QGIS in remote sensing set. Volume 2, QGIS and applications in agriculture and forest

Content: Introduction xi Chapter 1. Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas  1Mohammad EL HAJJ, Nicolas BAGHDADI, Mehrez ZRIBI and Hassan BAZZI 1.1. Context 1 1.2. Study site and satellite data  2 1.2.1. Radar images 2 1.2.2. Optical image 4 1.2.3. Land cover map 4 1.3. Methodology 5 1.3.1. Inversion approach of radar signal for estimating soil moisture 5 1.3.2. Segmentation of crop and grasslands areas 6 1.3.3. Soil moisture mapping  8 1.4. Implementation of the application via QGIS . 10 1.4.1. Layout 10 1.4.2. Radar images 14 1.4.3. Optical image 20 1.4.4. Land cover map 26 1.4.5. Segmentation of crop   s areas and grasslands 26 1.4.6. Elimination of small spatial units  29 1.4.7. Mapping soil moisture  33 1.4.8. Soil moisture maps  43 1.5. Bibliography 44 Chapter 2. Disaggregation of Thermal Images 47Mar BISQUERT and Juan Manuel SANCHEZ 2.1. Definition and context  47 2.2. Disaggregation method  48 2.2.1. Image pre-processing  48 2.2.2. Disaggregation 50 2.3. Practical application of the disaggregation method . 53 2.3.1. Input data 53 2.3.2. Step 1: pre-processing  54 2.3.3. Step 2: disaggregation  63 2.4. Results analysis 73 2.5. Bibliography 75 Chapter 3. Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB 77Jean-Marc GILLIOT, Camille LE PRIOL, Emmanuelle VAUDOUR and Philippe MARTIN 3.1. Context 77 3.2. Method of AP extraction  79 3.2.1. Formatting the RPG data  79 3.2.2. Classification of SPOT satellite images  81 3.2.3. Intersect overlay between extracted AP and FB with crop validation 81 3.3. Practical application of the AP extraction  82 3.3.1. Software and data 83 3.3.2. Setting up the Python script  86 3.3.3. Step 1: formatting the RPG data  89 3.3.4. Step 2: classification of SPOT satellite Images . 97 3.3.5. Step 3: intersect overlay between extracted AP and FB and crop validation 110 3.4. Acknowledgements 116 3.5. Bibliography 116 Chapter 4. Land Cover Mapping Using Sentinel-2 Images and the Semi-Automatic Classification Plugin: A Northern Burkina Faso Case Study 119Louise LEROUX, Luca CONGEDO, Beatriz BELLON, Raffaele GAETANO and Agnes BEGUE 4.1. Context 119 4.2. Workflow for land cover mapping  120 4.2.1. Introduction to SCP and S2 images  120 4.2.2. Pre-processing 122 4.2.3. Land cover classification  126 4.2.4. Classification accuracy assessment and post-processing 129 4.3. Implementation with QGIS and the plugin SCP 131 4.3.1. Software and data 131 4.3.2. Step 1: data pre-processing  133 4.3.3. Step 2: land cover classification  139 4.3.4. Step 3: assessment of the classification accuracy and post-processing 144 4.4. Bibliography 150 Chapter 5. Detection and Mapping of Clear-Cuts with Optical Satellite Images  153Kenji OSE 5.1. Definition and context  153 5.2. Clear-cuts detection method  154 5.2.1. Step 1: change detection     geometric and radiometric pre-processing 154 5.2.2. Steps 2 and 3: forest delimitation  160 5.2.3. Step 4: clear-cuts classification  160 5.2.4. Steps 5 and 6: export in vector mode  162 5.2.5. Step 7: statistical evaluation.  164 5.2.6. Method limits 166 5.3. Practical application 166 5.3.1. Software and data 166 5.3.2. Step 1: creation of the changes image  168 5.3.3. Steps 2 and 3: creation, merging and integration of masks 170 5.3.4. Step 4: clear-cuts detection  174 5.3.5. Step 5: vector conversion  177 5.4. Bibliography 180 Chapter 6. Vegetation Cartography from Sentinel-1 Radar Images  181Pierre-Louis FRISON and Cedric LARDEUX 6.1. Definition and context  181 6.2. Classification of remote sensing images  183 6.3. Sentinel-1 data processing  185 6.3.1. Radiometric calibration  186 6.3.2. Ortho-rectification of calibrated data  186 6.3.3. Clip over a common area  187 6.3.4. Filtering to reduce the speckle effect  187 6.3.5. Generation of color compositions based on different polarizations 188 6.4. Implementation of the processing within QGIS 189 6.4.1. Downloading data  194 6.4.2. Calibration, ortho-rectification and stacking of Sentinel-1 data over a common area  198 6.4.3. Speckle filtering 201 6.4.4. Other tools 202 6.5. Data classification 205 6.6. Bibliography 212 Chapter 7. Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park  215Nicolas KARASIAK and Pauline PERBET 7.1. Context and definition  215 7.1.1. Global context 215 7.1.2. Species 216 7.1.3. Remote sensing images available  217 7.1.4. Software 219 7.1.5. Method implementation  219 7.2. Software installation 220 7.2.1. Dependencies installation available in OsGeo . 220 7.2.2. Installation of scikit-learn  221 7.2.3. Dzetsaka installation  222 7.3. Method 222 7.3.1. Image processing 223 7.3.2. Cloud mask creation  225 7.4. Processing 227 7.4.1. Creating training plots  227 7.4.2. Classification with dzetsaka plugin  230 7.4.3. Post-classification  236 7.5. Final processing 239 7.5.1. Synthesis of predicted images  240 7.5.2. Global synthesis and cleaning unwanted areas . 242 7.5.3. Statistical validation     limits  244 7.6. Conclusion 245 7.7. Bibliography 245 Chapter 8. Physiognomic Map of Natural Vegetation 247Samuel ALLEAUME and Sylvio LAVENTURE 8.1. Context 247 8.2. Method 247 8.2.1. Segmentation of the VHSR mono-date image . 249 8.2.2. Calculation of temporal variability indices 249 8.2.3. Extraction of natural vegetation using time series 251 8.2.4. Vegetation densities  252 8.2.5. Maximum productivity index of herbaceous areas 255 8.3. Implementation of the application  256 8.3.1. Study area 256 8.3.2. Software and data 257 8.3.3. Step 1: VHSR image processing  259 8.3.4. Step 2: calculation of the variability indices on the time series 264 8.3.5. Step 3: extraction of the natural vegetations from the time series of Sentinel-2 image by thresholding method 267 8.3.6. Step 4: classification of vegetation density by supervised classification SVM 274 8.3.7. Step 5: extraction of the level of productivity of grasslands 277 8.3.8. Step 6: final map 279 8.4. Bibliography 282 Chapter 9. Object-Based Classification for Mountainous Vegetation Physiognomy Mapping  283Vincent THIERION and Marc LANG 9.1. Definition and context  283 9.2. Method for detecting montane vegetation physiognomy 284 9.2.1. Satellite image pre-processing  286 9.2.2. Image segmentation  289 9.2.3. Sampling, learning and segmented image classification 291 9.2.4. Statistical validation of classification  295 9.2.5. Limits of the method  297 9.3. Application in QGIS 298 9.3.1 Pre-processing 299 9.3.2. Segmentation 312 9.3.3. Classification 319 9.4. Bibliography 337 List of Authors 341 Index 343 Scientific Committee 347