<p>Introduction xi</p> <p><b>Chapter 1. Monitoring Coastal Bathymetry Using Multispectral Satellite Images at High Spatial Resolution 1</b><br /><i>Bertrand LUBAC</i></p> <p>1.1. Definition, context and objective 1</p> <p>1.2. Description of the methodology 3</p> <p>1.2.1. Step 1: selection and preprocessing of MSI images 5</p> <p>1.2.2. Step 2: calibration of the bathymetry inversion model 7</p> <p>1.2.3. Step 3: preparation and application of the masks 8</p> <p>1.2.4. Step 4: characterization of the morphological evolution of the main sedimentary structures 9</p> <p>1.3. Practical application 10</p> <p>1.3.1. Software and data 10</p> <p>1.3.2. Step 1: extraction of the region of interest and preprocessing 13</p> <p>1.3.3. Step 2: calculation of bathymetry 20</p> <p>1.3.4. Step 3: preparation and application of masks 25</p> <p>1.3.5. Step 4: characterization of the morphological evolution of the main submarine sedimentary structures 31</p> <p>1.4. Bibliography 33</p> <p><b>Chapter 2. Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) 35</b><br /><i>Zeineb KASSOUK, Zohra LILI-CHABAANE, Benoit DEFFONTAINES, Mohammad EL HAJJ and Nicolas BAGHDADI</i></p> <p>2.1. Coastal wetland dynamic 35</p> <p>2.2. Ichkeul marshes wetland 36</p> <p>2.3. Object-oriented classification method integrating the topo-bathymetric terrain model 39</p> <p>2.3.1. Construction of the topo-bathymetric DTM 40</p> <p>2.3.2. Image preprocessing 44</p> <p>2.3.3. Segmentation 48</p> <p>2.3.4. Classification 49</p> <p>2.3.5. Limitations of the methodology 51</p> <p>2.3.6. Case example of topo-bathymetric transect with the associated vegetation communities 51</p> <p>2.3.7. Conclusion 53</p> <p>2.4. From a practical point of view in QGIS 53</p> <p>2.4.1. Software and data 53</p> <p>2.4.2. Computation of the topo-bathymetric DTM 55</p> <p>2.4.3. Image preprocessing 58</p> <p>2.4.4. Segmentation 65</p> <p>2.4.5. Classification 71</p> <p>2.5. Bibliography 76</p> <p><b>Chapter 3. Reservoir Hydrological Monitoring by Satellite Image Analysis 77</b><br /><i>Paul PASSY and Adrien SELLES</i></p> <p>3.1. Context and scientific issue 77</p> <p>3.1.1. Scientific issue 77</p> <p>3.1.2. Physical and human context 77</p> <p>3.1.3. The importance of water resources in Central India 78</p> <p>3.2. Methods and data set 78</p> <p>3.2.1. Methods 78</p> <p>3.2.2. Data set 79</p> <p>3.2.3. Data set preparation 80</p> <p>3.3. Extraction and quantification of the Singur reservoir area 82</p> <p>3.3.1. Calculation of the AWEI Index. 82</p> <p>3.3.2. Construction of the water–land binary raster 83</p> <p>3.3.3. Vectorization of the binary raster 84</p> <p>3.3.4. Selection of water polygons 85</p> <p>3.3.5. Calculation of the water area of the reservoir 86</p> <p>3.4. Characterization of vegetation 88</p> <p>3.4.1. Choosing an indicator of the state of vegetation 88</p> <p>3.4.2. Calculation of the SAVI on the study area 88</p> <p>3.4.3. Creating a land–water mask 89</p> <p>3.4.4. Statistics of the SAVI land surface index 90</p> <p>3.5. Automation of the processing chain via the construction of a QGIS model 91</p> <p>3.5.1. Model setting 91</p> <p>3.5.2. Construction of the chain of treatments for the extraction of the reservoir 92</p> <p>3.6. Conclusions 103</p> <p>3.7. Bibliography 103</p> <p><b>Chapter 4. Network Analysis and Routing with QGIS 105</b><br /><i>Hervé PELLA and Kenji OSE</i></p> <p>4.1. Introduction 105</p> <p>4.2. General notions 105</p> <p>4.2.1. Definition of a network 105</p> <p>4.2.2. Network topology 106</p> <p>4.2.3. Topological relationships 107</p> <p>4.2.4. Graph traversal – example of the shortest path (Dijkstra) 109</p> <p>4.3. Examples of development and analysis of hydrographic networks 109</p> <p>4.4. Thematic analysis 111</p> <p>4.4.1. Introduction 111</p> <p>4.4.2. Useful data 112</p> <p>4.4.3. Step 1: verification of network consistency 113</p> <p>4.4.4. Step 2: routes organization 119</p> <p>4.4.5. Step 3: alignment of points on a network 121</p> <p>4.4.6. Step 4: network classification 123</p> <p>4.4.7. Step 5: stations characterization 124</p> <p>4.4.8. Step 6: distance calculation between observation points 129</p> <p>4.4.9. Step 7: upstream path and drainage basins