Details

<b>Preface</b> <p><b>List of Contributors</b></p> <p>1          GIS and remote sensing integration: in search of a definition</p> <p><i>Victor Mesev and Alexandra Walrath</i></p> <p>1.1 Introduction</p> <p>1.2 In search of a definition</p> <p>1.2.1 Evolutionary integration</p> <p>1.2.2 Methodological integration</p> <p>1.3 Outline of the book</p> <p>1.4 Conclusions</p> <p><b>2          Integration taxonomy and uncertainty</b></p> <p><i>Manfred Ehlers</i></p> <p> <b>           </b> 2.1 Introduction</p> <p>2.2 Taxonomy issues</p> <p>2.2.1 Taxonomy of GIS operators</p> <p>2.2.2 Taxonomy of image analysis operators in remote sensing</p> <p>2.2.3 An integrated taxonomy</p> <p>2.3 Uncertainty issues</p> <p>2.3.1 Uncertainty in geographic information</p> <p>2.3.2 Uncertainty in the integration of GIS and remote sensing</p> <p>2.4 Modelling positional and thematic error in the integration of remote sensing and GIS</p> <p>2.4.1 Positional and thematic uncertainties</p> <p>2.4.2 Problem formulation</p> <p>2.4.3 Modelling positional uncertainty</p> <p>2.4.3.1 Line errors</p> <p>2.4.3.2 Confidence region for line segments</p> <p>2.4.3.3 Positional uncertainty of boundary line features</p> <p>2.4.3.4 Positional uncertainty of area objects</p> <p>2.4.4 Thematic uncertainties of a classified image</p> <p>2.4.5 Modelling the combined positional and thematic uncertainties</p> <p>2.5 Conclusions</p> <p><b>3          Data fusion related to GIS and remote sensing</b></p> <p><i>Paolo Gamba and Fabio Dell'Acqua</i></p> <p>3.1 Introduction</p> <p>3.2 Why do we need GIS–remote sensing fusion?</p> <p>3.2.1 Remote sensing output to GIS</p> <p>3.2.2 GIS input to remote sensing interpretation algorithms</p> <p>3.2.3 Example: urban planning check and update</p> <p>3.3 Problems in GIS–remote sensing data fusion</p> <p>3.3.1 Lack of consistent standards</p> <p>3.3.2 Inconsistency of GIS–remote sensing accuracy, legends and scales</p> <p>3.3.3 Different nature of the two sources</p> <p>3.3.4 Need for information rather than data fusion</p> <p>3.3.5 Example: population mapping through remote sensing</p> <p>3.4 Present and future solutions</p> <p>3.4.1 Multiscale analysis</p> <p>3.4.2 Fusion techniques</p> <p>3.4.2.1 Fuzzy-based framework retaining accuracy information</p> <p>3.4.2.2 Non-parametric approaches</p> <p>3.4.2.3 Knowledge-based approaches</p> <p>3.5 Conclusions</p> <p>3.5.1 Integration of remote sensing and GIS into a change detection module</p> <p>4          The importance of scale in remote sensing and GIS and its implications for data integration.</p> <p><i>Peter M. Atkinson</i></p> <p>4.1 Introduction</p> <p>4.2 Data models and scales of measurement</p> <p>4.2.1 Raster imagery</p> <p>4.2.1.1 Raster imagery and the RF model</p> <p>4.2.1.2 Scales of measurement in remotely sensed imagery</p> <p>4.2.2 Vector data</p> <p>4.2.2.1 Vector data and the object-based model</p> <p>4.2.2.2 Scales of measurement</p> <p>4.3 Scales of spatial variation</p> <p>4.3.1 Spatial variation in raster data</p> <p>4.3.1.1 Characterizing scales of spatial variation</p> <p>4.3.1.2 Characterizing error</p> <p>4.3.1.3 Upscaling and downscaling</p> <p>4.3.2 Scales of variation in vector data</p> <p>4.3.3 Processes in the environment</p> <p>4.3.3.1 Processes and forms</p> <p>4.3.3.2 Process modelling</p> <p>4.3.3.3 Scales of representation</p> <p>4.4 