CHF129.00
Download est disponible immédiatement
These four volumes present innovative thematic applications implemented using the open source software QGIS. These are applications that use remote sensing over continental surfaces. The 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.
Auteur
Nicolas Baghdadi, French Research Institute of Science and Technology for Environment and Agriculture, France Mehrez Zribi, CNRS and CESBIO, France Clément Maillet, ING, France
Contenu
Introduction xi
Chapter 1 Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas 1
Mohammad 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 47
Mar Bisquert and Juan Manuel Sánchez
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 77
Jean-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 119
Louise Leroux, Luca Congedo, Beatriz Bellón, Raffaele Gaetano and Agnès Bégué
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 153
Kenji 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 <p>...