Z. Li, Q. Zhu, C. Gold. "Digital Terrain Modeling. Principles and Methodology"

Digital terrain modeling is a process to obtain desirable models of the land surface. This book has serving as the text book in this area.




Terrain models have always appealed to military personnel, planners, landscape architects, civil engineers, as well as other experts in various earth sciences. Originally, terrain models were physical models, made of rubber, plastic, clay, sand, etc. Since the later 1950s, the computer has been introduced into this area and the modeling of terrain surface has since then been carried out numerically or digitally, leading to the current discipline— digital terrain modeling. Such models have found wide applications, since its origin in the late 1950s, in various disciplines such as mapping, remote sensing, civil engineering, mining engineering, geology, geomorphology, military engineering, land planning, and communications.

Contents
Preface
1 Introduction
1.1 Representation of Digital Terrain Surfaces
1.1.1 Representation of Terrain Surfaces
1.1.2 Representation of Digital Terrain Surfaces
1.2 Digital Terrain Models
1.2.1 The Concept of Model and Mathematical Models
1.2.2 The Terrain Model and the Digital Terrain Model
1.2.3 Digital Elevation Models and Digital Terrain Models
1.3 Digital Terrain Modeling
1.3.1 The Process of Digital Terrain Modeling
1.3.2 Development of Digital Terrain Modeling
1.4 Relationships Between Digital Terrain Modeling and Other Disciplines
2 Terrain Descriptors and Sampling Strategies
2.1 General (Qualitative) Terrain Descriptors
2.2 Numeric Terrain Descriptors
2.2.1 Frequency Spectrum
2.2.2 Fractal Dimension
2.2.3 Curvature
2.2.4 Covariance and Auto-Correlation
2.2.5 Semivariogram
2.3 Terrain Roughness Vector: Slope, Relief, and Wavelength
2.3.1 Slope, Relief, and Wavelength as a Roughness Vector
2.3.2 The Adequacy of the Terrain Roughness Vector for DTM Purposes
2.3.3 Estimation of Slope
2.4 Theoretical Basis for Surface Sampling
2.4.1 Theoretical Background for Sampling
2.4.2 Sampling from Different Points of View
2.5 Sampling Strategy for Data Acquisition
2.5.1 Selective Sampling: Very Important Points plus Other Points
2.5.2 Sampling with One Dimension Fixed: Contouring and Profiling
2.5.3 Sampling with Two Dimensions Fixed: Regular Grid and Progressive Sampling
2.5.4 Composite Sampling: An Integrated Strategy
2.6 Attributes of Sampled Source Data
2.6.1 Distribution of Sampled Source Data
2.6.2 Density of Sampled Source Data
2.6.3 Accuracy of Sampled Source Data
3 Techniques for Acquisition of DTM Source Data
3.1 Data Sources for Digital Terrain Modeling
3.1.1 The Terrain Surface as a Data Source
3.1.2 Aerial and Space Images
3.1.3 Existing Topographic Maps
3.2 Photogrammetry
3.2.1 The Development of Photogrammetry
3.2.2 Basic Principles of Photogrammetry
3.3 Radargrammetry and SAR Interferometry
3.3.1 The Principle of Synthetic Aperture Radar Imaging
3.3.2 Principles of Interferometric SAR
3.3.3 Principles of Radargrammetry
3.4 Airborne Laser Scanning (LIDAR)
3.4.1 Basic Principle of Airborne Laser Scanning
3.4.2 From Laser Point Cloud to DTM
3.5 Cartographic Digitization
3.5.1 Line-Following Digitization
3.5.2 Raster Scanning
3.6 GPS for Direct Data Acquisition
3.6.1 The Operation of GPS
3.6.2 The Principles of GPS Measurement
