Nicolas Baghdadi is Research Director at the National Research Institute of Science and Technology for the Environment and Agriculture (IRSTEA). He is currently Scientific Director of the Theia Land Data Centre (France).Clément Mallet is a senior researcher at the National Institute of Geographic and Forest Information (IGN). He is a member of the LaSTIG laboratory, a joint lab between IGN and the University of Paris-Est, France.Mehrez Zribi is Research Director at the National Center for Scientific Research (CNRS). He is currently working at the Center for the Study of the Biosphere from Space (CESBIO) in Toulouse (France) where he is also responsible for the "Observation Systems" team.

• Introduction xiChapter 1 Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas 1Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi And Hassan Bazzi1.1 Context 11.2 Study site and satellite data 21.2.1 Radar images 21.2.2 Optical image 41.2.3 Land cover map 41.3 Methodology 51.3.1. Inversion approach of radar signal for estimating soil moisture 51.3.2 Segmentation of crop and grasslands areas 61.3.3 Soil moisture mapping 81.4 Implementation of the application via QGIS 101.4.1 Layout 101.4.2 Radar images 141.4.3 Optical image 201.4.4 Land cover map 261.4.5 Segmentation of crop’s areas and grasslands 261.4.6 Elimination of small spatial units 291.4.7 Mapping soil moisture 331.4.8 Soil moisture maps 431.5 Bibliography 44Chapter 2 Disaggregation of Thermal Images 47Mar Bisquert and Juan Manuel Sánchez2.1 Definition and context 472.2 Disaggregation method 482.2.1 Image pre-processing 482.2.2 Disaggregation 502.3 Practical application of the disaggregation method 532.3.1 Input data 532.3.2 Step 1: pre-processing 542.3.3 Step 2: disaggregation 632.4 Results analysis 732.5 Bibliography 75Chapter 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 MARTIN3.1 Context 773.2 Method of AP extraction 793.2.1 Formatting the RPG data 793.2.2 Classification of SPOT satellite images 813.2.3. Intersect overlay between extracted AP and FB with crop validation 813.3 Practical application of the AP extraction 823.3.1 Software and data 833.3.2 Setting up the Python script 863.3.3 Step 1: formatting the RPG data 893.3.4 Step 2: classification of SPOT satellite Images 973.3.5 Step 3: intersect overlay between extracted AP and FB and crop validation 1103.4 Acknowledgements 1163.5 Bibliography 116Chapter 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 Bellón, Raffaele Gaetano and Agnès Bégué4.1 Context 1194.2 Workflow for land cover mapping 1204.2.1 Introduction to SCP and S2 images 1204.2.2 Pre-processing 1224.2.3 Land cover classification 1264.2.4 Classification accuracy assessment and post-processing 1294.3 Implementation with QGIS and the plugin SCP 1314.3.1 Software and data 1314.3.2 Step 1: data pre-processing 1334.3.3 Step 2: land cover classification 1394.3.4. Step 3: assessment of the classification accuracy and post-processing 1444.4 Bibliography 150Chapter 5 Detection and Mapping of Clear-Cuts with Optical Satellite Images 153Kenji Ose5.1 Definition and context 1535.2 Clear-cuts detection method 1545.2.1 Step 1: change detection – geometric and radiometric pre-processing 1545.2.2 Steps 2 and 3: forest delimitation 1605.2.3 Step 4: clear-cuts classification 1605.2.4 Steps 5 and 6: export in vector mode 1625.2.5 Step 7: statistical evaluation 1645.2.6 Method limits 1665.3 Practical application 1665.3.1 Software and data 1665.3.2 Step 1: creation of the changes image 1685.3.3 Steps 2 and 3: creation, merging and integration of masks 1705.3.4 Step 4: clear-cuts detection 1745.3.5 Step 5: vector conversion 1775.4 Bibliography 180Chapter 6 