise Point Positioning (PPP) service (CSRS-PPP, (NAD83 (CSRS) or ITRF). The observations 2013). The chosen total length of observations was processed are selected from the submitted RINEX file about 4 hrs as the previous studies suggest that after in the following order: this amount of observations almost no improvements in the PPP solution precision for static dual frequency 1. L1 and L2 pseudo-range and carrier phase measurements (Katrin Huber and Florian Heuberger, observations 2010) as shown in table (1). 2. L1 pseudo-range observation An L1 pseudo-range only solution will be performed in Table 1: PPP-Static accuracy variation with case of failure of the L1 and L2 pseudo-range and observation duration for dual-frequency measurements. carrier phase solution. (Katrin Huber and Florian Heuberger, 2010) PPP-Static accuracies for dual-frequency The PPP application can only process GPS measurements observations if precise GPS orbits and clocks products decimeter level after 15 to 30 min are available. It will use the best products available at a few cm level after 1 to 2 hours the time the data is submitted. It is important to note almost no improvement after 4 hours of bservations that the quality of the GPS orbit and clock estimates, and consequently of the PPP derived positions, has Table 2: PPP-Static precision variation with observation improved from 10cm and several nanoseconds in 1994 duration for GPS single-frequency measurements to about 2cm and 0.1 nanoseconds in 2003. Duration of Single frequency observations (L1) observations There is no minimum length for a GPS observation Sigma (95%) Sigma (95%) Sigma (95%) session. However, the quality of a PPP computed Latitude (m) Longitude Ellipsoidal position will not be optimal until the carrier phase (m) height (m) ambiguities have converged. For short data sets, 10 min. 2.954 2.545 6.634 positions will be calculated using only the pseudo- 20 min. 2.161 1.787 4.731 range observations. Longer data sets make it possible 30 min. 1.773 1.427 3.787 to resolve the ambiguities required to recover positions 45 min. 1.452 1.141 3.018 using the more precise carrier phase observations. Data 1 hour 1.251 0.975 2.576 sets up to six days long can be processed with PPP. 1.5 hour 1.007 0.791 2.067 2.0 hours 0.875 0.699 1.821 Since the ionosphere delays L1 CODE observations, 2.5 hours 0.760 0.624 1.665 an Ionospheric model is required for correction. The 3.0 hours 0.690 0.564 1.532 source of ionospheric corrections selected for the L1 3.5 hours 0.646 0.519 1.422 processing by the on-line application are the combined 3.875 hours 0.619 0.493 1.355 global ionospheric maps produced at 2-hour intervals in IONEX format by IGS with an accuracy of (± 2-8) 4. Results and discussion TECU-level (range errors in the order of 30 cm to 1 m) (Katrin Huber and Florian Heuberger, 2010). The Table 2 and Figure 1 Present PPP-Static precision L1&L2 processing uses the L1&L2 ionospheric-free variation with observation duration for single- combination of the code& phase observations and does frequency measurements resulting from this research. Journal of Geomatics 155 Vol.7 No.2 October 2013
Table 3 and Figure 2 present the PPP-Static precision single frequency observations. Dual frequency variation with observation duration for dual-frequency observations present a decimeter level precision for measurements resulting from this research. latitude and longitude coordinates with only 30 min. duration of observations. Using 1 hr duration of Table 3: PPP-Static precision variation with observations will yield a few centimeters level observation duration for GPS dual-frequency precision for latitude and longitude coordinates as well measurements as a decimeter level precision for height coordinate. Duration Dual frequency observations (L1/L2) Increasing duration of observations from 1 hr to nearly of Sigma Sigma Sigma (95%) 4 hrs will improve the precision down to 1-2 observations (95%) (95%) Ellipsoidal centimeters or even few millimeters. Latitude Longitude height (m) (m) (m) Improvement percentages in the precision of PPP- 10 min. 0.830 1.504 3.633 static solution in relation with observation duration for dual frequency GPS measurements with reference to 20 min. 0.331 0.607 1.788 (10 min.) observation duration time are superior to 30 min. 0.172 0.313 1.003 their single frequency observations counterparts. 30 45 min. 0.088 0.159 0.531 min. observation duration results in 76 % precision improvement for dual frequency observations while 1 hour 0.054 0.098 0.316 the percentage of improvement is only 40% for single 1.5 hour 0.029 0.054 0.157 frequency observations. (1 hr to 3 hrs) of observation 2.0 hours 0.019 0.038 0.092 duration will improve the precision to (92-98%) for dual frequency observations while it only improves the 2.5 hours 0.013 0.032 0.066 precision to (60 – 77%) for single frequency 3.0 hours 0.010 0.029 0.054 observations. Nearly 4 hrs of observations will result in 3.5 hours 0.008 0.027 0.048 precision improvement percentage of 99 % for dual 3.875 hours 0.007 0.025 0.047 frequency observations while it only improves the precision to 80 % for single frequency observations.
Tables 4 presents improvement percentages in the Tables 5 & 6 present the (Estimated – a priori) precision of PPP-static solution in relation with coordinate difference for single& dual -frequency observation duration for (single frequency & dual measurements respectively with different observation frequency) GPS measurements with reference to (10 duration times. The A-priori coordinate-values found min.) observation duration time. in the user RINEX file header or estimated at first epoch using code observations. In this particular The study results emphasize the quality of dual research, the A-priori coordinate-values found in the frequency observations over single frequency user RINEX file header. observations for PPP- static solution where observing only (20-30) min. of dual frequency observations will result in similar precision as observing nearly 4 hrs of Journal of Geomatics 156 Vol.7 No.2 October 2013
Table 4: Improvement percentages in the precision of PPP-static solution in relation with observation duration for (single frequency & dual frequency) GPS measurements with reference to (10 min.) observation duration time. Improvement percentages in the precision of PPP-static solution with observation duration for (single frequency & dual frequency) GPS measurements with reference to (10 min.) observation duration time. Duration of observations Latitude Longitude Ellipsoidal height General improvement improvement % improvement % improvement % %
Single 40 44 42 40 30 min. Dual 79 79 72 76 Single 57 61 61 60 1 hour Dual 93 93 91 92 Single 70 72 72 70 2.0 hours Dual 97 97 97 97 Single 76 77 77 77 3.0 hours Dual 98 98 98 98 3.875 Single 79 80 80 80 hours Dual 99 98 98 99
Table 5: (Estimated – a priori ) coordinate difference Table 6: (Estimated – a priori) coordinate difference for single-frequency measurements in relation with for dual-frequency measurements in relation with different observation duration times. different observation duration times. (Estimated – a priori ) coordinate (Estimated – a priori ) coordinate Duration of difference Duration of difference observations for single-frequency measurements observations for dual-frequency measurements Latitude Longitude Ellipsoidal Latitude Longitude Ellipsoidal (m) (m) height (m) (m) (m) height (m) 10 min. -1.330 -0.917 -1.673 10 min. -2.501 -0.930 -1.586 30 min. -1.423 -0.663 -2.036 30 min. -2.560 -1.091 -1.766 1 hour -1.493 -0.634 -2.296 1 hour -2.567 -1.018 -1.576 2.0 hours -1.480 -0.470 -2.830 2.0 hours -2.555 -1.046 -1.611 3.0 hours -1.658 -0.655 -2.546 3.0 hours -2.562 -1.042 -1.590 3.875 hours -1.816 -0.681 -2.260 3.875 hours -2.564 -1.041 -1.605 Journal of Geomatics 157 Vol.7 No.2 October 2013
5. Conclusions Limitations. International Association of Geodesy Symposia, Vol. 133 pp. 615-623, 2008. This research presents an effect analysis study of GPS observation Type and observation Duration on CSRS-PPP (2013). Canadian Spatial Reference System convergence behavior in PPP for static positioning. It (CSRS) Precise Point Positioning (PPP) service. proves that dual frequency observations gives better http://www.geod.nrcan.gc.ca/products- PPP solution precision comparing with single produits/ppp_e.php. Accessed (10/5/2013). frequency observations. The reason for this is the ability of dual frequency observations to eliminate the GPS-SPS (2008). GPS standard positioning service effect of ionospheric error (largest source of error (SPS) specifications. http://www.gps.gov/technical/ especially in near –equatorial geographic regions). ps/2008-SPS-performance-standard.pdf. Accessed Dual frequency observations of 20-30 min. duration (13/5/2013). time will result in similar precision from nearly 4 hrs of single frequency observations (precision of IGS (2013). International GNSS Service (IGS) decimeter level for latitude and longitude and of meter products. level for height). http://igscb.jpl.nasa.gov/components/prods.html. Accessed (10/5/2013). Static-PPP solution precision depends on the observation duration where more length of observation Katrin Huber and Florian Heuberger (2010). PPP: duration improves the precision of the solution up to Precise Point Positioning – Constraints and nearly 4 hrs. Longer observation duration will not have Opportunities. FIG Congress 2010 (Facing the noticeable effect. PPP-static solution using dual Challenges – Building the Capacity) Sydney, frequency observations provide better precision Australia, 11 16 April 2010. improvement percentages (76 – 99%) in relation with observation duration, while single frequency PPP (2013). CSRS-PPP user guide. observations provide smaller improvement percentages http://www.geod.nrcan.gc.ca/userguide/pdf/howtouse.p (40 – 80%). df. Accessed (15/4/2013).
Static-PPP solution using dual frequency observations TEQC (2013). TEQC-UNAVCO tutorial. provide bigger (Estimated – a priori ) coordinate http://facility.unavco.org/software/teqc/doc/UNAVCO difference for horizontal coordinates with different _Teqc_ Tutorial.pdf. Accessed (5/5/2013). observation duration times which proves its quality. While Static-PPP solution using single frequency Topcon GR-3 (2013). Topcon Positioning Systems( observations provide smaller (Estimated – a priori ) GR-3). http://www.topconpositioning.com/legacy/gr-3. coordinate difference for horizontal coordinates with Accessed (20/4/2013). different observation duration. Yves Mireault, Pierre Tétreault, François Lahaye, Pierre Héroux, and Jan Kouba (2008). online Precise References Point Positioning: A New, Timely Service from Natural Resources Canada. GPS world magazine, Abdel Salam (2005). Precise Point Positioning Using September 2008. Un-Differenced Code and Carrier Phase Observations. Ph.D thesis, University of Calgary, Canada. Zumberge, J. F., M. B. Heflin, D. C. Jefferson, M. M. Watkins and F. H. Webb (1997): Precise Point Bisnath S. and Y. Gao (2008). Current State of Processing for the Efficient and Robust Analysis of Precise Point Positioning and Future Prospects and GPS Data from Large Networks, J. Geophys. Res., 102(B3), 5005-5017. Journal of Geomatics 158 Vol.7 No.2 October 2013
Hydrological modelling to estimate rainfall based runoff in the lower Tapi basin
N.Goswami1,P.K.Gupta2 andAjai2 1K.S.School of Business Management, Gujarat University, Ahmedabad 2Space Applications Centre, ISRO, Ahmedabad Email: [email protected]
(Received: July 26, 2013; in final form October 8, 2013) Abstact: Rainfall based runoff modelling is crucial to study the magnitude of flood and inundation pattern in a river catchment. A set of models have been considered and evaluated based on several criteria to select the most appropriate one to obtain rainfall based runoff for lower Tapi basin, extending from Ukai dam to Surat city and covering an area of nearly 1679 km2. The Hydrologic Engineering Centre’s HEC-HMS model gave the most prominent response and was adopted for this study. A range of inputs such as rainfall, gauge discharge, basin characteristics, Ukai dam and Kakrapad Weir releases as well as the effect of topography were considered in the model to obtain runoff. The model is calibrated for the period of June to September for the year 2004 and validated for the same period for the year 2006 at Mandavi gauging site. Simulated and observed discharge results are compared and a very good match has been found with the acceptable limit of statistical parameters. Especially, river peaks are well simulated, hence model is considered validated for the study area. Keywords: Rainfall-runoff model, HEC-HMS, Gauge discharge, Topography
1. Introduction Station (CWPRS) are already involved in study of flood phenomena of Tapi River. But, it is also Urban and peri-urban areas along the banks of river or important to extend this study using GIS and RS located in a heterogeneous terrain could be prone to combined with different hydrological model. The floods due to heavy rainfall in the river catchment. objective of this study is to use the data generated from Even though floods often cause significant damage and geo-informatics techniques and to combine this data destruction, floods are acknowledged to be one of the with suitable hydrological model to estimate the most manageable disasters (Keys et al., 1996). Unlike rainfall based runoff that contributes to the river Tapi sudden impact disasters, such as, earthquake, forest fire in its lower basin. or bursting of dam, floods generally occur in predictable areas and one can plan in advance to 2. Study area mitigate anticipated or expected damages. With effective management, flood disaster can be managed. Tapi is a river in central India. It is one of the three Adequate knowledge of several parameters, such as, rivers in Peninsular India that runs from east to west. precipitation, infiltration, channel capacity and terrain Tapi river originates at Multai of Satpura range in topography are needed for assessing magnitude of Betul district of Madhya Pradesh at an elevation of 752 flood and inundation pattern and their impact. Based m above mean sea level (msl) and flows through on available data for precipitation, stream flow, flow Maharashtra and Gujarat before joining the Arabian rates and water levels, infiltration characteristics, slope Sea. The length of the river is about 724 km. The Tapi orientation, land use etc the extent of inundated area river basin encompasses an area of 65,145 km²; the can be forecast in advance for a catchment using a basin lies in the states of Maharashtra (51,504 km²), suitable model. The need of a model for rainfall-runoff Madhya Pradesh (9,804 km²) and Gujarat (3,837 km²). process arises as the hydrological measurement The Satpura range forms its northern boundary, Ajanta techniques have limitations and all the parameters of and Satmala forms it southern extremity and Mahadeo the hydrological system cannot be inferred correctly. hill forms its eastern boundary. This river basin may be Such a model can then be used for extrapolation into divided into three distinct parts: upper, middle and future with available measurement. At present, lower basins. The Lower Tapi Basin (LTB) modelling seems to be the only way to address encompasses the area from Ukai dam to Hazira and complex environmental and water resource problem at experiences periodic floods that repeat at every 3-4 he regional scales. Some of these inputs can be years interval. The land use/land cover of this area is of generated using geo-informatics techniques. mixed-forest, agricultural and fallow land, water body Combining these data with model, magnitude of flood and rural settlement. The topography of LTB and inundation pattern can be assessed for urban and comprises of narrow valleys and gently sloping land peri-urban environment. Gupta et al. (2012) have surface. The average rainfall of LTB is approximately shown the use of remote sensing (RS) data integrated 1376 mm. The flooding in this area is mainly due to model for the estimation of runoff at watershed scales. heavy rainfall and releases from Ukai dam at the time In this study rainfall based runoff has been modelled of high water level. The region between Ukai dam using the HEC-HMS (USACE, 2000) model for lower (21.2291N, 73.5819E) and Surat city is considered for Tapi basin. Many organisations like Central Water this study. The Mandavi gauging site is considered for Commission (CWC), Gujarat Engineering Research calibration and validation purpose. Study area is Institute (GERI), Central Water Power and Research presented in Fig. 1.
© Indian Society of Geomatics Journal of Geomatics 1 59 Vol.7 No.2 October 2013
RainRain gauge gauge RiverRainRiver gauge network network WatershedsRiverWatersheds network boundary boundary Watersheds boundary
Figure 1: Study area showing rain gauge locations, river network and sub-basin catchments with topographic variations
3. Model is assigned to each model as per the level of agreement by the models (Table 1) such as for high Several models such as SSARR, SLURP, HEC-HMS, parameterization requirements score was given 1 MIKE SHE, HSPF, SWAT, MODSIM, and SWMM because it further increases the complexity of the are considered for the study. Each of them are modelling, for land use if model is not taking into evaluated based on the objective of the study, number account land use variability score was given 1 because of parameters required, land use details, technical it affects the runoff generation process and crucial for support, usage, ease of use and learning of software runoff modelling. Hydrologic Engineering Centre’s and software cost by assigning range of weights Hydrologic Modeling System (HEC-HMS) gave between 1 to 5 for each criterion as per its importance prominent response. Hence HEC-HMS (Merwade, this study. A score between 0 and 5 for each criterion 2008) is selected.
Table 1: Model selection criteria Models HEC MIKE MOD- SSARR SLURP HSPF SWAT SWMM Weight Score Criteria HMS SHE SIM Not Satisfied: 0 Objective 3 3 4 4 3 2 2 2 5 Fully Satisfied:5 Parameteri High :1 3 4 5 2 2 2 4 4 4 -zation low:5 Absent:1 Land use 3 4 5 5 5 5 1 4 3 Effective:5 Tech. Absent:1 1 1 3 5 4 4 3 3 2 Support Good:5 Very little use:1 Usage 1 1 4 3 3 3 3 4 2 Widely use:5 Ease of use Challenging:1 1 3 4 3 2 2 4 4 4 and learn Easy:5 Software Not Free: 0 5 3 5 0 5 5 5 5 5 cost Free:5 Weighted 69 70 110 71 85 80 82 88 Score
4. Methodology and drainage lines. With the help of HEC-GeoHMS, An approach for the estimation of runoff is presented which is interfaced with GIS is used to prepare “Basin in Fig. 2. A Digital Elevation Model (SRTM DEM model” with sink, junction, outlets and imported to with 90m resolution) is used to delineate watershed HMS (Fig. 3). A total 24 watersheds and 19 junctions Journal of Geomatics 160 Vol.7 No.2 October 2013 are prepared. Meteorological model and “Time series model is run with the above inputs and runoff at each model” are constructed in HMS using 2004 rainfall watershed is simulated. For calibration of the model data obtained from the rain gauge stations at Amli, the simulated runoff of the watersheds that fall Bodhan, Godsamba, Kamrej, Kakrapad, Kadod, Surat, between Mandavi gauging site and Kakrapar is added Rander, Ukai, Mandavi, Zhankhvav, Valthan and to the Kakrapar releases and compared with the Uteva. Data from 1st June to 30th September is observed data at Mandavi during 1st June to 30th considered. Green and Ampt method is used for the September 2004, while validation is done for the same estimation of runoff whereas “Loss” is estimated using gauging site during the same period for the year 2006 the Clark’s unit hydrograph (Clark, 1945; Muhammad without changing the model calibrated parameters. et al., 2009) and “Transform” and “Lag” is used for Statistical parameters such as coefficient of routing the produced runoff down below the determination and modelling efficiency (Nash Sutcliffe topographic gradient. Time of concentration is coefficient) are also estimated to check the model estimated using the Kirpich formula (Kirpich, 1940). performance.
Locating Junctions and sink Delineation of watershed and drainage
Preparation of Basin model in HEC- GeoHMS
Determination of parameters
Preparation of meteorological model and Time series data in HECHMS
Assigning rain gauges Assigning rainfall
Running the model with specific control specification
Comparison of simulated and observed data
Figure 2: Approach for runoff estimation
Surat Mandavi Kakrapar Ukai
Figure 3: Basin model in HEC-HMS Journal of Geomatics 161 Vol.7 No.2 October 2013
5. Results and discussion induced runoff estimated by the model for the watersheds that belongs to between Kakrapad weir and Model is calibrated during monsoon period of the year Mandavi gauging site, are added with Kakrapar daily 2004 whereas validation is done during monsoon releases and then compared with observed daily period of the year 2006. Statistical parameters discharges at Mandavi. Model calibration was done by coefficient of determination and Nash Sutcliffe adjusting the model control parameters such as soil Efficiency (ASCE, 1983b) were used. saturated hydraulic conductivity, manning’s roughness, storage coefficients etc. to get the best fit between 5.1 Model calibration observed and simulated discharges during monsoon period of 2004 (low river flow year). Model calibration The HEC-HMS model was run using the daily rainfall result for Mandavi gauging site is presented in Figs 4- data for the period 1st June to 30th September for the 5. Coefficient of determination and Nash Sutcliff year 2004 and 2006. Simulated runoff at all the 24 efficiency are 0.92 and 0.77, respectively. watersheds is obtained on daily basis. The rainfall
Figure 4: Comparison of simulated and observed discharge during calibration period (1 June to 30 September, 2004) at Mandavi gauging site
Figure 5: Scatter plot between observed and simulated discharge during calibration period (1 June to 30 September, 2004) at Mandavi gauging site
5.2 Model validation not be measured at Mandavi gauging site during 8 - 20 August 2006. For remaining period, simulated results By keeping the model control parameters same, model are slightly over estimated (as storage coefficient was run is done for the monsoon period of the year 2006 tuned for high flow conditions) for low flow conditions (high river flow year) to see the model ability to whereas peak flows are very well simulated compared reproduce the performance which it gave during the to observed data. Coefficient of determination and calibration period i.e. monsoon period of the year modelling efficiency are 0.94 and 0.78, respectively. 2004. Validation period results for simulated and Considering good Nash Sutcliffe efficiency for the observed discharges are presented through Figures 6-7. validation period, simulated discharge pattern for There was heavy flood during early phase of the heavy flood situation (8-20 August 2006) can be August month. Due to heavy flood, observation could accepted in absence of the observed data. Journal of Geomatics 162 Vol.7 No.2 October 2013
Figure 6: Comparison of simulated and observed discharge during validation period (1 June to 30 September, 2006)at Mandavi gauging site
Figure 7: Scatter plot between observed and simulated discharge during validation period (1 June to 30 September, 2006) at Mandavi gauging site
6. Conclusion Forested Watershed using Remote Sensing and GIS. Journal of Hydrologic Engineering, American Society It is found that overall hydrograph was very well of Civil Engineers Vol. 17, No. 11, November 1, 2012. simulated, especially peak flows which show the ASCE, ISSN 1084-0699/2012/11-1255-1267. model capability to capture the hydrologic flow variability within the river system. Statistical Keys, C., D. Angus, and N. Benning, (1996). parameters calculated using observed and simulated Developing our expertise in the management of discharge data show that reasonably good result is flooding: some recent initiative. Australian Journal of obtained and the model is performing well in the study emergency management 11(4): 38-43. area. In future, validated model can be applied for the development of various hydrological scenarios for low, medium and high flow conditions. Kirpich, Z. P. (1940). Time of concentration of small agricultural watersheds. Civ. Eng. 10 (6): 362. References Merwade V. (2008). HMS model development Using ASCE Task Committee on Irrigation Canal Hec-GeoHMS. http://web.ics.purdue.edu. System Hydraulic Modeling (1993). Unsteady- flow modeling of irrigation canals. J. Irrig. and Muhammad, M. A., R.G.Abdul and A. Sajjad, (2009). Drain. Eng., ASCE, 119 (4): 615-630. Estimation of Clark's Instantaneous Unit Hydrograph Parameters and Development of Direct Surface Runoff Clark, C. O. (1945). Storage and the Unit Hydrograph Hydrograph. Water Resource Management 23: 2417- Trans. American Society of Civil Engineers (ASCE) 2435. 110: 1419-1446. USACE Hydrologic Modelling System HEC-HMS Gupta P. K., S. Punalekar, S. Panigrahy, A Sonakia (1998) Technical Reference Manual and J. S. Parihar, (2012). Runoff Modeling in an Agro- (www.hec.usace.army.mil/software/hec-hms). Journal of Geomatics 163 Vol.7 No.2 October 2013
FFT geoid models for Egypt using different modified kernels
RaaedMohamedKamelHassouna Department of CivilEngineering, Faculty ofEngineering in Shebin El-Kom, Minoufiya University, Shebin El-Kom, Minoufiya – 32511, Egypt Email: [email protected]
(Received: December 06, 2012; in final form August 31, 2013)
Abstract: In this research, the Meissl-modified, the spheroidal and the Meissl-modified spheroidal Stokes' kernels were applied for modelling the geoid surface over Egypt, utilizing the spherical multi-band FFT technique. Comparisons were held with the unmodified kernel geoid solution. It turned out that in general, the modified kernel solutions gave improved results over that of the unmodified case. Particularly, among the three studied modified kernels, the Wong- Gore kernel is the optimal one based on its efficiency to reduce the truncation error, and consequently, the resulting geoid accuracy. So, it is recommended to use such modified kernel in future geoid determinations in Egypt by the Stokes method.
