Nicolas Baghdadi, French Research Institute of Science and Technology for Environment and Agriculture, France. Clément Mallet, ING, France. Mehrez Zribi, CNRS and CESBIO, France.

• Introduction xiChapter 1. Monitoring Coastal Bathymetry Using Multispectral Satellite Images at High Spatial Resolution 1Bertrand LUBAC1.1. Definition, context and objective 11.2. Description of the methodology 31.2.1. Step 1: selection and preprocessing of MSI images 51.2.2. Step 2: calibration of the bathymetry inversion model 71.2.3. Step 3: preparation and application of the masks 81.2.4. Step 4: characterization of the morphological evolution of the main sedimentary structures 91.3. Practical application 101.3.1. Software and data 101.3.2. Step 1: extraction of the region of interest and preprocessing 131.3.3. Step 2: calculation of bathymetry 201.3.4. Step 3: preparation and application of masks 251.3.5. Step 4: characterization of the morphological evolution of the main submarine sedimentary structures 311.4. Bibliography 33Chapter 2. Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) 35Zeineb KASSOUK, Zohra LILI-CHABAANE, Benoit DEFFONTAINES, Mohammad EL HAJJ and Nicolas BAGHDADI2.1. Coastal wetland dynamic 352.2. Ichkeul marshes wetland 362.3. Object-oriented classification method integrating the topo-bathymetric terrain model 392.3.1. Construction of the topo-bathymetric DTM 402.3.2. Image preprocessing 442.3.3. Segmentation 482.3.4. Classification 492.3.5. Limitations of the methodology 512.3.6. Case example of topo-bathymetric transect with the associated vegetation communities 512.3.7. Conclusion 532.4. From a practical point of view in QGIS 532.4.1. Software and data 532.4.2. Computation of the topo-bathymetric DTM 552.4.3. Image preprocessing 582.4.4. Segmentation 652.4.5. Classification 712.5. Bibliography 76Chapter 3. Reservoir Hydrological Monitoring by Satellite Image Analysis 77Paul PASSY and Adrien SELLES3.1. Context and scientific issue 773.1.1. Scientific issue 773.1.2. Physical and human context 773.1.3. The importance of water resources in Central India 783.2. Methods and data set 783.2.1. Methods 783.2.2. Data set 793.2.3. Data set preparation 803.3. Extraction and quantification of the Singur reservoir area 823.3.1. Calculation of the AWEI Index. 823.3.2. Construction of the water–land binary raster 833.3.3. Vectorization of the binary raster 843.3.4. Selection of water polygons 853.3.5. Calculation of the water area of the reservoir 863.4. Characterization of vegetation 883.4.1. Choosing an indicator of the state of vegetation 883.4.2. Calculation of the SAVI on the study area 883.4.3. Creating a land–water mask 893.4.4. Statistics of the SAVI land surface index 903.5. Automation of the processing chain via the construction of a QGIS model 913.5.1. Model setting 913.5.2. Construction of the chain of treatments for the extraction of the reservoir 923.6. Conclusions 1033.7. Bibliography 103Chapter 4. Network Analysis and Routing with QGIS 105Hervé PELLA and Kenji OSE4.1. Introduction 1054.2. General notions 1054.2.1. Definition of a network 1054.2.2. Network topology 1064.2.3. Topological relationships 1074.2.4. Graph traversal – example of the shortest path (Dijkstra) 1094.3. Examples of development and analysis of hydrographic networks 1094.4. Thematic analysis 1114.4.1. Introduction 1114.4.2. Useful data 1124.4.3. Step 1: verification of network consistency 1134.4.4. Step 2: routes organization 1194.4.5. Step 3: alignment of points on a network 1214.4.6. Step 4: network classification 1234.4.7. Step 5: stations characterization 1244.4.8. Step 6: distance calculation between observation points 1294.4.9. Step 7: upstream path and drainage basins calculation 