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

353 Pages·2018·43.14 MB·English

by  Nicolas Baghdadi

QGIS and Applications in Agriculture and Forest
QGIS in Remote Sensing Set
coordinated by
André Mariotti
Volume 2
QGIS and Applications in
Agriculture and Forest

Edited by

Nicolas Baghdadi
Clément Mallet
Mehrez Zribi
First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as
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ISTE Ltd John Wiley & Sons, Inc.
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© ISTE Ltd 2018
The rights of Nicolas Baghdadi, Clément Mallet and Mehrez Zribi to be identified as the authors of this
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Library of Congress Control Number: 2017962204

British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-188-8
Contents
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
vi QGIS and Applications in Agriculture and Forest
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
Contents vii
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
Chapter 6. Vegetation Cartography from Sentinel-1 Radar
Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Pierre-Louis FRISON and Cédric 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
viii QGIS and Applications in Agriculture and Forest
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Nicolas 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 . . . . . . . . . . 247
Samuel 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
Contents ix
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 . . . . . . . . . . . . . . . . . . . . . . . 283
Vincent 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
Introduction
Agriculture and forestry are fields strongly involved in the use of spatial data,
which are essential for monitoring and restoring the spatial and temporal variability
of surface states. The latter are key parameters in the understanding and modeling of
different plant and soil processes, and in the management of agricultural or forest
resources. A very good knowledge of these environments is therefore fundamental
both from an economic and ecological point of view. Remote sensing, thanks to the
great diversity of spatial (from precision agriculture to global crop monitoring),
spectral (active and passive sensors) and temporal (from rapid mapping to annual
crop monitoring) resolutions, has become an inevitable support to address these
issues. In this context, the use of Geographic Information System (GIS) tools has
long been present in accompanying the exploitation of spatial imagery.
The aim of this second volume is to present different applications in agriculture
and forestry. The book, which is supported by scientists who are internationally
renowned in their fields, will help update knowledge and describe research and
development issues for years to come. It is intended for research teams in geomatics,
second-cycle students (engineering schools, master’s degrees) and postgraduate
studies (PhD students), and engineers involved in the monitoring and management
of agricultural or forestry resources and more fundamentally in the extraction of the
knowledge required for these needs. In addition to the texts of the proposed
chapters, readers will have access to the data, computer tools as well as screenshots
of all the windows, which illustrate all the steps necessary for the realization of each
application.
The first chapter of this volume concerns the estimation of the hydric state of the
soil by synergy of radar/optical satellite data. Chapter 2 deals with the
disaggregation of thermal data. The third chapter discusses the operational and