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 permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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John Wiley & Sons, Inc.
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The rights of Nicolas Baghdadi, Clément Mallet and Mehrez Zribi to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2017958933

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-78630-187-1

Table of Contents

Cover

Title

Introduction

1 Introduction to QGIS

1.1. History
1.2. QGIS graphical user interface
1.3. The processing module, the toolkit for spatial analysis

2 Introduction to GDAL Tools in QGIS

2.1. GDAL: the Swiss army knife of raster processing
2.2. GDAL tools: practical examples
2.3. Bibliography

3 GRASS GIS Software with QGIS

3.1. Presentation
3.2. GRASS GIS download and GRASS plugin in QGIS
3.3. GRASS GIS capabilities
3.4. Using Grass GIS functions from QGIS
3.5. Acknowledgments
3.6. Bibliography

4 The Use of SAGA GIS Modules in QGIS

4.1. SAGA GIS in QGIS
4.2. Using SAGA GIS to work with multispectral satellite images
4.3. Hydrological network extraction using SAGA GIS in QGIS
4.4. Interpolation using SAGA GIS
4.5. Bibliography

5 Orfeo ToolBox Applications

5.1. The Orfeo ToolBox
5.2. Using OTB applications
5.3. Exercises
5.4. Conclusion
5.5. Acknowledgments
5.6. Bibliography

6 Online Publication of a Land Cover Map Using LizMap

6.1. Context
6.2. Workflow to publish a map online using LizMap
6.3. Implementation with QGIS
6.4. Bibliography

7 GeoHealth and QuickOSM, Two QGIS Plugins for Health Applications

7.1. Background on the use of GIS for health and the development of dedicated plugins in QGIS
7.2. Methodology
7.3. Implementation: GeoHealth, assisted mapping with QGIS
7.4. Bibliography

List of Authors

Index

Scientific Committee

End User License Agreement

List of Tables

2 Introduction to GDAL Tools in QGIS

Table 2.1. Correspondence of GDAL commands with GdalTools and Processing Toolbox
Table 2.2. Download of Landsat image
Table 2.3. Download of Digital Terrain Model (DTM)
Table 2.4. Download of GEOFLA (IGN) and Synop (Météo France: the website is only available in French) data
Table 2.5. Reading of raster file header
Table 2.6. Projection change
Table 2.7. Image cropping
Table 2.8. Creation of a multiband raster file
Table 2.9. Creation of an image mosaic
Table 2.10. Use of VRT format
Table 2.11. Rasterization and interpolation
Table 2.12. Distance computation
Table 2.13. DTM preparation
Table 2.14. Slope and aspect computation from DTM
Table 2.15. Hillshade computation from DTM
Table 2.16. TRI and TPI computation from DTM
Table 2.17. Relief colorization
Table 2.18. Multicriteria query with GDAL raster calculator
Table 2.19. NDVI computation with GDAL raster calculator

4 The Use of SAGA GIS Modules in QGIS

Table 4.1. Landsat 8 band characteristics
Table 4.2. The land use of the region of Serre-Ponçon in 8 classes. For a color version of the table, see www.iste.co.uk/baghdadi/qgis1.zip
Table 4.3. Reclassification of the 8 classes land use as the 4 classes land use
Table 4.4. Equivalence between Strahler stream orders from raster and “real” Strahler orders

5 Orfeo ToolBox Applications

Table 5.1. Types of parameter values
Table 5.2. Types of parameters
Table 5.3. Convert application parameters
Table 5.4. Encoding
Table 5.5. Use of an application via the GUI
Table 5.6. Resampling an image with the superimpose application
Table 5.7. Concatenate images with the “ConcatenateImages” application
Table 5.8. Arithmetic operators
Table 5.9. Binary operators
Table 5.10. Computations pixel by pixel with the “BandMath” application
Table 5.11. Morphologic operators with the “BinaryMorphologicalOperation” application
Table 5.12. Smoothing an image with the “Smoothing” application
Table 5.13. Variables available in the application BandMathX
Table 5.14. Extended calculations over pixels with “BandMathX” applications
Table 5.15. Optical calibration with the “OpticalCalibration” application
Table 5.16. Download SRTM digital elevation model tiles with the “DownloadSRMTTiles” application
Table 5.17. Orthorectification of images with the “Orthorectification” application
Table 5.18. Extracting a region of an image with the associated sensor model, with the “ExtractROI” application
Table 5.19. Orthorectification of the extracted image with the “Orthorectification” application
Table 5.20. Extraction of homologous points with the “HomologousPointsExtraction” application
Table 5.21. Refining the sensor model with the “RefineSensorModel” application
Table 5.22. Orthorectification with the refined sensor model, using the "Orthorectification" application
Table 5.23. Pansharpening of optical image with the “Pansharpening” application
Table 5.24. Mosaic of images with the “Mosaic” application
Table 5.25. Vegetation indices
Table 5.26. Water indices
Table 5.27. Soil Indices
Table 5.28. Calculation of radiometric indices with the “RadiometricIndices” application
Table 5.29. Calculation of texture indices with the “HaralickTextureExtraction” application
Table 5.30. Spectral bands of Landsat-8
Table 5.31. Training data
Table 5.32. Training on a Landsat-8 image with the “TranImagesClassifier” application
Table 5.33. Classification of Landsat-8 image with the “ImageClassifier” application
Table 5.34. Calculation of classification performance with the “ComputeConfusionMatrix” application
Table 5.35. Generate a visualization image with the “ColorMapping” application

