Integration of GIS and Remote Sensing (Mastering GIS: Technology, Applications & Management)
By: Victor Mesev (author)Paperback
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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. This book is an invaluable reference for students and professionals dealing not only with GIS and remote sensing, but also computer science, civil engineering, environmental science and urban planning within the academic, governmental and commercial/business sectors.
Preface List of Contributors 1 GIS and remote sensing integration: in search of a definition Victor Mesev and Alexandra Walrath 1.1 Introduction 1.2 In search of a definition 1.2.1 Evolutionary integration 1.2.2 Methodological integration 1.3 Outline of the book 1.4 Conclusions 2 Integration taxonomy and uncertainty Manfred Ehlers 2.1 Introduction 2.2 Taxonomy issues 2.2.1 Taxonomy of GIS operators 2.2.2 Taxonomy of image analysis operators in remote sensing 2.2.3 An integrated taxonomy 2.3 Uncertainty issues 2.3.1 Uncertainty in geographic information 2.3.2 Uncertainty in the integration of GIS and remote sensing 2.4 Modelling positional and thematic error in the integration of remote sensing and GIS 2.4.1 Positional and thematic uncertainties 2.4.2 Problem formulation 2.4.3 Modelling positional uncertainty 18.104.22.168 Line errors 22.214.171.124 Confidence region for line segments 126.96.36.199 Positional uncertainty of boundary line features 188.8.131.52 Positional uncertainty of area objects 2.4.4 Thematic uncertainties of a classified image 2.4.5 Modelling the combined positional and thematic uncertainties 2.5 Conclusions 3 Data fusion related to GIS and remote sensing Paolo Gamba and Fabio Dell'Acqua 3.1 Introduction 3.2 Why do we need GIS-remote sensing fusion? 3.2.1 Remote sensing output to GIS 3.2.2 GIS input to remote sensing interpretation algorithms 3.2.3 Example: urban planning check and update 3.3 Problems in GIS-remote sensing data fusion 3.3.1 Lack of consistent standards 3.3.2 Inconsistency of GIS-remote sensing accuracy, legends and scales 3.3.3 Different nature of the two sources 3.3.4 Need for information rather than data fusion 3.3.5 Example: population mapping through remote sensing 3.4 Present and future solutions 3.4.1 Multiscale analysis 3.4.2 Fusion techniques 184.108.40.206 Fuzzy-based framework retaining accuracy information 220.127.116.11 Non-parametric approaches 18.104.22.168 Knowledge-based approaches 3.5 Conclusions 3.5.1 Integration of remote sensing and GIS into a change detection module 4 The importance of scale in remote sensing and GIS and its implications for data integration. Peter M. Atkinson 4.1 Introduction 4.2 Data models and scales of measurement 4.2.1 Raster imagery 22.214.171.124 Raster imagery and the RF model 126.96.36.199 Scales of measurement in remotely sensed imagery 4.2.2 Vector data 188.8.131.52 Vector data and the object-based model 184.108.40.206 Scales of measurement 4.3 Scales of spatial variation 4.3.1 Spatial variation in raster data 220.127.116.11 Characterizing scales of spatial variation 18.104.22.168 Characterizing error 22.214.171.124 Upscaling and downscaling 4.3.2 Scales of variation in vector data 4.3.3 Processes in the environment 126.96.36.199 Processes and forms 188.8.131.52 Process modelling 184.108.40.206 Scales of representation 4.4 Remote sensing and GIS data integration 4.4.1 Overlay and regression 220.127.116.11 Scales of measurement 18.104.22.168 Transformation 22.214.171.124 Geometric error 4.4.2 Remote sensing classification of land cover 126.96.36.199 Per-field classification 188.8.131.52 Soft classification and subpixel allocation 184.108.40.206 A note on downscaling and super-resolution mapping 4.5 Conclusion 5 Of patterns and processes: spatial metrics and geostatistics in urban analysis XiaoHang Liu and Martin Herold 5.1 Introduction 5.2 Geostatistics 5.3 Spatial metrics 5.4 Examples 5.4.1. Data preparation 5.4.2 Linkage from land cover to land use 220.127.116.11 Land use classification based on geostatistics 18.104.22.168 Land use classification based on spatial metrics 22.214.171.124 Land-use classification based on combined information 5.4.3 Linking urban form to population density 5.4.5 Linking characteristics of spatial patterns and processes 5.5 Conclusion 6 