Agroecological evaluation of agroforestry

Prof. Dr. Janos Tamás Water and Environmental Management Institute University of European Agroforestry Week Agrof-MM C5 - International training OVERVIEW

• AGROFORESTRY - ECOLOGICAL SERVICE • SOIL –SOIL EROSION • WHY GIS • HOW CAN BE USE GIS AS SDSS • SOFTWARE TOOLS Agroecology - Agroforestry Mind Map Silvopastoral system weed control – over grazing

FIGECZKY GÁBOR (WWF MAGYARORSZÁG) ÉS SZÉKELYHIDI TAMÁS SZERKESZTETTE: FIGECZKY GÁBOR Agroecological Capacity - Agroforestry • Soil Fertility • Water storage, infiltration an available water content • Provide nutrients to plants. (macro/micro) • Physical-Chemical-Biological parameters • Elevation • Slope • Aspects • Runoff - Erosion • Micro climate • Radiation • Precipitation • Temperature • Evapotranspiration • Optimization of agro technology • Genetics- Species • Cultivation • Water-Nutrition management • Plant protection • Harvesting Soil • Keys to Soil Taxonomy WRB Basic principles The classification of soils is based on soil properties defined in terms of: diagnostic horizons, diagnostic properties, diagnostic materials. 1. Soils with thick organic layers: HISTOSOLS 2. Soils with strong human influence Soils with long and intensive agricultural use: ANTHROSOLS Soils containing many artefacts: TECHNOSOLS Soil organic matter types to determine

Organic matter Living biomass under decomposition (biodiversity) Undecomposed organic matter under dry conditions under wet „easy fraction”, litter conditions(peats) Decomposed organic matter

humic non humic substances substances

Fulvic Humic Humin carbohidrates, acids acids proteines, etc

Eight threats for soil degradation

- erosion - decline of soil organic matter content - contamination -sealing - compaction - decline in soil biodiversity - salinization - landslides - desertification • Color • Texture • Structure • Bulk Density • Density • Porosity Soil Textural Triangle

10 % clay

60 % silt

30 % sand

Silt Loam Soil Water Content

Soil degradation Landuse change

Global landuse Arable land 11% Grassland 26% Forest 32% Others 31% Agroforestry in erosion control (Young, 1989) Water Erosion Indices of soil erobility for water erosion Strategy for Erosion Control

Source Hudson: Soil Erosion and Conservation Land use management (Poel and Kaya, 1991)

Agroforestry Soil Conservation Strategy (Dangler and Amstrong, 1982)

Agroforestry RUSLE Wischmeier and Smith's Empirical Soil Loss Model (USLE) Soil Erosion Model

IDRISI Soil Erosion

• Soil Erosion is a common term that is often confused with soil degradation as a whole, but in fact refers only to absolute soil losses in terms of topsoil and nutrients. • This is indeed the most visible effect of soil degradation, but does not cover all of its aspects. Soil erosion is a natural process in mountainous areas, but is often made much worse by poor management practices. Soil Erosion • Erosion models play critical roles in soil and water resource conservation and nonpoint source pollution assessments, including: sediment load assessment and inventory, conservation planning and design for sediment control, and for the advancement of scientific understanding. Data sources

USLE FAO documentation Title: Land husbandry - Components and strategy Division: Land and Water Division ISSN: 0253-2050 http://www.fao.org/3/a-t1765e/t1765e0e.htm

On lone calculation http://www.iwr.msu.edu/rusle/ RUSLE 2 Science Documentation Revised Universal Soil Loss Equation Version 2 (RUSLE2) https://www.ars.usda.gov/ARSUserFiles/60600505 /RUSLE/RUSLE2_Science_Doc.pdf

https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation- laboratory/watershed-physical-processes-research/research/rusle2/revised-universal-soil-loss- equation-2-rusle2-documentation/ RUSLE2 version WEPP (USDA - Water Erosion Prediction Project)

The WEPP erosion model computes soil loss along a slope and sediment yield at the end of a hillslope https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion- research/docs/wepp / Environmental Policy Integrated Climate (EPIC) Model

• Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion. EPIC simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management. • DOWNLOAD: http://epicapex.tamu.edu/epic/