1334.4.10. Step 8: downstream path 1354.4.11. Step 9: calculation of availability areas 1404.5. Bibliography 144Chapter 5. Representation of the Drainage Network in Urban and Peri-urban Areas Using a 2D Polygonal Mesh Composed of Pseudo-convex Elements 145Pedro SANZANA, Sergio VILLAROEL, Isabelle BRAUD, Nancy HITSCHFELD, Jorge GIRONAS, Flora BRANGER, Fabrice RODRIGUEZ, Ximena VARGAS and Tomas GOMEZ5.1. Definitions and context 1455.1.1. General context and objectives 1455.1.2. Derivation of input GIS layers 1485.1.3. Identification of badly-shaped HRUs and methodology to improve the model mesh quality 1495.2. Implementation of the TriangleQGIS module and general methodology 1535.2.1. Used technologies 1535.2.2. Context and general methodology 1535.2.3. Structure of the QGIS plugin 1555.2.4. Basic used library: MeshPy 1565.2.5. Installation of the plugin in Windows 1565.2.6. Installation of the virtual box, QGIS plugin and Geo-PUMMA 1605.3. Illustration of the TriangleQGIS plugin and some Geo-PUMMA scripts 1675.3.1. Insertion of nodes for long and thin polygons 1685.3.2. Triangulation using the TriangleQGIS plugin 1695.3.3. Dissolution of tirangulated elements 1785.3.4. Effect of the model mesh improvement 1815.4. Acknowledgments 1825.5. Bibliography 183Chapter 6. Mapping of Drought 185Mohammad EL HAJJ, Mehrez ZRIBI, Nicolas BAGHDADI and Michel LE PAGE6.1. Context 1856.2. Satellite data 1866.2.1. MODIS products 1866.2.2. Land cover map 1876.3. Drought index based on satellite NDVI data 1876.4. Methodology 1886.4.1. Preprocessing of MOD13Q1 images (step 1) 1896.4.2. Delimitation of drought zones (steps 2–5) 1896.4.3. Calculate the area of agricultural, urban and forest zones affected by the drought (step 6) 1906.5. Implementation of the application via QGIS 1916.5.1. Download MODIS MOD13Q1 data 1916.5.2. Preprocessing of MODIS MOD13Q1 data (step 1) 1936.5.3. Calculate VCI index (steps 1 and 2) 1956.5.4. Delimitation of drought zones (steps 2–5) 1996.5.5. Calculation of agricultural, forest and urban areas affected by drought (step 6) 2046.5.6. Visualization of results (step 7) 2066.6. Drought map 2126.7. Bibliography 213Chapter 7. A Spatial Sampling Design Based on Landscape Metrics for Pest Regulation: The Millet Head Miner Case Study in the Bambey Area, Senegal 215Valérie SOTI7.1. Definition and context 2157.2. The spatial sampling methodology 2177.2.1. Step 1: quantification of landscape metrics 2187.2.2. Step 2: sampling plan production 2217.2.3. Step 3: exportation of selected sampling sites to a GPS 2237.3. Practical application 2237.3.1. Software and data 2237.3.2. Step 1: landscape variables calculation 2247.3.3. Step 2: sampling plan production 2327.3.4. Step 3: integrating sampling points into a GPS device 2387.3.5. Limits of the method 2417.4. Bibliography 242Chapter 8. Modeling Erosion Risk Using the RUSLE Equation 245Rémi ANDREOLI8.1. Definition and context 2458.2. RUSLE model 2468.2.1. Climatic factor: rainfall aggressiveness R 2488.2.2. Topographic factor: slope length and gradient 2498.2.3. Soil types and land cover factors 2518.2.4. Estimation of soil losses A 2548.2.5. Limits of the method considered 2548.3. Implementation of the RUSLE model 2558.3.1. Software and data 2558.3.2. Step 1: R factor calculation 2578.3.3. Step 2: LS factor calculation 2638.3.4. Step 3: preparation of the K factor 2748.3.5. Step 4: C factor creation 2758.3.6. Step 5: soil loss A calculation from the RUSLE equation 2808.4. Bibliography 281List of Authors 283Index 285Scientific Committee 289

Nicolas Baghdadi, French Research Institute of Science and Technology for Environment and Agriculture, France. Clément Mallet, ING, France. Mehrez Zribi, CNRS and CESBIO, France.

