Integration of GIS and Remote Sensing

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.

Spis treści:
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     0;     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
2.4.3.1 Line errors
2.4.3.2 Confidence region for line segments
2.4.3.3 Positional uncertainty of boundary line features
2.4.3.4 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
3.4.2.1 Fuzzy–based framework retaining accuracy information
3.4.2.2 Non–parametric approaches
3.4.2.3 Knowledge–based approaches
3.5 Conclusion s
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
4.2.1.1 Raster imagery and the RF model
4.2.1.2 Scales of measurement in remotely sensed imagery
4.2.2 Vector data
4.2.2.1 Vector data and the object–based model
4.2.2.2 Scales of measurement
4.3 Scales of spatial variation
4.3.1 Spatial variation in raster data
4.3.1.1 Characterizing scales of spatial variation
4.3.1.2 Characterizing error
4.3.1.3 Upscaling and downscaling
4.3.2 Scales of variation in vector data
4.3.3 Processes in the environment
4.3.3.1 Processes and forms
4.3.3.2 Process modelling
4.3.3.3 Scales of representation
4.4 Remote sensing and GIS data integration
4.4.1 Overlay and regression
4.4.1.1 Scales of measurement
4.4.1.2 Transformation
4.4.1.3 Geometric error
4.4.2 Remote sensing classification of land cover
4.4.1.1 Per–field classification
4.4.1.2 Soft classification and subpixel allocation
4.4.1.3 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
5.4.2.1 Land use classification based on geostatistics
5.4.2.2 Land use classification based on spatial metrics
5.4.2.3 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
7.4.3.1 Population segregation analysis
7.4.3.2 Housing price modelling
7.5 Advantages and limitations of remote sensing technologies in socio–economic applications
7.5.1 Socio–economic information estimati