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Summary
Summary
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.
Author Notes
Dr. John P. Wilson is Professor of Spatial Sciences in the Dana and David Dornsife College of Letters, Arts and Sciences at the University of Southern California (USC) where he directs the Spatial Sciences institute as well as the Geographic Information Science Technology (GIST) Graduate Programs and GIS Research Laboratory, and also holds adjunct appointments as Professor in the School of Architecture and in the Viterbi School of Engineering's Departments of Computer Science and Civil Environmental Engineering.
Table of Contents
List of Figures | p. x |
List of Tables | p. xiv |
Preface | p. xvi |
Abbreviations | p. xviii |
1 Introduction | p. 1 |
1.1 Role of DEMs | p. 3 |
1.2 Role of Scale | p. 6 |
1.3 Survey of Applications | p. 12 |
1.4 Study Site and Software Tools | p. 16 |
1.5 Structure of Book | p. 20 |
2 Constructing Digital Elevation Models | p. 23 |
2.1 Elevation Data Networks | p. 23 |
2.2 Elevation Data Sources | p. 29 |
2.2.1 Ground Surveys | p. 31 |
2.2.2 Kinematic GPS Surveys | p. 32 |
2.2.3 Topographic Maps | p. 33 |
2.2.4 Photogrammetry Datasets | p. 35 |
2.2.5 Airborne Laser Scanning Datasets | p. 36 |
2.2.6 Interferometric Synthetic Aperture Radar Datasets | p. 37 |
2.2.7 Shuttle Radar Topographic Mission DEMs | p. 38 |
2.2.8 Advanced Spaceborne Thermal Emission and Reflectance Radiometer DEMs | p. 40 |
2.2.9 WorldDEM Datasets | p. 43 |
2.3 Fitness-For-Use | p. 43 |
2.4 Data Preprocessing and DEM Construction | p. 44 |
2.5 US National Elevation Dataset | p. 50 |
3 Calculating Land Surface Parameters | p. 53 |
3.1 Primary Land Surface Parameters | p. 54 |
3.1.1 Elevation and Surface Area | p. 54 |
3.1.2 Slope, Aspect, and Curvature | p. 59 |
3.1.3 Slope Direction and Width | p. 69 |
3.1.4 Flow Accumulation | p. 100 |
3.1.5 Elevation Residuals | p. 105 |
3.1.6 Statistical Parameters | p. 109 |
3.1.7 Upslope Parameters | p. 113 |
3.1.8 Downslope Parameters | p. 114 |
3.1.9 Visibility and Visual Exposure | p. 114 |
3.2 Secondary Land Surface Parameters | p. 115 |
3.2.1 Water Flow and Soil Redistribution | p. 116 |
3.2.2 Energy and Thermal Regimes | p. 135 |
3.3 Final Comments | p. 148 |
4 Delineating Land Surface Objects and Landforms | p. 150 |
4.1 Extracting and Classifying Specific Landform Elements | p. 152 |
4.1.1 Fuzzy Concepts and Fuzzy Classification Methods | p. 154 |
4.2 Extraction and Classification of Land Surface Objects Based on Flow Variables | p. 158 |
4.2.1 Drainage Networks and Channel Attributes | p. 159 |
4.2.2 Basin Boundaries and Attributes | p. 164 |
4.3 Extracting and Classifying Specific (Fuzzy) Landforms | p. 165 |
4.4 Extracting and Classifying Repeating Landform Types | p. 168 |
4.5 Discrete Geomorphometry: Coupling Multiscale Pattern Analysis and Object Delineation | p. 174 |
5 Measuring Error and Uncertainty | p. 179 |
5.1 Identification and Treatment of Error and Uncertainty | p. 180 |
5.1.1 Error | p. 182 |
5.1.2 Uncertainty | p. 194 |
5.2 Fitness-for-Use Revisited | p. 199 |
5.2.1 Predictive Vegetation Modeling | p. 199 |
5.2.2 Modeling Soil Erosion and Deposition | p. 203 |
5.2.3 Numerical Simulations of Landscape Development | p. 205 |
5.2.4 Modeling Soil-Water-Vegetation Interactions | p. 207 |
5.2.5 Modeling Global Wetlands | p. 209 |
5.3 Multiscale Analysis and Cross-scale Inference | p. 214 |
5.4 The US National Water Model | p. 223 |
6 Terrain Modeling Software and Services | p. 228 |
6.1 Changes in Data Capture and Computing Systems | p. 230 |
6.2 Esri's ArcGIS Ecosystem | p. 234 |
6.3 Third-party Esri Add-ons | p. 244 |
6.3.1 ArcGIS Geomorphometry Toolbox | p. 244 |
6.3.2 ArcGIS Geomorphometry and Gradient Metrics Toolbox | p. 245 |
6.3.3 ArcGeomorphometry Toolbox | p. 246 |
6.4 Other Software Choices | p. 248 |
6.4.1 GRASS | p. 248 |
6.4.2 ILWIS | p. 250 |
6.4.3 LandSerf | p. 251 |
6.4.4 MicroDEM | p. 252 |
6.4.5 QGIS | p. 253 |
6.4.6 RiverTools | p. 254 |
6.4.7 SAGA | p. 255 |
6.4.8 TauDEM | p. 257 |
6.4.9 Whitebox GAT | p. 258 |
6.5 Future Trends | p. 259 |
7 Conclusions | p. 261 |
7.1 Current State of the Art | p. 263 |
7.2 Future Needs and Opportunities | p. 269 |
7.2.1 Finding Ways to Use Provenance, Credibility, and Digital Terrain Modeling Application-context Knowledge | p. 269 |
7.2.2 Rediscovering and Using What We Already Know! | p. 270 |
7.2.3 Developing New Digital Terrain Methods | p. 272 |
7.2.4 Clarifying and Strengthening the Role of Theory | p. 274 |
7.2.5 Developing High-fidelity, Multi-resolution Digital Elevation Models | p. 275 |
7.2.6 Developing and Embracing New Visualization Opportunities | p. 275 |
7.2.7 Adopting and Using New Information Technologies and Workflows | p. 276 |
7.2.8 Solving "Wicked" Problems of Varying Magnitudes | p. 277 |
7.3 Call To Action | p. 278 |
References | p. 279 |
Index | p. 333 |