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0 (hodnocen0 x )

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BK

Boca Raton : CRC Press, c2005

xvi, 323 s. : il. ; 24 cm

ISBN 0-415-32462-9 (váz.)

Obsahuje tabulky a grafy

Bibliografie na s. 307-318, rejstřík

000176272

Preface xv // 1 Introduction 1 // 1.1 Representation of Digital Terrain Surfaces 1 // 1.1.1 Representation of Terrain Surfaces 1 // 1.1.2 Representation of Digital Terrain Surfaces 4 // 1.2 Digital Terrain Models 4 // 1.2.1 The Concept of Model and Mathematical Models 4 // 1.2.2 The Terrain Model and the Digital Terrain Model 6 // 1.2.3 Digital Elevation Models and Digital Terrain Models 7 // 1.3 Digital Terrain Modeling 9 // 1.3.1 The Process of Digital Terrain Modeling 9 // 1.3.2 Development of Digital Terrain Modeling 9 // 1.4 Relationships Between Digital Terrain Modeling and // Other Disciplines 11 // 2 Terrain Descriptors and Sampling Strategies 13 // 2.1 General (Qualitative) Terrain Descriptors 13 // 2.2 Numeric Terrain Descriptors 14 // 2.2.1 Frequency Spectrum 14 // 2.2.2 Fractal Dimension 15 // 2.2.3 Curvature 16 // 2.2.4 Covariance and Auto-Correlation 17 // 2.2.5 Semivariogram 17 // 2.3 Terrain Roughness Vector: Slope, Relief, and Wavelength 18 // 2.3.1 Slope, Relief, and Wavelength as a Roughness Vector 18 // 2.3.2 The Adequacy of the Terrain Roughness Vector for // DTM Purposes 19 // 2.3.3 Estimation of Slope 20 // 2.4 Theoretical Basis for Surface Sampling 21 // 2.4.1 Theoretical Background for Sampling 21 // 2.4.2 Sampling from Different Points of View 22 // 2.5 Sampling Strategy for Data Acquisition 24 // 2.5.1 Selective Sampling: Very Important Points plus // Other Points 24 // 2.5.2 Sampling with One Dimension Fixed: Contouring and Profiling 25 // 2.5.3 Sampling with Two Dimensions Fixed: Regular Grid and // Progressive Sampling 25 // 2.5.4 Composite Sampling: An Integrated Strategy 26 // 2.6 Attributes of Sampled Source Data 26 // 2.6.1 Distribution of Sampled Source Data 26 // 2.6.2 Density of Sampled Source Data 28 // 2.6.3 Accuracy of Sampled Source Data 28 // 3 Techniques for Acquisition of DTM Source Data 31 // 3.1 Data Sources for Digital Terrain Modelling 31 //

3.1.1 The Terrain Surface as a Data Source 31 // 3.1.2 Aerial and Space Images 32 // 3.1.3 Existing Topographic Maps 34 // 3.2 Photogrammetry 35 // 3.2.1 The Development of Photogrammetry 35 // 3.2.2 Basic Principles of Photogrammetry 36 // 3.3 Radargrammetry and SAR Interferometry 39 // 3.3.1 The Principle of Synthetic Aperture // Radar Imaging 40 // 3.3.2 Principles of Interferometric SAR 43 // 3.3.3 Principles of Radargrammetry 48 // 3.4 Airborne Laser Scanning (LIDAR) 50 // 3.4.1 Basic Principle of Airborne Laser Scanning 53 // 3.4.2 From Laser Point Cloud to DTM 55 // 3.5 Cartographic Digitization 56 // 3.5.1 Line-Following Digitization 56 // 3.5.2 Raster Scanning 57 // 3.6 GPS for Direct Data Acquisition 58 // 3.6.1 The Operation of GPS 58 // 3.6.2 The Principles of GPS Measurement 60 // 3.6.3 The Principles of Traditional // Surveying Techniques 61 // 3.7 A Comparison between DTM Data from Different Sources 62 // 4 Digital Terrain Surface Modeling 65 // 4.1 Basic Concepts of Surface Modeling 65 // 4.1.1 Interpolation and Surface Modeling 65 // 4.1.2 Surface Modeling and DTM Networks 66 // 4.1.3 Surface Modeling Function: General Polynomial 66 // 4.2 Approaches for Digital Terrain Surface Modeling 67 // 4.2.1 Surface Modeling Approaches: A Classification 68 // 4.2.2 Point-Based Surface Modeling 68 // CONTENTS // vii // 4.2.3 Triangle-Based Surface Modeling 69 // 4.2.4 Grid-Based Surface Modeling 70 // 4.2.5 Hybrid Surface Modeling 71 // 4.3 The Continuity of DTM Surfaces 72 // 4.3.1 The Characteristics of DTM Surfaces: A Classification 72 // 4.3.2 Discontinuous DTM Surfaces 72 // 4.3.3 Continuous DTM Surfaces 73 // 4.3.4 Smooth DTM Surfaces 74 // 4.4 Triangular Network Formation for Surface Modeling 75 // 4.4.1 Triangular Regular Network Formation from Regularly Distributed Data 75 //