• APEX – Agricultural Policy/Environmental eXtender Model http://epicapex.tamu.edu/apex/ SWAT

http://swat.tamu.edu/software/

Erosion 2015 Europe SOIL LOSS MODELING

E = R * K * C * LS * P

Where

E: Annual average soil loss (t ha-1 yr-1), R: Rainfall Erosion factor (MJ mm ha-1 h-1 yr-1), K: Soil Erodibility factor (t ha h ha-1 MJ-1 mm-1), EUROPEAN SOIL DATA CENTRE (ESDAC) C: Cover-Management factor (dimensionless), LS: Slope Length and Slope Steepness factor (dimensionless), P: Support practices factor (dimensionless). Farm level model - RUSLE TERRSET IDRISI • RUSLEDEM.rst + FIELDS (transparency)

The data used in this example is derived from a dairy farm in Rutland, Massachusetts (about 10 miles (16 km) north of Worcester in Central Massachusetts). • Kfactor; Rfactor; Cfactor; Pfactor

C= 0.25 maize C=0.005 hay C= 0.01 tree line RUSLE parameters: DEM: RUSLEDEM field: fields R: rfactor K: kfactor C: cfactor P: pfactor slope threshold: 3.000 aspect threshold: 3.000 length limit: 200.000 unit: feet area threshold: 43560.000 average soil: yes rounding: to shorter patch table: run1PatchTable.txt patch ID: run1PatchID.txt patch total loss: run1PatchTotalSoilLoss.txt field table: run1FieldTable.txt field unit loss: run1FieldAverageSoilLoss.txt field total loss: run1FieldTotalSoilLoss.txt

unit soil loss total soil loss ID # of patches area(ac) (t/ac/yr) (t/field/yr) 1 29 7.616 1.878 14.305 •Slope Threshold = 3, Maximum slope 2 9 2.363 0.112 0.265 3 18 4.932 3.179 15.678 length = 200 (feet), select round to 4 3 2.832 0.140 0.397 shorter, set the aspect threshold to 3, the 5 13 3.981 2.784 11.082 6 8 2.276 4.545 10.343 smallest patch size to 43,560 (ft2), the 7 5 5.581 2.371 13.233 default background to 0, and check the unit soil loss total soil loss box to average soil factor within patches. ID # of patches area(ac) (t/ac/yr) (t/field/yr)

Total 85 29.579 65.304 Patch Total soil loss 1. What is the maximum and minimum soil loss (tons/acre/year) that occurs on the seven fields? 2 Look at the C, K, P, and R values for the seven fields. Which of these four factors contains the most explanatory value for the low average soil loss for these two fields? 3 Which field has the highest average soil loss per acre? Which factor (L, S, C, K, P, R) is the likely major contributing factor for this field’s average soil loss? 4 Which patch had the highest soil loss? In what field is this patch located? 5. How changed total soil loss if we apply Agroforestry trees? RUSLE parameters: DEM: RUSLEDEM 6, How changed total soil loss if we apply higher R value ID # of patches area(ac) (t/ac/yr) (t/field/yr) to simulate climate change? 1 29 7.616 2.087 15.895 2 9 2.363 6.956 16.435 3 18 4.932 1.295 6.388 4 3 2.832 0.140 0.397 5 13 3.981 1.340 5.336 6 8 2.276 0.168 0.383 7 5 5.581 0.439 2.451 unit soil loss total soil loss ID # of patches area(ac) (t/ac/yr) (t/field/yr) Total 85 29.579 47.284 Why GIS?

• The Geographic Information System (GIS) technology is one of the most important examination methods for decision support to solve of the global or local environmental problems. • GIS data is a digital representation of objects or phenomena that take place on or below the surface. • It could provide different parameters of objects such as area, temperature, high, elevation; and categorize based on attributes. Why use GIS?

... because GIS can answer the following questions: • Where is? • What is there? • What has changes since? • What is the best route between? • What relations exist between? • What if? GIS as an Integrating Technology

• GIS is able to integrate • different data sources ( e.g. ground survey, remote sensing, etc.) • different disciplines • Obstacles: • „specialized“ software • missing exchange standards, etc. Forestry

• Had been among the first users of GIS • In the beginning just inventory of forest. • Now GIS is used for all areas of management.