Keywords: FFT, Geoid, Stokes formula, Modified kernel
1. Introduction estimates of the error variances of the Earth’s gravity data are not currently known in all areas (Featherstone, The spherical Stokes solution of the geodetic 2003). boundary-value problem requires a global integration of gravity anomalies to compute the geoid undulation The objective of this study is to apply three different at a certain point (Hofmann-Wellenhof and Moritz, deterministically modified Stokes' kernels to model the 2005). The geoid–ellipsoid separation has many geoid over Egypt, relative to the WGS-84 reference geodetic applications, such as the transformation of ellipsoid. In particular, beside the original Stokes Geographical Position System (GPS)-derived kernel, the Meissl-modified, the spheroidal and the ellipsoidal heights to orthometric heights. However, Meissl-modified spheroidal kernels are studied. This is the incomplete global coverage and/or unavailability accomplished via the spherical multi-band Fast Fourier of accurate terrestrial gravity data are common Transfrom (FFT) technique, using a unified integration problems, regarding a precise gravimetric cap size of 1°. For this purpose, the program determination of the geoid using the original Stokes' MODKERN for computing different types of modified formula. kernels (Featherstone, 2003) was launched as a subroutine into the SPFOUR program for geoid In 1962, Molodensky proposed an approach to reduce computation by FFT (Tscherning et al., 1992). Then, the truncation error that occurs when terrestrial gravity the features and accuracies of the associated four geoid data are used within a spherical cap of limited spatial solutions are investigated. Finally, the relevant extent about each computation point in Stokes' conclusions are drawn along with the recommended formula. This is achieved using a deterministic future work. modification of Stokes' integration kernel. Modifications to Stokes' kernel not only reduced the 2. The spherical Stokes' kernel truncation error in the computed geoid, but also adapted it to yield some preferable high-pass filtering All integral formulae assume that the entire Earth is properties (Vani ek and Featherstone, 1998). continuously covered with observed gravity data. Such However, Molodensky’s approach did not receive a data coverage is impossible. So, the integration is great deal of attention at that time because of the practically limited to a spherical cap of a suitable unavailability of global geoid undulations derived radius 0 around each computational point. Regarding from the analysis of the orbits of artificial Earth spherical Stokes' formula, the geoid undulation is satellites. These global geopotential models provide a computed by (Hoffmann-Wellenhof and Moritz, 2005) superior source of the low-frequency component of the 2 N = (R/4 ) S( ) g d geoid. When used in conjunction with regional 0 0 terrestrial gravity data via a truncated form of Stokes' integral, this can also reduce the truncation error 2 0 (Featherstone, 2003). = (R/4 ) S( ) g sin d d + 0 0 Several modifications to Stokes' kernel have been 2 presented (Featherstone and Olliver, 1993; Omang and (R/4 ) S( ) g sin d d , (1) Forsberg, 2002). These include both deterministic and 0 0 stochastic kernel modifications. The stochastic where modifications are rarely considered because reliable R the mean radius of the Earth (R 6371 km),
© Indian Society of Geomatics Journal of Geomatics 164 Vol.7 No.2 October 2013
g the gravity anomaly at the running point, truncation error resulting from using a limited the spherical distance between the data point integration domain even in a remove-restore sense. and the computational point, S( ) the spherical Stokes kernel, which is given as 3.1 Meissl's modification
S( ) = cosec( /2) – 6sin( /2) + 1 – 5cos( ) – This approach was first noted by Meissl (1971), in 3cos( ) ln [sin( /2) + sin2( /2)], (2) which the kernel is modified through a simple subtraction. Namely, Meissl’s modified Stokes kernel d the area element at the data point, Sm( ) is given by (Featherstone and Olliver, 1993) the normal gravity induced by the reference ellipsoid at the computational point. S ( ) - S( 0) for 0 0 Sm ( ) = The first term in Eq.(1) is called as the near zone 0 for 0 , contribution to the geoidal height. This inner zone term (5) corresponds to the gravity observations in the limited integration cap. The second term is referred to as the where 0 is the integration cap radius. Meissl's far zone part, which is induced by the gravity data truncation error is smaller than the spherical Stokes outside the integration cap and is supposed to cover the truncation error, when using a global geopotential rest of the Earth. model to provide the low-degree field (Featherstone, 2003). In modern geoid determination using the remove- restore scheme, the geoidal height is spectrally 3.2 The spheroidal Stokes kernel (Wong-Gore decomposed as follows (Featherstone et al., 1996) modification) r N = NM + N + NT
2 0 The spherical Stokes' kernel may also be expressed in r = NM + (R/4 ) S( ) g sin d d + NT, (3) terms of Legendre polynomials Pn as follows 0 0 (Hoffmann-Wellenhof and Moritz, 2005) where NM represents the second term in Eq. (1) and can be computed from a suitable geopotential model S( ) = [ (2n+1) / (n-1) ] Pn (cos ) for 0 n = 2 expansion up to a maximum degree M. And NT is the (6) short-wavelength geoid component that is computed from an appropriate digital elevation model (DTM). where n is the degree of the polynomial. The intermediate (medium frequency) component, Nr, is evaluated via the Stokes' formula, using the residual The definition of the spheroidal Stokes kernel, gravity anomalies gr, which are reduced for both the according to Wong and Gore (1969), involves geopotential model and the topographic gravity removing a low-degree Legendre polynomials up to anomaly effects. degree k from the spherical Stokes kernel. Namely, k k Due to the discontinuous coverage of the gravitational S ( ) = S ( ) - [ (2n+1) / (n-1) ] Pn (cos ) n = 2 observations within the integration cap, the integral in Eq.(3) is replaced by discrete summations as follows (Hofmann-Wellenhof and Moritz, 2005) = [ (2n+1) / (n-1) ] Pn (cos ), for 0 0, n = k+1 (7) Nr = (R/4 ) gr S( ) cos , (4a) where Sk ( ) denotes the Wong-Gore modified kernel (or spheroidal kernel) and k < M. If k = M (i.e. if k equals the maximum degree of the reference field used in which the spherical cap is efficiently replaced by a in the remove–restore scheme), such kernel could be rectangular data grid around the computation point, referred to as the generalized Stokes kernel with equal latitude and longitude spacing and . (Featherstone and Sideris, 1998; Featherstone, 1999). The contribution of a data point p can be computed by While increasing the degree of spheroidal modification r r N p = ( g p R / p) ( cos p / ). (4b) increases the amount of high-pass filtering, thus decreasing to some extent the effect of using a limited 3. Deterministically modified Stokes' kernels integration cap, the increased oscillation of the above kernel causes errors in the numerical solution of the In what follows, the mathematical concepts will be discretized integral Eq. (4a). Hence, it is recommended explained, regarding three deterministically modified that only spheroidal kernels with degree lower than that Stokes kernels that are used in the current study to of the removed geopotential model should be used replace the spherical kernel in Eqs. (3) and (4a). Each (Omang and Forsberg, 2002; Featherstone, 2003). So, kernel modification, in someway, represents an in the current study, the degree of kernel modification approximation that is intended for reducing the k was taken equal to 180. Journal of Geomatics 165 Vol.7 No.2 October 2013
* C nm the fully normalized spherical harmonic C- 3.3 The Meissl-modified spheroidal Stokes kernel coefficients of degree n and order m, reduced for the (Heck-Grüninger modification) even zonal harmonics of the reference ellipsoid, _ Snm the fully normalized spherical harmonic S- Heck and Grüninger (1987) presented a Meissl type of coefficients of degree n and order m,_ modification (Eq. 5) to the spheroidal Stokes kernel in Pnm(sin ) the fully normalized associated Legendre Eq. (7). Therefore, the Heck-Grüninger modified function of degree n and order m kernel Shg( ) can be expressed as and 2 g RTM = 2 G (H - Href.) – (G R /2) . k k 2 3 S ( ) - S ( 0) for 0 0, ( cos ) ((H' – H) /l ), (11) Shg ( ) = where 3 0 for 0 , the mean crustal density (taken 2.67 gm/cm ), (8) G the gravitational constant, l the distance between the computation point and which reduces the truncation error, if coupled with the the running point, remove–restore scheme (Featherstone and Olliver, H the elevation of the computation point, 1993). Also, a value k = 180 was selected to solve for Href the mean grid elevation of the computation point, the residual geoidal height in the current work, using H' the elevation of the running point, such modification. the geodetic latitude of the running point.
4. Input data The RTM anomaly effect was performed for each grid node, up to a cap size of 1º, using the detailed 30"x 30" A 1'x1' grid of free air gravity anomalies over the SRTM30 model; and from 1º to 1.5º, using a 15' x 15' Egypt was prepared for the current work, relative to the mean grid of the same model (which represents the WGS-84 geocentric ellipsoid. Such grid covers the reference topography). region bounded by (22° N 32° N; 25° E 36° E). Such data were compiled from all available 5. Multi-band FFT residual geoid solutions different Egyptian data sources. The original scattered data sets were of different types. Namely, gravity According to the 2D-FFT concept, the discrete anomalies, gravity disturbances, astronomical vertical integration in Eq. (4a) can be evaluated as follows deflection components and GPS-observed geoidal r -1 r heights were available relative to the WGS-84 datum. N ( p, p) = ( R /4 ) F2 { F2 ( g q cos q ) The average noises of these data types were 0.7 mgal, F2 [S( pq)]}, (12) 0.02 mgal, 1.6 arc-second and 3 cm, respectively. -1 Gravity anomalies and gravity disturbances were based where F2 and F2 denote the discrete 2D-Fourier on both absolute and relative gravity measurements. A transform and its inverse, respectively (Strang van least-squares collocation algorithm was followed to Hees, 1990). predict the above target gravity anomaly grid (Tscherning et al., 1992). The 2D multi-band FFT method calculates exact values along several reference parallels of latitude in the The low frequency contribution gM was removed considered area. In particular, ten reference latitudes from the gridded data, using the GRACE360C were used. The geoidal height at any latitude was geopotential model (e.g. Reigber et al., 2005). In obtained by the interpolation of its values at two addition, the residual topographic effect gRTM was reference latitudes i and i+1, as follows also subtracted. For this purpose, the 30" x 30" global terrain model (SRTM30) within the r r N ( ) = [ ( - i+1) / ( i - i+1) ] . N i , 2D + geographical window (21° N 33° N; 24° E r [ ( i - ) / ( i - i+1) ] . N i+1 , 2D. (13) 37° E) was used (USGS, 2006). Therefore, As an integration cap size of 1° was used, the four gr = g – g – g , (9) M RTM geoid solutions were computed at the nodes of a 1'x1' where grid only over the window (23° N 31° N; 26° E 35° E). Moreover, the input residual gravity M n 2 n * anomaly grid was extended with zeros by 50% in all gM = (GM/r ) (n-1) (a/r) (C nm cos m + n=0 m=0 directions (i.e. 100% zero padding) to avoid the effects of a cyclic convolution in the FFT technique Snm sin m ) Pnm(sin ), (10) with (Featherstone et al., 1996; Omang and Forsberg, 2002). the geocentric latitude, Such strategy was followed in all four solutions, each the geodetic longitude, with the relevant kernel function S( pq) as given by r the geocentric radius, Eqs. (2), (5), (7) and (8). M the maximum degree of the geopotential model (360), Figures 1 to 4 show contour maps for the residual GM the geocentric gravitational constant, geoid solutions pertaining to the unmodified, Meissl's modified, Heck-Grüninger modified and Wong-Gore a the equatorial radius, _ Journal of Geomatics 166 Vol.7 No.2 October 2013 modified kernels, respectively. Obviously, the Table 2: Accuracies of the four geoid solutions at unmodified kernel resulted in a residual geoid with independent GPS/Lev. check points (unit: meter) relatively low frequency features. On the other hand, Modification Mean Std. Min. Max. the modified kernels comprise better short wavelength Dev. structures. Specifically, the Wong-Gore solution gives None -0.290 1.236 -2.698 3.183 the most high frequency scheme. This could imply that Meissl -0.270 1.185 -2.606 3.004 the solution is associated with the best high-pass Heck- -0.251 1.138 -2.533 2.844 filtering characteristics, and consequently, the smallest Grüninger truncation error. Wong-Gore -0.245 1.127 -2.504 2.787
Moreover, Table 1 lists the statistics of the four residual geoid grid values, arranged in the same order. The mean and standard deviation of the residual geoid signal decreases in that order. So, also the smoothest residual geoid corresponds to the Wong-Gore kernel solution.
Table 1: Statistics of the four residual geoid solutions (unit: meter) Modification Mean Std. Min. Max. Dev. None -0.012 0.288 -0.888 1.295 Meissl -0.007 0.190 -0.875 0.905 Heck- -0.002 0.104 -0.772 0.613 Grüninger Wong-Gore 0.000 0.100 -0.823 0.639
6. Evaluation of the geoid models
A set of independent high-quality GPS/Levelling check Figure 1: Residual geoid contour map relevant to the points was used to evaluate the accuracy of the four unmodified kernel (Interval: 0.20 m) geoid solutions. Such points were selected to be well- distributed over the study area, as shown in Figure 5. For this purpose, the four residual geoid models were interpolated into the locations of the check points. This was accomplished via the weighted inverse distance method (to power 2). The interpolated values were then compared with the GPS geoid values, after restoring both the reference field and the RTM geoid effects. In particular,
r N computed = N interpolated + NM + NRTM, (14) with M n n * NM = (GM/r ) (a/r) (C nm cos m + n=0 m=0
Snm sin m ) Pnm(sin ), (15) and 2 2 NRTM = - ( G / ) (H - Href.) - (G R /6 ) . ( cos ) ((H'3 – H3)/l3). (16) Figure 2: Residual geoid contour map relevant to Table 2 shows the statistics of the discrepancies, Meissl modification (Interval: 0.20 m) relevant to the four solutions, among the observed and computed geoid at the check points. All features imply The geoid discrepancies at the check points were fitted an increase in geoid accuracy in the above order. to a bilinear trend. Table 3 lists the accuracies relevant Again, the optimum accuracy was achieved by the to the four methods after such fitting. Obviously, all spheroidal Stokes kernel. Particularly, using such standard deviations were greatly improved, compared kernel, a gain of 11 cm in geoid accuracy was to those in Table 2. Moreover, the above typical achieved, compared to the unmodified solution, in improvements, due to using the modified kernels, are terms of the standard deviation of differences. still preserved.
Journal of Geomatics 167 Vol.7 No.2 October 2013
Table 3: Accuracies of the four geoid solutions after a bilinear trend removal (unit: meter) Modification Mean Std. Min. Max. Dev. None 0.004 0.183 -0.323 0.614 Meissl 0.004 0.177 -0.335 0.584 Heck- 0.004 0.170 -0.344 0.556 Grüninger Wong-Gore 0.005 0.169 -0.347 0.546
7. Discussion
In the current study, the optimal result, pertaining to the Wong-Gore modified kernel, agrees with that obtained by Omang and Forsberg (2002). Also, such kernel modification yielded the better results in the previous work of Vani ek and Featherstone (1998), where it was found to be slightly better than the Figure 3: Residual geoid contour map relevant to spheroidal Molodensky kernels. Moreover, the Heck-Grüninger modification (Interval: 0.20 m) compromised enhanced approach, which was proposed by Featherstone (1999), leans on the spheroidal kernel modification.
However, it could be claimed that the optimality of a specific Stokes' kernel modification could vary according to the local structure of the Earth's gravity field over the geographical region under investigation, the local reliability of the used geopotential model, and the quality and resolution of the terrestrial gravitational data.
8. Conclusions and recommendations
Based on the current investigation, it can be concluded that the Stokes kernel modification is a valuable tool for reducing the truncation error, when combined with the remove-restore technique. It turned out that, among the three studied modified kernels, the Wong-Gore Figure 4: Residual geoid contour map relevant to kernel is the optimal one based the accuracy, Wong-Gore modification (Interval: 0.20 m) smoothness and high-pass filtering associated with the computed geoid. So, it is recommended to use such modified kernel in future Stokesian geoid determination in Egypt with additional new data. Also, modified kernels may be investigated, using a satellite- only global harmonic model as a reference field. Finally, it is worthwhile to apply the principle of kernel modification to both the Hotine and Deflection-Geoid formulas for geoid determination.
Acknowledgments
Prof. C.C. Tscherning, Geophysical Institute, University of Copenhagen, Denmark; is acknowledged for making the SPFOUR software available. Also, Prof. W.E. Featherstone, Western Australian Centre for Geodesy, Department of Spatial Sciences, Curtin University of Technology, is thanked for releasing the MODKERN software as public domain. Last, but not Figure 5: Distribution of the GPS/Levelling check least, three reviewers are acknowledged for their points critical review of the manuscript. Journal of Geomatics 168 Vol.7 No.2 October 2013
References Meissl, P. (1971). Preparations for the numerical evaluation of second-order Molodensky-type formulas. Featherstone, W.E. (1999). A comparison of Report 163, Department of Geodetic Science and gravimetric geoid models over Western Australia, Surveying, The Ohio State University, Columbus, OH. computed using modified forms of Stokes' integral. Journal of the Royal Society of Western Australia (82), Omang, O.C.D. and R. Forsberg (2002). The northern pp. 137-145. European geoid: A case study on long-wavelengt geoid Featherstone, W.E. (2003). Software for computing errors, Journal of Geodesy (76), pp. 369–380. five existing types of deterministically modified integration kernel for gravimetric geoid determination. Reigber, C., R. Schmidt, F. Flechtner, R. König, Computers and Geosciences (29)(2), pp. 183-193. U. Meyer, K.-H. Neumayer, P. Schwintzer and S.Y. Zhu (2005). An earth gravity field model complete to Featherstone, W.E. and J.G. Olliver (1993). The degree and order 150 from GRACE: EIGEN- gravimetric geoid of the British Isles computed using GRACE02S. Journal of Geodynamics (39)(1), pp. 1- a modified Stokes’ integral. In Forsberg, R. and H. 10. Denker (eds), The European geoid determination, Kort-og Martikelstyrelsen, Copenhagen, Denmark, Strang van Hees, G.L. (1990). Stokes formula using 1993, pp. 19-25. Fast Fourier Techniques. Manuscripta Geodaetica Featherstone, W.E. and M.G. Sideris (1998). Modified (15), pp. 235-239. kernels in spectral geoid determination: First results from Western Australia. In Forsberg, R., M. Feissl Tscherning, C.C., R. Forsberg and P. Knudsen (1992). and R. Dietrich (eds), Geodesy on move: Gravity, The GRAVSOFT package for geoid determination, Geoid, Geodynamics and Antarctica, Springer, Press. 1. Continental Workshop on the European Berlin, 1998, pp. 188-193. Geoid, Prague, May, 1992.
Featherstone, W.E., K. Alexander and M.G. Sideris USGS (2006). SRTM30. Available: http: //dds .cr. (1996). Gravimetric geoid refinement using high usgs. gov/srtm/ resolution gravity and terrain data. Geomatics Research Australasia (64), June, pp. 75-99. Vani ek, P. and W.E. Featherstone (1998). Heck, B. and W. Grüninger (1987). Modification of Performance of three types of Stokes' kernel in the Stokes’s integral formula by combining two classical combined solution for the geoid. Journal of Geodesy approaches. Proceedings of the XIX General Assembly (72), pp. 684-697. of the IUGG, Vancouver, Canada (2), 1987, pp. 309– 337. Wong, L. and R. Gore (1969). Accuracy of geoid heights from modified Stokes kernels. Geophysical Hofmann-Wellenhof, B. and H. Moritz (2005). Journal of the Royal Astronomical Society (18), pp. Physical geodesy. Springer-Verlag, Wien. 81–91. Journal of Geomatics 169 Vol.7 No.2 October 2013
A web based solution for online application processing for mining information system- A pilot study for Kadapa district, Andhra Pradesh, India
V.Raghu1 and K. Mruthyunjaya Reddy2 1A.P. State Remote Sensing Applications Centre,Ameerpet, Hyderabad – 500 038 2National Remote Sensing Centre,Hyderabad – 500 625 Email: [email protected] ; [email protected]
(Received: October 23, 2012; in final form September 10, 2013)
Abstract: The Department of Mines and Geology (DMG) has various functions and processes relating to processing of mineral concessions for grant of mining leases, approving of mining plans with the consent for establishment, operation of mines and environmental clearance certificates from Pollution Control Board, Govt. of A.P. for grant of mining leases and collection of mineral revenue for the minerals extracted by the entrepreneurs. In order to have an effective and accountable service and to create a facilitative environment to the entrepreneurs, the DMG has taken a major progressive step in introducing an online processing system that leverages the strength of information technology to seamlessly integrate the business functions of the department thereby reducing the mine lease applications processing time, creating a miners friendly environment, be proactive to the use of information technology and bring in work discipline, efficiency and transparency in the entire chain of the decision making. Andhra Pradesh State Remote Sensing Applications Centre developed a web based GIS solution for Kadapa district as a pilot study to facilitate prospecting of minerals, which provides information about the existing and applied mine leases, an online facility for any applicant to apply for mine lease and a software solution to process the application for grant of lease to the applicant.