4.4.2 Triangular Irregular Network Formation from Regularly Distributed Data 77 // 4.4.3 Triangular Irregular Network Formation from Irregularly Distributed Data 79 // 4.4.4 Triangular Irregular Network Formation from Specially Distributed Data 80 // 4.5 Grid Network Formation for Surface Modeling 80 // 4.5.1 Coarser Grid Network Formation from Finer Grid Data: Resampling 81 // 4.5.2 Grid Network Formation from Randomly Distributed Data 82 // 4.5.3 Grid Network Formation from Contour Data 83 // 5 Generation of Triangular Irregular Networks 87 // 5.1 Triangular Irregular Network Formation: Principles 87 // 5.1.1 Approaches for Triangular Irregular Network Formation 87 // 5.1.2 Principles of Triangular Irregular Network Formation 88 // 5.2 Vector-Based Static Delaunay Triangulation 90 // 5.2.1 Selection of a Starting Point for Delaunay // Triangulation 90 // 5.2.2 Searching for a Point to Form a New Triangle 92 // 5.2.3 The Process of Delaunay Triangulation 93 // 5.3 Vector-Based Dynamic Delaunay Triangulation 94 // 5.3.1 The Principle of ?owyer-Watson Algorithm for Dynamic Triangulation 94 // 5.3.2 Walk-Through Algorithm for Locating the Triangle Containing a Point 95 // 5.3.3 Numerical Criterion for Edge Swapping 97 // 5.3.4 Removal of a Point from the Delaunay // Triangulation 98 // 5.4 Constrained Delaunay Triangulation 99 // 5.4.1 Constraints for Delaunay Triangulation: The Issue // and Solutions 99 // 5.4.2 Delaunay Triangulation with Constraints 101 // 5.5 Triangulation from Contour Data with Skeletonization 102 // 5.5.1 Extraction of Skeleton Lines from Contour Map 103 // 5.5.2 Height Estimation for Skeleton Points 104 // 5.5.3 Triangulation from Contour Data with Skeletons 106 // 5.6 Delaunay Triangulations via Voronoi Diagrams 107 // 5.6.1 Derivation of Delaunay Triangulations from Voronoi Diagrams 108 //