Using remote sensing and GIS integration to identify spatial characteristics of sprawl at the building-unit level John Hasse 6.1 Introduction 6.2 Sprawl in the remote sensing and GIS literature 6.2.1 Definitions of sprawl 6.2.2 Spatial characteristics of sprawl at a metropolitan level 6.2.3 Spatial characteristics of sprawl at a submetropolitan level 6.3 Integrating remote sensing and GIS for sprawl research 6.4 Spatial characteristics of sprawl at a building-unit level 6.5 A practical building-unit level model for analysing sprawl 6.5.1 Urban density 6.5.2 Leapfrog 6.5.3 Segregated land use 6.5.4 Highway strip 6.5.5 Community node inaccessibility 6.5.6 Normalizing municipal sprawl indicator measures 6.6. Future benefits of integrating remote sensing and gis in sprawl research 7 Remote sensing applications in urban socio-economic analysis Chiangshan Wu 7.1 Introduction 7.2 Principles of urban socio-economic studies using remote sensing technologies 7.3 Socio-economic information estimation 7.3.1 Population estimation 7.3.2 Employment estimation 7.3.3 GDP estimation 7.3.4 Electrical power consumption estimation 7.4 Socio-economic activity modelling 7.4.1 Population interpolation 7.4.2 Socio-economic index generation 7.4.3 Understanding and modelling socio-economic phenomena 126.96.36.199 Population segregation analysis 188.8.131.52 Housing price modelling 7.5 Advantages and limitations of remote sensing technologies in socio-economic applications 7.5.1 Socio-economic information estimation 7.5.2 Socio-economic information modelling 7.6 Conclusions 8 Integrating remote sensing, GIS and spatial modelling for sustainable urban growth management Xiaojun Yang 8.1 Introduction 8.2 Research methodology 8.2.1 Study area 8.2.2 Data acquisition and collection 8.2.3 Satellite image processing 8.2.4 Change analysis 8.2.5 Spatial statistical analysis 8.2.6 Dynamic spatial modelling 8.3 Results and discussion 8.3.1 Urban growth 8.3.2 Driving force 184.108.40.206 High-density urban use 220.127.116.11 Low-density urban use 8.3.3 Future growth scenario simulation 8.4 Conclusions 9 An integrative GIS and remote sensing model for place-based urban vulnerability analysis Tarek Rashed, John Weeks, Helen Couclelis and Martin Herold 9.1 Introduction 9.2 Analysis of urban vulnerability: what is it all about? 9.3 A conceptual framework for place-based analysis of urban vulnerability 9.4 Integrating GIS and remote sensing into vulnerability analysis 9.5 A GIS-remote sensing place-based model for urban vulnerability analysis 9.6 An illustrative example of model application 9.6.1 Study area 9.6.2 Data 9.6.3 Identifying vulnerability hot spots A 9.6.4 Deriving remote sensing measures of urban morphology in Los Angeles 18.104.22.168 MESMA 9.6.5 Deriving an index of wealth for Los Angeles County 9.6.6 Spatial filtering of variables 9.6.7 Generating place-based knowledge of urban vulnerability in Los Angeles< 22.214.171.124 Statistical models 126.96.36.199 Results of correlation between vulnerability and wealth 188.8.131.52 Results of regression models 9.6.8 To what extent do model results conform to universal knowledge of vulnerability? 9.7 Conclusions 10 Using GIS and remote sensing for ecological mapping and monitoring Jennifer Miller and John Rogan 10.1 Introduction 10.2 Integration of GIS and remote sensing in ecological research 10.3 GIS data used in ecological applications 10.3.1 Gradient analysis 10.3.2 Climate 10.3.3 Topography 10.4 Remotely sensed data for ecological applications 10.4.1 Spectral enhancements 10.4.2 Land cover 10.4.3 Habitat structure 10.4.4 Biophysical processes 10.5 Species distribution models 10.5.1 Biodiversity mapping 10.6 Change detection 10.6.1 Case study: using GIS and remote sensing for large-area change detection and efficient map updating 10.6.1.1 Study area 10.6.1.2 Data and methods 10.6.1.3 Results 10.6.1.4 Case study discussion 10.7 Conclusions 11 Remote sensing and GIS for ephemeral wetland monitoring and sustainability in southern Mauritania Tara Shine and Victor Mesev 11.1 Introduction 11.1.1 Ephemeral wetlands 11.1.2 Remote sensing of ephemeral wetlands 11.2 Ephemeral wetlands in Mauritania 11.2.1 Data and processing 11.2.2 Results 11.2.3 Implications for management 11.3 Conclusions
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