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 ixChapter 1. Design and Implementation of Automated Atlas 1Boris MERICSKAY1.1. From map to atlas 11.2. Automation of maps and indicators 21.2.1. Step 1: atlas template designing 41.2.2. Step 2: data preparation and indicators creation 41.2.3. Step 3: atlas implementation in QGIS project 81.2.4. Step 4: atlas implementation in print composer 131.2.5. Step 5: atlas publication 161.3. Implementation of the application 171.3.1. Software and data 171.3.2. Step 2: data preparation and indicators creation 191.3.3. Step 3: atlas implementation in QGIS project 251.3.4. Step 4: atlas implementation in print composer 291.3.5. Step 5: atlas publication 37Chapter 2. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL) 39Christina CORBANE, Panagiotis POLITIS, Martino PESARESI, Thomas KEMPER and Alice SIRAGUSA2.1. Context 392.2. The Land Use Efficiency (LUE) 402.3. Installation of the LUE indicator calculation tool 412.4. Method to calculate the LUE indicator 422.4.1. Preparation of the input layer 442.4.2. Delimitation of the area of interest and clip of input data 452.4.3. Calculation of the LUE indicator 472.4.4. Visualization and analysis of the results 482.4.5. One possible interpretation 502.5. Limits of the method 512.6. Bibliography 52Chapter 3. Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach 53Justin EMERY, Julita DUDEK, Ludovic GRANJON, Benjamin POHL, Yves RICHARD, Thomas THEVENIN and Nadège MARTINY3.1. The city–climate relationship through urban climate modeling 533.2. Representation of the urban space 563.2.1. Step 1: integrate urban morphology in a DTM 593.2.2. Step 2: generate land use in a urban space 623.2.3. Step 3: calculate the anthropization index 713.2.4. Discussions and perspectives: contribution of remote sensing to vegetation mapping 733.3. Practical case study of the processing chain 763.3.1. Software and database 763.3.2. Step 1: integrate the building heights into a DTM 773.3.3. Step 2: geographic data generation about natural and artificial areas 803.3.4. Step 3: calculation of the anthropization index 883.4. Bibliography 90Chapter 4. Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment 93Josselin AVAL and Thierry ERUDEL4.1. Context 934.2. Method 944.2.1. Data acquisition and preprocessing 964.2.2. Reference map definition 994.2.3. Feature extraction 1014.2.4. Classification 1034.2.5. Building a prediction map 1074.2.6. Performance assessment 1074.2.7. Limits of the proposed method 1084.3. Implementation of the application 1094.3.1. Software and data 1094.3.2. Step 1: creation of a georeferenced image 1104.3.3. Step 2: building a reference map 1144.3.4. Step 3: classification and prediction map 1164.4. Bibliography 122Chapter 5. Automation of Workflows for the Installation of a Wind Farm 125Boris MERICSKAY5.1. Automation of workflows 1255.2. Automation of workflows for the installation of a wind farm in Brittany 1265.2.1. Step 1: download data with WFSs 1275.2.2. Step 2: preparation of the population grid dataset 1305.2.3. Step 3: identification of inhabited areas 1315.2.4. Step 4: consideration of protected areas 1345.2.5. Step 5: consideration of regional wind policy and wind energy criteria 1375.2.6. Step 6: proximity to power lines 1395.3. Implementation of the application 1425.3.1. Software and data 1425.3.2. Step 1: downloading datasets 1455.3.3. Step 2: preparation of population grid dataset 1495.3.4. Step 3: identification of inhabited areas 1545.3.5. Step 4: consideration of protected areas 1575.3.6. Step 5: consideration of the regional wind policy and wind energy criteria 1625.3.7. Step 6: consideration of proximity to power lines 165Chapter 6. Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands 169Rémi ANDREOLI and Brice VAN HAAREN6.1. Definition and context 1696.2. Method 1706.2.1. Water catchment perimeter database (PPE) preparation 1726.2.2. Soil stabilization criterion: the erosion hazard parameter 1746.2.3. Water regulation criterion and ecosystem degradation: the dominant vegetation parameter 1776.2.4. Resilience criterion: forest fragmentation parameter 1816.2.5. Assessment of the forest function in the PPE 1866.2.6. Limits 1886.3. Forest function assessment implementation 1886.3.1. Software and data 1886.3.2. Step 1: PPE polygons creation 1906.3.3. Step 2: erosion hazard parameter determination 1996.3.4. Step 3: dominant vegetation type parameter determination 2056.3.5. Step 4: forest fragmentation parameter 2166.3.6. Step 5: forest function assessment for water protection 2316.4. Bibliography 234Chapter 7. Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS 239Sylvie LADET, David SHEEREN, Pierre-Alexis HERRAULT and Mathieu FAUVEL7.1. Introduction 2397.2. Principle of the approach 2397.3. Materials and methods 2427.3.1. Step 1: land cover map 2427.3.2. Step 2: definition of the relevant landscape descriptors 2447.3.3. Step 3: statistical modeling 2467.4. Application of the processing chain: effect of landscape on forest bird diversity 2477.4.1. “Birds” data and the variable to be explained 2477.4.2. “Landscape” data and the explanatory variables 2487.4.3. Implementation in QGIS environment 2507.5. Acknowledgments 2627.6. Bibliography 262List of Authors 265Index 269Scientific Committee 271

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 ixChapter 1. Design and Implementation of Automated Atlas 1Boris MERICSKAY1.1. From map to atlas 11.2. Automation of maps and indicators 21.2.1. Step 1: atlas template designing 41.2.2. Step 2: data preparation and indicators creation 41.2.3. Step 3: atlas implementation in QGIS project 81.2.4. Step 4: atlas implementation in print composer 131.2.5. Step 5: atlas publication 161.3. Implementation of the application 171.3.1. Software and data 171.3.2. Step 2: data preparation and indicators creation 191.3.3. Step 3: atlas implementation in QGIS project 251.3.4. Step 4: atlas implementation in print composer 291.3.5. Step 5: atlas publication 37Chapter 2. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL) 39Christina CORBANE, Panagiotis POLITIS, Martino PESARESI, Thomas KEMPER and Alice SIRAGUSA2.1. Context 392.2. The Land Use Efficiency (LUE) 402.3. Installation of the LUE indicator calculation tool 412.4. Method to calculate the LUE indicator 422.4.1. Preparation of the input layer 442.4.2. Delimitation of the area of interest and clip of input data 452.4.3. Calculation of the LUE indicator 472.4.4. Visualization and analysis of the results 482.4.5. One possible interpretation 502.5. Limits of the method 512.6. Bibliography 52Chapter 3. Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach 53Justin EMERY, Julita DUDEK, Ludovic GRANJON, Benjamin POHL, Yves RICHARD, Thomas THEVENIN and Nadège MARTINY3.1. The city–climate relationship through urban climate modeling 533.2. Representation of the urban space 563.2.1. Step 1: integrate urban morphology in a DTM 593.2.2. Step 2: generate land use in a urban space 623.2.3. Step 3: calculate the anthropization index 713.2.4. Discussions and perspectives: contribution of remote sensing to vegetation mapping 733.3. Practical case study of the processing chain 763.3.1. Software and database 763.3.2. Step 1: integrate the building heights into a DTM 773.3.3. Step 2: geographic data generation about natural and artificial areas 803.3.4. Step 3: calculation of the anthropization index 883.4. Bibliography 90Chapter 4. Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment 93Josselin AVAL and Thierry ERUDEL4.1. Context 934.2. Method 944.2.1. Data acquisition and preprocessing 964.2.2. Reference map definition 994.2.3. Feature extraction 1014.2.4. Classification 1034.2.5. Building a prediction map 1074.2.6. Performance assessment 1074.2.7. Limits of the proposed method 1084.3. Implementation of the application 1094.3.1. Software and data 1094.3.2. Step 1: creation of a georeferenced image 1104.3.3. Step 2: building a reference map 1144.3.4. Step 3: classification and prediction map 1164.4. Bibliography 122Chapter 5. Automation of Workflows for the Installation of a Wind Farm 125Boris MERICSKAY5.1. Automation of workflows 1255.2. Automation of workflows for the installation of a wind farm in Brittany 1265.2.1. Step 1: download data with WFSs 1275.2.2. Step 2: preparation of the population grid dataset 1305.2.3. Step 3: identification of inhabited areas 1315.2.4. Step 4: consideration of protected areas 1345.2.5. Step 5: consideration of regional wind policy and wind energy criteria 1375.2.6. Step 6: proximity to power lines 1395.3. Implementation of the application 1425.3.1. Software and data 1425.3.2. Step 1: downloading datasets 1455.3.3. Step 2: preparation of population grid dataset 1495.3.4. Step 3: identification of inhabited areas 1545.3.5. Step 4: consideration of protected areas 1575.3.6. Step 5: consideration of the regional wind policy and wind energy criteria 1625.3.7. Step 6: consideration of proximity to power lines 165Chapter 6. Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands 169Rémi ANDREOLI and Brice VAN HAAREN6.1. Definition and context 1696.2. Method 1706.2.1. Water catchment perimeter database (PPE) preparation 1726.2.2. Soil stabilization criterion: the erosion hazard parameter 1746.2.3. Water regulation criterion and ecosystem degradation: the dominant vegetation parameter 1776.2.4. Resilience criterion: forest fragmentation parameter 1816.2.5. Assessment of the forest function in the PPE 1866.2.6. Limits 1886.3. Forest function assessment implementation 1886.3.1. Software and data 1886.3.2. Step 1: PPE polygons creation 1906.3.3. Step 2: erosion hazard parameter determination 1996.3.4. Step 3: dominant vegetation type parameter determination 2056.3.5. Step 4: forest fragmentation parameter 2166.3.6. Step 5: forest function assessment for water protection 2316.4. Bibliography 234Chapter 7. Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS 239Sylvie LADET, David SHEEREN, Pierre-Alexis HERRAULT and Mathieu FAUVEL7.1. Introduction 2397.2. Principle of the approach 2397.3. Materials and methods 2427.3.1. Step 1: land cover map 2427.3.2. Step 2: definition of the relevant landscape descriptors 2447.3.3. Step 3: statistical modeling 2467.4. Application of the processing chain: effect of landscape on forest bird diversity 2477.4.1. “Birds” data and the variable to be explained 2477.4.2. “Landscape” data and the explanatory variables 2487.4.3. Implementation in QGIS environment 2507.5. Acknowledgments 2627.6. Bibliography 262List of Authors 265Index 269Scientific Committee 271

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 ixChapter 1. Design and Implementation of Automated Atlas 1Boris MERICSKAY1.1. From map to atlas 11.2. Automation of maps and indicators 21.2.1. Step 1: atlas template designing 41.2.2. Step 2: data preparation and indicators creation 41.2.3. Step 3: atlas implementation in QGIS project 81.2.4. Step 4: atlas implementation in print composer 131.2.5. Step 5: atlas publication 161.3. Implementation of the application 171.3.1. Software and data 171.3.2. Step 2: data preparation and indicators creation 191.3.3. Step 3: atlas implementation in QGIS project 251.3.4. Step 4: atlas implementation in print composer 291.3.5. Step 5: atlas publication 37Chapter 2. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL) 39Christina CORBANE, Panagiotis POLITIS, Martino PESARESI, Thomas KEMPER and Alice SIRAGUSA2.1. Context 392.2. The Land Use Efficiency (LUE) 402.3. Installation of the LUE indicator calculation tool 412.4. Method to calculate the LUE indicator 422.4.1. Preparation of the input layer 442.4.2. Delimitation of the area of interest and clip of input data 452.4.3. Calculation of the LUE indicator 472.4.4. Visualization and analysis of the results 482.4.5. One possible interpretation 502.5. Limits of the method 512.6. Bibliography 52Chapter 3. Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach 53Justin EMERY, Julita DUDEK, Ludovic GRANJON, Benjamin POHL, Yves RICHARD, Thomas THEVENIN and Nadège MARTINY3.1. The city–climate relationship through urban climate modeling 533.2. Representation of the urban space 563.2.1. Step 1: integrate urban morphology in a DTM 593.2.2. Step 2: generate land use in a urban space 623.2.3. Step 3: calculate the anthropization index 713.2.4. Discussions and perspectives: contribution of remote sensing to vegetation mapping 733.3. Practical case study of the processing chain 763.3.1. Software and database 763.3.2. Step 1: integrate the building heights into a DTM 773.3.3. Step 2: geographic data generation about natural and artificial areas 803.3.4. Step 3: calculation of the anthropization index 883.4. Bibliography 90Chapter 4. Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment 93Josselin AVAL and Thierry ERUDEL4.1. Context 934.2. Method 944.2.1. Data acquisition and preprocessing 964.2.2. Reference map definition 994.2.3. Feature extraction 1014.2.4. Classification 1034.2.5. Building a prediction map 1074.2.6. Performance assessment 1074.2.7. Limits of the proposed method 1084.3. Implementation of the application 1094.3.1. Software and data 1094.3.2. Step 1: creation of a georeferenced image 1104.3.3. Step 2: building a reference map 1144.3.4. Step 3: classification and prediction map 1164.4. Bibliography 122Chapter 5. Automation of Workflows for the Installation of a Wind Farm 125Boris MERICSKAY5.1. Automation of workflows 1255.2. Automation of workflows for the installation of a wind farm in Brittany 1265.2.1. Step 1: download data with WFSs 1275.2.2. Step 2: preparation of the population grid dataset 1305.2.3. Step 3: identification of inhabited areas 1315.2.4. Step 4: consideration of protected areas 1345.2.5. Step 5: consideration of regional wind policy and wind energy criteria 1375.2.6. Step 6: proximity to power lines 1395.3. Implementation of the application 1425.3.1. Software and data 1425.3.2. Step 1: downloading datasets 1455.3.3. Step 2: preparation of population grid dataset 1495.3.4. Step 3: identification of inhabited areas 1545.3.5. Step 4: consideration of protected areas 1575.3.6. Step 5: consideration of the regional wind policy and wind energy criteria 1625.3.7. Step 6: consideration of proximity to power lines 165Chapter 6. Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands 169Rémi ANDREOLI and Brice VAN HAAREN6.1. Definition and context 1696.2. Method 1706.2.1. Water catchment perimeter database (PPE) preparation 1726.2.2. Soil stabilization criterion: the erosion hazard parameter 1746.2.3. Water regulation criterion and ecosystem degradation: the dominant vegetation parameter 1776.2.4. Resilience criterion: forest fragmentation parameter 1816.2.5. Assessment of the forest function in the PPE 1866.2.6. Limits 1886.3. Forest function assessment implementation 1886.3.1. Software and data 1886.3.2. Step 1: PPE polygons creation 1906.3.3. Step 2: erosion hazard parameter determination 1996.3.4. Step 3: dominant vegetation type parameter determination 2056.3.5. Step 4: forest fragmentation parameter 2166.3.6. Step 5: forest function assessment for water protection 2316.4. Bibliography 234Chapter 7. Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS 239Sylvie LADET, David SHEEREN, Pierre-Alexis HERRAULT and Mathieu FAUVEL7.1. Introduction 2397.2. Principle of the approach 2397.3. Materials and methods 2427.3.1. Step 1: land cover map 2427.3.2. Step 2: definition of the relevant landscape descriptors 2447.3.3. Step 3: statistical modeling 2467.4. Application of the processing chain: effect of landscape on forest bird diversity 2477.4.1. “Birds” data and the variable to be explained 2477.4.2. “Landscape” data and the explanatory variables 2487.4.3. Implementation in QGIS environment 2507.5. Acknowledgments 2627.6. Bibliography 262List of Authors 265Index 269Scientific Committee 271