6 Online Publication of a Land Cover Map Using LizMap

Table 6.1. Steps for configuring a QGIS project for the WEB
Table 6.2. LizMap installation
Table 6.3. Steps for configuring layers for LizMap
Table 6.4. Steps for publishing the map by FTP

7 GeoHealth and QuickOSM, Two QGIS Plugins for Health Applications

Table 7.1. Creation of an incidence map with GeoHealth plugin. For a color version of the table, see www.iste.co.uk/baghdadi/qgis1.zip
Table 7.2. Geocoding addresses with GeoCoding plugin
Table 7.3. “Blurring” of point data with GeoHealth plugin
Table 7.4. Calculation of a distance matrix and the shortest path
Table 7.5. Online contribution to OpenStreetMap

List of Illustrations

1 Introduction to QGIS

Figure 1.1. Standard QGIS GUI (v2.16) with ROUTE 120® database
Figure 1.2. Settings: “Options” menu
Figure 1.3. “Manage Layers” toolbar
Figure 1.4. Toolbar “project”
Figure 1.5. “Map Navigation” toolbar
Figure 1.6. “Attributes” toolbar
Figure 1.7. Treatment toolbox
Figure 1.8. OTB activation in processing options
Figure 1.9. Processor chain in the modeler
Figure 1.10. Batch processing

2 Introduction to GDAL Tools in QGIS

Figure 2.1. GDAL API tutorial
Figure 2.2. GDAL documentation for Python
Figure 2.3. Summary and synopsis of gdal_translate command
Figure 2.4. Description of gdal_translate parameters and application examples
Figure 2.5. GDAL metadata description
Figure 2.8. Example of slope and aspect calculation
Figure 2.9. Example of hillshade calculation
Figure 2.10. Example of roughness and topographic position indices calculation

3 GRASS GIS Software with QGIS

Figure 3.1. Opening GRASS Tools in QGIS
Figure 3.2. The groups of GRASS modules available from GRASS Tools in QGIS
Figure 3.3. The groups of GRASS modules available from the QGIS Processing Toolbox
Figure 3.4. Access to GRASS GIS from the list of available programs
Figure 3.5. The WIND file showing definition parameters of the current mapset
Figure 3.6. GRASS Tools commands to modify region settings, raster map boundaries and resolution
Figure 3.7. GRASS Tools commands to import external raster data
Figure 3.8. GRASS Tools commands to import vector data and attributes
Figure 3.9. GRASS GIS commands to import external vector data available in QGIS Processing Toolbox
Figure 3.10. Activation of GRASS 7 plugin in QGIS
Figure 3.11. The icon to access GRASS Tools in QGIS
Figure 3.12. Display of a raster image in QGIS
Figure 3.13. Definition of a GRASS GIS database directory
Figure 3.14. Creation of a new GRASS location and selection of the geographical projection
Figure 3.15. Coordinates of the default GRASS region defined from their raster image displayed previously in QGIS
Figure 3.16. Display of the raster image after importation into GRASS
Figure 3.17. The i.group command used to regroup monoband rasters into a single multiband raster
Figure 3.18. QGIS display of a red–green–blue color composite of a three-band raster. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 3.19. Importating of a vector layer displayed in QGIS into GRASS
Figure 3.20. Display in QGIS of a raster image and superposition of a vector file after importation into GRASS. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 3.21. Display of extract of Landsat image (channel TM4) with superposition of a vector file of administrative units of Lyon conurbation
Figure 3.22. Extraction of a subimage using a vector file
Figure 3.23. Display of Landsat image (channel TM4) after masking areas outside Lyon conurbation
Figure 3.24. Creation of a multiband image from three Landsat images (TM4, TM3, TM2)
Figure 3.25. Color composition of the multiband Landsat image (red = TM4, green = TM3, blue = TM2) . For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 3.26. Histogram stretch using the GDAL translate command
Figure 3.27. Color composite image of channels TM4 (red), TM3 (green), TM2 (blue) after contrast enhancement. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 3.28. Calculation of NDVI vegetation index
Figure 3.29. Gray-scale image of NDVI (black = low NDVI, white = high NDVI) calculated from the extract of the LANDSAT image presented in Figure 3.27
Figure 3.30. Visualization of NDVI results using a color table. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 3.31. Visualization of the mean NDVI value per administrative unit. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip

4 The Use of SAGA GIS Modules in QGIS

Figure 4.1. SAGA GIS installation using the OSGeo installer
Figure 4.2. Installation of the Processing Toolbox
Figure 4.3. The “Processing Toolbox” on the right side of the QGIS window
Figure 4.4. Activation of the SAGA modules in the “Processing options”
Figure 4.5. Location of the Serre-Ponçon Lake in the French Alps
Figure 4.6. Installation of the module “Semi-Automatic Classification Plugin: SCP”
Figure 4.7. Setting of the radiometric correction in the SCP module. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.8. Organization of the different layers in QGIS
Figure 4.9. Clipping of the Landsat bands using the “Clip raster with polygons” module
Figure 4.10. The Landsat bands clipped according to the study area (44°37′ N, 5°57″ E)
Figure 4.11. Building of the virtual raster using GDAL. We can see (and edit) the GDAL syntax at the bottom of the window
Figure 4.12. Building a color composite from a virtual raster
Figure 4.13. Color composite in false colors with the near infrared in red. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.14. Calculation of the SAVI using the SAGA module “Vegetation index (slope based)”
Figure 4.15. Setting the style of the SAVI layer
Figure 4.16. SAVI of the area of Serre-Ponçon calculated from the Landsat 8 image on August 23, 2016. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.17. Calculation of the AWEI using the raster calculator function
Figure 4.18. AWEI of the region of Serre-Ponçon calculated from the Landsat 8 image on August 23, 2016. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.19. Decision tree used to classify the land use of the region of Serre-Ponçon in 8 classes. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.20. Implementation of the decision tree using the SAGA GIS raster calculator
Figure 4.21. Land use of the region of Serre-Ponçon in 8 classes, derived from the Landsat 8 image on August 23, 2016. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.22. Application of the majority filter on the 8 classes land use classification using SAGA GIS
Figure 4.23. Land use classification after the application of 5 × 5 square majority filter on the initial classification. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.24. Reclassification of the land use
Figure 4.25. Lookup table to reclass the 8 classes land use as the 4 classes land use
Figure 4.26. Land use reclassified into four main classes. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.27. Popup window to mosaic the two SRTM tiles of our area of interest
Figure 4.28. Projection into UTM zone 31 N coordinate reference system of the merged SRTM tiles layer
Figure 4.29. Setting the coordinate reference system of the project as UTM 31N
Figure 4.30. Clipping the SRTM dataset according to our area of interest
Figure 4.31. Filling sinks
Figure 4.32. River network extraction using Strahler’s ordination-based method
Figure 4.33. The hydrographic network extracted in the Serre-Ponçon area (in green) using a threshold of 5 on the Strahler order. The background image is the DEM (SRTM. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip)
Figure 4.34. Zoom on two sources of rivers (in red). The background raster is the Strahler ordination raster pixel by pixel “srtm_sp_strahler_utm31n.tif” (pixel size: 30 m x 30 m). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.35. Comparison between a network extracted with a threshold of 5 (green) and one with a threshold of 8 (yellow). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.36. Location of the Seine basin in the North of France
Figure 4.37. Selection of 50% of the measurement points
Figure 4.38. Setting of the interpolation using the B-spline method
Figure 4.39. Interpolation of the bicarbonate in the Seine basin (the limits of the basin are in orange) using the “B-spline approximation” method. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.40. Settings of the interpolation using the “IDW” method
Figure 4.41. Interpolation of the bicarbonate in the Seine basin using the “Inverse distance weighted” method. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.42. Settings of the interpolation using the “Thin plate spline” method
Figure 4.43. Interpolation of the bicarbonate in the Seine basin using the “Thin plate spline (global)” method. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 4.44. Interpolated data and measured data comparison
Figure 4.45. Calculation of differences between measurements and interpolated values using the field calculator
Figure 4.46. Calculation of the statistics for each field of difference between observed and interpolated values

