Dr. Victor Mesev. Chair, Department of Geography, Florida State University, USA

• Series Foreword. 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.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 Conclusions.3.5.1 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. 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 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.8.3.2.1 High-density urban use.8.3.2.2 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.9.6.4 Deriving remote sensing measures of urban morphology in Los Angeles.9.6.4.1 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.9.6.7.1 Statistical models.9.6.7.2 Results of correlation between vulnerability and wealth.9.6.7.2 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.Index.

Dr. Victor Mesev. Chair, Department of Geography, Florida State University, USA

• Series Foreword. 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.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 Conclusions.3.5.1 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. 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 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.8.3.2.1 High-density urban use.8.3.2.2 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.9.6.4 Deriving remote sensing measures of urban morphology in Los Angeles.9.6.4.1 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.9.6.7.1 Statistical models.9.6.7.2 Results of correlation between vulnerability and wealth.9.6.7.2 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.Index.

Dr. Victor Mesev. Chair, Department of Geography, Florida State University, USA

• Series Foreword. 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.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 Conclusions.3.5.1 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. 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 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.8.3.2.1 High-density urban use.8.3.2.2 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.9.6.4 Deriving remote sensing measures of urban morphology in Los Angeles.9.6.4.1 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.9.6.7.1 Statistical models.9.6.7.2 Results of correlation between vulnerability and wealth.9.6.7.2 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.Index.