Source: BUCKLEY, David J. (1997) Field sensors in Agroforestry Airborne data aquisition Spectral fingerprint

Common oak (Quercus robur)

Red oak (Quercus rubra)

Scots pine (Pinus sylvestris)

Glade area

Sparsely covered with vegetation parcel

Trees shadow Lidar TOPOGRAPHY Of Agroforestry Tree volume DSM-DEM Tree density Evapotranspiration Runoff Infiltration LIDAR based products

(a) True-color orthophoto (© http://maps.live.de), (b) slope-adaptive echo ratio (sER), (c) nDSM overlaid with segmentation result and mean values per segment of the (d) echo width

(sp;mean), (e) backscatter cross-section (mean), and (f) final tree species classification result (From Hollaus et al. (2009a)) Modeling Process

Step 1: Problem Statement

Step 4: Step 2: Evaluation Basic Items

Step 3: Relationships and Rules Environment Spatial thinking Real word Expert User knowledge knowledge

Spatial- Time

Spatial Mapping Map Cartographic Users’ concept language demand Potential Users Legend Paper map Map reading Scale Computer graphics Rigid, digital map Disciple Change treatment Physical model Interactív map Analysis

Database model WEBGIS Data Data collection integration Data warehouses Meta data level Spatially GIS Standards Thematically GPS RS EM Data mining Geo Information System Scales of Measurement

• Observations can be put into different scales of measurements: • Nominal (e.g. tree names, etc.) • Ordinal (e.g. wood quality ranks, etc.) • Metric (e.g. diameter, time, kilometers, etc.) • Define operation types performed on data! Components of GIS

• People and Organizations GIS experts, GIS operators, computer staff, etc. • Data locational, temporal, attribute • Methods statistics, spatial analysis, etc. • Hardware workstations, digitizers, plotters, etc. • Software to perform analysis, retrieval, etc.

Source. BUCKLEY, David J. (1997) Geographic Information Technologies

• Geographic information technologies are technologies for collecting and dealing with geographic information. • GPS: provides positional data • Remote Sensing - RS: captures spatial information without touching the surface • DBMS: applied for storage and retrieval of spatial data

Source: CCRS (1998) The structure of the GIS database (Source: Esri)

Vector data - feature's geometry (Sutton et al., 2009)

An example of different resolution raster data maps (Shekhar and Xiong, 2008) Model example

Map with different type of vector layers Map with vector and raster layers Map with a raster layer DEM- Parcells Orchard –grassland biomass map Újfehértó- Layer and objects structure of A.Forestry The four engines that power the Information Cloud are capture, process, share, and deliver

(Stojic, M. and Sims, J. 2011) Data Capture

• The first engine includes airborne sensors (airborne digital imaging, LiDAR, UAV), satellites, and terrestrial sensors (total station, GPS, video, terrestrial LiDAR, handheld devices). • With all these sources feeding the Information Cloud, the combined data and metadata are the fuel that is fed through the geospatial information life cycle into the second engine for data fusion, processing, and production. Process and Share

• The second engine includes the geospatial processing tools for fusing and integrating geospatial source content into software applications for the creation and update of geospatial data and information products. • The third engine powering the Information Cloud is the ability to manage, fuse, and share geospatial data across departments and regions, connecting to an organization's hub of geospatial data and information. Deliver

• The fourth engine for fully leveraging the Information Cloud enables the delivery of geospatial data AND dynamic information products. This is done through on-demand geoprocessing over the Internet, to mobile Primary and Secondary Data Acquisition

Primary Data Secondary data

Terrestrial survey Digitized data

Photogrammetrical Scanned data survey

Survey from space Objectives

• Where can I find spatial data? • What type of data can be found? • How can I gain access to it? • How will it be delivered?

http://www.pakissan.com/english/advisory/agro- forestry.conducive.for.better.crop.production.shtml Imagery Data

• Aerial photos • Satellite imagery • Digital elevation models (DEM) • Digital orthophotos

Source: EUMIRAGE Attribute Data

• Textual data • Tabular data • Information must be linked to geographic features (point, line, polygon)

Source: ESRI Vertical Surveys

• Vertical survey measures the difference in elevation between two locations. • Methods of vertical survey are: • Differential leveling • Trigonometric leveling

Source: ROBINSON, Arthur H. et al (1995), p. 122 GPS Operational Constellation

• Set of at least 24 satellites • Orbiting the earth in 12 hours • Six orbital planes • Positioning Systems: • GPS (USA) • GLONASS (Russia) • GALILEO (Europe)