Remote sensing and GIS data integration</p> <p>4.4.1 Overlay and regression</p> <p>4.4.1.1 Scales of measurement</p> <p>4.4.1.2 Transformation</p> <p>4.4.1.3 Geometric error</p> <p>4.4.2 Remote sensing classification of land cover</p> <p>4.4.1.1 Per-field classification</p> <p>4.4.1.2 Soft classification and subpixel allocation</p> <p>4.4.1.3 A note on downscaling and super-resolution mapping</p> <p>4.5 Conclusion</p> <p>5          Of patterns and processes: spatial metrics and geostatistics in urban analysis</p> <p><i>XiaoHang Liu and Martin Herold</i></p> <p>5.1 Introduction</p> <p>5.2 Geostatistics</p> <p>5.3 Spatial metrics</p> <p>5.4 Examples</p> <p>5.4.1. Data preparation</p> <p>5.4.2 Linkage from land cover to land use</p> <p>5.4.2.1 Land use classification based on geostatistics</p> <p>5.4.2.2 Land use classification based on spatial metrics</p> <p>5.4.2.3 Land-use classification based on combined information</p> <p>5.4.3 Linking urban form to population density</p> <p>5.4.5 Linking characteristics of spatial patterns and processes</p> <p>5.5 Conclusion</p> <p><b>6          Using remote sensing and GIS integration to identify spatial characteristics of sprawl at the building-unit level</b></p> <p><i>John Hasse</i></p> <p>6.1 Introduction</p> <p>6.2 Sprawl in the remote sensing and GIS literature</p> <p>6.2.1 Definitions of sprawl</p> <p>6.2.2 Spatial characteristics of sprawl at a metropolitan level</p> <p>6.2.3 Spatial characteristics of sprawl at a submetropolitan level</p> <p>6.3 Integrating remote sensing and GIS for sprawl research</p> <p>6.4 Spatial characteristics of sprawl at a building-unit level</p> <p>6.5 A practical building-unit level model for analysing sprawl</p> <p>6.5.1 Urban density</p> <p>6.5.2 Leapfrog</p> <p>6.5.3 Segregated land use</p> <p>6.5.4 Highway strip</p> <p>6.5.5 Community node inaccessibility</p> <p><a id="_Toc5878279" name="_Toc5878279"></a><a id="_Toc7273220" name="_Toc7273220"></a><a id="_Toc7274172" name="_Toc7274172"></a><a id="_Toc7274434" name="_Toc7274434"></a><a id="_Toc7274686" name="_Toc7274686"></a><a id="_Toc7274796" name="_Toc7274796"></a><a id="_Toc7854929" name="_Toc7854929">6.5.6 Normalizing municipal sprawl indicator measures</a></p> <p>6.6. Future benefits of integrating remote sensing and gis in sprawl research</p> <p><b>7          Remote sensing applications in urban socio-economic analysis</b></p> <p><i>Chiangshan Wu</i></p> <p>7.1 Introduction</p> <p>7.2 Principles of urban socio-economic studies using remote sensing technologies</p> <p>7.3 Socio-economic information estimation</p> <p>7.3.1 Population estimation</p> <p>7.3.2 Employment estimation</p> <p>7.3.3 GDP estimation</p> <p>7.3.4 Electrical power consumption estimation</p> <p>7.4 Socio-economic activity modelling</p> <p>7.4.1 Population interpolation</p> <p>7.4.2 Socio-economic index generation</p> <p>7.4.3 Understanding and modelling socio-economic phenomena</p> <p>7.4.3.1 Population segregation analysis</p> <p>7.4.3.2 Housing price modelling</p> <p>7.5 Advantages and limitations of remote sensing technologies in socio-economic applications</p> <p>7.5.1 Socio-economic information estimation</p> <p>7.5.2 Socio-economic information modelling</p> <p>7.6 Conclusions</p> <p><b>8          Integrating remote sensing, GIS and spatial modelling for sustainable urban growth