Table of Contents

Cover

Title

Introduction

1 Design and Implementation of Automated Atlas

1.1. From map to atlas
1.2. Automation of maps and indicators
1.3. Implementation of the application

2 Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL)

2.1. Context
2.2. The Land Use Efficiency (LUE)
2.3. Installation of the LUE indicator calculation tool
2.4. Method to calculate the LUE indicator
2.5. Limits of the method
2.6. Bibliography

3 Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach

3.1. The city–climate relationship through urban climate modeling
3.2. Representation of the urban space
3.3. Practical case study of the processing chain
3.4. Bibliography

4 Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment

4.1. Context
4.2. Method
4.3. Implementation of the application
4.4. Bibliography

5 Automation of Workflows for the Installation of a Wind Farm

5.1. Automation of workflows
5.2. Automation of workflows for the installation of a wind farm in Brittany
5.3. Implementation of the application

6 Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands

6.1. Definition and context
6.2. Method
6.3. Forest function assessment implementation

7 Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS

7.1. Introduction
7.2. Principle of the approach
7.3. Materials and methods
7.4. Application of the processing chain: effect of landscape on forest bird diversity
7.5. Acknowledgments
7.6. Bibliography

List of Authors

Index

Scientific Committee

End User License Agreement

List of Tables

1 Design and Implementation of Automated Atlas

Table 1.1. Summary of the data used
Table 1.2. Steps for the calculation of population density in the layer “Municipalities”
Table 1.3. Step for the calculation of forest area
Table 1.4. Steps for aggregating population data
Table 1.5. Steps for aggregating forest data
Table 1.6. Steps for aggregating agricultural census data
Table 1.7. Calculate area of local region and clean attribute table
Table 1.8. Steps for the preparation of the intermunicipal cooperations layer
Table 1.9. Steps for defining the coverage layer of the atlas
Table 1.10. Steps for the configuration of municipalities labels mask
Table 1.11. Steps for the configuration of intermunicipal cooperations labels mask
Table 1.12. Steps for the activation of atlas in print composer
Table 1.13. Steps for adding dynamic items
Table 1.14. Steps to configure municipal population density map
Table 1.15. Steps to configure intermunicipal cooperations map
Table 1.16. Steps to configure the overview map
Table 1.17. Steps for the publication of the atlas

3 Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach

Table 3.1. Synthesis of criteria for the characterization of urban space for the numerical simulation of the urban climate
Table 3.2. Size of sidewalks according to the number of lanes
Table 3.3. Pre-processing steps of the DTM
Table 3.4. Rasterization of buildings’ height
Table 3.5. Integration steps of buildings’ height to DTM
Table 3.6. Creation steps of transport infrastructures areas
Table 3.7. Creation steps of buildings’ footprints
Table 3.8. Pre-processing steps of natural areas
Table 3.9. Creation steps of natural areas
Table 3.10. Creation steps of artificial areas
Table 3.11. Calculation of the anthropization index of the territory

4 Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment

Table 4.1. Confusion matrix example
Table 4.2. Creation of a georeferenced image (step 1). For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 4.3. Building of reference map (step 2). For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 4.4. Classification and prediction map (step 3). For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 4.5. Overall accuracy (%) and Kappa for each of the classifiers and each of the spectral bands studied
Table 4.6. Confusion matrix averaged by the number of iterations of SVM applied to RGB bands
Table 4.7. Confusion matrix averaged by the number of iterations of RF applied to RGB bands
Table 4.8. Confusion matrix averaged by the number of iterations of SVM applied to RGB-IR bands
Table 4.9. Confusion matrix averaged by the number of iterations of RF applied to RGB-IR bands
Table 4.10. Confusion matrix averaged by the number of iterations of SVM applied to IR band
Table 4.11. Confusion matrix averaged by the number of iterations of the RF applied to IR band

5 Automation of Workflows for the Installation of a Wind Farm

Table 5.1. Summary of model criteria
Table 5.2. Steps for downloading data from the Britanny Region WFS
Table 5.3. Steps for downloading data from the Britanny DREAL WFS
Table 5.4. Steps for downloading data from CARMEN WFS
Table 5.5. Steps for downloading data with QuickOSM
Table 5.6. Steps for the preparation of population grid dataset
Table 5.7. Steps for the identification of inhabited areas
Table 5.8. Steps of the union of different protected areas
Table 5.9. Steps to apply a difference between protected areas union layer and areas without population layer
Table 5.10. Steps for the consideration of regional wind policy
Table 5.11. Steps for the consideration of criteria for wind energy

