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In an age of unprecedented proliferation of data from disparate sources the urgency is to create efficient methodologies that can optimise data combinations and at the same time solve increasingly complex application problems.
Integration of GIS and Remote Sensing explores the tremendous potential that lies along the interface between GIS and remote sensing for activating interoperable databases and instigating information interchange. It concentrates on the rigorous and meticulous aspects of analytical data matching and thematic compatibility - the true roots of all branches of GIS/remote sensing applications. However closer harmonization is tempered by numerous technical and institutional issues, including scale incompatibility, measurement disparities, and the inescapable notion that data from GIS and remote sensing essentially represent diametrically opposing conceptual views of reality.
The first part of the book defines and characterises GIS and remote sensing and presents the reader with an awareness of the many scale, taxonomical and analytical problems when attempting integration. The second part of the book moves on to demonstrate the benefits and costs of integration across a number of human and environmental applications.
PrefaceList of Contributors1 GIS and remote sensing integration: in search of a definitionVictor Mesev and Alexandra Walrath1.1 Introduction1.2 In search of a definition1.2.1 Evolutionary integration1.2.2 Methodological integration1.3 Outline of the book1.4 Conclusions2 Integration taxonomy and uncertaintyManfred Ehlers 2.1 Introduction2.2 Taxonomy issues2.2.1 Taxonomy of GIS operators2.2.2 Taxonomy of image analysis operators in remote sensing2.2.3 An integrated taxonomy2.3 Uncertainty issues2.3.1 Uncertainty in geographic information2.3.2 Uncertainty in the integration of GIS and remote sensing2.4 Modelling positional and thematic error in the integration of remote sensing and GIS2.4.1 Positional and thematic uncertainties2.4.2 Problem formulation2.4.3 Modelling positional uncertainty18.104.22.168 Line errors22.214.171.124 Confidence region for line segments126.96.36.199 Positional uncertainty of boundary line features188.8.131.52 Positional uncertainty of area objects2.4.4 Thematic uncertainties of a classified image2.4.5 Modelling the combined positional and thematic uncertainties2.5 Conclusions3 Data fusion related to GIS and remote sensingPaolo Gamba and Fabio Dell'Acqua3.1 Introduction3.2 Why do we need GIS-remote sensing fusion?3.2.1 Remote sensing output to GIS3.2.2 GIS input to remote sensing interpretation algorithms3.2.3 Example: urban planning check and update3.3 Problems in GIS-remote sensing data fusion3.3.1 Lack of consistent standards3.3.2 Inconsistency of GIS-remote sensing accuracy, legends and scales3.3.3 Different nature of the two sources3.3.4 Need for information rather than data fusion3.3.5 Example: population mapping through remote sensing3.4 Present and future solutions3.4.1 Multiscale analysis3.4.2 Fusion techniques184.108.40.206 Fuzzy-based framework retaining accuracy information220.127.116.11 Non-parametric approaches18.104.22.168 Knowledge-based approaches3.5 Conclusions3.5.1 Integration of remote sensing and GIS into a change detection module4 The importance of scale in remote sensing and GIS and its implications for data integration.Peter M. Atkinson4.1 Introduction4.2 Data models and scales of measurement4.2.1 Raster imagery22.214.171.124 Raster imagery and the RF model126.96.36.199 Scales of measurement in remotely sensed imagery4.2.2 Vector data188.8.131.52 Vector data and the object-based model184.108.40.206 Scales of measurement4.3 Scales of spatial variation4.3.1 Spatial variation in raster data220.127.116.11 Characterizing scales of spatial variation18.104.22.168 Characterizing error22.214.171.124 Upscaling and downscaling4.3.2 Scales of variation in vector data4.3.3 Processes in the environment126.96.36.199 Processes and forms188.8.131.52 Process modelling184.108.40.206 Scales of representation4.4 Remote sensing and GIS data integration4.4.1 Overlay and regression220.127.116.11 Scales of measurement18.104.22.168 Transformation22.214.171.124 Geometric error4.4.2 Remote sensing classification of land cover126.96.36.199 Per-field classification188.8.131.52 Soft classification and subpixel allocation184.108.40.206 A note on downscaling and super-resolution mapping4.5 Conclusion5 Of patterns and processes: spatial metrics and geostatistics in urban analysisXiaoHang Liu and Martin Herold5.1 