Integration of GIS and remote sensing (Record no. 17137)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 07942nam a2200217Ia 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0470864109 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.3678 |
| Item number | MES |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mesev, Victor Ed. |
| 245 ## - TITLE STATEMENT | |
| Title | Integration of GIS and remote sensing |
| Medium | Book |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Chichester,West Sussex etc |
| Name of publisher, distributor, etc | John Wiley & Sons |
| Date of publication, distribution, etc | 2007 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvi,296p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Mastering GIS : technology, applications and management series |
| 9 (RLIN) | 61240 |
| 500 ## - GENERAL NOTE | |
| General note | CONTENTS Series Foreword xi Preface xiii List of Contributors xv 1. CIS and remote sensing integration: in search of a definition 1 Victor Mesev and Alexandra Walrath 1.1. Introduction 1 1.2. In search of a definition 2 1.2.1. Evolutionary integration 4 1.2.2. Methodological integration 5 1.3. Outline of the book 8 1.4. Conclusions 13 2. Integration taxonomy and uncertainty 17 Manfred Ehlers 2.1. Introduction 17 2.2. Taxonomy issues 19 2.2.1. Taxonomy of CIS operators 19 2.2.2. Taxonomy of image analysis operators in remote sensing 20 2.2.3. An integrated taxonomy 20 2.3. Uncertainty issues 22 2.3.1. Uncertainty in geographic information 22 2.3.2 Uncertainty in the integration of CIS and remote sensing 23 2.4. Modelling positional and thematic error in the integration of remote sensing and GIS 27 2.4.1. Positional and thematic uncertainties 27 2.4.2. Problem formulation 28 2.4.3. Modelling positional uncertainty 29 2.4.4. Thematic uncertainties of a classified image 34 2.4.5. Modelling the combined positional and thematic uncertainties 35 2.5. Conclusions 38 3. Data fusion related to CIS and remote sensing 43 Poolo Gamba and Fabio Dell'Acqua 3.1. Introduction 43 3.2. Why do we need CIS-remote sensing fusion? 43 3.2.1. Remote sensing output to CIS 44 3.2.2. CIS input to remote sensing interpretation algorithms 45 3.2.3. Example: urban planning check and update 46 3.3. Problems in GIS-remote sensing data fusion 47 3.3.1. Lack of consistent standards 48 3.3.2. Inconsistency of GIS-remote sensing accuracy, legends and scales 49 3.3.3. Different nature of the two sources 51 3.3.4. Need for information rather than data fusion 53 3.3.5. Example: population mapping through remote sensing 54 3.4. Present and future solutions 55 3.4.1. Multiscale analysis 55 3.4.2. Fusion techniques 57 3.5. Conclusions 60 3.5.1. Integration of remote sensing and GIS into a change detection module 61 4. The importance of scale in remote sensing and GIS and its implications for data integration 69 Peter M. Atkinson 4.1. Introduction 69 4.2. Data models and scales of measurement 70 4.2.1. Raster imagery 70 4.2.2. Vector data 74 4.3. Scales of spatial variation 75 4.3.1. Spatial variation in raster data 75 4.3.2. Scales of variation in vector data 79 4.3.3. Processes in the environment 79 4.4. Remote sensing and GIS data integration 80 4.4.1. Overlay and regression 80 4.4.2. Remote sensing classification of land cover 84 4.5. Conclusion 87 5. Of patterns and processes: spatial metrics and geostatistics in urban analysis 93 XiaoHang Liu and Martin Herald 5.1. Introduction 93 5.2. Geostatistics 95 5.3. Spatial metrics 96 5.4. Examples 100 5.4.1. Data preparation 100 5.4.2. Linkage from land cover to land use 103 5.4.3. Linking urban form to population density 107 5.4.5. Linking characteristics of spatial patterns and processes 109 5.5. Conclusion 112 6. Using remote sensing and GIS integration to identify spatial characteristics of sprawl at the building-unit level 117 John Hasse 6.1. Introduction 117 6.2. Sprawl in the remote sensing and GIS literature 118 6.2.1. Definitions of sprawl 119 6.2.2. Spatial characteristics of sprawl at a metropolitan level 122 6.2.3. Spatial characteristics of sprawl at a submetropolitan level 125 6.3. Integrating remote sensing and GIS for sprawl research 127 6.4. Spatial characteristics of sprawl at a building-unit level 133 6.5. A practical building-unit level model for analysing sprawl 135 6.5.1. Urban density 138 6.5.2. Leapfrog 138 6.5.3. Segregated land use 140 6.5.4. Highway strip 141 6.5.5. Community node inaccessibility 141 6.5.6. Normalizing municipal sprawl indicator measures 142 6.6. Future benefits of integrating remote