Keywords: File monitoring system, Mineral, Mining, Online application processing, Web based solution
1. Introduction Minerals (Development & Regulation) Act, 1957, Mineral Concession Rules, 1960 and A.P. Minor Minerals are the basic raw materials for any Minerals Concession Rules, 1966. infrastructural growth. The Government of Andhra Pradesh (A.P.) has identified the Department of Mines Presently, the entrepreneurs have to file application and Geology (DMG) as one of the nine growth engines before the district authority concerned in a prescribed for the development of the state and providing proforma and the officers at district level have to necessary administrative and infrastructure support for process the said application and submit their proposals speedy development. A.P. is one of the richest mineral to the Director of Mines & Geology or Deputy bearing areas in the country and it occupies the second Director of Mines & Geology as the case may be. In position. By virtue of its diverse geological setting, terms of minor minerals such as limestone slabs, road A.P. is a repository of 48 varieties of minerals and has metal, gravel, the Deputy Director of Mines & tremendous potential for further development in Geology will be granting the leases for all the minerals mining sector (DMG, 2012). In terms of mineral except granite and marble for which the Director of revenue to the state, it occupies first position in the Mines & Geology is the granting authority. In terms of country. For the past several years, DMG is looking all other major minerals like limestone, dolomite, for innovative methods to cut short the delays in steatite, quartz, feldspar, silica sand under un- processing of the applications, since many scheduled category and iron, manganese, bauxite, entrepreneurs are filing applications seeking leasehold beach sand under scheduled category of minerals the rights for various minerals. On an average, DMG is Government of India gives the letter of intent for the processing about 5000 applications in the year. During state government to issue a grant letter. 2008-09, DMG disposed of 8547 mineral concession applications and in 2009-10, the department cleared The DMG felt that the critical success factors to 7375 applications up to December 2009 (DMG, 2010). achieve are identification, demarcation and assessment of mineral wealth, publication, dissemination and The processing of applications requires various stages guidance of mineral information for promotion of such as identification of the area by the entrepreneur in mineral based industries. As a rregulatory authority, respect of the said area for availability of the mineral, granting of leases under mineral concession rules, extent, survey numbers, category of the land and other approving the mining plan, monitoring of production particulars from the Revenue department / Forest and dispatch of various minerals, vigilance over department and finally depending on the feasibility for mining and transportation are necessary. In terms of mining, allocating rights to the said entrepreneur for mineral revenue, monitoring of mineral revenue exploitation of minerals with the terms and conditions targets, achievements, demand, collection and balance, as laid down under statutory provisions of Mines & monitoring of mineral dispatches, issue of permits and
© Indian Society of Geomatics Journal of Geomatics 170 Vol.7 No.2 October 2013 mineral revenue assessment, issuance of mineral dues and dissemination of mineral information for clearance certificate should be carried out. promotion of mineral based industries. However, online issuance of mineral dues clearance certificate, In order to streamline the said procedure for online monitoring of demand collection and balance is transparency in the administration, the DMG planned not addressed in the present system. to develop a software system wherein entrepreneurs will have the information of the areas available for The main purpose of the work is to create a web based different minerals right from the village level and GIS application to facilitate online mineral concession survey number wise and depending on the prospects in application processing system. This developed various areas to file application by way of an online application helps wider reach to the mining system, so that, entrepreneurs need not waste their time entrepreneurs and streamlines work process facilitating in moving from pillar to post. The development in the department. Work involves developing geospatial information technology is expected to yield results in database using satellite imagery, cadastral maps, GSI this sector. district resource map, Survey of India (SOI) topomaps, mine maps for Kadapa district of A.P.. The thematic 1.1 Mineral production and revenue maps include cadastral parcels with survey numbers, administrative (district / mandal / village) boundaries, The DMG used to function as a technical and scientific base map features (transport network, rivers, streams & department by carrying out yearly field investigation water bodies), SOI toposheet grid index, forest and exploration programmes with dedicated manpower boundaries, maps of Reconnaissance Permit (RP), and also rendering advice to the state government on Prospect Lease (PL), Mine Lease (ML), lithology and the feasibility of grant of mineral concessions. Of late, mineral locations. These GIS layers facilitate online due to priorities and recent policies of the government, application processing system and File Management DMG has been mainly focusing for the last few years System (FMS). only on the mineral regulatory work and realization of revenue against the targets fixed by the government on 3. Study area both major and minor minerals. As a result, the mineral and mining sector of A.P. has contributed Rs.1660.79 Kadapa district with a geographical area of 15,380 sq. crores of mineral revenue to the state exchequer during km is situated in the Rayalaseema area of southern part 2007-08 and achieved Rs.1754.51 crores of mineral of A.P.. It is bordered by Chittoor district to its south, revenue during 2008-09 during the economic recession Nellore district to the east, Kurnool and Prakasam and melt down in the country. During the 2008-09, districts to the north and Anantapur district to the west A.P. stands first in mineral revenue among the (Fig. 1). This district has 51 mandals and 1008 revenue important mineral producing states of the country viz., villages. Jharkhand (Rs.1465 Crores), Rajasthan (Rs.1266 Crores), Chattisgarh (Rs.1217 Crores) and Karnataka (Rs.493 Crores) (DMG, 2009a and 2009b). During 2009-10 the DMG was able to collect mineral revenue of Rs.1445 crores up to January 2010 (DMG, 2010).
The mineral consumption is increasing due to promotion of various industries and manufacture of Figure 1: Location map of the study area mineral based products. A.P. produces about 100 to 110 million tonnes of industrial minerals and 200 4. Existing system for grant of leases million cubic meters of dimensional stones and building material and A.P. stands first in barytes and Earlier, the entrepreneur submits the application for limestone production in the country. The state stands mining lease to the Assistant Director of Mines and first in value of minor mineral production and second Geology (ADMG) department of the concerned region. in total value of mineral production in the country, The ADMG gives an acknowledgement informing the contributing about 9 to 10 percent (Rs.15966 Crores- date of inspection to the site. Meanwhile ADMG refers 2008-09) to the country’s mineral value production and the area applied for no objection certificate (NOC) state has exported mineral and mineral products with a from the concerned Tahasildar. After getting the report value of exports of Rs.2711 Crores during 2007-08 from the Tahasildar and inspection of the site, ADMG (DMG, 2009a and 2009b). submits his findings to the Director of Mines and Geology (DMG). Within thirty days of the receipt of 2. Scope and objective the report from the ADMG, the DMG grants/rejects the file for granite and marble. Proposals concerning to The objective of the study is to develop and implement major minerals will be sent to the Department of integrated information and file management system for Industries and Commerce within a month and this Kadapa district, A.P.. The study includes preparation department conforms grant/reject of the proposal of zoning atlas for mineral bearing areas and mine within 30 days. In case of minor minerals other than lease areas with available information, online grant of granite and marble, ADMG submits his leases under mineral concession rules, and guidance recommendations to the Deputy Director, Mines and Journal of Geomatics 171 Vol.7 No.2 October 2013
Geology (DDMG). Within 30 days of the receipt of the position. Map annotation is fully enabled with options report from ADMG, the DDMG sanctions the grant or to manage the display, determine styles, change reject of the proposal. In case the applied area falls in coloring, and display statistics and classification the forest land, ADMG refers the case to the Divisional methods. Forest Officer (DFO) who in turn submits his report within 30 days to the Chief Conservator of Forest (CCF) and DMG. Subsequently, the CCF submits his recommendations to the Environment and Forest Department of the State Government. The report of the Environment and Forest Department will be sent to Principal Chief Conservator of Forest (PCCF). According to the report of the PCCF, the Government of India clears the file and the state government issues an order.
5. Methodology
The methodology involves procurement of available published mineral maps, geology maps from statutory organizations like Geological Survey of India (GSI), Indian Bureau of Mines (IBM). Based on the information collected, a mineral resources atlas of Kadapa district was prepared. Mine lease maps, quarry lease / prospecting lease maps (granted and applied), reconnaissance permits and mine lease applications of Figure 2: Flow chart showing the application entire Kadapa district were collected from DMG, Govt. of A.P. The cadastral maps of Kadapa district collected ArcGIS Server adds extensive data editing tools from Survey Settlements and Land Records office, similar to the editing tools available in ArcMap. This Govt. of A.P. were scanned, digitized and attribute allows users to edit map files on a server that can be data were incorporated. The other thematic vector accessed by multiple people from remote locations. layers used in this study were district, mandal, village Because the database files are stored remotely and and hill boundaries and forest boundary. From SOI accessible via a network connection, multiuser geo- toposheets on 1:50,000 scale, drainage, surface water database editing is possible. This enables full online bodies, settlement locations, rail and road network are collaborations where data files can be updated derived. Further, these maps were geo-referenced with remotely by multiple people. In addition, ArcGIS IRS P6 LISS-IV satellite data and SOI toposheets and Server is much faster and is more compatible with brought to UTM projection with WGS 84 as datum. access points. Additionally, ArcGIS Server can be Each cadastral map was mosaiced into a mandal and accessed by other clients using REST endpoints to all the mandals were mosaiced into a district. The mine reference data layers and pull them into other lease maps were geo-referenced with cadastral maps applications. and satellite data. The entire geo-database is brought into GIS format using ArcMap. A file monitoring and 6. Geology of Kadapa district management system is developed using C#.Net language and deployed in ArcGIS server. The geo- The oldest rocks of the area belong to late Archaean or database of Kadapa district thus generated is integrated early Proterozoic eras which are succeeded by rocks of with file monitoring and management system and a Dharwarian age and both are traversed by dolerite web enabled application is developed. The steps dykes. The older rocks are overlain by rocks of the involved in the application development in file Cuddapah Super Group and Kurnool Group belonging monitoring system are shown in Fig. 2. to middle and Upper Proterozoic age. The major part of the district is occupied by the Cuddapah Basin The technological advancements of the ArcGIS server which is a huge depression formed over the denuded and its increased ability to serve data on the internet surfaces of older rocks extending into neighboring over other servers are briefly explained. ArcGIS server districts. has several application web services which include SOAP and Develop Custom .NET Web Services and The Archaean comprises the Peninsular Gneissic adds the use of spatial bookmarks from .mxd files, a Complex (PGC), represented by granite, granodiorite, query option to set maximum return records, and find granite-gneiss and the migmatite. These rock types options that calculate drive time and drive distance occur in the southwestern part of the district (Fig.3). polygons. There are numerous dykes and quartz veins traversing the Archaeans. The rocks of Dharwar Super Group ArcGIS has many more options for thematic vector range in age from Archaean to Lower Proterozoic and data classification and surface displays like slope, hill are represented by metabasalt and banded ferruginous shade, aspect, elevation and modifying the Sun chert. The Dharwar Super Group of rocks occurs as Journal of Geomatics 172 Vol.7 No.2 October 2013 minor bands trending NNW-SSE, within the 74 million tonnes and 96% of country’s barytes reserve Peninsular Gneissic Complex country in the is contained in A.P. southwestern part of the district (GSI, 2001). The district has extensive reserves of building material The Cuddapah Super Group is divided into four groups in quartzites, limestones and dolomites, which are of which the lower three groups occur in the District. quarried throughout the district for building purpose. The contact at the base of the Cuddapah’s and Limestone occurs in Pulivendla, Kadapa, Muddanur, Archaean is marked by a period of hiatus known as the Yerraguntla and Jammalamadugu mandals. They occur “Eparchaean unconformity” (DMG and APMDC, in Vempalle Formation of the Cuddapah Super Group 1993). The lowest is the Papaghni Group which and Narji and Koilkuntla Formations of the Kurnool includes a) Gulcheru Formation comprising quartzite, Group. Narji Limestones, exposed in Muddanur, arkose and conglomerate, b) Vempalle Formation Jammalamadugu and Kamalapuram mandals provide comprising dolomites, chert, mudstone and quartzite. good reserves of cement grade limestone in the district. The top of the Vempalle formation is occupied by A total reserve of about 143 million tonnes of all flows of basalt of andesitic composition and sills of categories is estimated in the district (GSI, 2001). dolerite. The Chitravati Group includes a) Pulivendla Granites and gneisses exposed in the southern part of Formation comprising quartzite with conglomerate and Rayachoti are also quarried for construction material. b) Gandikota Formation comprising quartzite and shale. The Nallamalai Group includes a) Bairenkonda Numerous old workings of lead, zinc and copper in the (Nagari) Formation comprising quartzite and shale and form of ventilation shafts, crosscuts, open quarries etc, b) Cumbum Formation comprising shale with dating back to the Moghul period, exist in a linear belt phyllites, dolomites, limestone and quartzite. stretching over 45 km in Varikunta (Kalasapadu mandal) and Zangamrajupalli (Brahmamgarimatam The Kurnool Group is seen in the western part and mandal). The estimated reserves of base metals in this includes Banaganapalle Quartzite, Narji Limestone, district is 1, 41,000 tonnes. Kadapa district has fairly Owk Shale, Paniam Quartzite, Koilkuntla Limestone good reserves of clays in Kodur, Rajampet and Kadapa and Nandyal Shale (GSI, 2001). mandals. Near Gadela (Obulavaripalli mandal), Hastavaram and Thallapaka (Rajampet mandal) white 6.1 Mineral resources of Kadapa district and impure clays in various shades of brown, yellow and purple with calcareous material deposits of white Kadapa district is a repository of vast mineral deposits clay, formed as a result of weathering of shales are which include asbestos, barytes, clay, diamond, gold, noticed. Around 2.1 million tonnes of clay reserves are iron ore, lead, zinc, limestone of cement grade, ochre, estimated in this area (DMG and APMDC,1993). talc, steatite and tungsten (Fig.3). Of these deposits, Iron ore derived from the ferruginous quartzite barytes, limestone and asbestos are the major ones intercalations within Pulivendla Quartzite occurs near (GSI, 2001). Chabali (Chakrayapeta mandal), Pendlimarri, Pagadalapalli (Pendlimarri mandal), Mantapampalli Asbestos deposits of Kadapa district are situated in (Ontimitta mandal) and Erraguntla Kota (Kodur Pulivendla, Lingala and Virapunayunipalli mandals. It mandal). A low grade crystalline magnesite body is the only asbestos producing district in the state occurs at the base of Vempalle Formation, 3 km south (DMG and APMDC, 1993). Asbestos occurs in the of Vempalle town and about 2.3 km SE of serpentinized dolomites belonging to Vempalli Kumarampalle (Vempalli mandal). Minor occurrences Formation and the mineralization is localized at the of poor quality steatite are recorded within dolomites contacts of dolomites with doleritic sills. In near the contacts of dykes near Nagayapalli Virapunayunipalli mandal, asbestos mineralization is (Pendlimarri mandal), Tangedupalli and Rajupalem noticed near Rajupalem at the upper contact of dolerite (Virapunayunipalli mandal). sill in Vempalli dolomites. Kadapa district has an in situ reserve of 0.25 million tones of chrysotile asbestos Uranium mineralization occurs in the dolomite of of which the recoverable reserve is nearly 0.1 million Vempalle Formation of the Cuddapah Super Group tonnes (Ramam, 1999). and extends intermittently over 60 km between Mabbuchintalapalli and Tummalapalli in the south east Kadapa district is the major mining center for barytes of Pulivendla. The estimated reserves of Uranium in not only in the state but also in the country as a whole this zone include 49,000 tonnes. (Ramam, 1999). Barytes occurs in Obulavaripalli, Kadapa, Pulivendla, Badvel and Rajampet mandals. In The large mineral resources of limestone, barytes and Mangampet area of Obulavaripalli mandal, barytes asbestos have helped the entrepreneurs to establish occurs as bedded and massive type in the Pullampet major Cement Units, asbestos based crushing and Formations of the Cuddapah Super Group. In other screening units, barytes based pulverizing units, slab areas, it occurs as veins. Over 90% of the barytes cutting and polishing units and others which include available in the district is off-coloured variety. The in shale, chalk crayon, tiles, brick units in Kadapa situ reserve of barytes in Kadapa district is estimated at district. Journal of Geomatics 173 Vol.7 No.2 October 2013
7. Use of mining information system accessibility with other departments like Survey Settlements and Land Records, Forest Department of A web based solution is developed to facilitate Govt. of A.P. involved in online processing of mineral prospecting of minerals which provide information concession applications (DMG, 2011). With the help about the existing and applied mine leases, an online of such provision the success of the application facility for any applicant to apply for mine lease and a developed will improve significantly. software solution to process the application for grant of lease to the applicant. The schematic diagram of the 8. Conclusions solution architecture is given in Fig. 4. The web based GIS solution offers a seamless integration of The solution, first of its kind in the country facilitates Tenements and Registry Systems and a facilitative online grant of lease under mineral concession rules, environment for the mining entrepreneurs by providing guidance and dissemination of mineral information for prospective information on mineral availability for promotion of mineral based industries, online exploration. This will reduce the cumbersome legacy information about the minerals availability and process of going to the field to know about the mineral building the geo database of minerals for Kadapa which the users would like to prospect for. The users district. Keeping in view of the above facilities, the can browse for the prospect zones, apply for lease DMG is envisaging to achieve a facilitative permits, access the map service portal to query, edit environment by providing Mineral Exploratory and analyze the displayed map data at anytime and Information thereby attracting domestic and from anywhere, know the status of existing application international companies for business investments, filed. The Table of content tool allows the user to introducing a transparent and seamlessly integrated manage the layers displayed in the application for File Processing System as per the recommendations of visualization. The proximity analysis tools help the the Ministry of Mines, Govt. of India, reducing the entrepreneur and users to know more information with processing time of mine lease applications thereby proximity from a known location. The information of improving the efficiency of the Department. all the layers is available for the user to analyze as they are loaded into the applications table of contents and at Acknowledgements known level of detail. The solution facilitates all the required information for entrepreneur and users to The authors thank the Director, Mines & Geology know before applying for a permit. Department, Govt. of A.P. for sponsoring the project, “Design and Development of Online Application An automated work flow powered by the Geospatial Processing for Mining Information System” (Project Information System facilitate the DMG and the Khanija) and Dr.G. Madhukar, former Joint Director, Government to have a Management Information DMG for his help and fruitful suggestions during the System (MIS), helpful to both the mining industry and execution of the project. The authors also acknowledge the Government as it offers a Decision Support with thanks for the valuable comments of the two System. Government can envisage an integrated anonymous reviewers which have helped in improving approach for the processing of Mine Lease applications the earlier version of the manuscript. by Departments of Environment and Forests, Revenue, and Mines and Geology. References
Registry System has been developed after a thorough DMG & APMDC (Department of Mines and Geology understanding with the existing processes of the DMG and A.P. Mineral Development Corporation Ltd.) and the functionalities at various offices across the (1993). Geology and mineral resources of Cuddapah state. The registry component developed is integrated district, Andhra Pradesh, 25p. with the GIS database. The system facilitates for submission of applications for grant of mining DMG (Department of Mines and Geology) (2009a). tenements on-line, submission of fee through challans Andhra Pradesh mineral resources, activities, and also facilitates submission of various statutory programmes and projects, 22p. returns and processing the same on-line. Various DMG (Department of Mines and Geology) (2009b). application formats required for an entrepreneur to file nd application and other statutory procedures to be 42 Andhra Pradesh state geological programming followed by the officers of DMG to process the board meeting for mines and minerals, Agenda Notes, application (Seshagiri Rao, 2008) are also incorporated 225p. in the web based solution. A smooth functioning of the DMG (Department of Mines and Geology) (2010). 43rd department can be envisaged by virtue of which Andhra Pradesh state geological programming board performance of the department can be more effective meeting for mines and minerals, Agenda notes, 198p. and efficient. DMG (Department of Mines and Geology) (2011). 44th The basic problem faced in implementation of this Andhra Pradesh state geological programming board system is that certain inputs such as creating the meeting for mines and minerals, Agenda Notes, 168p. Journal of Geomatics 174 Vol.7 No.2 October 2013
DMG (Department of Mines and Geology) (2012) 45th Ramam, P.K. (1999). Mineral resources of Andhra Andhra Pradesh state geological programming board Pradesh, Geological Society of India, 252p. meeting for mines and minerals, Agenda Notes, 139p. Seshagiri Rao, P. (2008). Law of mines and minerals, GSI (Geological Survey of India) (2001). District 15th edn., vol. I&II, Asia Law House, Hyderabad, resource map, Cuddapah district, Andhra Pradesh. 1924p.
Figure 3: Map showing geology and minerals of Kadapa district, Andhra Pradesh
Figure 4: The schematic diagram of the solution architecture Journal of Geomatics 175 Vol.7 No.2 October 2013
Reservoir impact assessment on land use/land cover in the catchment of upper Tunga reservoir in Shimoga taluk and district, Karanataka, India, using remote sensing and GIS
P. D. Jayakumar, Govindaraju and D. C. Lingadevaru Department of Applied Geology, Kuvempu University, Jnana Sahyadri, Shankaraghatta - 577 451 Email: [email protected] ; [email protected]
(Received: March 23, 2013; in final form August 19, 2013)
Abstract: The study aims to estimate the changes of land use / land cover (LU/LC) due to submergence by construction of upper Tunga reservoir. The land use/land cover was classified by onscreen digitization using visual image interpretation techniques with regional knowledge. Standard LU/LC codification implemented up to level III, using multi temporal IRS LISS III 1C/D and P6 satellite images of year 1997, 2000 and 2009. Digital image processing techniques like NDVI, NDWI, band rationing were performed for different season data. From the data analysis of 1997, 2000 and 2009, it was inferred that forest land has decreased while agricultural land has increased. Water spread area and built-up land have increased whereas wasteland has decreased. The submergence area before the construction of upper Tunga dam was 1337.09 ha and after, increased by 2238.22 ha. The study shows that remote sensing and GIS are useful and effective tools in the monitoring land resources in the catchment area of other reservoir.
