Keywords:
Landscape assessment -- Remote sensing -- Europe.
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (436 pages)
Edition:
1st ed.
ISBN:
9789400779693
Series Statement:
Remote Sensing and Digital Image Processing Series ; v.18
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=1783740
DDC:
554
Language:
English
Note:
Intro -- Preface -- Acknowledgements -- Contents -- Part I: Framework Conditions -- Chapter 1: Remote Sensing in Support of the Geo-information in Europe -- 1.1 How Policy Feeds into the Development of Information Services -- 1.2 Current Status and Challenges -- 1.3 Future Trends and Conclusions -- References -- Chapter 2: Global Land Cover Mapping: Current Status and Future Trends -- 2.1 Introduction -- 2.2 Status and Improvements for Land Cover Maps -- 2.2.1 Existing Land Cover Maps -- 2.2.2 What Needs to Be Improved -- 2.3 Moving Forward -- 2.3.1 Satellite Missions Allow Moving to Inclusion of Multiple Sensors, Finer Scale and Longer Time-Series Products -- 2.3.2 Novel Global Land Cover Products Are Being Developed -- 2.3.3 International Coordination and Harmonization Remain Vital -- 2.3.4 GLC Validation Is Becoming Operational -- 2.3.5 New Services and Tools -- 2.3.6 The First Global Assessments of Land Cover Change -- 2.3.7 New Users and Applications -- 2.4 Conclusion -- References -- Chapter 3: The Users´ Role in the European Land Monitoring Context -- 3.1 A Bit of History About the European Land Monitoring Process -- 3.2 Policies Influencing the Establishment of a European Land Monitoring Program -- 3.3 Which Users Have Been Involved in the European Land Monitoring Program and What Have Been Their Roles? -- 3.4 Shaping the Role of the Users in the Mid-to Long-Term -- 3.5 Current Situation and Aspects Still to Be Addressed in the Future -- References -- Chapter 4: Towards an European Land Cover Monitoring Service and High-Resolution Layers -- 4.1 GMES/Copernicus - The European Contribution to Global Environmental Monitoring -- 4.2 geoland2 -- 4.2.1 Background -- 4.2.2 The Project -- 4.3 The Continental LMCS -- 4.3.1 Service Definition Development -- 4.3.1.1 Service Specification -- 4.3.2 Expected Benefits -- References.
,
Part II: Operational European Mapping and Monitoring Services -- Chapter 5: CORINE Land Cover and Land Cover Change Products -- 5.1 Introduction -- 5.2 Technical Specification -- 5.2.1 Minimum Mapping Unit and Minimum Mapping Width -- 5.2.2 Nomenclature -- 5.3 History of CORINE Land Cover -- 5.4 Components of CORINE Land Cover Inventories -- 5.4.1 Satellite Image Acquisition and Processing -- 5.4.2 In-Situ Data -- 5.4.3 CLCC Mapping and CLC Production -- 5.4.3.1 Methodology of Change Mapping -- 5.4.3.2 Change Mapping by Means of CAPI -- 5.4.3.3 Production of the New Status Layer (CLC_year2) -- 5.4.3.4 Semi-automatic Approaches Applied in CLC -- 5.4.3.5 Quality Control -- 5.5 Validation -- 5.5.1 Data Dissemination -- 5.6 CORINE Land Cover 2012 -- 5.7 Future of CORINE Land Cover -- 5.8 Conclusions -- References -- Chapter 6: European Area Frame Sampling Based on Very High Resolution Images -- 6.1 Introduction -- 6.2 AFS Objectives and Design -- 6.3 Classification -- 6.4 Change Detection -- 6.5 Conclusion -- References -- Chapter 7: European Forest Monitoring Approaches -- 7.1 Introduction -- 7.2 Legacy of European Forest Monitoring -- 7.3 Pan-European Products from the European Forest Data Centre -- 7.4 The GMES Service Element Forest Monitoring -- 7.5 Development of Pre-operational Services: geoland2 -- 7.6 Operational Forest Monitoring: The GIO HR Forest Layer -- 7.7 Customised Applications: The Forest Downstream Sector -- 7.8 Conclusions and Outlook -- References -- Chapter 8: The European Urban Atlas -- 8.1 Overview and Main Characteristics -- 8.2 Analysis of User´s Requirements -- 8.3 Urban Atlas: Main Features -- 8.4 Methodology Description for UA Update -- 8.4.1 General Change Detection Considerations -- 8.4.2 Change Detection Algorithms -- 8.4.3 Change Detection Within the geoland2 Project -- 8.5 Conclusions -- References.