3.6.3 The Principles of Traditional Surveying Techniques
3.7 A Comparison between DTM Data from Different Sources
4 Digital Terrain Surface Modeling
4.1 Basic Concepts of Surface Modeling
4.1.1 Interpolation and Surface Modeling
4.1.2 Surface Modeling and DTM Networks
4.1.3 Surface Modeling Function: General Polynomial
4.2 Approaches for Digital Terrain Surface Modeling
4.2.1 Surface Modeling Approaches: A Classification
4.2.2 Point-Based Surface Modeling
4.2.3 Triangle-Based Surface Modeling
4.2.4 Grid-Based Surface Modeling
4.2.5 Hybrid Surface Modeling
4.3 The Continuity of DTM Surfaces
4.3.1 The Characteristics of DTM Surfaces: A Classification
4.3.2 Discontinuous DTM Surfaces
4.3.3 Continuous DTM Surfaces
4.3.4 Smooth DTM Surfaces
4.4 Triangular Network Formation for Surface Modeling
4.4.1 Triangular Regular Network Formation from Regularly Distributed Data
4.4.2 Triangular Irregular Network Formation from Regularly Distributed Data
4.4.3 Triangular Irregular Network Formation from Irregularly Distributed Data
4.4.4 Triangular Irregular Network Formation from Specially Distributed Data
4.5 Grid Network Formation for Surface Modeling
4.5.1 Coarser Grid Network Formation from Finer Grid Data: Resampling
4.5.2 Grid Network Formation from Randomly Distributed Data
4.5.3 Grid Network Formation from Contour Data
5 Generation of Triangular Irregular Networks
5.1 Triangular Irregular Network Formation: Principles
5.1.1 Approaches for Triangular Irregular Network Formation
5.1.2 Principles of Triangular Irregular Network Formation
5.2 Vector-Based Static Delaunay Triangulation
5.2.1 Selection of a Starting Point for Delaunay Triangulation
5.2.2 Searching for a Point to Form a New Triangle
5.2.3 The Process of Delaunay Triangulation
5.3 Vector-Based Dynamic Delaunay Triangulation
5.3.1 The Principle of Bowyer–Watson Algorithm for Dynamic Triangulation
5.3.2 Walk-Through Algorithm for Locating the Triangle Containing a Point
5.3.3 Numerical Criterion for Edge Swapping
5.3.4 Removal of a Point from the Delaunay Triangulation
5.4 Constrained Delaunay Triangulation
5.4.1 Constraints for Delaunay Triangulation: The Issue and Solutions
5.4.2 Delaunay Triangulation with Constraints
5.5 Triangulation from Contour Data with Skeletonization
5.5.1 Extraction of Skeleton Lines from Contour Map
5.5.2 Height Estimation for Skeleton Points
5.5.3 Triangulation from Contour Data with Skeletons
5.6 Delaunay Triangulations via Voronoi Diagrams
5.6.1 Derivation of Delaunay Triangulations from Voronoi Diagrams
5.6.2 Vector-Based Algorithms for the Generation of Voronoi Diagram
5.6.3 Raster-Based Algorithms for the Generation of Voronoi Diagram
6 Interpolation Techniques for Terrain Surface Modeling
6.1 Interpolation Techniques: An Overview
6.2 Area-Based Exact Fitting of Linear Surfaces
6.2.1 Simple Linear Interpolation
6.2.2 Bilinear Interpolation
6.3 Area-Based Exact Fitting of Curved Surface
6.3.1 Bicubic Spline Interpolation
6.3.2 Multi-Surface Interpolation (Hardy Method)
6.4 Area-Based Best Fitting of Surfaces
6.4.1 Least-Squares Fitting of a Local Surface
6.4.2 Least-Squares Fitting of Finite Elements
6.5 Point-Based Moving Averaging
6.5.1 The Principle of Point-Based Moving Averaging
6.5.2 Searching for Neighbor Points
6.5.3 Determination of Weighting Functions
6.6 Point-Based Moving Surfaces
6.6.1 Principles of Moving Surfaces