Key words: Land use/land cover, Dynamic Change, Remote sensing, GIS
1. Introduction ogee wire dam known as Tunga anicut, which is twelve km from Shimoga city. Water resource projects bring socio-economic sustainability as well as affect on natural environment. 3. Material and methodology The water level in a reservoir is manipulated according to the purpose for which it built, within the constraints The materials used for the study are Survey of India imposed by the prevailing climatic conditions. This toposheets namely 48 O/5, O/6, O/7, O/9 and O/10 of causes reservoir driven land use/land cover (LU/LC) 1:50000 scale. ASTER data is used for the extraction of change in the catchment. Land cover is the natural contour using ARC GIS 3D analysis; Multi-Temporal vegetation covered on the earth surface and land use is remote sensing data of IRS 1C/1D and P6 LISS-III the part of land used for human activities. Therefore, were used as shown in Table-1. Software used were Land use change is one of the problems in hydraulic AutoCAD Map 2000, Arc GIS, ERDAS and Global engineering projects because of changing process of Mapper. Projections used for mapping are Universal nature. In this regard, catchment of the reservoir is the Transverse Mercator (UTM), Datum WGS 1984 and sensitive zone of human social activities on land cover Zone 43 North. change; this brings the chain-reaction such as the forestland area reduction and the agriculture land area Table 1: Satellite data products and their acquisition increase (Zhao et al., 2010; Amirin and Hasmadi, dates. 2010). The environmental related research, LU/LC Year Path Seasons(LISS III 1C/1D & P6) information is used as inputs to predict other factors. /Row Spectral resolution-4 bands, Spatial The remote sensing technique by virtue of its synoptic, resolution-23.5, Quantaisation-8 bit multispectral coverage of terrain on a repetitive basis 1997 98/64 19-Oct-1997, 21-Feb-1997, 10-Apr- provides spatial and temporal information about 1997 LU/LC of a region and thus the changes taking place 2000 98/64 16-Nov-2000, 21-Jan-2000, 30-Apr- therein, and GIS helps to keep the spatial database in 2000 digital format up to date for future analysis, 2009 98/64 28-Oct-2009, 06-Feb-2009, 19-Apr- manipulation and modeling. 2009 2. Study area Satellite images were georeferenced using third order The upper Tunga dam was constructed across river polynomial equation in ERDAS and radiometrically Tunga in the year 2004-2005 to provide water for corrected by dark pixel subtraction technique. The land drinking and irrigation purpose .The total aerial extent use and land cover layers were extracted from images of the catchment of reservoir is 1042.88 km2. A part of of 1997, 2000 and 2009 using ARC GIS to analyze the catchment area that covers about 180.33 km2 is taken changing dynamics. In order to avoid confusion for detail analysis. The area lies between 75o 25’ between land/water and vegetation pixels, digital image 24.386”E -75o 36’ 49.296”E, and 13o 51’ 16.145”N- classification techniques such as spatial models NDVI 13o 44’ 19.085” N as shown in location map (Fig.1). = (NIR-RED) / (NIR+RED) & NDWI = (GREEN-RED) The dam is constructed 100 m downstream to the old / (GREEN+RED) were used. © Indian Society of Geomatics Journal of Geomatics 176 Vol.7 No.2 October 2013
4. Results and discussion Tunga dam by 333.43 hectares. Built-up land increased from 84.29, 88.82 and 90.61 hectares. Wasteland Most of the study area is occupied with forest and decreased from 61.85, to 60.67 and to 59.97 ha small village, which are going to be submerged due to respectively (Table 2). the construction of upper tunga reservoir. The LU/LC change analysis, land use change dynamics and 4.2 Land use change dynamics submergence analysis were carried out for the catchment area. The dynamic degree of land refers to the amount change of certain types of land-use in a certain interval 4.1 Change analysis of time; this is factor in modeling of land use for future prediction (Zhan Chunxiao et al., 2008). LU/LC status during 1997, 2000 and 2009 (in Hectares) is shown in Table 2. It can be seen that The expression for computation is, forestland has decreased from 15009.08, 14793.29 and -1 -1 14630.98 hectares in 1997, 2000 and 2009 respectively, LC = ( Ub - Ua ) * Ua *T *100% which shows that, forest is the most affected area, because of submergence and agricultural encroachment where, LC represents dynamic degree of a certain (see Fig. 2, 3, 4). The agricultural land has increased type of land use within specific time, from 2020.50, 2043.00 and 2069.49 hectares in these years. Water spread area increased from 849.09 ha to Ua and Ub represent the number of the certain land-use 1034.67 ha and to 1182.52 hectares. The water spread type at the beginning and at the end of the research, T area has increased after the construction of upper represents the specific time. Table 2: Land use land cover changes during 1997, 2000 and 2009(in Hectares) 1997 2000 2009 LULC Area % Area % Area % Agricultural Land 2020.50 11.21 2043.00 11.34 2069.49 11.48 Built Up 84.29 0.47 88.82 0.49 90.61 0.50 Forest 15009.08 83.27 14793.29 82.08 14630.98 81.13 Wastelands 61.67 0.34 60.85 0.34 59.97 0.33 Waterbodies 849.09 4.71 1036.67 5.75 1182.52 6.56 Total 18024.63 100.00 18022.63 100.00 18033.56 100.00 Journal of Geomatics 177 Vol.7 No.2 October 2013
The land use dynamics of the study area is presented to be done to refine in the catchment zone and its in Table 3. It can be seen that forest and wasteland relationship between land use and hydrology using have negative value, which shows decrease of forest high-resolution satellite data in large scale for and wasteland. Agricultural land shows positive value protecting the catchment of the reservoir. which is encroached within the notified forest region. Built up and waterbodies show positive values, Table 4: LU/LC submergence from 1997 to 2000 and indicating increase in area under these classes in the to 2009 (Area in hectares) catchment of the reservoir. The forest submergence in LU/LC Class 1997 2000 2009 this catchment is in dense deciduous forest, which Agriculture 162.93702 180.0975 394.9339 cannot be replaced. Natural and manmade factors are Built-up 5.807678 3.41737 5.841085 driving force in long time scale for the LU/LC changes, Forest 364.719213 473.3908 777.216 but in a short time scale, reservoir is the main driving Water body 793.699139 921.8077 1045.915 factors for land use changes. In future, it may affect the Wasteland 9.931192 10.04493 14.32203 water balance in the reservoir by increased surface Total 1337.09424 1588.758 2238.228 runoff.
Table 3: Change dynamics from 1997 to 2009 Acknowledgement Land Use Type 1997-2000 2000-2009 Agricultural land 0.371 0.144 Authors would like to thank the Chairman and all Built up 1.795 0.223 senior faculty members for their support and to utilize Forest -0.479 -0.122 the facilities in the Department of Applied Geology, Wastelands -0.442 -0.162 Kuvempu University, Jnanasahyadri, Shankaraghatta- Waterbodies 7.364 1.563 577 451.
4.3 Submergence analysis References
Digital elevation model gives realistic visualization for Amirin, M.K. and I.M Hasmadi (2010). Land use model-based analysis. Slope and aspect parameters changes in Perak catchment zone using remote sensing were derived from inbuilt algorithms to understand the and GIS technique. Journal of GIS Trends, 1(1), 15-19. trend and nature of terrain. LU/LC maps of different years were overlaid on the DEM and water spread NRSA (2006). Manual of national land use land cover polygons were generated using contour information mapping using multi-temporal satellite data. National and satellite images. Based on the water spread area Remote Sensing Agency, Dept. of Space, Govt. of and full reservoir level during the study period, the land India, Technical Document Number NRSA/RSGIS- under submergence was studied and results are A/NRC/NLULC-L3/TECHMAN/R02/May-06. presented in Table 4. As the water spread increases from 1997 to 2009, maximum part of both agricultural Qinghe Zhao, Shiliang Liu and Shikui Dong (2010). land and forestland comes under submergence. Effect of dam construction on spatial-temporal change
5. Conclusion of land use: A case study of Manwan, Lancang River, Yunnan. Procedia Environmental Sciences 2 (2010) The change of the land use pattern in the study area 852–858. indicates that there is a need for regular monitoring in a proper interval of time. Due to the construction of the Zhan Chunxiao, Liu Zhiming and Zeng Nan (2008). reservoir, 2.35% of Sakrebailu, Chorana edehalli and Using remote sensing and GIS to investigate land use Bommanahalli reserve forest area came under dynamic change in western plain of Jilin province. The submergence. Future problems can be avoided by International Archives of the Photogrammetry, Remote taking necessary action using dynamic degree of Sensing and Spatial Information Sciences. Vol. positive or negative change. Hence, lot of work needs XXXVII. Part B7. Beijing.
Journal of Geomatics 178 Vol.7 No.2 October 2013
Energy balance modeling for ablation estimation of Gangotri glacier
Gunjan Rastogi and Ajai Marine, Geo and Planetary Sciences Group, Space Applications Centre (ISRO), Ahmedabad-380 015 Email: [email protected]
(Received: July 10, 2013; in final form September30, 2013)
Abstract: Surface energy balance of a glacier describes physical connection between ice/snow ablation and climate forcing. To understand the response of glacier to climate variations, energy balance equations were formulated for the Gangotri glacier in the Indian Himalaya. Various components of the energy balance were computed using inputs from satellite data and in-situ measurements. Melting, sublimation and ablation were computed from the energy balance components for the period January to December, 2011. Melting rates computed from energy balance model were compared with the those obtained from the temperature-index model and it shows good agreement.
Keywords: Energy balance, Mass balance, Melting, Sublimation, Ablation, Gangotri glacier
1. Introduction Melting rate computed from energy balance model was The behavior of glaciers is a manifestation of compared with the one obtained from the temperature- fluctuations in the climate system and thereby making index model. Temperature-index model rests upon them important indicators of climate change. The close relationship between snow and ice melt and air Himalayas have the highest concentration of glaciers temperature usually expressed in the form of positive outside the polar region and thus holds one of the most temperatures. As air temperature is generally the most important natural resources of water in frozen form. It readily available data that is why such models are is important from the point of view of water and widely used for snow and ice melt computations energy security of India and many other countries in (Hock, 2003) the region. In addition, it also regulates the regional climate and the environment. These glaciers are 2. Location and data used sensitive to climate change. In view of the above, one needs to study and monitor the status of these glaciers. Gangotri glacier is one of the largest glaciers in the Establishing the physical relationship between glacier central Himalaya. Landsat image of Gangotri glacier is and climate requires the study of its surface energy shown in figure 1. It is located in Uttarkashi District, balance (Favier et.- al., 2004). Surface energy balance Uttarakhand, India. This is a valley type glacier in is a vital element in computation of melting or Ganga basin and the source of a major river system sublimation processes of the glacier and snow. Small Ganga in northern India. This glacier is bounded changes in the surface energy balance can lead to between longitude 78 59 30 and 79 17 45 E and dramatic changes in the snow and ice cover. (Luers and latitude 30 43 00 and 30 57 15 N. The glacier has Bareiss, 2010). an estimated volume of over 27 cubic kilometers. This glacier, flowing in north-west direction is about 27 km In the complex relationship between glaciers and long and 2 to 3 km wide. climate, one of the key processes is melt of snow and ice at the glacier surface. Melting followed by runoff accounts for most of the ablation on many glaciers (Anslow et.-al., 2005). In addition to it, snowmelt runoff estimates are needed for forecasting seasonal water yields, river regulation and storage works, planning flood control programs, etc (Arnold et.- al., 1996).
Both, process-based models (derived from a surface energy balance) and empirical models, which correlate melt with temperature and to some extent the radiation, were developed for glacierized regions. The energy balance incorporates radiative fluxes, turbulent fluxes and the energy flux in the subsurface. The present paper deals with the formulation of energy balance model on Gangotri glacier in Ganga basin of Himachal Pradesh, India. Various components of the energy Figure 1: Landsat image showing the location of balance were computed. Finally melting, sublimation Gangotri glacier and ablation were computed from the energy balance. © Indian Society of Geomatics Journal of Geomatics 1 79 Vol.7 No.2 October 2013
The input data sources used to compute energy balance where is albedo, G is global radiations, a and s are components in the present study are described as air and surface emissivities respectively, is Stefan’s -8 2 4 under: constant and its value is 5.67*10 W/m K and Ta, Ts are air and surface temperatures respectively (Singh 2.1 Satellite data and Singh, 2001).All these parameters are detailed below: Surface reflectance and land surface temperature products (at 500m and 1km resolution respectively) of 3.1.1 Albedo MODIS, onboard TERRA satellite are used in this study. MODIS has 36 spectral bands with spatial The conversion formula for the total shortwave resolutions as: 250m (band1 – 2), 500m (band3 – 7), broadband albedo for MODIS is given as under (Liang, 1000m (band8 – 36) and quantization is 12 bits 2000) – (Suzanne et.- al., 2006). MODIS = 0.160 1 + 0.291 2 + 0.243 3 + 0.116 4 + 2.2 Meteorological data 0.112 5 + 0.081 7 – 0.0015 (3)
Meteorological parameters such as wind speed, air where 1, 2, 3, 4, 5 and 7 are spectral narrowband temperature and relative humidity were obtained albedos in 1, 2, 3, 4, 5 and 7 bands respectively. through an Automatic Weather Station (AWS) of Snow and Avalanche Study Establishment (SASE) 3.1.2 Global radiations located at Bhojbasa in Gangotri sub-basin. For clear sky G can be calculated using the following 3. Calculation of the energy balance equation (Samani et.- al., 2007):
A unit volume of glacier is defined from the surface to G = (as + (bs* n/N)) Ra (4) a depth where there are no significant heat fluxes. On this volume, for a unit of time, and assuming a lack of where n is actual duration of sunshine (hour), N is horizontal energy transfers, the surface energy balance maximum possible duration of sunshine or daylight equation is written as follows, where the fluxes toward hours (hour), n/N is relative sunshine duration, Ra is -2 -1 the surface are positive (e.g., Oke, 1987, p. 90): extraterrestrial radiation (M J m day ), as is regression constant, expressing the fraction of R + H + LE + G +P = Qm + Qs = Q (1) extraterrestrial radiations reaching the earth on overcast days (n=0), (as + bs) fraction of extraterrestrial R is the net all-wave radiation, H is the turbulent radiation reaching the earth on clear days (n=N). sensible heat flux, LE is the turbulent latent heat flux. Commonly used values for as and bs are 0.25 and 0.50 The conductive heat flux in the snow/ice G can be respectively (Singh and Singh, 2001). Equation disregarded as the glacier is isothermal. The heat number (4) for the clear sky radiation reaching the advected by precipitation P is insignificant compared earth on clear sky days (n=N) can be written as: to the other terms (e.g., Wagnon et al., 1999). Qm is the latent heat storage change due to melting and G = (as + bs) Ra (5) freezing and Qs is the net convergence or divergence of sensible heat fluxes within the volume. The change Now the extraterrestrial radiation, Ra , for each day of of the energy Q is stored in the volume or utilized in the year and for different latitudes can be estimated the melting process. If the top layers of a glacier have a from the solar constant, the solar declination angle and 0 temperature below 0 C, then Q corresponds to a the time of the year by the following formulation: temperature change within the surface layers. If these 0 layers are at 0 C, then Q is available for the melting Ra = (24*60/ ) * Gsc * dr * ( s sin sin + cos cos process (Favier et.- al., 2004). sin s) (6)
3.1 Net radiations - Where Gsc is solar constant and given as 0.0820 M J m 2 -1 Solar radiation is the major energy source and is able min , dr is inverse relative earth-sun distance and is to change large quantities of liquid water into water calculated as- vapour. The net radiation is the balance of the incident and reflected short-wave radiation and the incident and dr = 1+ 0.033* cos(2 J/365) (7) emitted long-wave. It can be expressed as- where J is Julian day of the year, s is sunset hour
R = (1 – )G + (Li – Lo) (2a) angle given by-
-1 According to Stefan’s Boltzmann law above equation s = cos (-tan tan ) in radians (8) can be expressed as (Sellers et.- al., 1997)- is latitude in radians, is solar declination (rad), 4 4 R = (1 – )G + a Ta – s Ts (2b) which is calculated as- Journal of Geomatics 18 0 Vol.7 No.2 October 2013
= 0.409 * sin ((2 J/365) – 1.39) (9) aerodynamic method was used to calculate turbulent fluxes of sensible and latent heat. It employs bulk By substituting values of parameters from equations transfer coefficients derived from flux gradient (7), (8) and (9) in equation (6), equation (5) can be relationships. The bulk transfer coefficients explicitly expressed as: account for variable stability, surface conditions and measurement heights, and are used for melt estimation. G = 0.75 * Ra in M J m-2 day-1 (10) Stability of the surface layer is assessed by calculating the bulk Richardson number, Rb, which relates the 3.1.3 Incoming long wave radiations relative effects of buoyancy to mechanical forces (Reid and Brock, 2010): They are emitted by the atmosphere, primarily by 2 water vapour, CO2 and ozone. Wavelength range is 4- Rb = (g*(Ta – Ts) * (za – z0m)) / (Tm u ) (16) 120μm. During higher temperature and more cloudy conditions, long wave radiations are high. It is where Ta and u are the values of air temperature (in calculated as (Hock, 2010)- °K) and horizontal wind speed (in m/s) respectively at the level of measurement z; g is the acceleration due to 4 -2 Li = a Ta (11) gravity (g = 9.8 m s ); Ts is the surface temperature (in °K); Tm is the mean absolute air temperature between a is calculated using following formulation- the surface and the measurement level z (in °K); z0m is the surface roughness length for momentum transfer. -5 a = 0.7 + 5.95 * 10 * ea exp(1500/Ta) (12) The roughness length is defined as the height above a surface at which the extrapolated horizontal wind where a is emissivity of air and Ta, ea are surface air speed profile reaches zero. temperature in °K and vapour pressure in kPa at that temperature at about 1.0- 2.0m above the surface Assuming that local gradients of mean horizontal wind respectively. Based on the linear regression between speed u, mean air temperature Ta and mean vapour surface air temperature obtained from AWS and pressure are equal to the finite differences between the MODIS land surface temperature, surface air measurement level and the surface, it is possible to temperatures are estimated from 1km MODIS land give analytical expressions for the turbulent fluxes. surface temperature. ea (vapour pressure) at a given air (Oke, 1987): temperature is calculated as- 2 H = {( Cpk u (Ta – Ts)) / (ln (za/z0m) * ln (za/z0t))} * -1 ea = Rh * es/100 (13) ( m h) (17)
2 Where Rh is relative humidity and is obtained through LE = {( Lvk u (qa – qs)) / (ln (za/z0m) * ln (za/z0q))} * -1 AWS data and es is saturated vapour pressure and it is ( m ) (18) related with Ta through relation given below- where qa and qs are specific humidities at the level of es = 6.11 * exp((17.27 * Ta) / (237.3 + Ta)) (14) measurement z and surface, respectively. is the air density; Cp is the specific heat capacity for air at and es is in kPa and is Stefan Boltzmann constant and constant pressure; k is the von Karman's constant (k = its value is 5.67 * 10-8 W/m2 0.4) and Lv is the Latent heat of vapourization (Lv = 2.476 * 106 J/kg). The surface roughness lengths for 3.1.4 Outgoing long wave radiations heat z0t and humidity z0q were considered equal to z0m. Long wave radiations are emitted by earth’s surface. It is a function of temperature of the ice/snow surface and As the surface roughness length for momentum it can’t exceed 316W/m2 because ice/snow can’t be transfer (z0m), the surface roughness length for heat warmer than zero degrees. It is calculated as- (z0t) and humidity (z0q) were considered equal so it can be expressed as: 4 Lo = s Ts (15) z0m = z0t = z0q = z0 (19) where s is surface emissivity taken from literature as 0.965 (average value) and Ts is land surface The nondimensional stability functions for momentum temperature obtained as from Terra-MODIS data ( m), heat ( h) and moisture ( ) are expressed as (MOD11A1 data product). functions of Rb (Brutsaert, 1982; Oke, 1987): 3.2 Sensible and latent heat fluxes For a stable surface layer, Rb 0 The transport of heat and moisture in the surface -1 -1 2 boundary layer of the atmosphere is dominated by ( m h) = ( m ) = (1-5Rb) (20) turbulent motions and that is why sensible and latent heat fluxes are called turbulent heat fluxes. Bulk For an unstable surface layer, Rb < 0 : Journal of Geomatics 18 1 Vol.7 No.2 October 2013
-1 -1 0.75 ( m h) = ( m ) = (1-16Rb) (21) where M is the depth of melt water (mm/day). Similarly the sublimation rate can be calculated using In equation (17) and (18), wind speed at height z is following relation: obtained from AWS, installed near the snout of the glacier. LST product from Terra-MODIS wasused. For LE = Ls * S (24) entire analysis period the roughness length for where LE is the Latent Heat Flux calculated by momentum transfer (z0m) is taken from literature equation (18) and Ls is the Latent heat of sublimation 5 (Brock et.- al., 2006). Ls (Ls = 28.3 * 10 J/kg). The ablation (A) can be calculated as: 3.3 Ground heat flux A = M + S (25) The ground heat flux or the conductive heat transfer within a glacier tends to be small when compared to 4. Results and discussions radiative or turbulent fluxes (Marks and Dozier, 1992). The thermal energy stored by the ground during the Results of the computation of each of the component of summer period, when there is no snow cover over the energy balance equation are discussed below: ground, is released during winter and spring which contributes to the melting of the overlying snowpack. 4.1 Net radiations The temperature of the ground surface is reduced due to existing snowpack in comparison to the lower part Average monthly values of net radiations for the entire of the ground, which results in a temperature gradient. study period (January to December, 2011) are shown in Thus the heat is supplied by ground in the upward figure-2a. The net radiation is increasing during direction. In case the temperature of snow just above January to June while it is decreasing during July to the ground is below 00C, the ground heat flux raises the December. It can be explained through corresponding temperature and makes it ready for melting. The variation of net shortwave and net longwave radiations. vertical flux of heat at the snow surface is described as From January to June net radiation varies from 67W/m2 to 176W/m2 and this increasing trend is G = -K * T/ z (22) because of higher albedo values, predominance of clear sky conditions also net shortwave radiations dominate where K is the thermal conductivity of snow/ice (in W net longwave radiations and thus the net radiations are -1 -1 m K ), T is the snow/ice temperature, and z is the high while from July to December it varies from depth (Wagon et al., 1999). 165W/m2 to 82W/m2 and it is because of lower albedo values and predominance of cloudy sky conditions. 3.4 Surface energy balance of snowpack
The internal energy change of the glacier ( Qm + 4.2 Sensible heat flux Qs) is calculated using equation (1). If the left hand side of the equation (1) is positive, energy is available The variation of sensible heat flux for the entire to the snow/ice. This energy is first used to increase the analysis period is shown in figure-2b. Sensible heat snow/ice temperature until it reaches 00C ( Qs > 0 and flux is negative in April, 2011 which shows that the Qm = 0), and as soon as temperature reaches 00C, the surface boundary layer is in unstable condition. It is melting of snow/ice starts ( Qm > 0). If the LHS is positive in all other months which indicate that the negative then the reverse situation is observed, it surface boundary layer is almost in stable condition. indicates that the melted water of the glacier refreezes Positive value also signifies that it is the source of ( Qm < 0) and then snow/ice temperature decreases energy at the glacier surface. Fluctuations in sensible ( Qs < 0). In equation (1), Qs is the rate of gain/loss heat flux mainly results from variations in wind, air of heat in a vertical column extending from the surface and surface temperatures. to the depth of snowpack. Therefore considering daily means, Qs usually remains zero because the gain of Unstable boundary conditions (negative sensible heat heat during the day is compensated by the loss of heat flux) are found in the early morning (around 0800 – at night. Therefore looking at daily means, the change 0900 LT) while glacier surface is heated by incident of the internal energy of glacier is reduced to the latent shortwave radiations and air temperature is still heat storage change. Now Q (kJ/m2d) is converted to negative. After a while, the temperature at the surface reaches its upper limit 00C, air temperature keeps mass units using the Latent heat of fusion Lf (Lf = 333.5 kJ/kg). increasing to positive values and then the lower atmosphere becomes stable with positive sensible heat M = 0.0031 * Q (23) flux (Wagon et al., 1999). Journal of Geomatics 18 2 Vol.7 No.2 October 2013
Figure 2a: Temporal distribution of Net Radiations between January, 2011 and December, 2011. Energy flux densities represent the mean value for each month.