,
Part III: State of the Art Mapping Methods -- Chapter 9: A Review of Modern Approaches to Classification of Remote Sensing Data -- 9.1 Introduction -- 9.2 Classification Techniques for Hyperspectral Images -- 9.3 Classification Techniques for Very High Geometrical Resolution Images -- 9.4 Advanced Techniques for Classification of Remote Sensing Images -- 9.5 Semisupervised Learning Techniques for Classification of Remote Sensing Images -- 9.6 Active Learning Techniques for Classification of Remote Sensing Images -- 9.7 Domain Adaptation Techniques for Classification of Remote Sensing Images -- 9.8 Discussion and Conclusion -- References -- Chapter 10: Recent Advances in Remote Sensing Change Detection - A Review -- 10.1 Introduction -- 10.2 Input Data -- 10.3 Preprocessing: Radiometric and Geometric Requirements -- 10.3.1 Radiometric Requirements -- 10.3.2 Geometric Requirements -- 10.4 Change Extraction Algorithms -- 10.4.1 Algebra Methods -- 10.4.1.1 Image Differencing and Vegetation Index Differencing -- 10.4.1.2 Ratioing -- 10.4.1.3 Normalized Difference Change Detection (NDCD) -- 10.4.1.4 Regression -- 10.4.1.5 Change Vector Analysis (CVA) -- 10.4.1.6 Phase-Related SAR Change Extraction Methods -- Persistent Scatterer Interferometry (PSI) -- Coherence Change -- Differential SAR Interferometry (DInSAR) -- 10.4.1.7 SAR Backscatter-Related Change Extraction Methods -- Differential Radargrammetry -- Speckle Decorrelation -- Offset Tracking -- Speckle Tracking -- 10.4.2 Transformation-Based Methods -- 10.4.2.1 Principal Component Analysis (PCA) -- 10.4.2.2 Gramm-Schmidt Transformation -- 10.4.2.3 Iteratively-Reweighted Alteration Detection (IR-MAD) -- 10.4.2.4 Other Transformations -- 10.4.2.5 Wavelets, Curvelets and Other - Lets -- 10.4.3 Classification-Based Methods -- 10.4.3.1 Post-Classification Comparison (PCC).
,
10.4.3.2 Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) -- 10.4.3.3 Multitemporal Spectral Mixture Analysis (SMA) -- 10.4.4 Time Series Analysis (TSA) -- 10.4.4.1 Trajectory Analysis -- 10.4.4.2 Time Series Segmentation -- 10.4.4.3 Breaks for Additive Seasonal and Trend (BFAST) -- 10.4.5 Object-Based Approaches -- 10.5 Change Labeling -- 10.5.1 Categorization of Change Extraction Algorithms Regarding Their Suitability for Change Labeling -- 10.5.2 Change Labeling Approaches -- 10.5.2.1 Pre-CE-Labeling: Setting the Ground -- 10.5.2.2 Post-CE-Labeling: Labeling Change Types and Change Intensities and Interpretation of Change Extraction Results -- 10.5.2.3 Concurrent Labeling: Labeling Change Classes -- 10.6 Accuracy Assessment -- 10.7 Conclusion -- References -- Chapter 11: Synergies from SAR-Optical Data Fusion for LULC Mapping -- 11.1 Introduction -- 11.2 Background -- 11.2.1 Random Forests -- 11.2.2 imageRF Classification Software -- 11.3 Study Site and Data Set -- 11.4 Results and Discussion -- 11.4.1 Experimental Setup -- 11.4.2 Classification Results, Using Single-Source Data Sets -- 11.4.3 Classification Results Using Multisensor Data Sets -- 11.4.4 Impact of RF Parameters -- 11.5 Conclusion -- References -- Chapter 12: Application of an Object-Oriented Method for Classification of VHR Satellite Images Using a Rule-Based Approach and Texture Measures -- 12.1 Introduction -- 12.2 Satellite Data and Study Areas -- 12.3 Methodical Approach -- 12.3.1 General Assumptions -- 12.3.2 Hierarchical Classification of Land Cover Categories -- 12.3.3 Accuracy Assessment -- 12.4 Results and Discussion -- 12.5 Conclusions -- References -- Chapter 13: Remote Sensing of Vegetation for Nature Conservation -- 13.1 Introduction -- 13.1.1 Which Remote Sensing Data Is Useful? -- 13.1.2 On the Uniqueness of Plant Species Appearances.
,
13.1.3 Species Mapping - Looking for a Needle in the Haystack? -- 13.1.4 Mapping Vegetation Types and Ecotones -- 13.1.5 Diversity and Different Ways to Tackle It -- 13.2 Synthesis -- References -- Chapter 14: Modeling Urban Sprawl -- 14.1 Introduction -- 14.2 Urban Sprawl -- 14.2.1 Patterns, Processes, Problems, and Policies -- 14.2.2 Trends of Urban Sprawl in European Cities -- 14.3 Models of Urban Growth and Urban Dynamics -- 14.3.1 Scope and Objectives of Urban Growth Models -- 14.3.2 Theories and Modeling Techniques -- 14.3.2.1 Cellular Automata (CA) -- 14.3.2.2 Multi Agent Systems (MAS) -- 14.3.2.3 Statistical Regression (SR) -- 14.3.2.4 Spatial Optimization -- 14.3.2.5 Machine Learning (ML) -- 14.3.3 Remote Sensing and Models of Urban Growth -- 14.4 Example: Combined Use of Two Land-Use Change Models -- 14.5 Perspectives in Urban Growth Modeling -- References -- Part IV: National Practice Examples -- Chapter 15: Land Information System Austria (LISA) -- 15.1 Current Status of Land Monitoring in Austria -- 15.2 LISA - Development of a Countrywide Approach -- 15.2.1 Why LISA? -- 15.2.2 National Spatial Data Infrastructure -- 15.2.3 European Spatial Data Infrastructure -- 15.2.4 Future Potential of Very High Resolution Satellite Imagery -- 15.2.5 Object-Orientated Data Model -- 15.2.6 Land Cover Mapping -- 15.2.7 Land Use Mapping -- 15.2.8 Change Mapping -- 15.3 LISA - Selected Applications -- 15.3.1 Upscaling of LISA-Data Sets to CORINE Land Cover -- 15.3.2 Accounting of Land Reserves Available for Construction -- 15.3.3 Classification of Coverage Types in Settlements -- 15.4 From Project Status to Operational Roll-Out -- 15.5 Lessons Learnt -- References -- Chapter 16: Digital Land Cover Model for Germany - DLM-DE -- 16.1 Introduction: Background and Motivation of the DLM-DE.
,
16.1.1 Federal Structure and Land Surveying Responsibilities in Germany.
Permalink