5 Orfeo ToolBox Applications

Figure 5.1. Segmentation of a medical image with ITK (source: ITK). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.2. Segmentation of remote sensing image with OTB (source: Cookbook). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.3. Description of the mechanism with a median filter: the output image is generated region by region (right), each one obtained from a sub-region of the input image (left) adapted to the kernel size of the filter (radius of 2 pixels) (source: [IBE16])
Figure 5.4. Example of multithreading on a region (Region 2) with two CPUs (CPU 1, CPU 2) (source: [IBE16], adapted by the authors)
Figure 5.5. Graphical user interface of the “Convert” application. The current tab allows us to edit the application parameters
Figure 5.6. Graphical user interface of the “Convert” application. The current tab provides the application description (in red), the information about its parameters (in green) and an example of use (in blue). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.7. Screenshot of the Monteverdi software. Two superimposed image layers are loaded and a local transparency tool is used. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.8. A region of interest is defined with its origin (startx, starty) and its size (sizex, sizey) in pixel coordinates. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.14. Aliasing caused by resampling (source: Wikipedia)
Figure 5.19. a) An optical image (Spot 6)¹⁰; b) binary image resulting from thresholding one channel of the initial optical image. In the binary image, a series of morphological operators are applied with a structuring element of 5 pixels of radius; c) binary image after application of the erosion operator; d) binary image after application of the dilatation operator; e) binary image after application of the opening operator; f) binary image after application of the closing operator
Figure 5.20. a) Initial image (Spot 6)¹¹ on which filters have been applied; b) initial image after application of the mean filter; c) initial image after application of the Gaussian filter; d) initial image after application of the Anisotropic diffusion
Figure 5.21. Interaction between solar radiation, atmosphere and the Earth’s surface (from [OSE 16])
Figure 5.22. SAR acquisition principle. Target P (at distance h) is seen multiple times by the radar on the moving platform providing a resolution corresponding to a synthetic antenna of length M (source; Wikipedia)
Figure 5.23. Tilt displacement is the shift in an object’s image position on a tilted photo from its theoretical position on a truly vertical photo. This results from the photo plane being tilted with respect to the datum plane at the time of exposure. This effect is demonstrated in the figure above. Though the distances between A–B and C–D are identical on the datum plane, their corresponding representations on the photo plane are not (i.e. distances between a–b and c–d are not equivalent) (source: OSSIM¹²).
Figure 5.24. Relief displacement is the shift in an object’s image position caused by its elevation above a particular datum. For vertical or near vertical photography the shift occurs radially from the nadir point. This effect is demonstrated in the figure above. Though the distances between A–B and C–D are identical on the datum plane, their corresponding representations on the photo plane are not (i.e. distances between A–B and C–D are not equivalent) (source: OSSIM)
Figure 5.25. a) Multispectral Raster Sensor image (Spot 6)¹³; b) image after orthorectification
Figure 5.26. Example of the result of the matching of two images by the SIFT method (Fantasia ou Jeu de la poudre, devant la porte d'entrée de la ville de Méquinez, by Eugène Delacroix, 1782) (source: Wikipedia)
Figure 5.28. RCS pansharpening method (source: Cookbook)
Figure 5.30. a) Mosaic in natural color without harmonization (132 RapidEye scenes, 5m spatial resolution); b) natural color mosaic with harmonization. For a color version of this figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.32. Direction of calculation (offset parameter)
Figure 5.34. a) Panchromatic Spot 6¹⁶ image; b) “Inertia” feature computed along north direction; c) “Inertia” feature computed along east direction
Figure 5.35. Training sample visualization (each point corresponds to a pixel selected within a training polygon). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 5.40. Result of supervised pixel-based classification applied to the whole Landsat-8 time series. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip

6 Online Publication of a Land Cover Map Using LizMap

Figure 6.1. Workflow for the publication of land cover map online using LizMap. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 6.2. Illustration of baselayers available in LizMap (all maps presented in the framework of this chapter are available online here: www.e-watch.pro/formation-lizmap)
Figure 6.3. Illustration of the land cover map published online. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip
Figure 6.4. Illustration of the transparency management under LizMap. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis1.zip

7 GeoHealth and QuickOSM, Two QGIS Plugins for Health Applications

Figure 7.1. GeoHealth plugin home window
Figure 7.2. Blurring process of point data implemented in the “Blur” tool
Figure 7.3. Typical example of the use of QGIS software for mapping and spatial analysis of health data
Figure 7.4. Access to the QGIS Plugins Dialog Window
Figure 7.5. QGIS Plugins Dialog Window

Introduction

This series, QGIS in Remote Sensing, aims to facilitate the appropriation and operational use of Quantum Geographic Information System (QGIS) software for the specific field of remote sensing. QGIS is an open-source and cross-platform software (Windows, Android, BSD, Linux, Mac OS X, Unix) that has been rapidly developed and steadily adopted over the last 10 years. Thanks to a growing international community of programmers, both in terms of fundamental tools and thematic applications, QGIS is increasingly used with strong and continuous growth in terms of users.