5.6.2 Vector-Based Algorithms for the Generation of Voronoi Diagram 108 // 5.6.3 Raster-Based Algorithms for the Generation of Voronoi Diagram 111 // 6 Interpolation Techniques for Terrain Surface Modeling 115 // 6.1 Interpolation Techniques: An Overview 115 // 6.2 Area-Based Exact Fitting of Linear Surfaces 117 // 6.2.1 Simple Linear Interpolation 117 // 6.2.2 Bilinear Interpolation 117 // 6.3 Area-Based Exact Fitting of Curved Surface 119 // 6.3.1 Bicubic Spline Interpolation 119 // 6.3.2 Multi-Surface Interpolation (Hardy Method) 120 // 6.4 Area-Based Best Fitting of Surfaces 123 // 6.4.1 Least-Squares Fitting of a Local Surface 123 // 6.4.2 Least-Squares Fitting of Finite Elements 126 // 6.5 Point-Based Moving Averaging 127 // 6.5.1 The Principle of Point-Based Moving Averaging 127 // 6.5.2 Searching for Neighbor Points 128 // 6.5.3 Determination of Weighting Functions 129 // 6.6 Point-Based Moving Surfaces 130 // 6.6.1 Principles of Moving Surfaces 131 // 6.6.2 Selection of Points 131 // 7 Quality Control in Terrain Data Acquisition 133 // 7.1 Quality Control: Concepts and Strategy 133 // 7.1.1 A Simple Strategy for Quality Control in Digital // Terrain Modeling 133 // 7.1.2 Sources of Error in DTM Source (Raw) Data 134 // 7.1.3 Types of Error in DTM Source Data 134 // 7.2 On-Line Quality Control in Photogrammetric Data Acquisition 135 // 7.2.1 Superimposition of Contours Back to the // Stereo Model . 135 // 7.2.2 Zero Stereo Model from Orthoimages 135 // 7.2.3 Trend Surface Analysis 136 // 7.2.4 Three-Dimensional Perspective View for Visual Inspection 136 // 7.3 Filtering of the Random Errors of the Original Data 136 // 7.3.1 The Effect of Random Noise on the Quality of DTM Data 137 // 7.3.2 Low-Pass Filter for Noise Filtering 139 // 7.3.3 Improvement of DTM Data Quality by Filtering 140 // 7.3.4 Discussion: When to Apply a Low-Pass Filtering 141 //

7.4 Detection of Gross Errors in Grid Data Based on Slope Information 142 // 7.4.1 Gross Error Detection Using Slope Information: An // Introduction 143 // 7.4.2 General Principle of Gross Error Detection Based on an // Adaptive Threshold 143 // 7.4.3 Computation of an Adaptive Threshold 145 // 7.4.4 Detection of Gross Error and Correction of a Point 146 // 7.4.5 A Practical Example 147 // 7.5 Detection of Isolated Gross Errors in Irregularly // Distributed Data 147 // 7.5.1 Three Approaches for Developing Algorithms for Gross // Error Detection 148 // 7.5.2 General Principle Based on the Pointwise Algorithm 149 // 7.5.3 Range of Neighbors (Size of Window) 149 // 7.5.4 Calculating the Threshold Value and Suspecting a Point 150 // 7.5.5 A Practical Example 150 // 7.6 Detection of a Cluster of Gross Errors in Irregularly // Distributed Data 151 // 7.6.1 Gross Errors in Cluster: The Issue 151 // 7.6.2 The Algorithm for Detecting Gross Errors in Clusters 153 // 7.6.3 A Practical Example 154 // 7.7 Detection of Gross Errors Based on Topologie Relations of Contours 155 // 7.7.1 Gross Errors in Contour Data: An Example 155 // 7.7.2 Topological Relations of Contours for Gross Error Detection 156 // 8 Accuracy of Digital Terrain Models 159 // 8.1 DTM Accuracy Assessment: An Overview 159 // 8.1.1 Approaches for DTM Accuracy Assessment 159 // 8.1.2 Distributions of DTM Errors 160 // 8.1.3 Measures for DTM Accuracy 161 // 8.1.4 Factors Affecting DTM Accuracy 163 // 8.2 Design Considerations for Experimental Tests on DTM Accuracy 165 // 8.2.1 Strategies for Experimental Tests 165 // 8.2.2 Requirements for Checkpoints in Experimental Tests 166 // 8.3 Empirical Models for the Accuracy of the DTM Derived from Grid Data 170 // 8.3.1 Three ISPRS Test Data Sets 170 // 8.3.2 Empirical Models for the Relationship between DTM Accuracy and Sampling Intervals 170 //