• Introduction xiChapter 1. Monitoring Coastal Bathymetry Using Multispectral Satellite Images at High Spatial Resolution 1Bertrand LUBAC1.1. Definition, context and objective 11.2. Description of the methodology 31.2.1. Step 1: selection and preprocessing of MSI images 51.2.2. Step 2: calibration of the bathymetry inversion model 71.2.3. Step 3: preparation and application of the masks 81.2.4. Step 4: characterization of the morphological evolution of the main sedimentary structures 91.3. Practical application 101.3.1. Software and data 101.3.2. Step 1: extraction of the region of interest and preprocessing 131.3.3. Step 2: calculation of bathymetry 201.3.4. Step 3: preparation and application of masks 251.3.5. Step 4: characterization of the morphological evolution of the main submarine sedimentary structures 311.4. Bibliography 33Chapter 2. Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) 35Zeineb KASSOUK, Zohra LILI-CHABAANE, Benoit DEFFONTAINES, Mohammad EL HAJJ and Nicolas BAGHDADI2.1. Coastal wetland dynamic 352.2. Ichkeul marshes wetland 362.3. Object-oriented classification method integrating the topo-bathymetric terrain model 392.3.1. Construction of the topo-bathymetric DTM 402.3.2. Image preprocessing 442.3.3. Segmentation 482.3.4. Classification 492.3.5. Limitations of the methodology 512.3.6. Case example of topo-bathymetric transect with the associated vegetation communities 512.3.7. Conclusion 532.4. From a practical point of view in QGIS 532.4.1. Software and data 532.4.2. Computation of the topo-bathymetric DTM 552.4.3. Image preprocessing 582.4.4. Segmentation 652.4.5. Classification 712.5. Bibliography 76Chapter 3. Reservoir Hydrological Monitoring by Satellite Image Analysis 77Paul PASSY and Adrien SELLES3.1. Context and scientific issue 773.1.1. Scientific issue 773.1.2. Physical and human context 773.1.3. The importance of water resources in Central India 783.2. Methods and data set 783.2.1. Methods 783.2.2. Data set 793.2.3. Data set preparation 803.3. Extraction and quantification of the Singur reservoir area 823.3.1. Calculation of the AWEI Index. 823.3.2. Construction of the water–land binary raster 833.3.3. Vectorization of the binary raster 843.3.4. Selection of water polygons 853.3.5. Calculation of the water area of the reservoir 863.4. Characterization of vegetation 883.4.1. Choosing an indicator of the state of vegetation 883.4.2. Calculation of the SAVI on the study area 883.4.3. Creating a land–water mask 893.4.4. Statistics of the SAVI land surface index 903.5. Automation of the processing chain via the construction of a QGIS model 913.5.1. Model setting 913.5.2. Construction of the chain of treatments for the extraction of the reservoir 923.6. Conclusions 1033.7. Bibliography 103Chapter 4. Network Analysis and Routing with QGIS 105Hervé PELLA and Kenji OSE4.1. Introduction 1054.2. General notions 1054.2.1. Definition of a network 1054.2.2. Network topology 1064.2.3. Topological relationships 1074.2.4. Graph traversal – example of the shortest path (Dijkstra) 1094.3. Examples of development and analysis of hydrographic networks 1094.4. Thematic analysis 1114.4.1. Introduction 1114.4.2. Useful data 1124.4.3. Step 1: verification of network consistency 1134.4.4. Step 2: routes organization 1194.4.5. Step 3: alignment of points on a network 1214.4.6. Step 4: network classification 1234.4.7. Step 5: stations characterization 1244.4.8. Step 6: distance calculation between observation points 1294.4.9. Step 7: upstream path and drainage basins calculation 1334.4.10. Step 8: downstream path 1354.4.11. Step 9: calculation of availability areas 