Figure 2b: Temporal distribution of Sensible Heat Flux between January, 2011 and December, 2011. Energy flux densities represent the mean value for each month.
Figure 2c: Temporal distribution of Latent Heat Flux between January, 2011 and December, 2011. Energy flux densities represent the mean value for each month.
Figure 2d: Temporal distribution of Melting rate between January, 2011 and December, 2011. Journal of Geomatics 1 83 Vol.7 No.2 October 2013
Figure 2e: Temporal distribution of Sublimation rate between January, 2011 and December, 2011.
Figure 2f: Temporal distribution of Ablation rate between January, 2011 and December, 2011
Figure 3a: Melt rate estimated from Energy Balance (EB) model and Temperature Index (TI) model.
Figure 3b: Comparison of melt rate estimated from Energy Balance Model and Temperature Index Model Journal of Geomatics 1 84 Vol.7 No.2 October 2013
4.3 Latent heat flux mentioned two models shown in figure-3b. It is observed from the figure-3b that there is a good Monthly variation of latent heat flux for the study agreement in daily averaged melt rates as computed period is shown in figure-2c. The highly variable wind from the two models. speed, air temperature and specific humidity cause strong fluctuations in latent heat fluxes. Indeed, in the 6. Conclusion morning, as soon as the Sun rises, the atmosphere is dry and latent is highly negative. Positive values of Energy balance equation was formulated for Gangotri latent heat flux signify that it is the source of energy at glacier. Various components of energy balance the glacier surface. Negative sign represents heat sink equation (net radiations, sensible heat flux and latent and it also indicates that snow/ice surface loses mass heat flux) were computed at the glacier surface. The by sublimation and also infers that the stratification of inputs used are surface reflectance and land surface the lower atmosphere is moderately stable. temperature (retrieved from satellite data) and weather parameters (relative humidity, wind speed and air 4.4 Melting, sublimation and ablation temperature) from in-situ measurements. These components were used to compute melting and Monthly average values of melt rate are given in sublimation rates. The results reveal that energy figure-2d. The fluctuations in the melting are because balance is dominated by radiative exchange which is of the variation of the total energy received at the governed by the variation in net shortwave radiation. glacier surface. Higher the energy received at the The turbulent latent heat flux is found to be negative surface higher the melting takes place. most of the time during the study period which indicates continuous sublimation. The turbulent The variation in average monthly sublimation rate is sensible heat flux is little bit prominent in comparison shown in figure-2e. The fluctuations in the sublimation to latent heat flux, which indicates the marginal role of are because of the variation of the latent heat flux at the local air temperature by itself on ablation. The glacier surface. The negative values of latent heat flux comparison of melt rates from energy balance model correspond to sublimation while positive values of and temperature index model shows good agreement latent heat flux correspond to condensation on the over the entire observation period. The results of the glacier surface. present study facilitate a better understanding of the glacier’s dynamics and the response of glaciers to The variation of average monthly ablation rate for the change in atmospheric variables. entire analysis period is shown in figure-2f. The fluctuations in the ablation are because of the variation Acknowledgements of the melting and sublimation at the glacier surface, as ablation is sum of melting and sublimation. We are thankful to Shri. A.S. Kiran Kumar, Director, SAC for his interest in this study. We are also thankful 5. Comparison of energy balance model with to Dr. J.S. Parihar, DD, EPSA; Dr. Prakash Chauhan, temperature index model Head, PMD/ MPSG/EPSA and Dr. I.M.Bahuguna, Scientist, GSD/MPSG/EPSA, for their support and Temperature index model is simple and has potential to fruitful discussions. We owe our sincere thanks to predict mass balances and discharges. It is based on Director, SASE for providing Automatic Weather the assumption that melt rates are linearly related to air Station data for our study. temperature, which is considered as an integrated index of the total energy available for melt. The factor of References proportionality is called degree day factor, DDF (mm 0C-1 per time step). The melt rate based on this model Anslow, F.S., S. Hostetler, W.R. Bidlake and P.U. is given as under (Pellicciotti et al., 2005): Clark (2005). Analysis of meteorological data and the surface energy balance of McCall Glacier, Alaska, M = DDF snow/ice T T > TT USA. Journal of Glaciology. 51, 451-461.
0 T TT Arnold, N.S., I.C. Willis, M.J. Sharp, K.S. Richards and W.J. Lawson (1996). A distributed surface energy where M is the melt rate (mm water equivalent per unit balance model for a small valley glacier. Journal of of time), T is the mean air temperature of each time Glaciology. 42, 77-89. 0 step ( C) and TT is a threshold temperature above which melt is assumed to occur (e.g. 10C). The degree Brock, B.W., I.C. Willis and M.J. Sharp (2006). day factors for the analysis period were optimized for Measurement and parameterization of roughness our study area. The results from energy balance model length variations at Haut Glacier. Journal of and temperature index model were compared for Glaciology, 52, 1-17. Gangotri glacier. Comparison of daily average melt rate from energy balance model and temperature index Brutsaert, B. (1982). Evapouration in the Atmosphere, model are shown in figure-3a. The correlation Theory, History and Applications. Kluwer Acad., between the melt rates computed from the above Norwell, Mass, 299pp. Journal of Geomatics 1 85 Vol.7 No.2 October 2013
Favier, V., P. Wagnon, J. Chazarin, L. Maisincho and Reid, T.D. and B.W. Brock (2010). An energy balance A. Coudrain (2004). One-year measurements of model for debris covered glaciers including heat surface heat budget on the ablation zone of Antizana conduction through the debris layer. Journal of Glacier 15, Ecuadorian Andes. Journal of Geophysical Glaciology. 56, 903-916. Research. 109, D18105. Samani, Z., A.S. Bawazir, M. Bleiweiss, R. Skaggs, Hock, R. (2003). Temperature index melt modelling in and V.D. Tran (2007). Estimation daily net radiations mountain areas. Journal of Hydrology. 282, 104-115. over vegetation canopy through remote sensing and climatic data. Journal of Irrigation and drainage Hock, R. (2010). Glacier meteorology energy balance. engineering. 133, 291-297. Summer school in glaciology, 1-10. Sellers, P.J., R.E. Dickinson, D.A. Randall, A.K. Liang, S. (2000). Narrowband to broadband Betts, F.G. Hall, J.A. Berry, G.J. Collatz, A.S. conversions of land surface albedo I algorithms. Denning, H.A. Mooney, C.A. Nobre, N. Sato, C. Field Remote Sensing of Environment. 76, 213-238. and A.H. Sellers (1997). Modeling the exchanges of energy, water, and carbon between continents and the Marks, D. and J. Dozier (1992). Climate and energy atmosphere. Journal of Glaciology. 51, 25-36. exchange at the snow surface in the Alpine Region of Singh, P. and V.P. Singh (2001). Snow and Glacier the Sierra Nevada: 2. Snow cover energy balance. Hydrology. The Netherlands: Kluwer Academic, Water. Resour. Res. 28, 3043-3054. (Chapter 6).
Oke, T.R. (1987). Boundary Layer Climates, 2nd ed., Suzanne, W.S., Eva, E.B., Jun, L., Menzel, W.P. and 435pp., Routledge, New York. Liam, E.G. (2006). MODIS atmospheric profile retrieval algorithm theoretical basis document. Pellicciotti, F., B. Brock, U. Strasser, P. Burlando, M. Cooperative Institute for Meteorological Satellite Funk and J. Corripio (2005). An enhanced Studies,University of Wisconsin-Madison, Version-6. temperature-index glacier melt model including the Wagon, P., P. Ribstein, R.E.B. Francou and B. shortwave radiation balance: Development and testing Pouyaud (1999). Annual cycle of energy balance of for Haut Glacier d’Arolla, Switzerland. Journal of Zongo Glacier, Cordillera Real, Bolivia. Journal of Glaciology. 51, 573-587. Geophysical Research. 104, 3907-3923. Journal of Geomatics 1 86 Vol.7 No.2 October 2013
Flood simulation for ungauged basin: A case study of lower Tapi basin, India
SudhakarSharma1,Anupam K Singh2 and Akshay O Jain2 1Department of Civil Engineering, Nirma University, Ahmedabad 2Department of Civil Engineering, Pandit Deendayal Petroleum University, Gandhinagar Email: [email protected] ; [email protected] ; [email protected]
(Received: August 29, 2012; in final form September 5, 2013)
Abstract: In India, most of the watersheds up to 500km2 geographical area can be termed as ungauged catchments. The estimation of surface runoff and hydrological behavior of these catchments is not only erroneous rather it is impossible to understand the hydrological response. This is mainly due to absence of historical data records on rainfall-runoff process and physical behavior of watershed for a given hydrological event. In this research paper, simulation of the flood events was carried out for a short time period using GIS-based hydrologic model from US Army Corps of Engineering- HEC-HMS and HEC-GeoHMS. These models were used to simulate flood response for an ungauged rural watershed having geographic area of 437 km2. The topographic, soil and land use data for the study area were processed using the GIS tool to estimate potential flood discharge. Intensity- Duration- Frequency (I-D-F) curves for 2, 5, 10 and 25-years of return period were generated for Varekhadi catchment. Thus I-D-F curves were used for simulating the flooding potential for various return periods. Model input parameters on topography, soil and land use were subject to in-situ field verification. The simulated peak runoff and time of peak at five ungauged locations were simulated using HEC-HMS model. Model was validated for Godsamba discharge site for a 2-days rainfall event during 6-7 August, 2010. It can be concluded that model has over-estimated in the range of 11-13% in comparison to measured discharge. It was observed that HEC-HMS was able to simulate discharge at ungauged site satisfactorily.
Keywords: Flood modeling, GIS, HEC-GeoHMS, HEC-HMS, LTB
1. Introduction maximum precipitation using intensity- duration-
2 frequency (I-D-F) data as input to frequency storm In India, watersheds up to 500km of geographical area method of HEC-HMS. USACE (2003) has opined that can be classified as ungauged catchments. These hydrographs produced by HEC-HMS can be used for watersheds have no organized past data records on flow forecasting, flood damage reduction, floodplain depth-discharge relationship or rainfall-runoff process. regulation, and systems operation in conjunction with Due to insufficient data, the estimation of surface pre-processing software such as HEC-GeoHMS. Yener runoff and hydrological behavior of these catchments et al. (2007) remarked that model parameters should be is not only erroneous, but impossible to understand the checked and updated for more precise simulation hydrological response. The hydrological response from results. They have generated hydrologic model in each catchments helps in river routing vis-à-vis in HEC-GeoHMS using DEM and stream network of the flood modeling and flood forecasting. Ogden et al. study region. Ogden et al. (2001) mentioned that HEC- (2001) cited few models on discharge estimation for GeoHMS is useful in determining drainage paths and ungauged catchments, which require determination of watershed boundaries which is needful in determining physical parameters of a catchment. They also the watershed response to rainfall events. mentioned various models which take advantage of spatially distributed data in a geographic information Hammouri and El-Naqa (2007) did hydrological system (GIS) format for watershed analysis and modeling of ungauged wadis in arid environments hydrologic modeling such as HEC-HMS, WMS, using HEC-HMS with GIS for Wadi Madoneh in TOPAZ, AGNPS, and HEC-1. The Hydrologic Jordan. HEC-HMS was used to predict the surface Modeling System (HEC-HMS) by US Army Corps of runoff as result of different design storms. Later the Engineering can simulate the rainfall-runoff processes simulated model results were calibrated against of dendritic watershed system. It helps in determining measured runoff events. They found out in their study flow volume at a point in a river which is of vital for that HEC-HMS model is unbiased in predicting the flood modeling and forecasting. simulated runoff. Yusop et al. (2007) have also simulated HEC-HMS model for modelling storm flow Yener et al. (2007) observed that rainfall-runoff hydrograph in an oil palm catchment, Malaysia and the models are widely used for gauged catchments through model performance was found to be satisfactory with the last century to formulate a reliable relationship in-situ data. Oleyiblo and Li (2010) used HEC-HMS between the rainfall (input of the model) and runoff with HEC-GeoHMS for flood forecasting in Misai and (output of the model). They took Yuvacik basin Wan’an catchments in China. They examined located in southeastern part of Marmara region of applicability, capability and suitability of HEC-HMS Turkiye as study area and computed runoffs that for flood forecasting followed by validation of correspond to different return periods and probable simulated results. They were of the opinion that HEC-
© Indian Society of Geomatics Journal of Geomatics 1 87 Vol.7 No.2 October 2013
HMS has holistic application for flood forecasting. watershed consist of 2 major surface water reservoirs This research study uses HEC-HMS with pre- Issar and Amli dams which are located in the study processing model HEC-GeoHMS for flood simulation area. The dam storage is mainly used for flood control in Varekhadi watershed in absence of gauged data. during monsoon season and for irrigation during the post-monsoon through gravity canal system. The right 2. Study area bank canal from Kakrapar weir located 30km upstream of Varekhadi confluence also passes through Tapi basin covers a geographical area of 65145 km2 watershed and is predominantly used for irrigation and is the India’s second largest westward draining purpose. inter-state river in Arabian Sea. Basin covers three states having an area of 51504 km2 in Maharashtra, The geographic coordinates of the study area are 0 0 0 0 9804 km2 in Madhya Pradesh and 3837 km2 in the 21 14'N 73 07'E to 21 30'N 73 30'E as lower left and Gujarat. The Tapi river basin can be classified in three upper right corners. The study area receives an average zones, viz. Upper Tapi basin, Middle Tapi Basin, and yearly rainfall of 1376 mm, minimum and maximum Lower Tapi Basin (LTB). The area between Ukai dam temperature of 22°C and 40°C respectively and relative to Arabian Sea has been considered as LTB, mainly humidity values as 89% maximum and 32% minimum occupying Surat and Hazira twin city along with tens over the year. of small towns and villages along the river course. LTB having a geographical area of 2920km2 has Major landuse/landcover categories are built-up, experienced periodic floods. The Surat and Hazira twin agriculture, forest, fallow, water bodies and other. cities are almost 106km downstream of Ukai dam and Hydrological soil group of B and C is available in were affected by recurrence floods. One among the Varekhadi catchment. Major problem in study area is major causes of flood in LTB is attributed to early flood in low laying areas near Wareli village at the peak discharge from various tributaries such as junction of Varekhadi and Tapi river. The recent flood Varekhadi, Anjana khadi, Serul khadi, Mau khadi and during August 2006 in Tapi catchment caused greater Gal khadi. damage to life and property resulting into 300 people deaths and US$ 4.5 billion value property damage (Singh and Sharma, 2009).
3. Methodology
The research objective of current study is to understand hydrological response from watershed and to devise a method for rainfall-runoff characterization. Varekhadi watershed is delineated into 5 sub- watersheds i.e. Zankhawa, Amli, Vishdalia, Wareli and Godsambha using DEM and stream network data. HEC-GeoHMS was used for extracting physical parameters of sub-watersheds scale. Later, hydrological modeling was done using HEC-HMS. I- D-F curves were generated for storm frequency for 2, 5, 10 and 25-years of return period. These data were input to hydrological model and results were obtained. Following methodology was adopted for flood simulation in HEC-HMS model as shown in figure 2.
3.1 Geodatabase generation
The geo-database for Varekhadi was created using topological maps, satellite remote sensing images and field surveys using GPS. Topographical maps at 1:50000 scales were collected, geo-referenced and Figure 1: Study area digitized for various themes such as contours, level points, streams and watershed boundary. Based on Varekhadi stream is a tributary of Tapi river information obtained from maps, attribute properties to confluences near Mandvi town. The stream confluence these themes were assigned. The geo-data base on is 40 km upstream of Surat city as depicted in Figure 1. above listed themes was cross-checked with field and The study area is a part of LTB in Gujarat state, India. attributes were revised. Later, a digital elevation model The length of Varekhadi tributary is approximately 50 (DEM) for 50m cell size and 2.5m vertical accuracy km, covering a geographical area is 437 km2 (figure 1). for LTB was generated. Integrated map of DEM and The sub-watershed consists of 1 urban centre stream network with varekhadi sub-watershed Zankhwaw along with 150 rural settlements. The boundary is shown in figure 3. Journal of Geomatics 18 8 Vol.7 No.2 October 2013
For generating hydrological soil group map, soil survey of study area was conducted. Thirty points were selected for soil sample and were analyzed in the university laboratory. Soil properties were identified and hydrological soil group map was prepared. There are two types of soil in study area, namely group B and C, as shown in figure 5.
Figure 2: Methodology for HEC-HMS model
Figure 5: Hydrological soil group map
Generating Curve Number (CN) map, hydrologic soil group field from the soil map and the land use field from the land use map were used for generating CN map as shown in figure 6.
Figure 3: DEM with stream network
Land use/land cover map was generated using remote sensing satellite data. The image of Landsat-7 ETM+ (10 Nov, 2001) Band 2,3,4 with 30 meter spatial resolution and PAN with 15 meter ground resolution were selected for land use/land cover mapping. Land use/land cover categories in the study area were built- up land, agriculture, forest, fellow land, water bodies and other as shown in figure 4.
Figure 6: Curve number map
3.2 HEC-HMS modeling
After creating above layers, further analysis was done using Arc-hydro for generating various rasters useful for flood simulation modeling such as flow direction, flow accumulation, stream segment and sub-watershed delineation which were exported in HEC-GeoHMS for extracting various hydrological parameters such as watershed area, slop, stream length, centroid length, centroid, basin lag, time lag and later imported into HEC-HMS model for further analysis as shown in Figure 4: Land use/land cover map figure 7. Journal of Geomatics 1 89 Vol.7 No.2 October 2013
Table1: HEC-HMS model output for 5 sub-watersheds Watershed Watershed Area Peak Time of ID Name (km2 ) Discharge Peak (m3/s) W130 Zankhawa 112.25 147.8 07Aug2010 , 06:30 W170 Amli 101.83 160.1 07Aug2010 , 01:30 W180 Vishdalia 92.66 233.2 06Aug2010 , 21:00 W200 Godsamba 62.43 246.8 06Aug2010 , 18:30 W220 Wareli 67.89 269 06Aug2010 , 18:30
Zankhawa sub-watershed has highest geographical area of 112.5 km2 among all 5 sub-watershed and has peak discharge of 147.8 m3/sec. Godsamba sub- Figure 7: HEC-HMS model watershed is the smallest one having geographical area of 62.43 km2 and Wareli has highest peak discharge Soil Conservation Service (SCS) hydrograph method among all 5 sub-watersheds (i.e. 269.0 m3/sec) as was used for hydrograph analysis. Twentyfive year shown in figure 9. It is observed that peak discharge is rainfall analysis was done and I-D-F curve was inversely proportional to geographical area of sub- generated as shown in figure 8. This frequency storm watershed. Peak discharge and time of peak were data was used for flood simulation. I-D-F curve shows found out in HEC-HMS using rainfall data of I-D-F 123mm/hr intensity for 25 year. Simulation results are curve along with other physical parameters of discussed in result section. watershed for 2 day event (i.e. 6-7 Aug, 2010).