Source: DANA, Peter (1999) New graphics Remotely Sensed Data

• Measuring technique without physical contact to observed features • Type of data depending on: • Imaging device • Its sensitivity • (...) • Sensor platform

Source: CRUM, Shannon (1997) Data integration - Georeferencing

• All spatial data file in GIS are georeferenced. Describing the correct location of features requires a framework for defining real-world location. • This process, and the assigning map coordinates to the image data is called georeferencing. • Georeferencing allows combining dataset representing information about the same location

Different type of projection (Source: ESRI) Convert one coordinate system to another RMS Error Placement of GCPs

Properties of the projection Metadata

• Why was this spatial data gathered? • What was collected? Source: BUREAU of LAND MANAGEMENT, Denver • Who collected it? • How was it collected? • How current is it? • Where is this data at?

https://earthenginepartners.appspot.com/science-2013-global-forest Surface modeling – Site Selection

• Nowadays the GIS models use numerous applications to evaluate or calculate the effect of the environmental phenomenon. • However the modelling of the natural, hydrological, erosion process needs digital surface data, which is generated in general from vector databases (contour lines, elevation points). Consequently, the creating of surface model is fundamental in numerous area of agroforestry. • Digital Terrain Models (DTMs) are getting more and more important in geographical data processing and analysis: they allow modelling, analysis and visualization of phenomena related to the territory morphology (or to any other characteristic of the territory different from elevation) (Gomarasca, 2009). • DSM describes the terrestrial surface, including the objects covering it like buildings, vegetation and DEM

• The digital surface model is a representation of features, either real or hypothetical, in three-dimensional space. • A 3D surface is usually derived, or calculated, using specially designed algorithms that sample point, line or polygon data and convert them into a digital 3D surface. • GIS softwares can create and store different types of surface model: TIN, raster, terrain and voxel based. DEM -

• TINs (Triangulated Irregular Networks) are a vector data structure that partitions geographic space into contiguous, non-overlapping triangles. • A TIN is a complete planar graph that maintains topological relationships between its constituent elements: nodes, edges, and triangles. • The vertices of each triangle are sample data points with x-, y-, and z- values (used to represent elevations). These sample points are connected by lines to form Delaunay triangles. (Shekhar and Xiong, 2008) DEM - TIN creation

(a) TIN (Triangulated Irregular Network) derived from scattered points on two-dimensional plane based on Delaunay’s triangulation. If the points have altitude information (z coordinates), generated TIN can be used for perspective viewing, (b) TIN with original scattered points overlap, (c) contour lines overlapping the TIN of generation. This data structure allows data to be displayed as three-dimensional surface, or to be used for terrain analysis including contouring and visibility mapping (Gomarasca, 2009). DEM -Raster

• The most common method of acquiring elevation data in digital raster format is digitizing contours from a topographic map and applying an interpolation method to transform the contour data into a DEM. • Common raster DEM databases usually store data in several meters pixel resolution having vertical accuracy around several meters. • The intensity values of pixels represent the height above the sea level. • The one of the most important operation related to DEM accuracy is interpolation method. Objectives of GIS (SDSS)

• What kind of questions can be asked? • What is Map Algebra? • What kind of „horizontal“ and „vertical“ operations are available?

Source: Institute of Water Research Michigan State University (1997) Extraction of new Information

• Spatial analysis can be regarded as extracting information embodied in data distributed over space. • Spatial phenomena are always in relation to each other. Analyzing Spatial Relationships

• Geometric Relationships: • requires coordinate information • length, perimeter, distance, etc. • Topological Relationships • requires non-spatial information • adjacency, connectivity, overlap, containment

Source: NCGIA (2000) Basic Queries

Basic queries are: • What is it? = query by locational information • Where is it? = query by attribute information

Source: SPENCER, John (2000) Advanced Queries

• Which is the shortest path? • Where is the most suitable location for a new shop? • Where is the nearest facility? ...

Source: Institute of Water Research Michigan State University (1997) Classification of Analysis Techniques • Geometrical vs. Topological • Vertical vs. Horizontal • Discrete vs. Continuous • Map Algebra • Manner of classification depends on user‘s perspective!

Analysis Input Output Technique Map Algebra I

Analysis functions can be divided into four categories: • Local operators • Focal operators • Zonal operators • Global operators Map Algebra II

• Zonal operators require a zonal and a value theme. • Global operators take the whole study area into account.