1404.5. Bibliography 144Chapter 5. Representation of the Drainage Network in Urban and Peri-urban Areas Using a 2D Polygonal Mesh Composed of Pseudo-convex Elements 145Pedro SANZANA, Sergio VILLAROEL, Isabelle BRAUD, Nancy HITSCHFELD, Jorge GIRONAS, Flora BRANGER, Fabrice RODRIGUEZ, Ximena VARGAS and Tomas GOMEZ5.1. Definitions and context 1455.1.1. General context and objectives 1455.1.2. Derivation of input GIS layers 1485.1.3. Identification of badly-shaped HRUs and methodology to improve the model mesh quality 1495.2. Implementation of the TriangleQGIS module and general methodology 1535.2.1. Used technologies 1535.2.2. Context and general methodology 1535.2.3. Structure of the QGIS plugin 1555.2.4. Basic used library: MeshPy 1565.2.5. Installation of the plugin in Windows 1565.2.6. Installation of the virtual box, QGIS plugin and Geo-PUMMA 1605.3. Illustration of the TriangleQGIS plugin and some Geo-PUMMA scripts 1675.3.1. Insertion of nodes for long and thin polygons 1685.3.2. Triangulation using the TriangleQGIS plugin 1695.3.3. Dissolution of tirangulated elements 1785.3.4. Effect of the model mesh improvement 1815.4. Acknowledgments 1825.5. Bibliography 183Chapter 6. Mapping of Drought 185Mohammad EL HAJJ, Mehrez ZRIBI, Nicolas BAGHDADI and Michel LE PAGE6.1. Context 1856.2. Satellite data 1866.2.1. MODIS products 1866.2.2. Land cover map 1876.3. Drought index based on satellite NDVI data 1876.4. Methodology 1886.4.1. Preprocessing of MOD13Q1 images (step 1) 1896.4.2. Delimitation of drought zones (steps 2–5) 1896.4.3. Calculate the area of agricultural, urban and forest zones affected by the drought (step 6) 1906.5. Implementation of the application via QGIS 1916.5.1. Download MODIS MOD13Q1 data 1916.5.2. Preprocessing of MODIS MOD13Q1 data (step 1) 1936.5.3. Calculate VCI index (steps 1 and 2) 1956.5.4. Delimitation of drought zones (steps 2–5) 1996.5.5. Calculation of agricultural, forest and urban areas affected by drought (step 6) 2046.5.6. Visualization of results (step 7) 2066.6. Drought map 2126.7. Bibliography 213Chapter 7. A Spatial Sampling Design Based on Landscape Metrics for Pest Regulation: The Millet Head Miner Case Study in the Bambey Area, Senegal 215Valérie SOTI7.1. Definition and context 2157.2. The spatial sampling methodology 2177.2.1. Step 1: quantification of landscape metrics 2187.2.2. Step 2: sampling plan production 2217.2.3. Step 3: exportation of selected sampling sites to a GPS 2237.3. Practical application 2237.3.1. Software and data 2237.3.2. Step 1: landscape variables calculation 2247.3.3. Step 2: sampling plan production 2327.3.4. Step 3: integrating sampling points into a GPS device 2387.3.5. Limits of the method 2417.4. Bibliography 242Chapter 8. Modeling Erosion Risk Using the RUSLE Equation 245Rémi ANDREOLI8.1. Definition and context 2458.2. RUSLE model 2468.2.1. Climatic factor: rainfall aggressiveness R 2488.2.2. Topographic factor: slope length and gradient 2498.2.3. Soil types and land cover factors 2518.2.4. Estimation of soil losses A 2548.2.5. Limits of the method considered 2548.3. Implementation of the RUSLE model 2558.3.1. Software and data 2558.3.2. Step 1: R factor calculation 2578.3.3. Step 2: LS factor calculation 2638.3.4. Step 3: preparation of the K factor 2748.3.5. Step 4: C factor creation 2758.3.6. Step 5: soil loss A calculation from the RUSLE equation 2808.4. Bibliography 281List of Authors 283Index 285Scientific Committee 289

Nicolas Baghdadi, French Research Institute of Science and Technology for Environment and Agriculture, France. Clément Mallet, ING, France. Mehrez Zribi, CNRS and CESBIO, France.