calculation 133</p> <p>4.4.10. Step 8: downstream path 135</p> <p>4.4.11. Step 9: calculation of availability areas 140</p> <p>4.5. Bibliography 144</p> <p><b>Chapter 5. Representation of the Drainage Network in Urban and Peri-urban Areas Using a 2D Polygonal Mesh Composed of Pseudo-convex Elements 145</b><br /><i>Pedro SANZANA, Sergio VILLAROEL, Isabelle BRAUD, Nancy HITSCHFELD, Jorge GIRONAS, Flora BRANGER, Fabrice RODRIGUEZ, Ximena VARGAS and Tomas GOMEZ</i></p> <p>5.1. Definitions and context 145</p> <p>5.1.1. General context and objectives 145</p> <p>5.1.2. Derivation of input GIS layers 148</p> <p>5.1.3. Identification of badly-shaped HRUs and methodology to improve the model mesh quality 149</p> <p>5.2. Implementation of the TriangleQGIS module and general methodology 153</p> <p>5.2.1. Used technologies 153</p> <p>5.2.2. Context and general methodology 153</p> <p>5.2.3. Structure of the QGIS plugin 155</p> <p>5.2.4. Basic used library: MeshPy 156</p> <p>5.2.5. Installation of the plugin in Windows 156</p> <p>5.2.6. Installation of the virtual box, QGIS plugin and Geo-PUMMA 160</p> <p>5.3. Illustration of the TriangleQGIS plugin and some Geo-PUMMA scripts 167</p> <p>5.3.1. Insertion of nodes for long and thin polygons 168</p> <p>5.3.2. Triangulation using the TriangleQGIS plugin 169</p> <p>5.3.3. Dissolution of tirangulated elements 178</p> <p>5.3.4. Effect of the model mesh improvement 181</p> <p>5.4. Acknowledgments 182</p> <p>5.5. Bibliography 183</p> <p><b>Chapter 6. Mapping of Drought 185</b><br /><i>Mohammad EL HAJJ, Mehrez ZRIBI, Nicolas BAGHDADI and Michel LE PAGE</i></p> <p>6.1. Context 185</p> <p>6.2. Satellite data 186</p> <p>6.2.1. MODIS products 186</p> <p>6.2.2. Land cover map 187</p> <p>6.3. Drought index based on satellite NDVI data 187</p> <p>6.4. Methodology 188</p> <p>6.4.1. Preprocessing of MOD13Q1 images (step 1) 189</p> <p>6.4.2. Delimitation of drought zones (steps 2–5) 189</p> <p>6.4.3. Calculate the area of agricultural, urban and forest zones affected by the drought (step 6) 190</p> <p>6.5. Implementation of the application via QGIS 191</p> <p>6.5.1. Download MODIS MOD13Q1 data 191</p> <p>6.5.2. Preprocessing of MODIS MOD13Q1 data (step 1) 193</p> <p>6.5.3. Calculate VCI index (steps 1 and 2) 195</p> <p>6.5.4. Delimitation of drought zones (steps 2–5) 199</p> <p>6.5.5. Calculation of agricultural, forest and urban areas affected by drought (step 6) 204</p> <p>6.5.6. Visualization of results (step 7) 206</p> <p>6.6. Drought map 212</p> <p>6.7. Bibliography 213</p> <p><b>Chapter 7. A Spatial Sampling Design Based on Landscape Metrics for Pest Regulation: The Millet Head Miner</b> <b>Case Study in the Bambey Area, Senegal 215</b><br /><i>Valérie SOTI</i></p> <p>7.1. Definition and context 215</p> <p>7.2. The spatial sampling methodology 217</p> <p>7.2.1. Step 1: quantification of landscape metrics 218</p> <p>7.2.2. Step 2: sampling plan production 221</p> <p>7.2.3. Step 3: exportation of selected sampling sites to a GPS 223</p> <p>7.3. Practical application 223</p> <p>7.3.1. Software and data 223</p> <p>7.3.2. Step 1: landscape variables calculation 224</p> <p>7.3.3. Step 2: sampling plan production 232</p> <p>7.3.4. Step 3: integrating sampling points into a GPS device 238</p> <p>7.3.5. Limits of the method 241</p> <p>7.4. Bibliography 242</p> <p><b>Chapter 8. Modeling Erosion Risk Using the RUSLE Equation 245</b><br /><i>Rémi ANDREOLI</i></p> <p>8.1. Definition and context 245</p> <p>8.2. RUSLE model 246</p> <p>8.2.1. Climatic factor: rainfall aggressiveness R 248</p> <p>8.2.2. Topographic factor: slope length and gradient 249</p> <p>8.2.3. Soil types and land cover factors 251</p> <p>8.2.4. Estimation of soil losses A 254</p> <p>8.2.5. Limits of the method considered 254</p> <p>8.3. Implementation of the RUSLE model 255</p> <p>8.3.1. Software and data 255</p> <p>8.3.2. Step 1: R factor calculation 257</p> <p>8.3.3. Step 2: LS factor calculation 263</p> <p>8.3.4. Step 3: preparation of the K factor 274</p> <p>8.3.5. Step 4: C factor creation 275</p> <p>8.3.6. Step 5: soil loss A calculation from the RUSLE equation 280</p> <p>8.4. Bibliography 281</p> <p>List of Authors 283</p> <p>Index 285</p> <p>Scientific Committee 289</p>