6 Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands

Table 6.1. Thresholds for the definition of erosion hazard classes [AND 16]
Table 6.2. Definition of the erosive hazard classes assigned to the PPE of New Caledonia [AND16]
Table 6.3. Aggregation of land use classes in large vegetation classes in New Caledonia [AND 16]
Table 6.4. Dominant vegetation determination of New Caledonia’s PPE [AND 16]
Table 6.5. Threshold values used to define forest fragmentation classes of New Caledonia’s PPE [AND16]
Table 6.6. Install QGIS extensions
Table 6.7. Merging shapes according to an attribute
Table 6.8. Geometry check. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.9. Remove small polygons using GRASS. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.10. Creation of the mask erosion hazard
Table 6.11. Calculation of zonal statistics of erosion hazard
Table 6.12. Estimation of the erosion hazard for each PPE. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.13. Clipping of the land cover map according to the extent of the study area
Table 6.14. Large vegetation classes grid codes and their corresponding land cover classes
Table 6.15. Update the attribute table of the land cover map. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.16. Rasterizing of the land cover vector file
Table 6.17. Creation of large vegetation types binary masks
Table 6.18. Calculation of zonal statistics for each large vegetation type
Table 6.19. Determination of the dominant vegetation in each PPE. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.20. Clipping forests in the PPE. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.21. Calculation of perimeters and areas of forest polygons
Table 6.22. Calculation of group statistics by PPE
Table 6.23. D component calculation
Table 6.24. Diversity index calculation
Table 6.25. Join the table of the diversity index to the vector layer of PPE
Table 6.26. Shape index calculation
Table 6.27. Forest fragmentation estimate for each PPE. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Table 6.28. Forest function state assessment. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip

7 Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS

Table 7.1. Comparison of the characteristics of some land cover maps of France
Table 7.2. Features of metrics calculated under the LecoS plugin. Some computable descriptors (about twenty) describe composition (variety and abundance of the elements) and configuration (spatial characteristics and distribution of the elements) of the landscape. They are inspired by the software Fragstats
Table 7.3. Nomenclature of the classes of the OSO 2016 map with associated codes. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Table 7.4. Six metrics selected for the case study

List of Illustrations

1 Design and Implementation of Automated Atlas

Figure 1.1. Atlas of Corsica local regions. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.2. Workflow for calculating indicators
Figure 1.3. Handling flow for atlas implementation
Figure 1.4. Atlas template
Figure 1.5. Example of topological errors between the boundaries of municipalities and local regions
Figure 1.6. Polygons spatial join based on centroids. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.7. Example of topological error following the transformation
Figure 1.8. Attribute table of Final Local regions layer after attribute table cleaning
Figure 1.9. Principle of atlas coverage layer. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.10. Example of layout of main map. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.11. Example of municipal population density map. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.12. Example of an Intermunicipal cooperation map in Corsica. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.13. Add main map to the atlas template. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.14. Add title and indicators to the atlas template. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.15. Add municipal population density map to the atlas template. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.16. Add Intermunicipal cooperation map to the atlas template. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.17. Example of completed atlas template. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.18. Overview of the pages of the atlas. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 1.19. QGIS extensions

2 Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL)

Figure 2.1. Display of the LUE indicator calculation tool in the QGIS Toolbox
Figure 2.2. Steps for the calculation of the LUE
Figure 2.3. Preparation of the input layer
Figure 2.4. Delimitation of the area of interest and clip of input data
Figure 2.5. The interface of the LUE tool
Figure 2.6. Batch process execution window
Figure 2.7. Calculation of the LUE
Figure 2.8. Choosing the backup option
Figure 2.9. Visualization and analysis of the results
Figure 2.10. Layer property window in QGIS
Figure 2.11. Results of the calculation of the LUE indicator for two intervals:Id_1990–2000 and Id_2000–2014 (cells of 250 m × 250 m). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 2.12. Example of backyard shacks that are formal government-built housing, but where over-occupation corresponds to an average of 15 people living in a four-room dwelling [GER 13]. This explains the increase in the value of the LUE indicator in these areas. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip

3 Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach

Figure 3.1. Processing chain used to calculate the anthropization index in Dijon
Figure 3.2. Buildings’ heights rasterization process from BD TOPO®. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.3. Buildings’ heights raster (at the top left) and DTM (at the bottom left) combined in the same raster (to the right)
Figure 3.4. Geometric overlaps between roads and railways. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.5. Buildings’ geometric modeling in BD TOPO® (source: [IGN 16])
Figure 3.6. Various buffer radius tests (10, 15 and 20 m) in dense building area: City center of Dijon. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.7. Buffer allocation in mixed areas including a residential zone in the northeast, and an industrial zone in the southwest: the tertiary activity zone of Quetigny (Lat: 6692129.501971; Long: 858612.392790) located in the suburbs of Dijon. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.8. Complementarity between vegetation from OpenStreetMap and BD TOPO® in open landscape: the University campus in Dijon. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.9. Anthropization map at the end of step 2. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.10. Illustration of intersection GIS processing in accordance with a regular grid (QGIS functionality: Intersection). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.11. Anthropization index at a resolution of 150 m in the study area (in brackets: the number of features associated at each % class). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.12. Anthropization index of the territory at 30 m of spatial resolution. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 3.13. Batch process with QGIS
Figure 3.14. Reprojection of layers in batch process mode
Figure 3.15. Clipping of layers in batch process mode

4 Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment

Figure 4.1. Method for studying the potential of airborne optical remote sensing for pool mapping in an urban environment, For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.2. Spectral reflectance of relatively clear and very turbid water [LOI 13]. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.3. Spectral reflectance of road, bare soil, vegetation and roof [NCR 13]. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.4. Simulated data that illustrate how SVM works. The dashed line represents the decision boundary of a linear SVM between the data of the two classes. Below, this is the pool class; above, this is the not-pool class. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.5. Simulated data that illustrate how RF works. The dashed lines represent the decision boundaries of RF with at most two nodes (see decision trees in Figure 4.6). Each line represents a branch of a tree. For example, the red line should be related to the red tree (Figure 4.6). Indeed, considering this tree and in a similar way to the SVM, below it is the pool class while above it is the not-pool class. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.6. Example of three decision trees in connection with Figure 4.5. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.7. Illustration of the hyperspectral image used in this study acquired on a study site of 0.496km×0.980km on the city of Toulouse (France, X = 375,906.54; Y = 4,827,256.61). It is an atmospheric-corrected reflectance image (R = 632 nm, G = 559nm, B = 486nm)
Figure 4.8. GCP table
Figure 4.9. Atmospheric-corrected and georeferenced hyperspectral image (R = 632 nm, G = 559 nm, B = 486 nm) acquired on study site of 0.496 km × 0.980 km on Toulouse (France, X = 375,906.54 ; Y= 4,827,256.61)
Figure 4.10. Overlapping of the reference map and the georeferenced hyperspectral image: the swimming pools are blue and the non-pools are red. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.11. Illustration of selected pixels within polygons. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.12. Not-pools are red. Top left: SVM RGB; top right: RF RGB; middle left: SVM RGB-IR; middle-right: RF RGB-IR; bottom left: SVM IR; bottom right: RF IR. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 4.13. Pools are blue. Top: reflectance image; bottom left: SVM IR; bottom right: RF IR. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip

5 Automation of Workflows for the Installation of a Wind Farm

Figure 5.1. Global workflow of chapter
Figure 5.2. WFS and associated datasets
Figure 5.3. Query for download Power lines of Brittany
Figure 5.4. Workflow for preparation of grid population dataset
Figure 5.5. Brittany population grid. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.6. Principle of symmetrical difference. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.7. Principle of difference. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.8. Workflow for identification of inhabited areas
Figure 5.9. Mapping result of uninhabited areas. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.10. Workflow of protected areas union
Figure 5.11. Mapping result of different protected areas union. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.12. Workflow of the difference of protected areas
Figure 5.13. Mapping result of the difference of protected areas. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.14. Workflow for consideration of regional wind policy and wind energy potentialities
Figure 5.15. Mapping result of the workflow. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 5.16. Workflow for consideration of proximity to power lines
Figure 5.17. Map of the three optimal areas for installation of new wind farm
Figure 5.18. Zoom on three optimal areas with satellite imagery
Figure 5.19. Workflow concatenation