Source: ALLEN, Chris (1997) Distance Analysis

• Euclidean Distance: • measuring distance between objects • Buffer: • discrete zone of fixed distance from an object • there are different kinds of buffers (e.g. doughnut buffer) • Distance can also be expressed in units such as travel time. Network Analysis

• Attribute information can be attached to line segments which restrict movement speed along the line. • Applications are e.g. the determination of • shortest path • best route • capacity zones • closest facilites

Source: Nicholas School of the Environment (2000) Surface Analysis

• How can effective (non-euclidean) distance be calculated on a raster? By the technique of „accumulated distance“ • Cells are assigned cost/friction values. • „Distance“ is regarded as the difference between adjacent cell values.

Source: Nicholas School of the Environment (2000) Terrain Analysis • Terrain analyses are concerned with the determination of: • slope • aspect • visibility • watersheds • Terrain information may be provided by DEMs and TINS.

Source: CHRISMAN, Nicholas (1999) Interpolation I

• Interpolation is concerned with predicting unknown values based on known ones. • Tobler‘s Law of Geography: Locations close together are more likely to have similar values than locations far apart.

Source: WHITTEN, E.H.T. (1981) Interpolation II

• Exact vs. approximate interpolations: Interpolated surface passes (or not) through all known points. • Thiessen Polygons: Value of a point measurement is assigned to an area defined according to the Delauney triangulation.

Source: CHRISMAN, Nicholas (1999) Geostatistics

• Geostatistics deals with the problem that spatial data are not independent (Tobler‘s Law). • Simple descriptive techniques (e.g. frequency count, minimum, maximum, average) and more advanced techniques (e.g. autocorrelation, kriging) are applied.

Source: GEOVARIANCES (2000) Overlay I

• Analysis of multiple layers • Results are represented in a new (virtual) layer • Usage of boolean operators and map algebra • Masking: absence/presence of characteristic

Source: UNESCO (1999) Landuse Overlay II 3 3 1

2 1 1 1 x • Weights are used if layers’ importance 3 2 1 differ 3 3 2 • Matrix Overlay 2 x 1 1 1

Elevation 1 1 1 11 11 7 2 2 2 5 4 4 1 x 1 1 1 8 5 4 Result Slope 3 1 1 Boolean Operators

• Layers are combined by operators like and, or , not, ... • Often inherent in software commands like „intersect“, „union“, etc.

Source: CHRISMAN, Nicholas (1999)

Source: Denis White (1997) SDSS

= Spatial Decision Support System • It is an interactive and computer-based system. • Aim: higher effectiveness of decision making and minimizing uncertainties related to decisions.

Source: MALCZEWSKI, Jack (1997) MCE

= Multi-Criteria Evaluation • MCE ranks criteria in terms of their importance. • Implemented MCE keeps the decision-finding process more objectiveto others.

Source: IDRISI Regional Decision Support System in Reforestration using GIS

Settlement - Forest areas Soil genetic cadastral map database

Digitising Digitising

Regional Local Regional Government forest areas Coalitions

Selection of forest Regional soil Settlement areas bigger than genetic database cadastral system 1ha

Erdőtelepítésre kijelölt régiók

#

# # Nyíradony Regional forest Hajdúhadháztéglás #

# # # # É # #

# # areas, # # Bánháza

# #

# #

# # Dankótelep Nyíracsád # # # settlements

# # #

#

#

# Fülöp Sámsonikert # Martinka #

#

Hajdúsámson # #

#

Nagyhegyes #

# Debrecen # # Vámospércs

Bagamér Soil genetic maps of # Újléta

#

# Mikepércs Erdőspusztai Önkormányzatok regions outside Álmos# d Településeinek Társulása Monostorpályi # # Sáránd settlements and forest # # Kokad # Dél-Nyírség - Ligetalja HajdúbagosHosszúpályi # Települési Önkormányzatok Létavértes Egyesülete areas

Messzelátó Cserekert Északhajdúsági Településszövetség # # és Térségfejlesztési Társulása

0 10 20 30 40 50 60 70 80 Kilométer Selection of soils Selection of Selection of sand with bad and highly and drift and humus extremely bad moderately acid soil water balances soils

Selection of soils Selection of areas with organic bigger than 1 ha in matter content the residual areas less than 50 t/ha