There was a need of actual rainfall data for flood simulation of particular event for given study area. Due to inadequate rainfall data availability, rain gauge station was installed to obtain rainfall data for a particular event (i.e. 6-7 Aug, 2010). Well laid down selection criteria (representative, secure,with minimum obstacles, of uniform wind speed etc.) were followed for selecting location of rain gauge station. Later, this model results were validated using rainfall data for 2- day event (i.e. 6-7 Aug, 2010) obtained from rain- gauge installed in study area. Results in form of tables, graphs and maps were generated which are presented and discussed in result section. Figure 9: Rainfall-Runoff modeling at Godsamba
It was required to validate simulated results with observed flood data. As given watershed was ungauged, there were inadequate availability of flood data. It was needed to install stream gauge sensor for flood data collection. There are certain site selection criteria such as downstream location, rock surface, straight river reaches, easy accessibility etc. were followed for selecting stream gauge sensor. It was observed that Godsamba site fulfilled all above mentioned criteria. Later, simulated results were validated at Godsamba site for single 2-day rainfall event with stream gauge sensor data which shows good Figure 8: I-D-F curve of Varekhadi Sub-watershed fit. Simulated results were compared with observed stream gauge sensor data were in the range of 11-13 % 4. Results and discussions for that particular event. Comparative predicated peak discharge and observed peak discharge value at Varekhadi watersehd was delineated into 5 sub- Godsamba are shown in figure 10. watersheds (i.e. Zhankhaw, Amli, Vishdalia, Godsamba, Wareli) and their properties such as area, A probable reason for this over prediction might be time of peak were found out as shown in table 1. low accuracy of DEM and error during installation of Journal of Geomatics 190 Vol.7 No.2 October 2013 stream gauge sensor. It was also observed that Superintending Engineer, Surat Irrigation Circle for his behavior of flood response in both simulated and whole hearted support in facilitating the research work. observed case is similar. It can be concluded that Our sincere thanks Mr B.K Cheba and B.P Patel both presented research methodology satisfactorily predicts Additional Assistant Engineers at Mandvi and runoff for a given rainfall event under ungauged Tadkeshwar Sub-division Irrigation Department, condition. Major limitation of the research work is that government of Gujarat for logistics and field assistant. there is limited history of time series for comparison purpose as there was only 1-year data available for the References purpose of validation. It can be stated that HEC-HMS model helps in flood prediction in the absence of Hammouri, N. and A. El-Naqa (2007). Hydrological sufficient hydrological data availability. modeling of ungauged wadis in arid environments using GIS: a case study of Wadi Madoneh in Jordan Revista Mexicana de Ciencias Geológicas, 24 (2), 185- 196.
Ogden, F.L., J. Garbrecht, P.A. DeBarry and L.E. Johnson (2001). GIS and distributed watershed models. Journal of Hydrologic Engineering, 6 (6), 515- 523.
Oleyiblo, J.O. and Z. Li (2010). Application of HEC- HMS for flood forecasting in Misai and Wan’an catchments in China. Water Science and Engineering, 3(1), 14-22.
Singh, A.K. and A.K. Sharma (2009). GIS and a Figure 10: Result validation at Godsamba site remote sensing based approach for urban flood-plain 5. Conclusion mapping for the Tapi catchment, India. IAHS Publ. 331, 389-394. This research study used HEC-HMS with pre- processing model HEC-GeoHMS for flood simulation USACE-HEC (2003). Geospatial hydrologic modeling in Varekhadi watershed in absence of gauged data. extension, HEC-GeoHMS v1.1. User’s Manual, US This model predicted flood response from 5 sub- Army Corps of Engineers, Hydrologic Engineering watersheds. Later, the simulated results were validated Center. with the observed data from the automatic stream gauge sensor at Godsambha site. It was found that Yusop, Z., C.H. Chan and A. Katimon (2007). Runoff model was over predicting flood discharge in the range characteristics and application of HEC-HMS for of 11-13 % for that particular event. It can be stated modelling storm flow hydrograph in an oil palm that HEC-HMS model helps in flood prediction in the catchment. Water Science & Technology, 56 (8), 41– absence of sufficient hydrological data availability. 48. Acknowledgement Yener, M.K., A.A. orman and T. Gezgin (2007). This research work was carried out under ISRO- Modeling studies with HEC-HMS and runoff scenarios RESPOND research grant sanctioned to Dr-Ing. in Yuvacik Basin, Turkiye. Proceedings of Anupam K. Singh wide sanction no.ISRO/RES/541 International Congress River Basin Management /07- 08. We are thankful to Mr K. B. Rabadia Antalya, Turkey, March 22-24, 2007. pp 621-63.
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Site suitability analysis for a central wastewater treatment plant in Accra metropolitan area using geographic information system
Alex Barimah Owusu and Paulina Ansaa Asante Department of Geography and Resource Development, University of Ghana, LG 59, Legon, Accra, Ghana, West Africa Email: [email protected] ; [email protected]
(Received: March 13, 2013; in final form September 4, 2013)
Abstract: This study presents a site suitability analysis for a central wastewater treatment plant (CWTP) using Geographic Information Systems (GIS) technology. The main objective was to identify locations suitable for the construction of a CWTPs in the Accra metropolitan area to solve the problems relating to the general absence and malfunctioning of wastewater treatment plants in the metropolis and assess the perceptions people hold relating the projects. For this purpose, several variables including, elevation, distance to surface water bodies, land cover, distance to major roads, airport facilities and existing populated communities were analysed using GIS technology in order to accept or reject a particular area within the study area. The results subsequently combined all areas of interest into a final composite map showing suitable areas and unsuitable areas for the construction of a CWTP. The GIS analysis finds that there are many small-size lands that can be used. However, given the size requirements, few areas constituting about 20km2 of the total land surface were suitable for the construction of the CWTPs. Most of the time, public does not support construction of such a plant because they are not well informed.
Keywords: Geographic Information Systems; Site suitability, Accra metropolitan area, Waste water
1. Introduction treatment of wastewater in 1996 and prepared a feasibility study which was financed by the African The problem of wastewater treatment is becoming Development Bank, to improve on sewerage, effluent more exigent since it has critical consequences for both disposal and sanitation in Accra. surface and ground water resources. In most cities in Sub-Saharan Africa, large amounts of wastewater is The feasibility study made recommendations regarding discharged into the environment without adequate improvements on the off-site and on-site sanitation treatment (Hutton et al., 2007). Untreated wastewater facilities in the city of Accra. Thereafter, in 2004, a in Ghana is mostly discharged directly into drainage proposed solution for wastewater treatment in the systems that empty into water bodies such as rivers, metropolitan area was unveiled and central to the lagoon and streams in the country, especially in the proposal was the recommendation for the construction urban and peri-urban areas. Such pollutants impair the of two central or off-site wastewater treatment plants aesthetics of beaches, destroy aquatic life and have based on stabilization pond (SP) technology, which serious health implications to people exposed to them. combine low-cost, low-maintenance, simple and Industrial wastewater is generated from breweries, reliable operation and high removal efficiencies textile, chemical and pharmaceuticals and mining (Gemitzi et al., 2007). The question now is which areas industries. Most of these industries empty their are suitable for the location of these central wastewater wastewater into nearby drains without treatment. treatment plants (CWTPs)? Another question that Domestic and storm water in both cities and villages arises is what relevant factors need to be taken into are discharged into open drains which finally ends up consideration when selecting suitable sites for the in water bodies without treatment. project?
Accra, the largest city in Ghana has a number of As is the case with most facility planning exercises, industries and growing population which implies that it site selection typically involves a screening process in produces a lot of wastewater. With an estimated which several alternative locations are evaluated population of about 3 million, waste quantities are against a set of planning/design criteria, or constrains, increasing rapidly and may double by the next decade in order to arrive at a recommended site. Since the (EPA, 2002). Disposing of wastewater is becoming a constraints can often times be represented spatially, major concern in the metropolis. A number of Geographic Information Systems (GIS) has proved to treatment plants in the past were constructed to treat be a powerful tool for performing the site suitability the expected increase in wastewater in the city, but analyses. majority of the plants are in deplorable state. As far back as the year 2000, about 80% of wastewater Selecting sites for waste water treatment plant requires treatment facilities in the city were not functioning considerations of numerous geographic factors and leading to disposal of untreated wastewater into their interactions. This usually involves numerous streams and rivers in the city (EPA, 2000). This has led government agencies with varied requirements and to pollution of water bodies in the metropolitan area. criteria. These criteria for site selection include The Government of Ghana recognized the need for the physical characteristics as well as socioeconomic,
© Indian Society of Geomatics Journal of Geomatics 19 2 Vol.7 No.2 October 2013 ecological and land use (social) factors. The views of region makes the area more favorable for the the public, who are the main beneficiaries of such establishment of CWTPs. projects, are required to be taken into account in the site selection process. In light of this the study 2. Methodology examined the following questions: 2.1 General 1. What factors should be considered in selecting sites for CWTPs? The study was approached in two ways. The first part 2. Which area(s) in Accra Metropolitan Area involved stakeholder interviews and criteria (AMA) may be suitable for the installation of a assessment to identify common criteria that need to be CWTP based on these factors? considered in suitable site selection. The second part 3. What are the perceptions of residents around was the application of GIS-based multicriteria decision wastewater treatment plants? making in modelling these factors in order to arrive at locations that are suitable for locating wastewater In this study, suitable areas within the AMA for the treatment plant. construction of a CWTPs were identified using GIS In collecting data and evaluating factors and technology. Sites selection was based on a selection constraints to be included, the following agencies were process application that takes into account both contacted: physical/environmental and socio-economic factors such as topography, land cover, distance to major a. The AMA waste management department residential areas, distance to surface water bodies, b. The Environmental Protecting Agency (EPA) distance to airport facilities and distance to major c. Accra Sewerage Improvement Project (ASIP) roads. Also the views of residents living around an old wastewater treatment plant were sought. Based on factor analysis by AMA, ASIP, EPA and some residents, the following factors emerged as the The study was conducted in the AMA of the Greater essential criteria for site selection: Accra region of Ghana, West Africa. The study area is a major city in Sub-Saharan Africa with a total land • Slope surface area of approximately 231km2 and lies in a • Land cover coastal zone. The area experiences two rainy seasons, a • Distance to existing road wet and dry season in a year. With a tropical climate, a • Distance to water bodies (rivers and lagoons) total population of 3 million (GSS, 2002) and lack of • Distance to major populated communities wastewater treatment facilities in most parts of the • Distance to Airport Facility
Figure 1: Map of the AMA showing various communities and sub-metropolitan areas Journal of Geomatics 1 93 Vol.7 No.2 October 2013
The specific requirements of each criterion and how it plants in Ghana and residents living around wastewater was used are discussed in the appropriate sections. Also treatment plants were interviewed. The Government the stakeholders were divided on the exact size of the Agencies include, AMA waste management department, area requirement since it is tied to the type of technology ASIP and EPA. Each had varying siting requirements and used. However a minimum of 20 km2 was commonly criteria. The requirements that were common to all the recommended. three were selected and used for the analysis. The variables identified are listed below. The modeling of data based on multicriteria decision making was done as follows: • Land availability • Slope Spatial decisions problems such as the selection of sites • Soil issues for major public facility projects that have high risk to • Hydrology both the environment and public, typically involves a • Community issues large set of feasible alternatives and multiple, conflicting • Cost of Land and incommensurate evaluation criteria. These • Territorial set up: proximity to roads, airport alternatives are often evaluated by a number of facilities and populated communities individuals (Decision-makers, managers, interest groups and stakeholders) who may have unique preferences with The laws in Ghana have no specified minimum respect to the relative importance of criteria on the basis requirement for any of the above mentioned variables of which the alternative are evaluated. Accordingly, many when it comes to selecting sites for CWTP. The spatial decision problems give rise to the GIS-based requirements used in this study are mainly adapted from multicriteria decision analysis (Malczewski and Rinner, the Ghana landfills guidelines and views of officials in 2005). the Government agencies. The specific requirements of each criterion and how it was used are discussed in the Site selection is a spatial problem that requires inputs of appropriate sections. large volumes of environmental, economic and socio- political data. GIS have proven to support the 2.2 Preparation of spatial data in GIS management and analysis of large volumes of data and so is often recognized ‘as a decision support system The data required were the spatial and attribute data in involving the integration of spatially referenced data in a the right format capable of being handled in an ArcGIS problem solving environment’ (Cowen, 1988). In GIS environment. The data was collected from AMA and system there is multicriteria decision making algorithm EPA. We performed quality checks to ensure precision, that provides a rich collection of techniques and accuracy and lineage to understand the data. procedures for structuring decision problems and designing, evaluating and prioritizing alternative Selecting locations for wastewater treatment plants was decisions. For this study, in order to select optimal done by thematic vector layers analysis in ArcGIS 9.3. locations for wastewater treatment plants, a spatial Slopes were obtained from Digital Elevation Model multicriteria model integrated with GIS data analysis (DEM) of the AMA. Land cover polygon layer was was made in an effort to identify the optimal location in obtained from land cover map and from which suitable the AMA, Greater Accra region where the general land cover type was selected in ArcGIS vector analysis. absence of wastewater treatment plants have become a Areas covered by human settlements, major roads and the significant issue to the city administration and residents. airport facility were obtained from 1: 50,000 land use Identification of optimal location for CWTPs for the map obtained from the EPA. study area was done by GIS-based multi-criteria analysis methodology involving the following main steps: 2.3 GIS multicriteria decision making
i) Identification of variables that influence the siting The type of decision problems that interests spatial of wastewater treatment plant. planners typically involve numerous and conflicting ii) Criteria identification and definition of the upper criteria and a set of alternatives evaluated by different or lower limits of each variable. people characterized by unique preferences with respect iii) Preparation of necessary spatial data in GIS and to the relative importance of criteria considered. GIS is a creation of thematic maps. geo database system that uses computers to collect, store, iv) Application of GIS in modelling these factors in manipulate, analyze and display geographic information. order to arrive at locations that are suitable for GIS has been used as a tool in this research for handling locating wastewater treatment plant. and manipulating both spatial data and non-spatial data such as road networks, rivers, community coverage, land 2.1.1 Identification of variables that influence the cover, location of airport facility and surface slope sitting of Treatment plant analysis. The major aim of the GIS application is to support comprehensive decision making. The GIS plays a In identifying the factors that influence site selection, major role in the site suitability analysis for the officials from three Government agencies who are mainly wastewater treatment plant by storing and manipulating in charge of selecting sites for wastewater treatment the large amounts of spatial and non-spatial data and also Journal of Geomatics 1 94 Vol.7 No.2 October 2013 performing the analysis task during the final decision they were covered by forest, lagoon and in a coastline. making. From the land cover, two areas were selected as ideal for the construction of the treatment plant facility. The maps Spatial multicriteria decision analysis can be thought of below (Figure 4 and 5) show the land cover of the area as a process that combines and transforms geographical and the selected land cover area. Figure 4 shows the land data (input) into a resultant decision (output). cover of AMA and Figure 5 shows the selected land (Malczewski, 2004). The efforts to integrate GIS and cover. MCDA began mostly in late 1980s and early 1990s. This development can be associated with the proliferation stage of the GIS development (see Waters, 1998; Malczewski, 2004). Researchers have often applied GIS- MCDA for mostly ‘Spatial Decision Support Systems (SDSS)’, ‘Collaborative Spatial Decision Making’ and ‘GIS and Society’ projects.
In this study the GIS based multicriteria analysis process involved the creation and analysis of several grids of different themes (Siddiqui et al., 1996). The GIS analysis resulted in the creation of thematic maps which satisfy criteria used in selection of sites. Variables analyzed include slope, land cover, distance to existing major roads, distance to rivers and lagoon, distance to existing major populated communities and distance to airport Figure 2: Slope map of AMA in five groups (%) facilities. The process was divided into two steps, first, a map was created for each variable based on specific criteria and the second step looked at the actual analysis of the created files. To increase the speed of the final analysis, for each variable, the created maps showed only the areas of interest. The scale of capture of the topographic data was 1:250,000.
A map was created for each of the above mentioned variables based on criteria identified. For each criterion, two discrete categories (suitable and unsuitable categories) were created. The study applies exclusionary criteria (Kontos et al., 2003). Integration of the factors within functions was achieved using an overlay procedure. A final composite map was created showing areas within AMA that satisfied all suitability criteria for citing CWTP. ArcGIS software extensions such as Figure 3: Selected areas based on slope ArcMap, ArcScene and ArcCatalog were used for analyzing spatial datasets.
2.3.1 Slope
Mild slopes would result in higher hydraulic residence times and thus higher pollutant removal capacity (Economopoulou and Tsihrintzis, 2002, 2004). To minimize pumping costs and improve the overall efficiency of the wastewater treatment plant a maximum slope value of 10% was considered. Steeper areas would not be economically appropriate because they would require excessive excavation and pumping costs is likely to increase. Figure 2 and Figure 3 show slope analysis.
2.3.2 The land cover Figure 4: Land cover of Accra metropolitan area Any potential treatment facility must have as little impact as possible on the existing population (Gemitzi et al., 2.3.3 The distance to major surface water bodies 2007). Non-forested areas, such as agricultural, non- populated areas and grasslands land are considered Streams, rivers, lakes and the coastline offer the main acceptable for the construction of wastewater treatment disposal options for effluents (an outflow of water from systems. The rest of the land cover areas were rejected, as natural body of water or human made structure like a Journal of Geomatics 1 95 Vol.7 No.2 October 2013 wastewater treatment plant) after treatment, if an 2.3.4 The distance to major roads irrigation alternative is not possible. Thus, for practical reasons, the proposed facilities should not be far away A buffer zone of 300 meters was created around main from the effluent disposal area for instance rivers, sea and highways (figure 7). This distance was created mainly for so on. However, according to the Ghana landfill visual impacts. The presence of a wastewater treatment guidelines for landfill sites, a 90-m buffer zone should be plant have no influence on traffic as is in the case of maintained around river and 300-m buffer zone around sanitary landfills which goes with solid particles and lakes, lagoons. In the study area a 90-m buffer zone was attracting birds that might influence transportation in one created around the rivers and 300m around lagoon as way or the other. It should be noted that the law in Ghana shown in fig 6. This is to help in protecting surface does not specify any minimum setback distance between waters from a possible leakage of untreated wastewater as wastewater treatment plants and major roads. well as protecting the facilities from flooding. 2.3.5 Communities issues
In order to minimize any public health risk and inconvenient effects such as bad odour, spillage of wastewater and any visual effects while the plant is under construction or after construction, it is necessary to locate such facilities at least 500m away from existing community areas. The law in Ghana does not specify minimum distances from community’s limits to wastewater treatment facilities. This distance was then selected based on the Ghana landfill guidelines for sanitary landfills (EPA, 2002). Accordingly, a 2000-m buffer was then created around major populated areas as shown in fig 8; since the data used have limited information on all the communities in AMA. Also a 3000-m buffer was created around the airport facility to Figure 5: Selected areas based on land cover avoid any visual effects as a result of the presence of the plant (figure 9 illustrates).
Figure 6: Buffer around major surface water bodies Figure 8: Buffer around major populated areas in Accra Metropolitan Area (AMA)
Figure 7: Buffer around major roads in study area Figure 9: Buffer around airport area Journal of Geomatics 1 96 Vol.7 No.2 October 2013
3. Results 4. Discussions and conclusion
Having all the different variables examined in the study This study presented the selection of suitable sites for in grid format. The final analysis was carried out using CWTP based on identified socio-economic and the overlay function in, ArcMap. The result is showing environmental factors that influence the selection of sites the areas that meet all the previously specified criteria for the plants using GIS technology. The methodology and are suitable sites for the construction of CWTPs (fig was applied to Accra Metropolitan Area in the Greater 10). This selection is only based on the available variable Accra Region, an area of about 231km2. In covering the datasets used in the analysis. Other variables may also be whole area a scale of 1:50000- 1:250000 were used. A taken under consideration if available, for instance, data more accurate study can be done applying the same on aquifers and soil quality and so on. The source of the methodology at the selected areas using finer scale maps spatial datasets used for analysis is the Environment and applying non-exclusionary criteria. Protection Agency Accra Metropolitan Area Mapping in 2009. This implies that as at 2009, these areas based on The methodology applied derives from the use of GIS criteria used were suitable for the construction of technology. GIS technology was used in the creation of CWTPs. The total land area for suitable sites is about the thematic maps and making the final analysis where 20km2. areas of interest were highlighted based on relevant criteria. Criteria used in the selection process in the Accra Metropolis were from guidelines used in selection of Landfills sites and opinions of officials in Environmental Protection Agency Ghana, Accra Sewerage Improvement Project, Ghana, Waste Management Department of Accra Metropolitan Assembly, Ghana and Residents living around old wastewater treatment plants in the area.
In conclusion, the present study offers a simple and fast way to examine large areas and to highlight possible locations of CWTPs, using only exclusionary criteria, as the application of non- exclusionary criteria would require the adoption of fine scale data, complex computation, and more time which may not be ideal for large area planning.
Figure 10: Final composite map showing suitable sites However, any final decision by the sub-metro level that can be used for central wastewater treatment plants should be based on more detailed examination on each selected site, involving a ranking of all possible sites 3.1 Public participation analysis using non-exclusionary criteria and taking into account the opinion of the people in the local communities. The involvement of the public in decision making is very Education of the local people is also very vital as it important when undertaking any project that is of informs them on the essence of the installation of the particular interest to them and how it can impact their treatment plants and how they would have to handle lives. The views of residents living around old central them. wastewater treatment facilities were sought to know the perceptions they hold about such projects. This could Reference influence their actions in supporting such a project. These residents were selected using simple random sampling Cowen, D. (1988). GIS versus CAD versus DBMS: what and interviewed. are the differences. Photogrammetric Engineering and Remote Sensing, 54, pp. 1551–1555. A sample of fifty (50) residents was taken out of the Economopoulou, M.A. and V.A. Tsihrintzis (2002). entire population of residents living around such facilities Sensitivity analysis of stabilization pond system design in the AMA. The sample agreed that although the parameters. Environmental Technology 23 (3), pp. 273– construction of treatments plant is important they can 286. have adverse effects in future if not properly maintained and therefore recommended that Government should Economopoulou, M.A. and V.A. Tsihrintzis (2004). choose locations away from public places and ensure that Design methodology of free water surface constructed the public is also educated on how to handle themselves wetlands. Water Resources Management, 18 (6), pp. 541– around such facilities. Also 86% (43) of the sample 565 advocated for the use of on-site facilities such as septic tanks to be used instead of CWTPs (off-site facilities). Environmental Protection Agency. (2002). Manual for The remaining 14% (7) supported the construction of the preparation of district waste management plans. CWTPs. Accra: Ghana. Journal of Geomatics 19 7 Vol.7 No.2 October 2013
Gemitzi, A., A. Vassilios, O. Christou and C. Petalas Malczewski, J. (2004). GIS-based land-use suitability (2007). Use of GIS in siting stabilization pond facilities analysis: a critical overview. Progr. Plann. 62 (1), pp. 3– for domestic wastewater treatment. Journal of 65. Environmental Management, 82, 155-166. Malczewski, J. and C. Rinner (2005). Exploring GSS (2002). 2000 population and housing census: multicriteria decision strategies in GIS with linguistic summary report of final results. Ghana Statistical Service, quantifiers: A case study of residential quality evaluation. Accra, Ghana. J. Geogr. Syst., 7 (2), 249–268.
