Details

QGIS and Applications in Territorial Planning


QGIS and Applications in Territorial Planning


1. Aufl.

von: Nicolas Baghdadi, Clément Mallet, Mehrez Zribi

144,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 15.02.2018
ISBN/EAN: 9781119510437
Sprache: englisch
Anzahl Seiten: 288

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Beschreibungen

<p>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.</p>
<p>Introduction ix</p> <p><b>Chapter 1. Design and Implementation of Automated Atlas </b><b>1<br /></b><i>Boris MERICSKAY</i></p> <p>1.1. From map to atlas 1</p> <p>1.2. Automation of maps and indicators 2</p> <p>1.2.1. Step 1: atlas template designing 4</p> <p>1.2.2. Step 2: data preparation and indicators creation 4</p> <p>1.2.3. Step 3: atlas implementation in QGIS project 8</p> <p>1.2.4. Step 4: atlas implementation in print composer 13</p> <p>1.2.5. Step 5: atlas publication 16</p> <p>1.3. Implementation of the application 17</p> <p>1.3.1. Software and data 17</p> <p>1.3.2. Step 2: data preparation and indicators creation 19</p> <p>1.3.3. Step 3: atlas implementation in QGIS project 25</p> <p>1.3.4. Step 4: atlas implementation in print composer 29</p> <p>1.3.5. Step 5: atlas publication 37</p> <p><b>Chapter 2. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL) </b><b>39<br /></b><i>Christina CORBANE, Panagiotis POLITIS, Martino PESARESI, Thomas KEMPER and Alice SIRAGUSA</i></p> <p>2.1. Context 39</p> <p>2.2. The Land Use Efficiency (LUE) 40</p> <p>2.3. Installation of the LUE indicator calculation tool 41</p> <p>2.4. Method to calculate the LUE indicator 42</p> <p>2.4.1. Preparation of the input layer 44</p> <p>2.4.2. Delimitation of the area of interest and clip of input data 45</p> <p>2.4.3. Calculation of the LUE indicator 47</p> <p>2.4.4. Visualization and analysis of the results 48</p> <p>2.4.5. One possible interpretation 50</p> <p>2.5. Limits of the method 51</p> <p>2.6. Bibliography 52</p> <p><b>Chapter 3. Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach </b><b>53<br /></b><i>Justin EMERY, Julita DUDEK, Ludovic GRANJON, Benjamin POHL, Yves RICHARD, Thomas THEVENIN and Nadège MARTINY</i></p> <p>3.1. The city–climate relationship through urban climate modeling 53</p> <p>3.2. Representation of the urban space 56</p> <p>3.2.1. Step 1: integrate urban morphology in a DTM 59</p> <p>3.2.2. Step 2: generate land use in a urban space 62</p> <p>3.2.3. Step 3: calculate the anthropization index 71</p> <p>3.2.4. Discussions and perspectives: contribution of remote sensing to vegetation mapping 73</p> <p>3.3. Practical case study of the processing chain 76</p> <p>3.3.1. Software and database 76</p> <p>3.3.2. Step 1: integrate the building heights into a DTM 77</p> <p>3.3.3. Step 2: geographic data generation about natural and artificial areas 80</p> <p>3.3.4. Step 3: calculation of the anthropization index 88</p> <p>3.4. Bibliography 90</p> <p><b>Chapter 4. Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment </b><b>93<br /></b><i>Josselin AVAL and Thierry ERUDEL</i></p> <p>4.1. Context 93</p> <p>4.2. Method 94</p> <p>4.2.1. Data acquisition and preprocessing 96</p> <p>4.2.2. Reference map definition 99</p> <p>4.2.3. Feature extraction 101</p> <p>4.2.4. Classification 103</p> <p>4.2.5. Building a prediction map 107</p> <p>4.2.6. Performance assessment 107</p> <p>4.2.7. Limits of the proposed method 108</p> <p>4.3. Implementation of the application 109</p> <p>4.3.1. Software and data 109</p> <p>4.3.2. Step 1: creation of a georeferenced