• Introduction xiChapter 1. Monitoring Coastal Bathymetry Using Multispectral Satellite Images at High Spatial Resolution 1Bertrand LUBAC1.1. Definition, context and objective 11.2. Description of the methodology 31.2.1. Step 1: selection and preprocessing of MSI images 51.2.2. Step 2: calibration of the bathymetry inversion model 71.2.3. Step 3: preparation and application of the masks 81.2.4. Step 4: characterization of the morphological evolution of the main sedimentary structures 91.3. Practical application 101.3.1. Software and data 101.3.2. Step 1: extraction of the region of interest and preprocessing 131.3.3. Step 2: calculation of bathymetry 201.3.4. Step 3: preparation and application of masks 251.3.5. Step 4: characterization of the morphological evolution of the main submarine sedimentary structures 311.4. Bibliography 33Chapter 2. Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) 35Zeineb KASSOUK, Zohra LILI-CHABAANE, Benoit DEFFONTAINES, Mohammad EL HAJJ and Nicolas BAGHDADI2.1. Coastal wetland dynamic 352.2. Ichkeul marshes wetland 362.3. Object-oriented classification method integrating the topo-bathymetric terrain model 392.3.1. Construction of the topo-bathymetric DTM 402.3.2. Image preprocessing 442.3.3. Segmentation 482.3.4. Classification 492.3.5. Limitations of the methodology 512.3.6. Case example of topo-bathymetric transect with the associated vegetation communities 512.3.7. Conclusion 532.4. From a practical point of view in QGIS 532.4.1. Software and data 532.4.2. Computation of the topo-bathymetric DTM 552.4.3. Image preprocessing 582.4.4. Segmentation 652.4.5. Classification 712.5. Bibliography 76Chapter 3. Reservoir Hydrological Monitoring by Satellite Image Analysis 77Paul PASSY and Adrien SELLES3.1. Context and scientific issue 773.1.1. Scientific issue 773.1.2. Physical and human context 773.1.3. The importance of water resources in Central India 783.2. Methods and data set 783.2.1. Methods 783.2.2. Data set 793.2.3. Data set preparation 803.3. Extraction and quantification of the Singur reservoir area 823.3.1. Calculation of the AWEI Index. 823.3.2. Construction of the water–land binary raster 833.3.3. Vectorization of the binary raster 843.3.4. Selection of water polygons 853.3.5. Calculation of the water area of the reservoir 863.4. Characterization of vegetation 883.4.1. Choosing an indicator of the state of vegetation 883.4.2. Calculation of the SAVI on the study area 883.4.3. Creating a land–water mask 893.4.4. Statistics of the SAVI land surface index 903.5. Automation of the processing chain via the construction of a QGIS model 913.5.1. Model setting 913.5.2. Construction of the chain of treatments for the extraction of the reservoir 923.6. Conclusions 1033.7. Bibliography 103Chapter 4. Network Analysis and Routing with QGIS 105Hervé PELLA and Kenji OSE4.1. Introduction 1054.2. General notions 1054.2.1. Definition of a network 1054.2.2. Network topology 1064.2.3. Topological relationships 1074.2.4. Graph traversal – example of the shortest path (Dijkstra) 1094.3. Examples of development and analysis of hydrographic networks 1094.4. Thematic analysis 1114.4.1. Introduction 1114.4.2. Useful data 1124.4.3. Step 1: verification of network consistency 1134.4.4. Step 2: routes organization 1194.4.5. Step 3: alignment of points on a network 1214.4.6. Step 4: network classification 1234.4.7. Step 5: stations characterization 1244.4.8. Step 6: distance calculation between observation points 1294.4.9. Step 7: upstream path and drainage basins calculation 