QGIS is regularly improved with many new features to better meet the needs of users. This is accomplished through the integration of a wide range of extensions in geographic information science, e.g., GDAL, GRASS, SAGA, Orfeo ToolBox (OTB), etc. as well as the development of its own QGIS Server, and a real Pythonbased script engine.

This set of four books describes, in the first volume, the operating principle of QGIS and the fundamental libraries most frequently used in image processing and geomatics: GDAL, GRASS, SAGA and OTB. This volume presents numerous core functionalities that will be implemented in several practical cases of remote sensing and spatial analysis presented in the other three volumes of the set: management and processing of raster and vector formats, georeferencing, geoprocessing tools, statistical analysis, spatial analysis (on networks, on surfaces) and classifications, starting from core processing up to visualization and cartographic editing.

Volumes 2 to 4 are devoted entirely to the description of various applications that use spatialized data, mainly satellite images. Each chapter in the four volumes is structured into three parts: (1) description of the application context, (2) methodology, and (3) implementation in QGIS and its libraries. Each of these volumes has a particular thematic flavor. Volume 2 is dedicated to applications in agriculture and forestry Volume 3 includes applications related to land use planning, whereas Volume 4 deals with applications covering water and risk management issues.

The use of GIS is becoming a fundamental tool for the scientific community and the public authorities responsible for the implementation, monitoring and evaluation of public policies. Indeed, GIS have evolved tremendously from systems originally planned for the visualization or representation of spatialized information, and the analysis of maps and related products, to more complete and versatile systems containing the most common tools for image processing and spatial analysis. Nowadays, GIS not only allow us to highlight the research results or an application project, but also to carry out the different stages of creation of the information and the knowledge necessary for these projects. QGIS, through its free and multiplatform philosophy, is a fertile breeding ground for these developments. It is even more so with the new paradigm in remote sensing: today, access to geospatial imagery is facilitated for a large number of actors; the cornerstone of studies, projects and research is becoming the capacity to process this large amount of data.

Consequently, the needs of the user community for teaching materials are very strong and we hope, through the publication of this set of books, to help facilitate the adoption of QGIS for beginners and to help those who already have some previous experience in the use of QGIS and its libraries to broaden their expertise.

Chapter 1 of this volume provides an introduction to QGIS. Four other Chapters (2, 3, 4 and 5) are devoted to the description of different libraries. Chapter 2 presents the GDAL library and its main functionalities, followed by Chapter 3 on GRASS, and Chapter 4 dedicated to SAGA, presenting their main visualization and processing tools. Chapter 5 is devoted to the OTB, mainly oriented toward image processing. Chapter 6 describes the use of Lizmap for the dissemination of an online land-use map, and Chapter 7 describes the two GeoHealth and QuickOSM extensions for health applications.

This work, carried out by scientists of a high technical level, is addressed to students (masters, engineering schools, PhD), engineers and researchers who have already adopted GIS. In addition to the texts of the proposed chapters, readers will have access to the data and tools allowing the complete realization of the scientific procedure described in each chapter, as well as access to screenshots of all the windows illustrating each step necessary to the realization of each application. Through this educational work, we hope to contribute to the appropriation of this tool for remote sensing purposes.

A supplement to the chapters, including databases and screenshots illustrating the practical application of the chapters, is available at the following address:

Using Internet Explorer: ftp://193.49.41.230

Using a FileZilla client: 193.49.41.230

Username: vol1_en

Password: 1loe34Nv@

We would like to thank everyone who contributed to the preparation of this book, first of all the authors of the chapters of course, but also the experts of the reading committee for their feedback on the chapters, the exercises, and the corrections made. This project was carried out thanks to the support of IRSTEA (French National Research Institute of Science and Technology for the Environment and Agriculture), CNRS (French National Center for Scientific Research), IGN (National Institute of Geographic and Forest Information) and CNES (French National Center for Space Studies).

We are very grateful to Airbus Defense and Space, CNES and Equipex Geosud for providing us with SPOT 5/6/7 images. Please note that these images may only be used for research and training purposes. Any commercial activity from the data provided is strictly prohibited.

Our thanks also go to our families for their encouragement and to André Mariotti (Professor Emeritus, Pierre and Marie Curie University) and Pierrick Givone (President, IRSTEA) for their encouragement and support for the realization of this project.

Nicolas BAGHDADI

Clément MALLET

Mehrez ZRIBI