Vegetation Cartography from Sentinel-1 Radar Images 181Pierre-Louis Frison and Cédric Lardeux6.1 Definition and context 1816.2 Classification of remote sensing images 1836.3 Sentinel-1 data processing 1856.3.1 Radiometric calibration 1866.3.2 Ortho-rectification of calibrated data 1866.3.3 Clip over a common area 1876.3.4 Filtering to reduce the speckle effect 1876.3.5. Generation of color compositions based on different polarizations 1886.4 Implementation of the processing within QGIS 1896.4.1 Downloading data 1946.4.2 Calibration, ortho-rectification and stacking of Sentinel-1 data over a common area 1986.4.3 Speckle filtering 2016.4.4 Other tools 2026.5 Data classification 2056.6 Bibliography 212Chapter 7 Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park 215Nicolas Karasiak and Pauline Perbet7.1 Context and definition 2157.1.1 Global context 2157.1.2 Species 2167.1.3 Remote sensing images available 2177.1.4 Software 2197.1.5 Method implementation 2197.2 Software installation 2207.2.1 Dependencies installation available in OsGeo 2207.2.2 Installation of scikit-learn 2217.2.3 Dzetsaka installation 2227.3 Method 2227.3.1 Image processing 2237.3.2 Cloud mask creation 2257.4 Processing 2277.4.1 Creating training plots 2277.4.2 Classification with dzetsaka plugin 2307.4.3 Post-classification 2367.5 Final processing 2397.5.1 Synthesis of predicted images 2407.5.2 Global synthesis and cleaning unwanted areas 2427.5.3 Statistical validation – limits 2447.6 Conclusion 2457.7 Bibliography 245Chapter 8 Physiognomic Map of Natural Vegetation 247Samuel Alleaume and Sylvio Laventure8.1 Context 2478.2 Method 2478.2.1 Segmentation of the VHSR mono-date image 2498.2.2 Calculation of temporal variability indices 2498.2.3 Extraction of natural vegetation using time series 2518.2.4 Vegetation densities 2528.2.5 Maximum productivity index of herbaceous areas 2558.3 Implementation of the application 2568.3.1 Study area 2568.3.2 Software and data 2578.3.3 Step 1: VHSR image processing 2598.3.4 Step 2: calculation of the variability indices on the time series 2648.3.5 Step 3: extraction of the natural vegetations from the time series of Sentinel-2 image by thresholding method 2678.3.6 Step 4: classification of vegetation density by supervised classification SVM 2748.3.7 Step 5: extraction of the level of productivity of grasslands 2778.3.8 Step 6: final map 2798.4 Bibliography 282Chapter 9 Object-Based Classification for Mountainous Vegetation Physiognomy Mapping 283Vincent Thierion and Marc Lang9.1 Definition and context 2839.2 Method for detecting montane vegetation physiognomy 2849.2.1 Satellite image pre-processing 2869.2.2 Image segmentation 2899.2.3 Sampling, learning and segmented image classification 2919.2.4 Statistical validation of classification 2959.2.5 Limits of the method 2979.3 Application in QGIS 2989.3.1 Pre-processing 2999.3.2 Segmentation 3129.3.3 Classification 3199.4 Bibliography 337List of Authors 341Index 343Scientific Committee 347

Nicolas Baghdadi is Research Director at the National Research Institute of Science and Technology for the Environment and Agriculture (IRSTEA). He is currently Scientific Director of the Theia Land Data Centre (France).Clément Mallet is a senior researcher at the National Institute of Geographic and Forest Information (IGN). He is a member of the LaSTIG laboratory, a joint lab between IGN and the University of Paris-Est, France.Mehrez Zribi is Research Director at the National Center for Scientific Research (CNRS). He is currently working at the Center for the Study of the Biosphere from Space (CESBIO) in Toulouse (France) where he is also responsible for the "Observation Systems" team.