image 110</p> <p>4.3.3. Step 2: building a reference map 114</p> <p>4.3.4. Step 3: classification and prediction map 116</p> <p>4.4. Bibliography 122</p> <p><b>Chapter 5. Automation of Workflows for the Installation of a Wind Farm </b><b>125<br /></b><i>Boris MERICSKAY</i></p> <p>5.1. Automation of workflows 125</p> <p>5.2. Automation of workflows for the installation of a wind farm in Brittany 126</p> <p>5.2.1. Step 1: download data with WFSs 127</p> <p>5.2.2. Step 2: preparation of the population grid dataset 130</p> <p>5.2.3. Step 3: identification of inhabited areas 131</p> <p>5.2.4. Step 4: consideration of protected areas 134</p> <p>5.2.5. Step 5: consideration of regional wind policy and wind energy criteria 137</p> <p>5.2.6. Step 6: proximity to power lines 139</p> <p>5.3. Implementation of the application 142</p> <p>5.3.1. Software and data 142</p> <p>5.3.2. Step 1: downloading datasets 145</p> <p>5.3.3. Step 2: preparation of population grid dataset 149</p> <p>5.3.4. Step 3: identification of inhabited areas 154</p> <p>5.3.5. Step 4: consideration of protected areas 157</p> <p>5.3.6. Step 5: consideration of the regional wind policy and wind energy criteria 162</p> <p>5.3.7. Step 6: consideration of proximity to power lines 165</p> <p><b>Chapter 6. Ecosystemic Services Assessment: Application to Forests for the Preservation of Water Resources in Tropical Islands </b><b>169<br /></b><i>Rémi ANDREOLI and Brice VAN HAAREN</i></p> <p>6.1. Definition and context 169</p> <p>6.2. Method 170</p> <p>6.2.1. Water catchment perimeter database (PPE) preparation 172</p> <p>6.2.2. Soil stabilization criterion: the erosion hazard parameter 174</p> <p>6.2.3. Water regulation criterion and ecosystem degradation: the dominant vegetation parameter 177</p> <p>6.2.4. Resilience criterion: forest fragmentation parameter 181</p> <p>6.2.5. Assessment of the forest function in the PPE 186</p> <p>6.2.6. Limits 188</p> <p>6.3. Forest function assessment implementation 188</p> <p>6.3.1. Software and data 188</p> <p>6.3.2. Step 1: PPE polygons creation 190</p> <p>6.3.3. Step 2: erosion hazard parameter determination 199</p> <p>6.3.4. Step 3: dominant vegetation type parameter determination 205</p> <p>6.3.5. Step 4: forest fragmentation parameter 216</p> <p>6.3.6. Step 5: forest function assessment for water protection 231</p> <p>6.4. Bibliography 234</p> <p><b>Chapter 7. Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS </b><b>239<br /></b><i>Sylvie LADET, David SHEEREN, Pierre-Alexis HERRAULT and Mathieu FAUVEL</i></p> <p>7.1. Introduction 239</p> <p>7.2. Principle of the approach 239</p> <p>7.3. Materials and methods 242</p> <p>7.3.1. Step 1: land cover map 242</p> <p>7.3.2. Step 2: definition of the relevant landscape descriptors 244</p> <p>7.3.3. Step 3: statistical modeling 246</p> <p>7.4. Application of the processing chain: effect of landscape on forest bird diversity 247</p> <p>7.4.1. “Birds” data and the variable to be explained 247</p> <p>7.4.2. “Landscape” data and the explanatory variables 248</p> <p>7.4.3. Implementation in QGIS environment 250</p> <p>7.5. Acknowledgments 262</p> <p>7.6. Bibliography 262</p> <p>List of Authors 265</p> <p>Index 269</p> <p>Scientific Committee 271</p>
<strong>Nicolas Baghdadi</strong>, French Research Institute of Science and Technology for Environment and Agriculture, France. <p><strong>Clément Mallet</strong>, ING, France. <p><strong>Mehrez Zribi</strong>, CNRS and CESBIO, France.

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