6 Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands

Figure 6.1. Forest function assessment for water protection within water catchment perimeter algorithm. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.2. Water catchment protection perimeters. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.3. PPE database preparation. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.4. Erosion hazard assessment for each PPE: a) soil loss estimates from RUSLE equation in t/ha/year (from white, values near 0 to red, high soil loss estimates); b) erosion hazard map per pixel according to soil loss estimates: low (green), moderate (yellow), high (orange), extreme (red); c) PPE erosion hazard classification (yellow, low to moderate; orange, moderate to high; red, high to extreme). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.5. Dominant vegetation determination of each simplified PPE
Figure 6.6. Forest fragmentation index determination method
Figure 6.7. Examples of forest diversity indices in New Caledonia. a) Diversity index = 0.02. b) Diversity index = 0.90. c) Diversity index = 1.95. d) Diversity index = 3.56. For a color version of the table, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.8. Examples of forest shape indices in New Caledonia. a) Shape index = 39. b) Shape index = 95. c) Shape index = 965. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.9. PPE’s forest fragmentation decision tree. For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.10. The decision tree used to assess forest function state of the PPE (dominant vegetation 1: landscape dominated by tree and shrub formations; Dominant vegetation 2: mosaic of tree, shrub and herbaceous formations with the presence of bare soils and predominance of shrub and herbaceous formations; Dominant vegetation 3: landscape with the large presence of bare soil where the vegetation is sparse and consists of herbaceous formations, with some shrub and tree formations from degraded forests). For a color version of the figure, see www.iste.co.uk/baghdadi/qgis3.zip
Figure 6.11. The Different checks supported by the plugin

7 Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS

Figure 7.1. The two components of spatial heterogeneity: composition and configuration. The heterogeneity of composition increases with the number of patches of various types (from left to right). The heterogeneity of configuration increases with the complexity of the organization of the patches (from bottom to top)
Figure 7.2. Landscape ecology analysis approach and translation for geomatics processing chain in three steps
Figure 7.3. The land cover French map OSO 2016 (43°16′23.24″ N; 0°48′05.75″ E) with an area of 17 km². For the associated nomenclature, see www.iste.co.uk/baghdadi/qgis3.zip

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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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: 2017961026

British Library Cataloguing-in-Publication Data

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

ISBN 978-1-78630-189-5

Introduction

Scientific and technical issues related to territorial planning have long been firmly accompanied by the software for the graphical representation of the results of these studies. The software is a key element for communication both upstream and downstream of a project, for the phases of design, consultation or conciliation with public and private actors in connection with the infrastructures in question. In this way, Geographic Information Systems (GISs) have, over the last 15 years, taken a central place for all applications and issues related to territorial planning. With a growing number of tools, stakeholders (geomaticians, users, decision makers, scientists), data and references available, we have moved from a mainly cartographic use to more and more advanced analysis, simulation and diagnostics. All spatial scales are concerned, from the very small to the very large, from land use on a European scale to local city planning maps.

This third volume is devoted to the presentation and implementation in QGIS (Quantum Geographic Information System) and its libraries of several applications in relation to territorial planning. The proposed chapters illustrate the great diversity of study cases, spatial scales and actors involved in this problem: from the global scale to the city level, from tailored specific object detection to more general infrastructure location studies, in urban and agroforestry environments, without neglecting the coastal areas.

This work, carried out by scientists of a high technical level, is addressed to geomatics research teams, second-level students (engineering schools, master’s) and postgraduate studies (PhD students), and engineers involved in the management of water and territory resources. In addition to the texts of the proposed chapters, readers will have access to the data and tools, as well as screenshots of all the QGIS windows, which illustrate the manipulations necessary to carry out each step of each application. Through this educational work, we wish to contribute to the appropriation of the free software GIS, in connection with remote sensing applications.

A supplement to the chapters, including datasets 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: vol3_en

Password: 34voL@en3

We would like to thank everybody who contributed to the development of this book; first of all, the scientists, authors of the chapters of course, but also the experts of the scientific committee for their inspection of the chapters and the corrections made. This project was carried out thanks to the support of IRSTEA (French Research Institute of Science and Technology for Environment and Agriculture), IGN (French National Institute for Geographic and Forestry Information), CNRS (French National Center for Scientific Research) 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 in a research and training framework and any commercial activity based on the data provided is strictly prohibited.

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

Nicolas BAGHDADI

Clément MALLET

Mehrez ZRIBI