• Introduction xiChapter 1 Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas 1Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi And Hassan Bazzi1.1 Context 11.2 Study site and satellite data 21.2.1 Radar images 21.2.2 Optical image 41.2.3 Land cover map 41.3 Methodology 51.3.1. Inversion approach of radar signal for estimating soil moisture 51.3.2 Segmentation of crop and grasslands areas 61.3.3 Soil moisture mapping 81.4 Implementation of the application via QGIS 101.4.1 Layout 101.4.2 Radar images 141.4.3 Optical image 201.4.4 Land cover map 261.4.5 Segmentation of crop’s areas and grasslands 261.4.6 Elimination of small spatial units 291.4.7 Mapping soil moisture 331.4.8 Soil moisture maps 431.5 Bibliography 44Chapter 2 Disaggregation of Thermal Images 47Mar Bisquert and Juan Manuel Sánchez2.1 Definition and context 472.2 Disaggregation method 482.2.1 Image pre-processing 482.2.2 Disaggregation 502.3 Practical application of the disaggregation method 532.3.1 Input data 532.3.2 Step 1: pre-processing 542.3.3 Step 2: disaggregation 632.4 Results analysis 732.5 Bibliography 75Chapter 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 MARTIN3.1 Context 773.2 Method of AP extraction 793.2.1 Formatting the RPG data 793.2.2 Classification of SPOT satellite images 813.2.3. Intersect overlay between extracted AP and FB with crop validation 813.3 Practical application of the AP extraction 823.3.1 Software and data 833.3.2 Setting up the Python script 863.3.3 Step 1: formatting the RPG data 893.3.4 Step 2: classification of SPOT satellite Images 973.3.5 Step 3: intersect overlay between extracted AP and FB and crop validation 1103.4 Acknowledgements 1163.5 Bibliography 116Chapter 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 Bellón, Raffaele Gaetano and Agnès Bégué4.1 Context 1194.2 Workflow for land cover mapping 1204.2.1 Introduction to SCP and S2 images 1204.2.2 Pre-processing 1224.2.3 Land cover classification 1264.2.4 Classification accuracy assessment and post-processing 1294.3 Implementation with QGIS and the plugin SCP 1314.3.1 Software and data 1314.3.2 Step 1: data pre-processing 1334.3.3 Step 2: land cover classification 1394.3.4. Step 3: assessment of the classification accuracy and post-processing 1444.4 Bibliography 150Chapter 5 Detection and Mapping of Clear-Cuts with Optical Satellite Images 153Kenji Ose5.1 Definition and context 1535.2 Clear-cuts detection method 1545.2.1 Step 1: change detection – geometric and radiometric pre-processing 1545.2.2 Steps 2 and 3: forest delimitation 1605.2.3 Step 4: clear-cuts classification 1605.2.4 Steps 5 and 6: export in vector mode 1625.2.5 Step 7: statistical evaluation 1645.2.6 Method limits 1665.3 Practical application 1665.3.1 Software and data 1665.3.2 Step 1: creation of the changes image 1685.3.3 Steps 2 and 3: creation, merging and integration of masks 1705.3.4 Step 4: clear-cuts detection 1745.3.5 Step 5: vector conversion 1775.4 Bibliography 180Chapter 6 Vegetation Cartography from Sentinel-1 Radar Images 181Pierre-Louis Frison and Cédric Lardeux6.1 Definition and context 1816.2 Classification of remote sensing images 1836.3 Sentinel-1 data processing 1856.3.1 Radiometric calibration 1866.3.2 Ortho-rectification of calibrated data 1866.3.3 Clip over a common area 1876.3.4 Filtering to reduce the speckle effect 1876.3.5. Generation of color compositions based on different polarizations 1886.4 Implementation of the processing within QGIS 1896.4.1 Downloading data 1946.4.2 Calibration, ortho-rectification and stacking of Sentinel-1 data over a common area 1986.4.3 Speckle filtering 2016.4.4 Other tools 2026.5 Data classification 2056.6 Bibliography 212Chapter 7 Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park 215Nicolas Karasiak and Pauline Perbet7.1 Context and definition 2157.1.1 Global context 2157.1.2 Species 2167.1.3 Remote sensing images available 2177.1.4 Software 2197.1.5 Method implementation 2197.2 Software installation 2207.2.1 Dependencies installation available in OsGeo 2207.2.2 Installation of scikit-learn 2217.2.3 Dzetsaka installation 2227.3 Method 2227.3.1 Image processing 2237.3.2 Cloud mask creation 2257.4 Processing 2277.4.1 Creating training