1334.4.10. Step 8: downstream path 1354.4.11. Step 9: calculation of availability areas 1404.5. Bibliography 144Chapter 5. Representation of the Drainage Network in Urban and Peri-urban Areas Using a 2D Polygonal Mesh Composed of Pseudo-convex Elements 145Pedro SANZANA, Sergio VILLAROEL, Isabelle BRAUD, Nancy HITSCHFELD, Jorge GIRONAS, Flora BRANGER, Fabrice RODRIGUEZ, Ximena VARGAS and Tomas GOMEZ5.1. Definitions and context 1455.1.1. General context and objectives 1455.1.2. Derivation of input GIS layers 1485.1.3. Identification of badly-shaped HRUs and methodology to improve the model mesh quality 1495.2. Implementation of the TriangleQGIS module and general methodology 1535.2.1. Used technologies 1535.2.2. Context and general methodology 1535.2.3. Structure of the QGIS plugin 1555.2.4. Basic used library: MeshPy 1565.2.5. Installation of the plugin in Windows 1565.2.6. Installation of the virtual box, QGIS plugin and Geo-PUMMA 1605.3. Illustration of the TriangleQGIS plugin and some Geo-PUMMA scripts 1675.3.1. Insertion of nodes for long and thin polygons 1685.3.2. Triangulation using the TriangleQGIS plugin 1695.3.3. Dissolution of tirangulated elements 1785.3.4. Effect of the model mesh improvement 1815.4. Acknowledgments 1825.5. Bibliography 183Chapter 6. Mapping of Drought 185Mohammad EL HAJJ, Mehrez ZRIBI, Nicolas BAGHDADI and Michel LE PAGE6.1. Context 1856.2. Satellite data 1866.2.1. MODIS products 1866.2.2. Land cover map 1876.3. Drought index based on satellite NDVI data 1876.4. Methodology 1886.4.1. Preprocessing of MOD13Q1 images (step 1) 1896.4.2. Delimitation of drought zones (steps 2–5) 1896.4.3. Calculate the area of agricultural, urban and forest zones affected by the drought (step 6) 1906.5. Implementation of the application via QGIS 1916.5.1. Download MODIS MOD13Q1 data 1916.5.2. Preprocessing of MODIS MOD13Q1 data (step 1) 1936.5.3. Calculate VCI index (steps 1 and 2) 1956.5.4. Delimitation of drought zones (steps 2–5) 1996.5.5. Calculation of agricultural, forest and urban areas affected by drought (step 6) 2046.5.6. Visualization of results (step 7) 2066.6. Drought map 2126.7. Bibliography 213Chapter 7. A Spatial Sampling Design Based on Landscape Metrics for Pest Regulation: The Millet Head Miner Case Study in the Bambey Area, Senegal 215Valérie SOTI7.1. Definition and context 2157.2. The spatial sampling methodology 2177.2.1. Step 1: quantification of landscape metrics 2187.2.2. Step 2: sampling plan production 2217.2.3. Step 3: exportation of selected sampling sites to a GPS 2237.3. Practical application 2237.3.1. Software and data 2237.3.2. Step 1: landscape variables calculation 2247.3.3. Step 2: sampling plan production 2327.3.4. Step 3: integrating sampling points into a GPS device 2387.3.5. Limits of the method 2417.4. Bibliography 242Chapter 8. Modeling Erosion Risk Using the RUSLE Equation 245Rémi ANDREOLI8.1. Definition and context 2458.2. RUSLE model 2468.2.1. Climatic factor: rainfall aggressiveness R 2488.2.2. Topographic factor: slope length and gradient 2498.2.3. Soil types and land cover factors 2518.2.4. Estimation of soil losses A 2548.2.5. Limits of the method considered 2548.3. Implementation of the RUSLE model 2558.3.1. Software and data 2558.3.2. Step 1: R factor calculation 2578.3.3. Step 2: LS factor calculation 2638.3.4. Step 3: preparation of the K factor 2748.3.5. Step 4: C factor creation 2758.3.6. Step 5: soil loss A calculation from the RUSLE equation 2808.4. Bibliography 281List of Authors 283Index 285Scientific Committee 289