8.3.3 Empirical Models for DTM Accuracy Improvement with // the Addition of Feature Data 172 // 8.4 Theoretical Models of DTM Accuracy Based on Slope and // Sampling Interval 173 // 8.4.1 Theoretical Models for DTM Accuracy: An Overview 174 // 8.4.2 Propagation of Errors from DTM Source Data to // the DTM Surface 178 // 8.4.3 Accuracy Loss Due to Linear Representation of Terrain // Surface 180 // 8.4.4 Mathematical Models of the Accuracy of DTMs Linearly Constructed from Grid Data 186 // 8.5 Empirical Model for the Relationship between Grid and Contour Intervals 188 // 8.5.1 Empirical Model for the Accuracy of DTMs Constructed from Contour Data 188 // 8.5.2 Empirical Model for the Relationship between Contour and Grid Intervals 189 // 9 Multi-Scale Representations of Digital Terrain Models 191 // 9.1 Multi-Scale Representations of DTM: An Overview 191 // 9.1.1 Scale as an Important Issue in Digital Terrain Modeling 191 // 9.1.2 Transformation in Scale: An Irreversible Process in Geographical Space 192 // 9.1.3 Scale, Resolution, and Simplification of Representations 194 // 9.1.4 Approaches for Multi-Scale Representations 195 // 9.2 Hierarchical Representation of DTM at Discrete Scales 196 // 9.2.1 Pyramidal Structure for Hierarchical Representation 196 // 9.2.2 Quadtree Structure for Hierarchical Representation 198 // 9.3 Metric Multi-Scale Representation of DTM at Continuous Scales: Generalization 200 // 9.3.1 Requirements for Metric Multi-Scale Representation of DTM 200 // 9.3.2 A Natural Principle for DTM Generalization 200 // 9.3.3 DTM Generalization Based on the Natural Principle 202 // 9.4 Visual Multi-Scale Representation of DTM at Continuous Scales: View-Dependent LOD 205 // 9.4.1 Principles for View-Dependent LOD 205 // 9.4.2 Typical Algorithms for View-Dependent LOD for DTM Data 207 // 9.5 Multi-Scale DTM at a National Level 208 //

9.5.1 Multi-Scale DTM in China 209 // 9.5.2 Multi-Scale DTM in the United States 209 // 10 Management of DTM Data 211 // 10.1 Strategies for management of DTM data 211 // 10.1.1 Strategy for Making DTM Data Management Operational 211 // 10.1.2 Strategy for Using Databases for DTM Data Management 212 // 10.2 Management of DTM Data with Files 213 // 10.2.1 File Structure for Grid DTM 213 // 10.2.2 File Structure for TIN DTM 214 // 10.2.3 File Structure for Additional Terrain Feature Data 216 // 10.3 Management of DTM Data with Spatial Databases 217 // 10.3.1 Organization of Tables for Grid DTM Data 218 // 10.3.2 Organization of Tables for TIN DTM Data 221 // 10.3.3 Organization of Tables for Additional Terrain Feature Data 223 // 10.3.4 Organization of Tables for Metadata 225 // 10.4 Compression of DTM Data 226 // 10.4.1 Concepts and Approaches for DTM Data Compression 226 // 10.4.2 Huffman Coding 227 // 10.4.3 Differencing Followed by Coding 228 // 10.5 Standards for DTM Data Format 229 // 10.5.1 Concepts and Principles of DTM Data Standards 230 // 10.5.2 Standards for DTM Data Exchange of the United States 231 // 10.5.3 Standards for DTM Data Exchange of China 231 // 11 Contouring from Digital Terrain Models 233 // 11.1 Approaches for Contouring from DTM 233 // 11.2 Vector-Based Contouring from Grid DTM 233 // 11.2.1 Searching for Contour Points 234 // 11.2.2 Interpolation of Contour Points 235 // 11.2.3 Tracing Contour Lines 236 // 11.2.4 Smoothing Contour Lines 238 // 11.3 Raster-Based Contouring from Grid DTM 238 // 11.3.1 Binary and Edge Contouring 239 // 11.3.2 Gray-Tone Contouring 241 // 11.4 Vector-Based Contouring from Triangulated DTM 241 // 11.5 Stereo Contouring from Grid DTM 243 // 11.5.1 The Principle of Stereo Contouring 243 // 11.5.2 Generation of Stereomate for Contour Map 245 // 12 Visualization of Digital Terrain Models 247 //