sensing and GIS in sprawl research 143 7. Remote sensing applications in urban socio-economic analysis 149 Changshan Wu 7.1. Introduction 149 7.2. Principles of urban socio-economic studies using remote sensing technologies 150 7.3. Socio-economic information estimation 153 7.3.1. Population estimation 153 7.3.2. Employment estimation 155 7.3.3. GDP estimation 155 7.3.4. Electrical power consumption estimation 156 7.4. Socio-economic activity modelling 157 7.4.1. Population interpolation 157 7.4.2. Socio-economic index generation 158 7.4.3. Understanding and modelling socio-economic phenomena 159 7.5. Advantages and limitations of remote sensing technologies in socio-economic applications 167 7.5.1. Socio-economic information estimation 167 7.5.2. Socio-economic information modelling 168 7.6. Conclusions 168 8. Integrating remote sensing, CIS and spatial modelling for sustainable urban growth management 173 Xiaojun Yang 8.1. Introduction 173 8.2. Research methodology 175 8.2.1. Study area 176 8.2.2. Data acquisition and collection 176 8.2.3. Satellite image processing 178 8.2.4. Change analysis 180 8.2.5. Spatial statistical analysis 181 8.2.6. Dynamic spatial modelling 182 8.3. Results and discussion 184 8.3.1. Urban growth 184 8.3.2. Driving force 187 8.3.3. Future growth scenario simulation 191 8.4. Conclusions 193 9. An integrative GIS and remote sensing model for place-based urban vulnerability analysis 199 Tarek Rashed, John Weeks, Helen Coudelis and Martin Herald 9.1. Introduction 199 9.2. Analysis of urban vulnerability: what is it all about? 201 9.3. A conceptual framework for place-based analysis of urban vulnerability 202 9.4. Integrating GIS and remote sensing into vulnerability analysis 205 9.5. A CIS-remote sensing place-based model for urban vulnerability analysis 206 9.6. An illustrative example of model application ' 208 9.6.1. Study area 209 9.6.2. Data 209 9.6.3. Identifying vulnerability hot spots 210 9.6.4. Deriving remote sensing measures of urban morphology in Los Angeles 212 9.6.5. Deriving an index of wealth for Los Angeles County 216 9.6.6. Spatial filtering of variables 217 9.6.7. Generating place-based knowledge of urban vulnerability in Los Angeles 218 9.6.8. To what extent do model results conform to universal knowledge of vulnerability? 222 9.7. Conclusions 224 10. Using GIS and remote sensing for ecological mapping and monitoring 233 Jennifer A. Miller and John Rogan 10.1. Introduction 233 10.2. Integration of GIS and remote sensing in ecological research 237 10.3. CIS data used in ecological applications 237 10.3.1. Gradient analysis 238 10.3.2. Climate 240 10.3.3. Topography 241 10.4. Remotely sensed data for ecological applications 242 10.4.1. Spectral enhancements 243 10.4.2. Land cover 244 10.4.3. Habitat structure 245 10.4.4. Biophysical processes 246 10.5. Species distribution models 247 10.5.1. Biodiversity mapping 251 10.6. Change detection 253 10.6.1. Case study: using CIS and remote sensing for large-area change detection and efficient map updating 253 10.7. Conclusions 260 11. Remote sensing and CIS for ephemeral wetland monitoring and sustainability in southern Mauritania 269 Fora Shine and Victor Mesev 11.1. Introduction 269 11.1.1. Ephemeral wetlands 269 11.1.2. Remote sensing of ephemeral wetlands 270 11.2. Ephemeral wetlands in Mauritania 272 11.2.1. Data and processing 274 11.2.2. Results 279 11.2.3. Implications for management 283 11.3. Conclusions 284 Index 291 |
| 600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 61241 |
| 890 ## - Country | |
| Country | India |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Tall buildings--Design and construction |
| -- | Geographic information systems |
| 9 (RLIN) | 97685 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deforestation -- Tropics |
| 9 (RLIN) | 97686 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Forest conservation -- Tropics |
| 9 (RLIN) | 97687 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Clearing of land -- Tropics |
| 9 (RLIN) | 97688 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Withdrawn status | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Total Renewals | Full call number | Barcode | Date last seen | Date last borrowed | Price effective from | Koha item type | Public note |
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| Dewey Decimal Classification | CEPT Library | CEPT Library | 27/07/2010 | Mobel Book Distributers | 4122.50 | 14 | 7 | 621.3678 MES | 006400 | 19/03/2023 | 21/01/2023 | 30/08/2013 | Book | Status:Catalogued;Bill No:7088 |