Potential areas Települések Erdőtelepítésre jav. Régió határa for forestation Vizsgált terület Országhatár

Települések Régió határa Országhatár SOFTWARES ESRI Spatial Analyst

http://www.esri.com/software/arcgis/extensions/spatialanalyst TRIMBLE Ecognition

http://www.ecognition.com/ ENVI - ENVI LIDAR

http://www.harrisgeospatial.com/docs/IntroductionLidar.html

IDRISI TERRSET Ecological services of Agroforestry

• The Water Yield model within ESM measures the average annual runoff or water yield from watersheds in a study region. • The Water Purification model estimates the contribution that vegetation and soil has on the purification of water through the removal of nutrient pollutants present in runoff. It then analyses the value of this vegetation based on the avoided cost of water treatment. • The Sediment Retention model to assess the dredging costs associated with removing accumulated sediment from waterways. Ecological services of Agroforestry

• The Carbon Storage and Sequestration panel to estimate the net amount of carbon stored, the total biomass removed from deforesting and harvesting, and the economic value of the carbon sequestered in the remaining carbon pools. • The Timber Harvest model evaluates the potential value of timber harvesting from multiple forest parcels in a study area. • The Habitat Quality and Rarity panel to assess the impacts that anthropogenic threats have on the quality and rarity of habitats. • The Crop Pollination panel to quantify the abundance of and services provided by wild pollinators to agricultural sites on LAStools

http://www.cs.unc.edu/~isenburg/lastools/

https://www.facebook.com/LAStools/ Fusion LIDAR FUSION Basic exercises with Fusion

• EXERCISE 1 Download and Install the Fusion Program and Data • EXERCISE 2 Getting Started with Fusion • EXERCISE 3 Extracting Plot Subsets • EXERCISE 4 Calculate Lidar Metrics •

(From Doneus et al. (2010)) DEM – Terrain model

Profile view of ALS point clouds with coloured amplitude The profile has a length of 100m and a width of 5m

Full-Waveform Airborne Laser Scanning Systems and Their Possibilities in Forest Applications Ath.: Markus Hollaus, Werner Mücke, Andreas Roncat, Norbert Pfeifer,and Christian Briese SURFER

http://www.goldensoftware.com/ • 3D Visualisation • Catchment delineation • Basic terrain analysis • Hydrological flow modelling • Reprojecting • Contrast enhancing