6.6.2 Selection of Points
7 Quality Control in Terrain Data Acquisition
7.1 Quality Control: Concepts and Strategy
7.1.1 A Simple Strategy for Quality Control in Digital Terrain Modeling
7.1.2 Sources of Error in DTM Source (Raw) Data
7.1.3 Types of Error in DTM Source Data
7.2 On-Line Quality Control in Photogrammetric Data Acquisition
7.2.1 Superimposition of Contours Back to the Stereo Model
7.2.2 Zero Stereo Model from Orthoimages
7.2.3 Trend Surface Analysis
7.2.4 Three-Dimensional Perspective View for Visual Inspection
7.3 Filtering of the Random Errors of the Original Data
7.3.1 The Effect of Random Noise on the Quality of DTM Data
7.3.2 Low-Pass Filter for Noise Filtering
7.3.3 Improvement of DTM Data Quality by Filtering
7.3.4 Discussion: When to Apply a Low-Pass Filtering
7.4 Detection of Gross Errors in Grid Data Based on Slope Information
7.4.1 Gross Error Detection Using Slope Information: An Introduction
7.4.2 General Principle of Gross Error Detection Based on an Adaptive Threshold
7.4.3 Computation of an Adaptive Threshold
7.4.4 Detection of Gross Error and Correction of a Point
7.4.5 A Practical Example
7.5 Detection of Isolated Gross Errors in Irregularly Distributed Data
7.5.1 Three Approaches for Developing Algorithms for Gross Error Detection
7.5.2 General Principle Based on the Pointwise Algorithm
7.5.3 Range of Neighbors (Size of Window)
7.5.4 Calculating the Threshold Value and Suspecting a Point
7.5.5 A Practical Example
7.6 Detection of a Cluster of Gross Errors in Irregularly Distributed Data
7.6.1 Gross Errors in Cluster: The Issue
7.6.2 The Algorithm for Detecting Gross Errors in Clusters
7.6.3 A Practical Example
7.7 Detection of Gross Errors Based on Topologic Relations of Contours
7.7.1 Gross Errors in Contour Data: An Example
7.7.2 Topological Relations of Contours for Gross Error Detection
8 Accuracy of Digital Terrain Models
8.1 DTM Accuracy Assessment: An Overview
8.1.1 Approaches for DTM Accuracy Assessment
8.1.2 Distributions of DTM Errors
8.1.3 Measures for DTM Accuracy
8.1.4 Factors Affecting DTM Accuracy
8.2 Design Considerations for Experimental Tests on DTM Accuracy
8.2.1 Strategies for Experimental Tests
8.2.2 Requirements for Checkpoints in Experimental Tests
8.3 Empirical Models for the Accuracy of the DTM Derived from Grid Data
8.3.1 Three ISPRS Test Data Sets
8.3.2 Empirical Models for the Relationship between DTM Accuracy and Sampling Intervals
8.3.3 Empirical Models for DTM Accuracy Improvement with the Addition of Feature Data
8.4 Theoretical Models of DTM Accuracy Based on Slope and Sampling Interval
8.4.1 Theoretical Models for DTM Accuracy: An Overview
8.4.2 Propagation of Errors from DTM Source Data to the DTM Surface
8.4.3 Accuracy Loss Due to Linear Representation of Terrain Surface
8.4.4 Mathematical Models of the Accuracy of DTMs Linearly Constructed from Grid Data
8.5 Empirical Model for the Relationship between Grid and Contour Intervals
8.5.1 Empirical Model for the Accuracy of DTMs Constructed from Contour Data
8.5.2 Empirical Model for the Relationship between Contour and Grid Intervals
9 Multi-Scale Representations of Digital Terrain Models
9.1 Multi-Scale Representations of DTM: An Overview
9.1.1 Scale as an Important Issue in Digital Terrain Modeling
9.1.2 Transformation in Scale: An Irreversible Process in Geographical Space