Hutton, G., L. Haller and J. Bartram (2007). Global cost- Siddiqui, M., J.M. Everett and B.E. Vieux (1996). benefit analysis of water supply and sanitation Landfill siting using Geographical Information Systems: interventions. Journal of Water and Health, 5(4), 481– A demonstration. Journal of Environmental Engineering, 502. 122 (6), 515-523.
Kontos, T.D., D.P. Komilis and C.P. Halvadakis (2003). Waters, N.M. (1998). Geographic information systems. In Siting MSW landfills on Lesvos Island with a GIS-based Encyclopedia of Library and Information Science, edited methodology. Waste Management and Research, 21 (3), by A. Kent, and C.M. Hall (Eds). New York, NY: Marcel pp. 262–278. Dekker. Journal of Geomatics 19 8 Vol.7 No.2 October 2013
Forest fire risk and degradation assessment using remote sensing and GIS
R. NambiManavalanand S. Jayalakshmi Institute of Remote Sensing, Department of Civil Engineering, Anna University Chennai Email: [email protected] ; [email protected]
(Received: February 6, 2013; in final form September 14, 2013) Abstract: Fire risk map is prepared by considering various factors such as presence of roads, settlements, NDVI, NDWI, temperature, slope and aspect. Nilgiri District forest area has been taken as the study area. Risk maps of individual factors are prepared and overlaid for getting the combined risk map using ArcGIS 9.3 and ERDAS IMAGINE 8.5. The accuracy of the risk map prepared is assessed by comparing with a damage extent map, of a fire occurrence in the same study area, which in-turn was prepared by overlaying pre-fire dated map with a post-fire dated map. About 75% of the degraded areas due to the recent forest fire fall under high risk zones in the prepared fire risk map.
Keywords: Fire risk, Damage extent map, High risk zones
1. Introduction 2.1 Ground fire Fire has been a source of disturbance for thousands of years. Forest and wild land fire has been taking place Ground fire occurs in the humus and peaty layers historically, shaping landscape structure, pattern and beneath the litter of undecomposed portion of forest ultimately the species composition of ecosystems. The floor with intense heat but practically no flame. Such ecological role of fire is to influence several factors fires are relatively rare and have been recorded such as plant community development, soil nutrient occasionally at high altitudes in Himalayan fir and availability and biological diversity. Forest and wild spruce forests. land fire are considered as the vital natural processes initiating natural exercises of vegetation succession. 2.2 Surface fire However uncontrolled fire can cause tremendous adverse impacts on the environment and the human Surface fire occurs on or near the ground in the litter, society (Maeda et al, 2011). ground cover and scrub. They are the most common type in all fire-prone forests of the country. India has a forest cover of about 20.55 % of its geographical area. It is enriched with an ample diversity of forests bloomed with a rich array of floral 2.3 Crown fire and faunal life forms (Roy, 2007). The Forest cover in Crown fire occurs in the crowns of trees. It consumes Tamil Nadu constitutes about 2.95% of the total forest foliage and kill the trees. It occurs most frequently in cover in India out of which more than 40% of the low level coniferous forests in the Siwaliks and forest cover fall under the Nilgiri district Himalayas (National Commission on Agriculture (http://www.forests.tn.nic.in/ForestAtGlance/forestatgl (NCA) Report, 1976). ance_home.html as on 23-01-2013).
The ecological and socio-economic consequences of Knowledge of the geographical and temporal wild land fire in India include- Loss of timber, bio- distribution of burning is critical for assessing the diversity, wildlife habitat, global warming, soil erosion, emission of gases and particulates to the atmosphere. fuel-wood and fodder, damage to water and other One of the important discoveries over the past years natural resources, natural regeneration etc. (Gao et based on a series of field experiment is that fires in al,2011). Crown and surface forest fire occurred diverse ecosystems differ widely in the production of recently in Nilgiri district (Latitude: 11o 29’ 40” N and gaseous and particulate emissions (Roy, 2007). Longitude: 76o 45’ 43” E ) on February 29, 2012 has Emissions depend on the type of ecosystem, moisture degraded 100 hectares of forest areas according to the content of the vegetation, nature, behavior and Forest Survey of India characteristics of the fire. (http://www.fsi.org.in/search1.php). Fire regimes in tropical forests and derived vegetation 2. Types of forest fire are characterized and distinguished by return intervals of fire (fire frequency), fire intensity (e.g. surface fires There are three kinds of fire which burn in forested vs. stand replacement fires) and impact on soil. Basic areas when conditions of fuel and weather permit tropical and subtropical fire regimes are determined by ignition and sustained combustion. They are surface ecological and anthropogenic (socio-cultural) fire, ground fires and crownfires (Roy, 2007). gradients.(Chuviecoa et al,2010). © Indian Society of Geomatics Journal of Geomatics 199 Vol.7 No.2 October 2013
3. Causes of forest fire
Forest fires are caused by Natural as well as Man-made processes. i) Natural causes: It includes lightning, high atmospheric temperature and low humidity. ii) Man-made causes: Fire is caused when a source of fire like naked flame, cigarette or bidi, electric spark or any other source of ignition comes into contact with inflammable material.
A combination of edaphic, climatic and human activities account for the majority of wild land fire. High terrain steepness along with high summer temperature supplemented with high wind velocity and the availability of highly flammable material in the forest floor can contribute to forest fire. The contribution of natural fires is insignificant in comparison to number of fires started by humans (Guang-xiong et al, 2007). Figure 1: Nilgiri district forest area In the mountain area, along with elevation raising, temperature will become lower and humidity will 5.1 Factors considered: increase, so the probability of forest fire reduces (Zhong-wei et al, 2009). Abrupt slope allows faster i. Slope surface runoff, dries the surface fuel and exacerbates the fire spread. Aspect and the received solar radiation Slope is an important physiographic factor, which is are partly correlated. Temperature, relative humidity related to wind behaviour, and hence affects the fire and wind force are the main meteorological factors proneness of the area. Fire travels most rapidly in the (Gai et al, 2011). up-slopes and least rapidly in the down-slopes. The Study area has an uneven land structure due to which it 4. Study area has many slopes. The Slope map is created using 3D Analysis in terms of percentage. The weights are The Nilgiris District (Figure 1) is in the assigned as per Table 1, based on various variables. Indian state of Tamil Nadu. Nilgiri (English: Blue Mountains) is also the name given to a range of ii. Settlements and roads mountains spread across the states of Tamilnadu, Karnataka and Kerala. The Hills are part Forest fire that is accidental / man-made can also be of a larger mountain chain known as the Western resulted by the movements of humans and vehicles. Ghats. The highest point is the mountain of Thus, forests that are near roads are fire prone. Many Doddabetta, with a height of 2,623 m. It has an area of roads are present in the study area. This can make the 2,452.50 km2 with an elevation of 2000 to 2,600 meters access of people from the roads, one of the reasons for above MSL. Its latitudinal and longitudinal dimensions the forest fire. are 130 KM (Latitude: 11° 7’ 48” to 11° 36’ 36” North) by 185 KM (Longitude: 76° 0’ 0” to 77° 9’ 0” Forests located near settlements can be said to be more East) respectively. fire prone since the people living there can cause an accidental fire. Crowded settlements are located within 5. Methodology the forest in the study area, so they can cause forest fires. Each factor is extracted from raw data like DEM, Satellite images, Road map and Settlement map. From Buffer zones are created around the roads and the DEM, slope and aspect maps are prepared using settlements. Forest areas closer to the roads and three-dimensional analysis and temperature-decrease settlements, will have more probability for a fire to map using the DEM pixel values. From the road and break out. According to this, buffer zones were settlement’s raw data, buffered zones are created integrated to fire rating classes and are assigned around every element of road or settlement. From the weights as per Table 1. satellite images the individual colour bands are extracted and analyzed for the NDVI and NDWI map iii. Temperature preparation. These individual maps serve as inputs to the combined processing for the preparation of the fire The environmental lapse rate (ELR), is the rate of risk map. decrease of temperature with altitude in the stationary Journal of Geomatics 20 0 Vol.7 No.2 October 2013 atmosphere at a given time and location. As an vii. Fuzzy clustering algorithm average, the International Civil Aviation Organization (ICAO) defines an international standard In Table 1 all the values belonging to a class have been atmosphere (ISA) with a temperature lapse rate of 6.49 assigned the same weightage. This results in crisp sets K(°C)/1,000 m (3.56 °F or 1.98 K(°C)/1,000 Ft) from of variables which subsequently gives low accuracy in sea level to 11 kilometres (36,000 ft). From 11 to 20 the final result (i.e. the fire risk map). For getting more kilometres (36,000 to 66,000 ft), the constant accuracy ‘Fuzzy Clustering Algorithm’ is used. The temperature is −56.5 °C (−69.7 °F), which is the lowest values of each factor, determined in the Table 1, are assumed temperature in the ISA. The standard multiplied with unique membership values as shown atmosphere contains no moisture. Unlike the idealized by equation (4). The membership values are computed ISA, the temperature of the actual atmosphere does not using equation (5). This whole process is called fuzzy always fall at a uniform rate with height. For example, clustering algorithm using triangular membership there can be an inversion layer in which the function (Dubois et al, 1980). temperature increases with height. FACTOR INDEX = [Factor] * [Membership value] (4) The fire risk layer for temperature is prepared using the For a class (a,c) let ‘x’ be a parameter’s variable and let fact that there is a temperature drop of 6.49 °C for ‘b’ be the mid-valu e. every 1 Km rise in elevation. The calculated values are 0 If x=a compared with field data for more accuracy. The (x-a)/(b-a) If a < x < b Resulting temperatures seem to be approximately equal Membership value = 1 If x = b to the directly measured values. The entire range of (c-x)/(c-b) If b < x < c temperature-decreases are then divided into five equal 0 If x=c (5) classes and assigned weights as per Table 1. 5.2 Damage extent map iv. Aspect
Aspect is the direction that the existing slope faces. Aspect can have a strong influence on temperature. This is because aspect affects the angle of the sun rays when they come in contact with the ground, and therefore affects the concentration of the sun's rays hitting the earth (amount of radiation divided by surface area, termed insolation).Each aspect can differently determine the fire risk severity because of the variability in the solar radiative energy received by the vegetation of the study area. From DEM layer aspect map is prepared using 3D Analysis. Weightages are assigned as per Table 1. Figure 2: Flow chart of the preparation of damage extent map v. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) A Last forest fire was occurred in Nilgiri district on February 29, 2012 (‘The Times of India-Coimbatore A NDVI and NDWI maps are prepared using Landsat- Edition’ dated 29-02-2012). For assessing the damage 7 ETM+. Each band data (i.e. Red, Green & NIR) is usage of high resolution satellite images can be better. prepared separately using equations (1) and (2) But as low resolution images were only available for respectively. such a recently occurred forest fire, (OCM-2 of resolution 360m) satellite images of the study area, on NDVI=(NIR-Red)/(NIR+Red) (1) December 2011 and April 2012, are considered and their pixel’s respective vegetation fraction (VF) values NDWI=(Green-NIR)/(Green+NIR) (2) are computed using equation (6) . The Resulting data (belonging to December 2011 and April 2012) are The entire range is then divided into five equal classes merged and the respective pixel’s VF values are finally and are assigned weights as per Table 1. subtracted (as shown in the Figure 2) to prepare the damage extent map. vi. Fire risk map Vegetation fraction = (NDVI - NDVI ) / (NDVI Fire risk map is prepared by using equation (3) SOIL VEG – NDVI ) (6) SOIL Risk Index= 7(NDVI+NDWI+Temperature) where NDVISOIL – NDVI value of a pure soil pixel, +5(Slope+Aspect) (3) and +3(Roads+Settlements) NDVI – NDVI value of a pure vegetation pixel. (Roy, 2007) VEG Journal of Geomatics 201 Vol.7 No.2 October 2013
5.3 Accuracy assessment risk index is 1.111753 and standard deviation of the indices being 0.989114. The 5 scale range pixel values The pixel values of ‘Fire risk map’ and ‘Damage extent have been divided equally into 3 categories namely map’ are converted to 5 scale range so that ‘0’ and ‘5’ low, medium and high classes. Thus due to slope, remain the minimum and maximum values 72.97 %, 24.34 %, 2.69 % of the study area fall under respectively. The pixel values of ‘Fire risk map’ are low, medium and high risks respectively. subtracted with the ‘damage extent map’. The regions with zero values are considered as higher in accuracy and the values either negative or positive, are considered to be lesser in accuracy.
Table 1: Fire risk factors and their weights Parameters Weight Classes Factors > % 35 5 % 35 - 25 4 Slope 5 % 25 - 10 3 % 10 - 5 2 < % 5 1 North-East 0 North 0.5 South-East 1 East 1.5 Aspect 5 North-West 2 West 3 South 3.5 South-West 4 1.94 - 4.97 5 4.97 – 7.99 4 Temperature 8.00 – 11.01 3 7 decrease 11.01 – 14.04 2
14.04 – 17.06 1 < 100 m 5 Figure 3: Risk index map due to slope 100 - 200 m 4 Distance 200 - 300 m 3 3 from Roads 300 - 400 m 2 6.2 Risk index map due to NDWI > 400 m 1 Distance < 1000 m 5 from 1000 – 2000m 4 Settlements 3 2000 - 3000 m 3 3000 - 4000 m 2 -1 to -0.6 1 -0.6 to -0.2 2 NDVI 7 -0.2 to 0.2 3 0.2 to 0.6 4 0.6 to 1.0 5 -1 to -0.6 5 -0.6 to -0.2 4 -0.2 to 0.2 3 NDWI 7 0.2 to 0.6 2 0.6 to 1.0 1
6. Results and discussions
6.1 Risk index map due to slope Figure 3 depicts the spatial distribution of various risk indices due to slope. The minimum slope angle in the study area is 0°0’0” and the maximum slope angle is 89°53’58.2”. The mean of all the slope variables is 15°49’12.96” and the standard deviation being 14°57’21”. The minimum fire risk index here is 0 and the maximum index being 4.999946. The mean fire Figure 4: Risk index map due to NDWI Journal of Geomatics 202 Vol.7 No.2 October 2013
Figure 4 depicts the spatial distribution of various risk fire risk index here is 0 and the maximum index being indices due to NDWI. The minimum NDWI in the 3.999991.The mean fire risk index is 1.299738 and study area is -0.9993 and the maximum NDWI is 1.The standard deviation of the indices being 1.273913. The 5 mean of all the NDWI variables is 0.34258 and the scale range pixel values have been divided equally into standard deviation being 0.41282. The minimum fire 3 categories namely low, medium and high classes. risk here is 0 and the maximum being 4.The mean fire Thus due to Aspect, 70.678 %, 16.798 %, 12.524 risk is 0.955768 and standard deviation being % of the study area fall under low, medium and high 0.843615. The 5 scale range pixel values have been risks respectively. divided equally into 3 categories namely low, medium and high classes. Thus due to NDWI, 77.64 %, 22.25 %, 0.109 % of the study area fall under low, medium and high risks respectively.
6.3 Risk index map due to NDVI
Figure 5 depicts the spatial distribution of various risk indices due to NDVI. The minimum NDVI in the study area is -1 and the maximum NDVI is 0.9998.The mean of all the NDVI variables is -0.247579 and the standard deviation being 0.45267. The minimum fire risk here is 0 and the maximum being 5.The mean fire risk is 1.128018 and standard deviation being 0.94932. The 5 scale range pixel values have been divided equally into 3 categories namely low, medium and high classes. Thus due to NDVI, 69.58 %, 29.44 %, 0.98 % of the study area fall under low, medium and high risks respectively.
Figure 6: Risk index map due to aspect
Figure 5: Risk index map due to NDVI
6.4 Risk index map due to aspect
Figure 6 depicts the spatial distribution of various risk indices due to Aspect. The minimum angle of aspect in the study area is 0°0’0” and the maximum angle of aspect is 359°59’59.6”. The mean angle of all the Aspect variables is 137°29’28.4” and the angle of standard deviation being 112°11’31.5”. The minimum Figure 7: Risk index map due to settlements Journal of Geomatics 203 Vol.7 No.2 October 2013
6.5 Risk index map due to settlement 1.735776 and standard deviation of the indices being 1.267033. The 5 scale range pixel values have been Figure 7 depicts the spatial distribution of various risk divided equally into 3 categories namely low, medium indices due to the Settlements. The minimum fire risk and high classes. Thus due to the temperature decrease, index here is 0.000005 and the maximum index being 52.58 %, 32.92 %, 14.5 % of the study area fall under 4.99999.The mean fire risk index is 2.041522 and low, medium and high risks respectively. standard deviation of the indices being 1.282319. The 5 scale range pixel values have been divided equally into 3 categories namely low, medium and high classes. Thus due to the settlements, 43.75 %, 37.28 %, 18.96 % of the study area fall under low, medium and high risks respectively.
6.6 Risk index map due to roads
Figure 8 depicts the spatial distribution of various risk indices due to the roads. The minimum fire risk index here is 0 and the maximum index being 4.999984.The mean fire risk index is 1.699174 and standard deviation of the indices being 1.265394. The 5 scale range pixel values have been divided equally into 3 categories namely low, medium and high classes. Thus due to the roads, 56.06 %, 30.431 %, 13.508 % of the study area fall under low, medium and high risks respectively.
Figure 9: Risk index map due to temperature decrement
6.8 Fire risk map
Figure 8: Risk index map due to roads
6.7 Risk index map due to temperature decrement
Figure 9 depicts the spatial distribution of various risk indices due to the temperature decrement. The minimum decrease in temperature in the study area is 0° C and the maximum decrease in temperature is 17.0687° C. The mean of all the temperature decreases is 9.026996° C and the standard deviation in the temperature decreases being 4.136436° C. The minimum fire risk index here is 0 and the maximum index being 4.999372. The mean fire risk index is Figure 10: Fire risk index map in Nilgiri district Journal of Geomatics 204 Vol.7 No.2 October 2013
Figure 10 depicts the spatial distribution of various risk damage extent map and it is found that 73.14% indices due to the combination of all the influencing coincidence. The damage extent map is prepared using parameters discussed previously. The minimum fire low resolution (of 360m) statelite image (OCM II) risk index in the study area here is 0 and the maximum which may be the reason for the reduction of index being 4.983414. The mean fire risk index is conincidence. 1.153604 and standard deviation of the indices being 0.930775. The 5 scale range pixel values have been divided equally into 3 categories namely low, medium and high classes. Thus due to all the parameters, 75.52 %, 20.378 %, 4.102 % of the study area fall under low, medium and high risks respectively.
6.9 Damage extent map
Figure 11 depicts the spatial distribution of various damage indices due to the forest fire that happened on 29/02/2012 in Nilgiri district. The minimum damage index due to the fire is 0 and the maximum damage index being 3.The mean damage index is 1.07185 and the standard deviation of the damage indices being 0.786572. The 5 scale range pixel values have been divided equally into 3 categories namely low, medium and high classes. Thus due to the forest fire 55.96 %, 28.33 %, 25.71 % of the study area have come under low, medium, and high damage respectively.
Figure 12: Accuracy assessment of forest fire risk map prepared by comparing with a forest fire that happened on 29/02/2012 in Nilgiri district
7. Conclusion
The Main influencing parameters like Slope, Aspect, Temperature-decrease, Distances from roads & settlements, NDVI, and NDWI have been identified and fused together to produce the final fire risk map. Then damage extent map of the study area, due to a recent forest fire was prepared, and compared with the fire risk map. After well scrutinization accuracy assessment map was prepared to detail the accuracy of the prepared fire risk map. It was observed that in the damage extent map, 73.164 % of the study area match with the high or low risk areas in the fire risk map. Accuracy might be to the fullest extent had other influencing parameters were also considered. Thus the Fire risk map prepared could have been more optimal Figure 11: Map showing the damage Index due to a had other parameters like relative humidity and wind forest fire that happened on 29/02/2012 in Nilgiri speed, had also been taken into account though its district implementation may be tougher at a district level as these parameters are, both dynamic and highly variable 6.10 Accuracy assessment map even within a town.
Figure 12 depicts the spatial distribution of various References accuracy indices of the fire risk map prepared. The minimum accuracy index is 0 and the maximum Chuviecoa, E., I. Aguadoa and M. Yebraa (2010). accuracy index being 4.9985.The Mean of the accuracy Development of a framework for fire risk assessment indices is 2.495019 and the standard deviation of the using remote sensing and geographic information same is 1.387119. The risk map is compared with the system technologies. Ecological Modelling, pp 46-58. Journal of Geomatics 205 Vol.7 No.2 October 2013
Dubois, D. and H. Prade (2000). Fundamentals of Peninsular Malaysia. Journal China University of fuzzy sets. Kluwer Academic Publishers, Boston, Mining and Technology, 17. London, Dordrecht. Zhong-wei, G., W. Changsha and P. Yan (2009). Gai, C., W. Weng and H. Yuan (2011). GIS-based Measurement of forest fire risk based on VaR. forest fire risk assessment and mapping. Fourth International Conference on Management Science & International Joint Conference on Computational Engineering (16th), pp 309-314. Sciences and Optimization, pp 1240-1244. Maeda, E.E., G.F.B Arcoverde, P.K.E Pellikka and Gao, X, X. Fei and H. Xie (2011). Forest fire risk Y.E. Shimabukuro (2011) fire risk assessment in the zone evaluation based on high spatial resolution RS Brazilian Amazon using MODIS imagery and change image in Liangyungang Huaguo Mountain Scenic vector analysis. Elsevier Journal, 76-84. Spot. Spatial Data Mining and Geographical Knowledge Services (ICSDM), IEEE International Roy, P.S. (2007). Forest fire and degradation Conference, pp 593-596. assessment using satellite remote sensing and geographic information system. Satellite Remote Guang-xiong, P., L. Jing and C. Yun-hao (2007). A Sensing and GIS Applications in Agricultural forest fire risk assessment using ASTER images in Meteorology, pp 361-400. Journal of Geomatics 206 Vol.7 No.2 October 2013
ISG Newsletter
Indian Society of Geomatics (ISG) brings out a newsletter which is very popular because of its popular content on geomatics. The newsletter has featured special themes like desertification, mountain ecosystem, watershed development, climate change etc.