Introduction5.2 Geostatistics5.3 Spatial metrics5.4 Examples5.4.1. Data preparation5.4.2 Linkage from land cover to land use220.127.116.11 Land use classification based on geostatistics18.104.22.168 Land use classification based on spatial metrics22.214.171.124 Land-use classification based on combined information5.4.3 Linking urban form to population density5.4.5 Linking characteristics of spatial patterns and processes5.5 Conclusion6 Using remote sensing and GIS integration to identify spatial characteristics of sprawl at the building-unit levelJohn Hasse6.1 Introduction6.2 Sprawl in the remote sensing and GIS literature6.2.1 Definitions of sprawl6.2.2 Spatial characteristics of sprawl at a metropolitan level6.2.3 Spatial characteristics of sprawl at a submetropolitan level6.3 Integrating remote sensing and GIS for sprawl research6.4 Spatial characteristics of sprawl at a building-unit level6.5 A practical building-unit level model for analysing sprawl6.5.1 Urban density6.5.2 Leapfrog6.5.3 Segregated land use6.5.4 Highway strip6.5.5 Community node inaccessibility6.5.6 Normalizing municipal sprawl indicator measures6.6. Future benefits of integrating remote sensing and gis in sprawl research7 Remote sensing applications in urban socio-economic analysisChiangshan Wu7.1 Introduction7.2 Principles of urban socio-economic studies using remote sensing technologies7.3 Socio-economic information estimation7.3.1 Population estimation7.3.2 Employment estimation7.3.3 GDP estimation7.3.4 Electrical power consumption estimation7.4 Socio-economic activity modelling7.4.1 Population interpolation7.4.2 Socio-economic index generation7.4.3 Understanding and modelling socio-economic phenomena126.96.36.199 Population segregation analysis188.8.131.52 Housing price modelling7.5 Advantages and limitations of remote sensing technologies in socio-economic applications7.5.1 Socio-economic information estimation7.5.2 Socio-economic information modelling7.6 Conclusions8 Integrating remote sensing, GIS and spatial modelling for sustainable urban growth managementXiaojun Yang8.1 Introduction8.2 Research methodology8.2.1 Study area8.2.2 Data acquisition and collection8.2.3 Satellite image processing8.2.4 Change analysis8.2.5 Spatial statistical analysis8.2.6 Dynamic spatial modelling8.3 Results and discussion8.3.1 Urban growth8.3.2 Driving force184.108.40.206 High-density urban use220.127.116.11 Low-density urban use8.3.3 Future growth scenario simulation8.4 Conclusions9 An integrative GIS and remote sensing model for place-based urban vulnerability analysisTarek Rashed, John Weeks, Helen Couclelis and Martin Herold9.1 Introduction9.2 Analysis of urban vulnerability: what is it all about?9.3 A conceptual framework for place-based analysis of urban vulnerability9.4 Integrating GIS and remote sensing into vulnerability analysis9.5 A GIS-remote sensing place-based model for urban vulnerability analysis9.6 An illustrative example of model application9.6.1 Study area9.6.2 Data9.6.3 Identifying vulnerability hot spots9.6.4 Deriving remote sensing measures of urban morphology in Los Angeles18.104.22.168 MESMA9.6.5 Deriving an index of wealth for Los Angeles County9.6.6 Spatial filtering of variables9.6.7 Generating place-based knowledge of urban vulnerability in Los Angeles<22.214.171.124 Statistical models126.96.36.199 Results of correlation between vulnerability and wealth188.8.131.52 Results of regression models9.6.8 To what extent do model results conform to universal knowledge of vulnerability?9.7 Conclusions10 Using GIS and remote sensing for ecological mapping and monitoringJennifer Miller and John Rogan10.1 Introduction10.2 Integration of GIS and remote sensing in ecological research10.3 GIS data used in ecological applications10.3.1 Gradient analysis10.3.2 Climate10.3.3 Topography10.4 Remotely sensed data for ecological applications10.4.1 Spectral enhancements10.4.2 Land cover10.4.3 Habitat structure10.4.4 Biophysical processes10.5 Species distribution models10.5.1 Biodiversity mapping10.6 Change detection10.6.1 Case study: using GIS and remote sensing for large-area change detection and efficient map updating10.6.1.1 Study area10.6.1.2 Data and methods10.6.1.3 Results10.6.1.4 Case study discussion10.7 Conclusions11 Remote sensing and GIS for ephemeral wetland monitoring and sustainability in southern MauritaniaTara Shine and Victor Mesev11.1 Introduction11.1.1 Ephemeral wetlands11.1.2 Remote sensing of ephemeral wetlands11.2 Ephemeral wetlands in Mauritania11.2.1 Data and processing11.2.2 Results11.2.3 Implications for management11.3 Conclusions