QGIS and Applications in Water and Risks

Our four volumes propose to present innovative thematic applications implemented using the open source software QGIS. These are applications that use remote sensing over continental surfaces. The four volumes detail applications of remote sensing over continental surfaces, with a first one discussing applications for agriculture. A second one presents applications for forest, a third presents applications for the continental hydrology, and finally the last volume details applications for environment and risk issues.

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