management</b></p> <p><i>Xiaojun Yang</i></p> <p>8.1 Introduction</p> <p>8.2 Research methodology</p> <p>8.2.1 Study area</p> <p>8.2.2 Data acquisition and collection</p> <p>8.2.3 Satellite image processing</p> <p>8.2.4 Change analysis</p> <p>8.2.5 Spatial statistical analysis</p> <p>8.2.6 Dynamic spatial modelling</p> <p>8.3 Results and discussion</p> <p>8.3.1 Urban growth</p> <p>8.3.2 Driving force</p> <p>8.3.2.1 High-density urban use</p> <p>8.3.2.2 Low-density urban use</p> <p>8.3.3 Future growth scenario simulation</p> <p>8.4 Conclusions</p> <p><b>9          An integrative GIS and remote sensing model for place-based urban vulnerability analysis</b></p> <p><i>Tarek Rashed, John Weeks, Helen Couclelis and Martin Herold</i></p> <p>9.1 Introduction</p> <p>9.2 Analysis of urban vulnerability: what is it all about?</p> <p>9.3 A conceptual framework for place-based analysis of urban vulnerability</p> <p>9.4 Integrating GIS and remote sensing into vulnerability analysis</p> <p>9.5 A GIS–remote sensing place-based model for urban vulnerability analysis</p> <p>9.6 An illustrative example of model application</p> <p>9.6.1 Study area</p> <p>9.6.2 Data</p> <p>9.6.3 Identifying vulnerability hot spots</p> <p> </p> <p>9.6.4 Deriving remote sensing measures of urban morphology in Los Angeles</p> <p>9.6.4.1 MESMA</p> <p>9.6.5 Deriving an index of wealth for Los Angeles County</p> <p>9.6.6 Spatial filtering of variables</p> <p>9.6.7 Generating place-based knowledge of urban vulnerability in Los Angeles< </p> <p>9.6.7.1 Statistical models</p> <p><a id="_Toc31500672" name="_Toc31500672">9.6.7.2 Results of correlation between vulnerability and</a> wealth</p> <h4>9.6.7.2 Results of regression models</h4> <p>9.6.8 To what extent do model results conform to universal knowledge of vulnerability?</p> <p>9.7 Conclusions</p> <p><b>10        Using GIS and remote sensing for ecological mapping and monitoring</b></p> <p><i>Jennifer Miller and John Rogan</i></p> <p>10.1 Introduction</p> <p>10.2 Integration of GIS and remote sensing in ecological research</p> <p>10.3 GIS data used in ecological applications</p> <p>10.3.1 Gradient analysis</p> <p>10.3.2 Climate</p> <p>10.3.3 Topography</p> <p>10.4 Remotely sensed data for ecological applications</p> <p>10.4.1 Spectral enhancements</p> <p>10.4.2 Land cover</p> <p>10.4.3 Habitat structure</p> <p>10.4.4 Biophysical processes</p> <p>10.5 Species distribution models</p> <p>10.5.1 Biodiversity mapping</p> <p><a id="OLE_LINK1" name="OLE_LINK1"></a><a id="OLE_LINK2" name="OLE_LINK2">10.6 Change detection</a></p> <p>10.6.1 Case study: using GIS and remote sensing for large-area change detection and efficient map updating</p> <p>10.6.1.1 Study area</p> <p>10.6.1.2 Data and methods</p> <p>10.6.1.3 Results</p> <p>10.6.1.4 Case study discussion</p> <p>10.7 Conclusions</p> <p><b>11        Remote sensing and GIS for ephemeral wetland monitoring and sustainability in southern Mauritania</b></p> <p><i>Tara Shine and Victor Mesev</i></p> <p>11.1 Introduction</p> <p>11.1.1 Ephemeral wetlands</p> <p>11.1.2 Remote sensing of ephemeral wetlands</p> <p>11.2 Ephemeral wetlands in Mauritania</p> <p>11.2.1 Data and processing</p> <p>11.2.2 Results</p> <p>11.2.3 Implications for management</p> <p>11.3 Conclusions</p>

<p><strong>Dr. Victor Mesev</strong>, Chair, Department of Geography, Florida State University, USA.

GB