9.1.3 Scale, Resolution, and Simplification of Representations
9.1.4 Approaches for Multi-Scale Representations
9.2 Hierarchical Representation of DTM at Discrete Scales
9.2.1 Pyramidal Structure for Hierarchical Representation
9.2.2 Quadtree Structure for Hierarchical Representation
9.3 Metric Multi-Scale Representation of DTM at Continuous Scales: Generalization
9.3.1 Requirements for Metric Multi-Scale Representation of DTM
9.3.2 A Natural Principle for DTM Generalization
9.3.3 DTM Generalization Based on the Natural Principle
9.4 Visual Multi-Scale Representation of DTM at Continuous Scales: View-Dependent LOD
9.4.1 Principles for View-Dependent LOD
9.4.2 Typical Algorithms for View-Dependent LOD for DTM Data
9.5 Multi-Scale DTM at a National Level
9.5.1 Multi-Scale DTM in China
9.5.2 Multi-Scale DTM in the United States
10 Management of DTM Data
10.1 Strategies for management of DTM data
10.1.1 Strategy for Making DTM Data Management Operational
10.1.2 Strategy for Using Databases for DTM Data Management
10.2 Management of DTM Data with Files
10.2.1 File Structure for Grid DTM
10.2.2 File Structure for TIN DTM
10.2.3 File Structure for Additional Terrain Feature Data
10.3 Management of DTM Data with Spatial Databases
10.3.1 Organization of Tables for Grid DTM Data
10.3.2 Organization of Tables for TIN DTM Data
10.3.3 Organization of Tables for Additional Terrain Feature Data
10.3.4 Organization of Tables for Metadata
10.4 Compression of DTM Data
10.4.1 Concepts and Approaches for DTM Data Compression
10.4.2 Huffman Coding
10.4.3 Differencing Followed by Coding
10.5 Standards for DTM Data Format
10.5.1 Concepts and Principles of DTM Data Standards
10.5.2 Standards for DTM Data Exchange of the United States
10.5.3 Standards for DTM Data Exchange of China
11 Contouring from Digital Terrain Models
11.1 Approaches for Contouring from DTM
11.2 Vector-Based Contouring from Grid DTM
11.2.1 Searching for Contour Points
11.2.2 Interpolation of Contour Points
11.2.3 Tracing Contour Lines
11.2.4 Smoothing Contour Lines
11.3 Raster-Based Contouring from Grid DTM
11.3.1 Binary and Edge Contouring
11.3.2 Gray-Tone Contouring
11.4 Vector-Based Contouring from Triangulated DTM
11.5 Stereo Contouring from Grid DTM
11.5.1 The Principle of Stereo Contouring
11.5.2 Generation of Stereomate for Contour Map
12 Visualization of Digital Terrain Models
12.1 Visualization of Digital Terrain Models: An Overview
12.1.1 Variables for Visualization
12.1.2 Approaches for the Visualization of DTM Data
12.2 Image-Based 2-D DTM Visualization
12.2.1 Slope Shading and Hill Shading
12.2.2 Height-Based Coloring
12.3 Rendering Technique for Three-Dimensional DTM Visualization
12.3.1 Basic Principles of Rendering
12.3.2 Graphic Transformations
12.3.3 Visible Surfaces Identification
12.3.4 The Selection of an Illumination Model
12.3.5 Gray Value Assignment for Graphics Generation
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
15.1 Digital Terrain Modeling with Complex Construction
15.1.1 Manual Addition of Constructions on Terrain Surface
15.1.2 Semiautomated Modification of the Terrain Surface
15.2 Digital Terrain Modeling on the Sphere
15.2.1 Generation of TIN and Voronoi Diagram on Sphere
15.2.2 Voronoi Diagram for Modeling Changes in Sea Level on Sphere
15.3 Three-Dimensional Volumetric Modeling Epilogue
References