http://www.saga-gis.org/en/index.html https://www.sketchup.com/ Sources • Objectives, Advanced Queries: Institute of Water Research, Michigan State University (1997): Geographic Information Systems – Background. http://www.iwr.msu.edu/edmodule/gis/history.html • Contents, Analyseablauf: ESRI (2000): Virtual Campus: working with Model Builder. http://campus.esri.com/ • Analyzing Spatial Relationships: NCGIA (2000): Spatial-Query-By-Sketch. http://www.spatial.maine.edu/~abl/SQBS/ • Basic Queries: SPENCER, John (2000): Geographic Information Systems. http://www.cpc.unc.edu/services/spatial/gis.htm • Map Algebra II: ALLEN, Chris (1997): Using Map Algebra. NCGIS GIS Core Curriculum for Technical Programs. Unit 42. http://www.ncgia.ucsb.edu/education/curricula/cctp/units/unit42/42_f.html • Boolean Operators: Denis White (1997): The polygon overlay operation. NCGIA Core Curriculum in GIScience, Unit 186. http://www.ncgia.ucsb.edu/giscc/units/u186/u186.html, posted October 7. 1997. CHRISMAN, Nicholas (1999): GIS Analysis. University of Washington, Geography 460. http://faculty.washington.edu/chrisman/G460/Lectures.html Network Analysis, Surface Analysis: Nicholas School of the Environment (2000): Computer Based Map Analysis with GIS. http://www.env.duke.edu/lel/env351/env351.html • Terrain Analysis, Interpolation II: CHRISMAN, Nicholas (1999): GIS Analysis. University of Washington, Geography 460. http://faculty.washington.edu/chrisman/G460/Lectures.html • Interpolation I: WHITTEN, E.H.T. (1981): Semivariograms and Kriging: Possible Usefuls Tools in Fold Description. In: R.G. Graig & M.L. Labovitz: Future Trends in Geomathematics. London: Pion, 41. • Overlay I: UNESCO (1999): GIS Project. Module A. http://gea.zvne.fer.hr/module/module_a/module_a5.html • Geostatistics: GEOVARIANCES (2000): Homepage. http://www.geovariances.fr/ • Simulations: Los Alamos National Laboratory (1999): Random Particle Transport And Diffusion (RAPTAD). http://www-tsa.lanl.gov/tsa4/aquality/mdesc.html • SDSS: MALCZEWSKI, Jack (1997): Spatial Decision Support Systems. NCGIA Core Curriculum in GIScience, Unit 127. http://www.ncgia.ucsb.edu/giscc/units/u127/u127.html • MCE: IDRISI Sources • Sources of Literature: • Aronoff, S. (1989): Geographic Information Systems: A Management perspective. Ottawa, Canada: WDL Publications. • Burrough, P.A. (1986): Principle of Geograhpical Information Systems for land resources assessment. Oxford, Oxford, University Press. • Cowen, D.J. (1988): GIS versus CAD versus DBMS: what are the differences? Photogrammetric Engineering and Remote Sensing, 54(1), 1441-1555. • Sources of figures: • Objectives: UNESCO (1999): Introduction to Geographic Information Systems. Training Modul A. http://gea.zvne.fer.hr/module/module_a/module_a1.html • Contents: Australian Antarctic Data Center AADC: What is GIS?. http://www-aadc.antdiv.gov.au/gis/what_gis.html • Geographic Information: ESRI (1998): About GIS. What can GIS do for you?. http://www.esri.com/library/gis/abtgis/gis_do.html • Data and Information/Digital Information: YEUNG, Albert K. (1998): Information Organization and Data Structure. NCGIA Core Curriculum Unit 51. http://www.ncgia.ucsb.edu/giscc/units/u051/ • Definitions/ Meaning of “GIS”: AEGIS, U.C. Berkeley College of Environmental Design (2000): Homepage. http://www5.ced.berkeley.edu:8005/aegis/ • Components of GIS/ GIS yesterday/ Forestry : BUCKLEY, David J. (1997): The GIS Primer. http://blaze.innovativegis.com/education/primer/concepts.html • GIS is not: Autodesk (2000): Design Examples. http://www.autodesk.com/prods/examples/autocad/html/index.htm • Geographic Information Technologies: Canada Center for Remote Sensing CCRS (1998): Fundamentals of Remote Sensing. http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/indexe.html • GIS today: POTTER, C. S./ KLOOSTER, S. A. (1997): Carbon Cycling in Amazon Rainforest. http://geo.arc.nasa.gov/sge/casa/regmdl.html • GIS tomorrow: GeoNorth (2000): Interactive Mapping. http://www.geonorth.com/products/mapoptix/index.cfm • Why use GIS: WHITE, Ben/ GREGORY, Ian/ SOUTHALL, Humphrey (1998): Analysing and Visualising Long-Term Change: Unlocking the potential of the Historical GIS. http://www.geog.qmw.ac.uk/gbhgis/gisruk98/ • GIS as an Integrating Technology: ONWORD Press (1999): Homepage Header. http://www.onwordpress.com/ • Utility Industry: South Pacific Applied Geoscience Commission SOPAC (1998): GIS for Power Utilities. http://www.sidsnet.org/mir/pacific/sopac/staff/wolf/GISPoUtl.html • Retail Trade: ESRI (1998): Getting to know Desktop GIS. http://www.esri.com/library/dtgis/ch1b.html#Desktop%20GIS%20puts%20it%20all%20together Sources • LILLESAND, Thomas M. / KIEFER, Ralph W. (1994): Remote Sensing and Image Interpretation, 3rd ed. John Wiley & Sons, Inc. New York, Chichester, Brisbane. 750 p. • Popular satellite imagery are gained from the U.S. LANDSAT and NOAA-AVHRR satellites, the French SPOT satellites, and the Indian IRS satellites. • Geographical Data Description Directory (GDDD): http://www.megrin.org/gddd/gddd.html

• Verwendete Literatur: • DAVIS, Bruce E. (1996): GIS – a visual approach. OnWord Press. Santa Fe, NM. 374 p. • ROBINSON, Arthur H. et al (1995): Elements of Cartography. 6th edition. John Wiley & Sons Inc. New York, Chichester, Brisbane. 674 p. • MONTGOMERY, Glenn E. / SCHUCH, Harold C. (1993): GIS Data Conversion Handbook. GIS World Inc. Fort Collins, CL. 292 p.

