A digital elevation model (DEM) is a digital representation of ground surface topography or terrain. It is also widely known as a digital terrain model (DTM). A DEM can be represented as a raster (a grid of squares) or as a vector based triangular irregular network (TIN). DEMs are commonly built using remote sensing techniques, but they may also be built from land surveying. DEMs are used often in geographic information systems, and are the most common basis for digitally-produced relief maps. The terrain surface can be described as compromising of two different elements; random and systematic. The random (stochastic) elements are the continuous surfaces with continuously varying relief. It would take an endless number of points to describe exactly the random terrain shapes, but these can be described in practice with a network of point. It is usual to use a network that creates sloping triangles or regular quadrants.
This book examines how the methods and data sources used to generate DEMs and calculate land surface parameters have changed over the past 25 years. The primary goal is to describe the state-of-the-art for a typical digital terrain modeling workflow that starts with data capture, continues with data preprocessing and DEM generation, and concludes with the calculation of one or more primary and secondary land surface parameters. Taken as a whole, this book covers the basic theory behind the methods, the instrumentation, analysis and interpretation that are embedded in the modern digital terrain modeling workflow, the strengths and weaknesses of the various methods that the terrain analyst must choose among, typical applications of the results emanating from these terrain modeling workflows, and future directions.
This book is intended for researchers and practitioners who wish to use DEMs, land surface parameters, land surface objects and landforms in environmental projects. The book will also be valuable as a reference text for environmental scientists who are specialists in related fields and wish to integrate these kinds of digital terrain workflows and outputs into their own specialized work environments.
List of Figures x
List of Tables xiv
Preface xvi
Abbreviations xviii
1 Introduction 1
1.1 Role of DEMS 3
1.2 Role of Scale 6
1.3 Survey of Applications 12
1.4 Study Site and Software Tools 16
1.5 Structure of Book 20
2 Constructing Digital Elevation Models 23
2.1 Elevation Data Networks 23
2.2 Elevation Data Sources 29
2.2.1 Ground Surveys 31
2.2.2 Kinematic GPS Surveys 32
2.2.3 Topographic Maps 33
2.2.4 Photogrammetry Datasets 35
2.2.5 Airborne Laser Scanning Datasets 36
2.2.6 Interferometric Synthetic Aperture Radar Datasets 37
2.2.7 Shuttle Radar Topographic Mission DEMs 38
2.2.8 Advanced Spaceborne Thermal Emission and Reflectance Radiometer DEMs 40
2.2.9 WorldDEM Datasets 43
2.3 Fitness?]For?]Use 43
2.4 Data Preprocessing and DEM Construction 44
2.5 US National Elevation Dataset 50
3 Calculating Land Surface Parameters 53
3.1 Primary Land Surface Parameters 54
3.1.1 Elevation and Surface Area 54
3.1.2 Slope, Aspect, and Curvature 59
3.1.3 Slope Direction and Width 69
3.1.4 Flow Accumulation 100
3.1.5 Elevation Residuals 105
3.1.6 Statistical Parameters 109
3.1.7 Upslope Parameters 113
3.1.8 Downslope Parameters 114
3.1.9 Visibility and Visual Exposure 114
3.2 Secondary Land Surface Parameters 115
3.2.1 Water Flow and Soil Redistribution 116
3.2.2 Energy and Thermal Regimes 135
3.3 Final Comments 148
4 Delineating Land Surface Objects and Landforms 150
4.1 Extracting and Classifying Specific Landform Elements 152
4.1.1 Fuzzy Concepts and Fuzzy Classification Methods 154
4.2 Extraction and Classification of Land Surface Objects Based on Flow Variables 158
4.2.1 Drainage Networks and Channel Attributes 159
4.2.2 Basin Boundaries and Attributes 164
4.3 Extracting and Classifying Specific (Fuzzy) Landforms 165
4.4 Extracting and Classifying Repeating Landform Types 168
4.5 Discrete Geomorphometry: Coupling Multiscale Pattern Analysis and Object Delineation 174
5 Measuring Error and Uncertainty 179
5.1 Identification and Treatment of Error and Uncertainty 180
5.1.1 Error 182
5.1.2 Uncertainty 194
5.2 Fitness?]for?]Use Revisited 199
5.2.1 Predictive Vegetation Modeling 199
5.2.2 Modeling Soil Erosion and Deposition 203
5.2.3 Numerical Simulations of Landscape Development 205
5.2.4 Modeling SoilWaterVegetation Interactions 207
5.2.5 Modeling Global Wetlands 209
5.3 Multiscale Analysis and Cross?]scale Inference 214
5.4 The US National Water Model 223
6 Terrain Modeling Software and Services 228
6.1 Changes in Data Capture and Computing Systems 230
6.2 Esris ArcGIS Ecosystem 234
6.3 Third?]party Esri Add?]ons 244
6.3.1 ArcGIS Geomorphometry Toolbox 244
6.3.2 ArcGIS Geomorphometry and Gradient Metrics Toolbox 245
6.3.3 ArcGeomorphometry Toolbox 246
6.4 Other Software Choices 248
6.4.1 GRASS 248
6.4.2 ILWIS 250
6.4.3 LandSerf 251
6.4.4 MicroDEM 252
6.4.5 QGIS 253
6.4.6 RiverTools 254
6.4.7 SAGA 255
6.4.8 TauDEM 257
6.4.9 Whitebox GAT 258
6.5 Future Trends 259
7 Conclusions 261
7.1 Current State of the Art 263
7.2 Future Needs and Opportunities 269
7.2.1 Finding Ways to Use Provenance, Credibility, and Digital Terrain Modeling Application context Knowledge 269
7.2.2 Rediscovering and Using What We Already Know! 270
7.2.3 Developing New Digital Terrain Methods 272
7.2.4 Clarifying and Strengthening the Role of Theory 274
7.2.5 Developing High?]fidelity, Multi?]resolution Digital Elevation Models 275
7.2.6 Developing and Embracing New Visualization Opportunities 275
7.2.7 Adopting and Using New Information Technologies and Workflows 276
7.2.8 Solving Wicked Problems of Varying Magnitudes 277
7.3 Call To Action 278
References 279
Index 333