plots 2277.4.2 Classification with dzetsaka plugin 2307.4.3 Post-classification 2367.5 Final processing 2397.5.1 Synthesis of predicted images 2407.5.2 Global synthesis and cleaning unwanted areas 2427.5.3 Statistical validation – limits 2447.6 Conclusion 2457.7 Bibliography 245Chapter 8 Physiognomic Map of Natural Vegetation 247Samuel Alleaume and Sylvio Laventure8.1 Context 2478.2 Method 2478.2.1 Segmentation of the VHSR mono-date image 2498.2.2 Calculation of temporal variability indices 2498.2.3 Extraction of natural vegetation using time series 2518.2.4 Vegetation densities 2528.2.5 Maximum productivity index of herbaceous areas 2558.3 Implementation of the application 2568.3.1 Study area 2568.3.2 Software and data 2578.3.3 Step 1: VHSR image processing 2598.3.4 Step 2: calculation of the variability indices on the time series 2648.3.5 Step 3: extraction of the natural vegetations from the time series of Sentinel-2 image by thresholding method 2678.3.6 Step 4: classification of vegetation density by supervised classification SVM 2748.3.7 Step 5: extraction of the level of productivity of grasslands 2778.3.8 Step 6: final map 2798.4 Bibliography 282Chapter 9 Object-Based Classification for Mountainous Vegetation Physiognomy Mapping 283Vincent Thierion and Marc Lang9.1 Definition and context 2839.2 Method for detecting montane vegetation physiognomy 2849.2.1 Satellite image pre-processing 2869.2.2 Image segmentation 2899.2.3 Sampling, learning and segmented image classification 2919.2.4 Statistical validation of classification 2959.2.5 Limits of the method 2979.3 Application in QGIS 2989.3.1 Pre-processing 2999.3.2 Segmentation 3129.3.3 Classification 3199.4 Bibliography 337List of Authors 341Index 343Scientific Committee 347

Nicolas Baghdadi is Research Director at the National Research Institute of Science and Technology for the Environment and Agriculture (IRSTEA). He is currently Scientific Director of the Theia Land Data Centre (France).Clément Mallet is a senior researcher at the National Institute of Geographic and Forest Information (IGN). He is a member of the LaSTIG laboratory, a joint lab between IGN and the University of Paris-Est, France.Mehrez Zribi is Research Director at the National Center for Scientific Research (CNRS). He is currently working at the Center for the Study of the Biosphere from Space (CESBIO) in Toulouse (France) where he is also responsible for the "Observation Systems" team.

• Introduction xiChapter 1 Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas 1Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi And Hassan Bazzi1.1 Context 11.2 Study site and satellite data 21.2.1 Radar images 21.2.2 Optical image 41.2.3 Land cover map 41.3 Methodology 51.3.1. Inversion approach of radar signal for estimating soil moisture 51.3.2 Segmentation of crop and grasslands areas 61.3.3 Soil moisture mapping 81.4 Implementation of the application via QGIS 101.4.1 Layout 101.4.2 Radar images 141.4.3 Optical image 201.4.4 Land cover map 261.4.5 Segmentation of crop’s areas and grasslands 261.4.6 Elimination of small spatial units 291.4.7 Mapping soil moisture 331.4.8 Soil moisture maps 431.5 Bibliography 44Chapter 2 Disaggregation of Thermal Images 47Mar Bisquert and Juan Manuel Sánchez2.1 Definition and context 472.2 Disaggregation method 482.2.1 Image pre-processing 482.2.2 Disaggregation 502.3 Practical application of the disaggregation method 532.3.1 Input data 532.3.2 Step 1: pre-processing 542.3.3 Step 2: disaggregation 632.4 Results analysis 732.5 Bibliography 75Chapter 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 MARTIN3.1 Context 773.2 Method of AP extraction 793.2.1 Formatting the RPG data 793.2.2 Classification of SPOT satellite images 813.2.3. Intersect overlay between extracted AP and FB with crop validation 813.3 Practical application of the AP extraction 823.3.1 Software and data 833.3.2 Setting up the Python script 863.3.3 Step 1: formatting the RPG data 893.3.4 Step 2: classification of SPOT satellite Images 973.3.5 Step 3: intersect overlay between extracted AP and FB and crop validation 1103.4 Acknowledgements 1163.5 Bibliography 116Chapter 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 