• Internetquellen: • Contents: KRAAK, Menno-Jan / ORMELING, Ferjan (1997): Cartography – Visualization of Spatial Data. Longman Ltd., Harlow, 222 p. • GIS – spatial data: BUCKLEY, David J. (1997): The GIS Primer. http://blaze.innovativegis.com/education/primer/primer.html • Remotely sensed data: CRUM, Shannon (1997): The Remote Sensing Core Curriculum, Volume 2. http://grouchy.geog.ucsb.edu/rscc/vol2/lec2/2lecture.html • Principles of Photogrammetry: Stereoscopy: ESTES, John E. / LAWLESS, Michael J. (1998): The Remote Sensing Core Curriculum. Volume 1, http://umbc7.umbc.edu/~tbenja1/santabar/vol1/lec8/8lecture.html • Aerial Photography: McClinton, Martin: Aerial Photography. http://www.brevard.cc.fl.us/BTR_Labs/educat/rs/6_photo.htm • Satellite Imagery, Scanner Types: JENSEN, John R. / JACKSON, Mark W.: The Remote Sensing Core Curriculum, Volume 3, http://www.cla.sc.edu/GEOG/rslab/rsccnew/index.html Products: NOAA - Aeronautical Survey Program http://www.ngs.noaa.gov/AERO/aero.html • GPS: DANA, Peter (1999): Global Positioning System Overview. http://www.utexas.edu/depts/grg/gcraft/notes/gps/gps.html • Receiver Performance, Real-Time Data: TRIMBLE (2000): GPS Tutorial. http://www.trimble.com/gps/fsections/aa_f1.htmDigitzing Equipment: ZHOU, Qiming (1998): Data input to a spatial information system. http://geog.hkbu.edu.hk/QZone/Teaching/geog3142.html#Lectures • Scanning: Canada Centre for Remote Sensing (1998): Fundamentals of Remote Sensing – Tutorial. http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/tcrede.html • Vectorization: McGWIRE, Kenneth, The Remote Sensing Core Curriculum; Volume 3, http://www.cla.sc.edu/GEOG/rslab/rsccnew/index.html • Metadata: Bureau of Land Management,Denver Federal Center (1995): http://www.blm.gov/gis/meta/barney/tut_met1.html • Checklist: FOOTE, Kenneth E. / LYNCH, Margaret (1999): Data Sources for GIS. The Geographer’s Craft Project. http://www.utexas.edu/depts/grg/gcraft/notes/sources/sources.html Sources • Contents: Standardiseringen I Sverige (1998): Different kind of standards. http://www.sis.se/english/stand/examples/diff_std_e.htm • Mapped Data: Bundesamt für Kartographie und Geodäsie: http://www.ifag.de/ • Image Data: EUMIRAGE: Calendar Images 1999. http://www.eurimage.com/ • Attribute Data: ESRI: Basics of Arc/Info. http://archi.kyungpook.ac.kr/~www/introai/basicai/Lesson02/content/t2con2. cfm • Geographic Information Infrastructure: COLEMAN, David J. / McLAUGHLIN, John: Defining global geospatial data infrastructure. http://www.eurogi.org/gsdi/ggdiwp1.html • GIS Formats: BUEHLER, Kurt / McKEE, Lance (1998) (ed.): The OpenGIS Guide – Introduction to interoperable Geoprocessing and the OpenGIs Specification by the Open GIS Consortium Technical Committee. www.opengis.org. • CAD Formats: Spatial Data Transfer Standard (2000): Senior Management Overview. http://mcmcweb.er.usgs.gov/sdts/training.html • Imagery Formats: U.S. Geological Survey (1999): Cities of the World – Athens. http://edcwww.cr.usgs.gov/customer.html, last update 12/99. • Graphic Files: GUARDIA, Neff/FURGUS, Nathaniel (1999): Popular Graphic Formats. http://www.ece2.engr.ucf.edu/~ngu/graphics.html • Geodata Market: BUCKLEY, David J.: The GIS Primer. http://blaze.innovativegis.com/education/primer/primer.html • Data Catalog: NOAA (2000): Live Access to Climate Data. http://ferret.wrc.noaa.gov/fbin/climate_server THANK YOU FOR ATTENTION