The forth coming issue of ISG Newsletter will feature popular geomatics articles of current interest. ISG invites articles of general interest on current topics related to geomatics. The articles may be sent to :
R. P. Dubey, Editor, ISG Newsletter
E-mail : [email protected] Phone : 02717-235434 Journal of Geomatics v Vol.7 No.2 October 2013
Reviewers for Journal of Geomatics, Volume 7 No. 1 & 2 Editorial Board places on record its sincere gratitude to the following peers for sparing their valuable time to review the papers for the Journal of Geomatics, Volume 7. Dr. Ajai Chief Editor
Shri Ritesh Agrawal Dr. Murli Mohan Dr. Syed Ashfaq Ahmed Dr. A.K. Muley Dr. A.S. Arya Dr. T.V.R. Murthy Shri R.J. Bhanderi Dr. R. Nagendra Dr. B.S. Chaudhary Dr. M.P. Oza Dr. Anup Das Shri R.P. Dubey Shri J.G. Patel Prof. S. Durbha Dr. M.B. Potdar Dr. R.S. Dwivedi Dr. Indra Prakash Prof. T.I. Eldho Dr. Raaed Hossouna Shri R. Gaikwad Dr. A.S. Rajawat Prof. J.K. Garg Dr. Rajkumar Prof. R. Ghosh Dr. T. Ravishankar Dr. L. Gnanappazham Dr. Champati Ray Dr. B. Gopalkrishna Dr. Pradip K. Ray Shri Bupesh Gupta Prof. Chandra Sekar Dr. P.K. Gupta Shri S.A. Sharma Prof. Jin-Tsong Hwang Dr. A.K. Sharma Shri Gaurav Jain Dr. Shivmohan Ms. Nirmala Jain Dr. Bipasha Shukla Dr. R.K. Jauhari Shri P. Jayaprasad Dr. T.S. Singh Dr. C. Jeganathan Shri C.P Singh Dr. C.S. Jha Shri K.M. Sreejith Dr. P.K. Joshi Dr Girja Shankar Srivastava Dr. S.P.S. Kushwaha Dr. A.V. Subba Rao Prof. Kusum Lata Prof. T.M.V. Suryanarayan Dr. T.J. Majumdar Dr. Syed Ashfaq Ahmed Shri K.R. Manjunath Dr. Balak Ram Thakur Shri I.C. Matieda Prof. P.M. Udani Shri Bhupendra Mishra Prof. Anjana Vyas Prof. Suman Mitra
Journal of Geomatics vi Vol.7 No.2 October 2013 Author Index Volume 7
Author Issue page no Ajai 1 25 Ajai, (see Lal, Disha) 2 134 Ajai, (see Goswami, N.) 2 158 Ajai, (see Rastogi, Gunjan) 2 178 Ajwaliya, Rajeshkumar J. 2 107 Arya, A.S. (see Lal, Disha) 2 134 Asante, Paulina Ansaa (see Owusu, Alex Barimah) 2 191 Baba, M. (see Ajai) 1 25 Bhattacharya, Satadru (see Ajai) 1 25 Chauhan, Prakash (see Lal, Disha) 2 134 Colney, Lalnunsiama 1 83 Dabas, Abhishek (see Gupta, Aviral Kumar) 1 77 Dasgupta, Arup (see Hiremath, Deepak B.) 1 13 Elmahdy, Samy Ismail 1 41 Farah, Ashraf 2 153 Ganeshaiah, K.N. (see Saran, Sameer) 2 138 Garg, P.K. (see Johar, Amita) 2 101 Garg, R.D. 2 120 Ghosh, Ranendu (see Gupta, Aviral Kumar) 1 77 Goel, Sudha (see Taudia, Debiprasad) 1 47 Goswami, N. 2 158 Govindaraju, (see Jaykumar, P.D.) 2 175 Gupta, Aviral Kumar 1 77 Gupta, P.K. (see Goswami, N.) 2 158 Hameed, Shashul (see Ajai) 1 25 Hassouna, Raaed Mohamed Kamel 1 7 Hassouna, Raaed Mohamed Kamel 2 163 Hiremath, Deepak B. 1 13 Jain, Akshay (see Sharma, Sudhakar) 2 186 Jain, S.S. (see Johar, Amita) 2 101 Jayakumar, P.D. 2 175 Jayalakshmi, S. (see Manavalan, R. Nambi) 2 198 Johar, Amita 2 101 Joshi, A.S. 1 33 Joy, Jose (see Mehmood, Khalid) 2 112 Kalubarme, Manik H. (see Mehmood, Khalid) 2 112 Kanga, Shruti 1 93 Krishnamurthy, Y.V.N. (see Saran, Sameer) 2 138 Journal of Geomatics vii Vol.7 No.2 October 2013 Author Issue page no Kurian, N.P. (see Ajai) 1 25 Kurtadikar, M.L. (see Joshi, A.S.) 1 33 Kushwaha, S.P.S. (see Saran, Sameer) 2 138 Lal , Disha 2 134 Lalwin, M. (see Sashikumar, M.C.) 1 19 Lingadevaru, D.C. (see Jaykumar, P.D.) 2 175 Manavalan, R. Namdi 2 198 Mantri, Prakash 1 63 Mehmood, Khalid 2 112 Mehra, Gulshan 2 145 Mohamed, Mohamed Mostafa (see Elmahdy, Sami Ismail) 1 41 Nathawat, Mahendra Singh (see Kanga, Shruti) 1 93 Nautiyal, B.P. (see Colney, Lalnunsiama) 1 83 Owusu, Alex Barimah 2 191 Palaniyandi, M. 2 126 Pandey, Prem Chandra (see Kanga, Shruti) 1 93 Patel, Ajay (see Mehmood, Khalid) 2 112 Pathan , S.K. (see Sankar Ram, M.) 1 69 Patil , Nitin Rangrao (see Hiremath, Deepak B.) 1 13 Raghu, V. 2 169 Rajawat, A.S. (see Ajai) 1 25 Rajeshwari, (see Mehra, Gulshan) 2 145 Raju, P.L.N. (see Saran, Sameer) 2 138 Ramakrishnan, Ratheesh (see Ajai) 1 25 Rastogi, Gunjan 2 178 Reddy, K. Mruthyunjaya (see Raghu, V.) 2 169 Sankar Ram , M. 1 69 Saran, Sameer 2 138 Sarat Chandra, M.D. (see Garg, R.D.) 2 120 Sashikumar, M.C. 1 19 Sasidhar, P. (see Sankar Ram, M.) 1 69 Sharma, Laxmi Kant (see Kanga, Shruti) 1 93 Sharma, S. K. (see Kanga, Shruti) 1 93 Sharma, Sudhakar 2 186 Shekhar, Shashi 1 1 Singh, Anupam K. (see Sharma, Sudhakar) 2 186 Singh, Hariom (see Saran, Sameer) 2 139 Sundar, D. (see Ajai) 1 25 Taudia, Debiprasad 1 47 Udani, P.M. 1 57 Udani, P.M. (see Ajwaliya, Rajeshkumar J.) 2 107 Unnikrishnan, A.S.(see Ajai) 1 25 Vyas, Prahlad Rai (see Mantri, Prakash) 1 63 Journal of Geomatics viii Vol.7 No.2 October 2013
ISG Annual Awards
National Geomatics Award
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ISG Chapter Award for Best Performance
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President’s Appreciation Medal for Contribution to the ISG
This award will be given to a member of the society, who has made noteworthy contribution to the growth of the ISG (its main body or any chapter). The Award consists of a Medal and a Citation.
Prof. Kakani Nageswara Rao Endowment Young Achiever Award
Indian Society of Geomatics instituted a new award from year 2013 named “Prof. Kakani Nageswara Rao Endowment Young Achiever Award”, to encourage young researchers/scientists/academicians pursuing research in the field of geospatial technology/applications. The award carries a cash prize of Rs.10,000/- along with a citation.
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Shri Pramod P. Kale, Pune Dr. George Joseph, Ahmedabad Dr. A.K.S. Gopalan, Hyderabad Dr. Prithvish Nag, Varanasi Dr. Baldev Sahai, Ahmedabad Prof. A.R. Dasgupta, Ahmedabad Dr. R.R. Navalgund, Bangluru Shri Rajesh Mathur, New Delhi Dr. Ajai , Ahmedabad
ISG - PATRON MEMBERS
P-1 Director, Space Applications Centre (ISRO), Jodhpur Tekra Satellite Road, Ahmedabad - 380 015 P-2 Settlement Commissioner, The Settlement Commissioner & Director of Land Records-Gujarat, Block No. 13, Floor 2, Old Sachivalay, Sector-10, Gandhinagar – 382 010 P-3 Commissioner, Mumbai Metropolitan Region Development Authority, Bandra-Kurla Complex, Bandra East, Mumbai - 400 051 P-4 Commissioner, land Records & Settlements Office, MP, Gwalior - 474 007 P-5 Director General, Centre for Development of Advanced Computing (C-DAC), Pune University Campus, Ganesh Khind, Pune - 411 007 P-6 Chairman, Indian Space Research Organization (ISRO), ISRO H.Q., Antariksha Bhavan, New BEL Road, Bangalore 560 231 P-7 Director General, Forest Survey of India, Kaulagarh Road, P.O. I.P.E., Dehra Dun – 248 195 P-8 Commissioner, Vadodara Municipal Corporation, M.S. University, Vadodara - 390 002 P-9 Director, Centre for Environmental Planning and Technology (CEPT), Navarangpura, Ahmedabad - 380 009 P-10 Managing Director, ESRI INDIA, NIIT GIS Ltd., 8, Balaji Estate, Sudarshan Munjal Marg, Kalkaji, New Delhi - 110 019 P-11 Director, Gujarat Water Supply and Sewerage Board (GWSSB), Jalseva Bhavan, Sector – 10A, Gandhinagar - 382 010 P-12 Director, National Atlas & Thematic Mapping Organization (NATMO), Salt Lake, Kolkata - 700 064 P-13 Director of Operations, GIS Services, Genesys International Corporation Ltd., 73-A, SDF-III, SEEPZ, Andheri (E), Mumbai - 400 096 P-14 Managing Director, Speck Systems Limited, B-49, Electronics Complex, Kushiaguda, Hyderabad - 500 062 P-15 Director, Institute of Remote Sensing (IRS), Anna University, Sardar Patel Road, Chennai - 600 025 P-16 Managing Director, Tri-Geo Image Systems Ltd., 813 Nagarjuna Hills, PunjaGutta, Hyderabad - 500 082 P-17 Managing Director, Scanpoint Graphics Ltd., B/h Town Hall, Ashram Road, Ahmedabad - 380 006 P-18 Secretary General, Institute for Sustainable Development Research Studies (ISDRS), 7, Manav Ashram Colony, Goplapura Mod, Tonk Road, Jaipur - 302 018 P-19 Commandant, Defense institute for GeoSpatial Information & Training (DIGIT), Nr. Army HQs Camp, Rao Tula Ram Marg, Cantt., New Delhi - 110 010 P-20 Vice President, New Rolta India Ltd., Rolta Bhavan, 22nd Street, MIDC-Marol, Andheri East, Mumbai - 400 093 P-21 Director, National Remote Sensing Centre (NRSC), Deptt. of Space, Govt. of India, Balanagar, Hyderabad - 500 037 P-22 Managing Director, ERDAS India Ltd., Plot No. 7, Type-I, IE Kukatpalli, Hyderabad - 500 072 P-23 Senior Manager, Larsen & Toubro Limited, Library and Documentation Centre ECC Constr. Gp., P.B. No. 979, Mount Poonamallee Road, Manapakkam, Chennai - 600 089. P-24 Director, North Eastern Space Applications Centre (NE-SAC), Department of Space, Umiam, Meghalaya 793 103 P-25 Progamme Coordinator, GSDG, Centre for Development of Advanced Computing (C-DAC), Pune University Campus, Pune – 411 007 P-26 Chief Executive, Jishnu Ocean Technologies, PL-6A, Bldg. No. 6/15, Sector – 1, Khanda Colony, New Panvel (W), Navi Mumbai – 410 206 P-27 Director General, A.P. State Remote Sensing Applications Centre (APSRAC), 8th Floor, “B” Block, Swarnajayanthi Complex, Ameerpet, Hyderabad- 500 038 P-28 Director, Advanced Data Processing Res. Institute (ADRIN), 203, Akbar Road, Tarbund, Manovikas Nagar P.O., Secunderabad – 500 009 P-29 Managing Director, LEICA Geosystems Geospatial Imaging Pvt. (I) Ltd., 3, Enkay Square, 448a Udyog Vihar, Phase-5, Gurgoan- 122 016 P-30 Director, Defense Terrain Research Limited (DTRL), Ministry of Defense, Govt. of India, Defense Research & Development Organisation, Metacafe House, New Delhi – 110 054 P-31 Chairman, OGC India Forum, E/701, Gokul Residency, Thakur Village, Kandivali (E), Mumbai – 400 101 P-32 Managing Director, ML Infomap Pvt. Ltd., 124-A, Katwaria Sarai, New Delhi – 110 016 P-33 Director, Rolta India Limited, Rolta Tower, “A”, Rolta Technology Park, MIDC, Andheri (E), Mumbai – 400 093 P-34 Director, State Remote Sensing Applications Centre, Aizawl – 796 012, Mizoram Journal of Geomatics xii Vol.7 No.2 October 2013
Instructions for Authors
The journal covers all aspects of Geomatics – geodata Use MS Word with English (UK/US) or English (Indian) acquisition, pre-processing, processing, analysis and dictionary. The page size should be A4 paper, with 2 cm margin publishing. Broadly this implies inclusion of areas like GIS, on all sides. Title, authors and affiliation should be centred. GPS, Photogrammetry, Cartography, Remote Sensing, Abstract should be justified across margins. The manuscript text Surveying, Spatial Data Infrastructure and Technology should be in two columns of 8.2 cm each with a gutter of 6mm including hardware, software, algorithms and model. It between them. Use only Times New Roman fonts. Title should endeavours to provide an international forum for rapid be 12 points bold. Authors and affiliation should be 9 points. All publication of developments in the field – both in technology other text including headings should be 10 points. Heading and applications. numbering scheme should be decimal e.g. 1., 1.1, 1.2.3, etc. Headings should be in bold. To begin with the frequency of publication will be six- monthly. However, depending on the response and interest, Normally length of a published paper should be about 6-10 frequency of publication may be reviewed. pages in A4 size including figures. Use of illustrations in colour should be restricted and resorted to only where it is absolutely A manuscript for publication must be based on original necessary and not for enhancing the look of the paper. If the research work done by the author(s). It should not have been number of colour illustrations exceeds five, authors’institution published in part or full in any type of publication nor should may be asked to reimburse the extra cost involved, which at it be under consideration for publication in any periodical. current rates is about Rs. 2500 per coloured Unsolicited review papers will not be published. figure/diagram/plate/illustration.
The Editorial Board or the Indian Society of Geomatics is not Submission of Manuscript responsible for the opinions expressed by the authors. Submissions should be in electronic form via email or a CD- Language ROM. The manuscript may be sent by email to [email protected]. In exceptional cases hard copy The language of the Journal will be English (Indian). submission in camera ready form may be allowed with the prior However, manuscripts in English (US) and English (British) permission of the Chief Editor. Submission in any other form will are also acceptable from authors from countries located be returned to the author. outside India. Guidelines for Citing References
Manuscript Format Names of all cited publications should be given in full. No abbreviations should be used. Following procedure is to be Each paper should have a title, name(s) of author(s), and adopted. affiliation of each of the authors with complete mailing address, e-mail address, an abstract, four to six keywords, and Journal Publications the text. The text should include introduction/background, research method, results, discussion, followed by Bahuguna, I.M. and A.V. Kulkarni (2005). Application of digital acknowledgements and references. The main text should be elevation model and orthoimages derived from IRS-1C Pan divided in sections. Section headings should be concise and stereo data in monitoring variations in glacial dimensions, numbered in sequence, using a decimal system for Journal of the Indian Society of Remote Sensing, 33(1), 107- subsections. Figures, images and their captions should be 112. (to be referred to in the text as Bahuguna and Kulkarni inserted at appropriate points of the text. Figures, images and (2005) or if more than two sets of authors are to be referred to, as tables should fit in two column format of the journal. If (Bahuguna and Kulkarni, 2005; Jain et al., 1994)) When more absolutely necessary, figures, images and tables can spread than two authors are to be referred to, use Jain et al. (1994). across the two columns. Figures and images, however, should However, in References, all authors are to be mentioned. not exceed half a page in height. A title should be provided for each Table, Image and Figure. All figures and images Publication in a Book should be in 600 dpi resolution and sized as per Misra, V.N. (1984). Climate, a factor in the rise and fall of the column/margin width. Authors must ensure that Indus Civilization – Evidence from Rajasthan and Beyond in diagrams/figures should not lose easy readability upon Frontiers of the Indus Civilization (B.B. Lal and S.P. Gupta: reduction to column size. The SI (metric) units and Chief Editors) Books and Books, New Delhi, pp. 461-489 international quantities should be used throughout Papers Published in Seminar/Symposium Proceedings the paper. In case measurements are given in any other system, equivalent measurements in SI (metric) units should be indicated in brackets. Journal of Geomatics xiii Vol.7 No.2 October 2013
Jain, A., A.R. Shirish, M. Das, K. Das, M.C. Porwal, and If the authors have used any copyright material in their P.S. Roy (1994). Remote Sensing and Geographic manuscript, it is understood that they have obtained permission Information System – An approach for the assessment of from the owner of the copyright material and they should biotic interference in the forest ecosystem. Proceedings. 15th convey the same along with the manuscript to the Chief Editor. Asian Conference on Remote Sensing, Bangalore, November 17-23, 1994, pp. 65-72. Certificate of Original Work
Books The authors will also provide a certificate that the paper is an original work, not published or being considered for publication Possehl, Gregory L. (1999). Indus Age: The elsewhere. beginnings. Oxford and IBH Publishing Corporation, New Delhi. In the event the certificate turns out to be false, the Journal shall ban the author(s) from publishing in the Journal for a period of Reviewing five years and inform the same to all other related publications.
Each paper will be reviewed by three peers. Papers Reprints forwarded by members of the Editorial or Advisory Boards along with their comments would get processed faster and Authors will be provided soft copy ( PDF)of their paper, no may be reviewed by two referees only. hard copy reprints will be provided.
Sample format for Authors is available in downloadable form at ISG website: www.isgindia.org/JOG/Sample_format.doc
Copyright
The copyright of the paper selected for publication will rest with the Indian Society of Geomatics. Corresponding author shall be required to sign a copyright assignment form, on behalf of all authors, once the paper is selected for publication. Authors are, however, at liberty to use this material elsewhere after obtaining permission from the Indian Society of Geomatics. Journal of Geomatics xiv Vol.7 No.2 October 2013
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TheSecretary IndianSocietyofGeomatics BuildingNo.40,RoomNo.17 SpaceApplicationsCentre(SAC)Campus JodhpurTekra, PO. AHMEDABADದ 380015 Sir,
I want to become a Member/ Life Member/ Sustaining Member/ Patron Member/ Foreign Member/ Student Member of the Indian Society of Geomatics, Ahmedabad for the year _____. Membership fee of Rs. _____ /- is being sent to you by Cash/ DD/ Cheque. (In case of DD/ Cheque No.______, drawn on Bank ______.(For outstation cheques, please,addclearingchargesofRs65.00) IagreetoabidebytheConstitutionoftheSociety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
BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB3,1 BBBBBBBBBBBBBBBBBBBBB 3URSRVHGE\ 0HPEHUಬV1DPHDQG1R 6LJQDWXUHRI3URSRVHU )RU2IILFH8VH ,6*0HPEHUVKLS1R ,6* 5HFHLSW1R 'DWH Journal of Geomatics xvi Vol.7 No.2 October 2013 MEMBERSHIP SUBSCRIPTION
Sr. Membership AdmissionFee AnnualSubscription
No. Category Rs.(Indian) US$(Foreign) Rs.(Indian)
1. Annual Member 10.00 - 300.00
2. Life Member a) Admitted before 45 years of age 2500.00 250.00 b) Admitted after 45 years of age 2000.00 200.00
3. Sustaining Member --- 2000.00
4. Patron Member 50000.00 3000.00
5. Student Member 10.00 - 100.00
MEMBERSHIP GUIDELINES
1. Subscription for Life Membership is also accepted in two equal installments payable within a duration of three months, if so desired by the applicant. In such a case, please specify that payment will be in installments and also the probable date for the second installment (within three months of the first installment). 2. A Member of the Society should countersign application of membership as proposer... 3. Subscription in DD or Cheque should be made out in the name of ‘INDIANSOCIETYOFGEOMATICS’and payable at Ahmedabad. 4. Outstation cheques must include bank-clearing charges of Rs. 65.00. 5. Financial year of the Society is from April 1 to March 31. 6. For further details, contact Secretary, Indian SocietyofGeomaticsat the address given above. 7. ISG has chapters already established at the following places. Ahmedabad, Ajmer, Bhagalpur, Bhopal, Chennai, Dehradun, Delhi, Hyderabad, Mangalore, Mumbai, Mysore, New Delhi, Pune, Srinagar, Tiruchirappalli, Vadodara and Visakhapatnam. Applicants for membership have the option to contact Secretary/Chairman of the local chapter for enrolment. Details can be found at the website of the Society: www.isgindia.org 8. Journal of the Society will be sent only to Patron Members, Sustaining Members and Life Members.