Bellón, Raffaele Gaetano and Agnès Bégué4.1 Context 1194.2 Workflow for land cover mapping 1204.2.1 Introduction to SCP and S2 images 1204.2.2 Pre-processing 1224.2.3 Land cover classification 1264.2.4 Classification accuracy assessment and post-processing 1294.3 Implementation with QGIS and the plugin SCP 1314.3.1 Software and data 1314.3.2 Step 1: data pre-processing 1334.3.3 Step 2: land cover classification 1394.3.4. Step 3: assessment of the classification accuracy and post-processing 1444.4 Bibliography 150Chapter 5 Detection and Mapping of Clear-Cuts with Optical Satellite Images 153Kenji Ose5.1 Definition and context 1535.2 Clear-cuts detection method 1545.2.1 Step 1: change detection – geometric and radiometric pre-processing 1545.2.2 Steps 2 and 3: forest delimitation 1605.2.3 Step 4: clear-cuts classification 1605.2.4 Steps 5 and 6: export in vector mode 1625.2.5 Step 7: statistical evaluation 1645.2.6 Method limits 1665.3 Practical application 1665.3.1 Software and data 1665.3.2 Step 1: creation of the changes image 1685.3.3 Steps 2 and 3: creation, merging and integration of masks 1705.3.4 Step 4: clear-cuts detection 1745.3.5 Step 5: vector conversion 1775.4 Bibliography 180Chapter 6 Vegetation Cartography from Sentinel-1 Radar Images 181Pierre-Louis Frison and Cédric Lardeux6.1 Definition and context 1816.2 Classification of remote sensing images 1836.3 Sentinel-1 data processing 1856.3.1 Radiometric calibration 1866.3.2 Ortho-rectification of calibrated data 1866.3.3 Clip over a common area 1876.3.4 Filtering to reduce the speckle effect 1876.3.5. Generation of color compositions based on different polarizations 1886.4 Implementation of the processing within QGIS 1896.4.1 Downloading data 1946.4.2 Calibration, ortho-rectification and stacking of Sentinel-1 data over a common area 1986.4.3 Speckle filtering 2016.4.4 Other tools 2026.5 Data classification 2056.6 Bibliography 212Chapter 7 Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park 215Nicolas Karasiak and Pauline Perbet7.1 Context and definition 2157.1.1 Global context 2157.1.2 Species 2167.1.3 Remote sensing images available 2177.1.4 Software 2197.1.5 Method implementation 2197.2 Software installation 2207.2.1 Dependencies installation available in OsGeo 2207.2.2 Installation of scikit-learn 2217.2.3 Dzetsaka installation 2227.3 Method 2227.3.1 Image processing 2237.3.2 Cloud mask creation 2257.4 Processing 2277.4.1 Creating training plots 2277.4.2 Classification with dzetsaka plugin 2307.4.3 Post-classification 2367.5 Final processing 2397.5.1 Synthesis of predicted images 2407.5.2 Global synthesis and cleaning unwanted areas 2427.5.3 Statistical validation – limits 2447.6 Conclusion 2457.7 Bibliography 245Chapter 8 Physiognomic Map of Natural Vegetation 247Samuel Alleaume and Sylvio Laventure8.1 Context 2478.2 Method 2478.2.1 Segmentation of the VHSR mono-date image 2498.2.2 Calculation of temporal variability indices 2498.2.3 Extraction of natural vegetation using time series 2518.2.4 Vegetation densities 2528.2.5 Maximum productivity index of herbaceous areas 2558.3 Implementation of the application 2568.3.1 Study area 2568.3.2 Software and data 2578.3.3 Step 1: VHSR image processing 2598.3.4 Step 2: calculation of the variability indices on the time series 2648.3.5 Step 3: extraction of the natural vegetations from the time series of Sentinel-2 image by thresholding method 2678.3.6 Step 4: classification of vegetation density by supervised classification SVM 2748.3.7 Step 5: extraction of the level of productivity of grasslands 2778.3.8 Step 6: final map 2798.4 Bibliography 282Chapter 9 Object-Based Classification for Mountainous Vegetation Physiognomy Mapping 283Vincent Thierion and Marc Lang9.1 Definition and context 2839.2 Method for detecting montane vegetation physiognomy 2849.2.1 Satellite image pre-processing 2869.2.2 Image segmentation 2899.2.3 Sampling, learning and segmented image classification 2919.2.4 Statistical validation of classification 2959.2.5 Limits of the method 2979.3 Application in QGIS 2989.3.1 Pre-processing 2999.3.2 Segmentation 3129.3.3 Classification 3199.4 Bibliography 337List of Authors 341Index 343Scientific Committee 347