12.1 Visualization of Digital Terrain Models: An Overview 247 // 12.1.1 Variables for Visualization 247 // 12.1.2 Approaches for the Visualization of DTM Data 250 // 12.2 Image-Based 2-D DTM Visualization 250 // 12.2.1 Slope Shading and Hill Shading 251 // 12.2.2 Height-Based Coloring 252 // 12.3 Rendering Technique for Three-Dimensional DTM Visualization 253 // 12.3.1 Basic Principles of Rendering 253 // 12.3.2 Graphic Transformations 254 // 12.3.3 Visible Surfaces Identification 256 // 12.3.4 The Selection of an Illumination Model 257 // 12.3.5 Gray Value Assignment for Graphics Generation 259 // 12.4 Texture Mapping for Virtual Landscape Generation // 12.4.1 Mapping Texture onto DTM Surfaces // 12.4.2 Mapping Other Attributes onto DTM Surfaces // 12.5 Animation Techniques for DTM Visualization // 12.5.1 Principles of Animation // 12.5.2 Seamless Pan-View on DTM in a Large Area // 12.5.3 “Fly-Through” and “Walk-Through” for DTM Visualization // 13 Interpretation of Digital Terrain Models // 13.1 DTM Interpretation: An Overview // 13.2 Geometric Terrain Parameters // 13.2.1 Surface and Projection Areas // 13.2.2 Volume // 13.3 Morphological Terrain Parameters // 13.3.1 Slope and Aspect // 13.3.2 Plan and Profile Curvatures // 13.3.3 Rate of Change in Slope and Aspect // 13.3.4 Roughness Parameters // 13.4 Hydrological Terrain Parameters // 13.4.1 Flow Direction // 13.4.2 Flow Accumulation and Flow Line // 13.4.3 Drainage Network and Catchments // 13.4.4 Multiple Direction Flow Modeling: A Discussion // 13.5 Visibility Terrain Parameters // 13.5.1 Line-of-Sight: Point-to-Point Visibility // 13.5.2 Viewshed: Point-to-Area Visibility // 14 Applications of Digital Terrain Models // 14.1 Applications in Civil Engineering // 14.1.1 Highway and Railway Design // 14.1.2 Water Conservancy // 14.2 Applications in Remote Sensing and Mapping //

14.2.1 Orthoimage Generation // 14.2.2 Remote Sensing Image Analysis // 14.3 Applications in Military Engineering // 14.3.1 Flight Simulation // 14.3.2 Virtual Battlefield // 14.4 Applications in Resources and Environment // 14.4.1 Wind Field Models for Environmental Study // 14.4.2 Sunlight Model for Climatology // 14.4.3 Flood Simulation // 14.4.4 Agriculture Management // 14.5 Marine Navigation // 14.6 Other Applications // 15 Beyond Digital Terrain Modeling 297 // 15.1 Digital Terrain Modeling with Complex Construction 297 // 15.1.1 Manual Addition of Constructions on Terrain Surface 297 // 15.1.2 Semiautomated Modification of the Terrain Surface 298 // 15.2 Digital Terrain Modeling on the Sphere 300 // 15.2.1 Generation of TIN and Voronoi Diagram on Sphere 300 // 15.2.2 Voronoi Diagram for Modeling Changes in Sea Level // on Sphere 301 // 15.3 Three-Dimensional Volumetric Modeling 302 // Epilogue 305 // References 307 // Index 319