Here's the first truly comprehensive guide to digital terrain modeling that provides you with a thorough, mathematically rigorous treatment of DTM generation, manipulation, and analysis techniques and applications in a single volume. It describes photogrammetric data capture, direct georeferencing systems, LIDAR, IFSAR and other data acquisition techniques, and explains how to determine the most appropriate DTM generation technique for any given project. You find a detailed treatment of DTM data structures, including storage and compression techniques for gridded data, as well as data filtering and smoothing procedures. Various data interpolation methods are covered, including Kriging analysis techniques. The mathematical theory behind DTM construction is developed to the extent that it has general applications to all surfaces. Emphasis on quality control helps you gain essential insight into the kind of problems and errors to expect in elevation. Moreover, this unique reference features a full range of highly illustrative examples and covers real-world applications that give you a clear understanding of DTM's current capabilities and limitations in environmental modeling, mapping, and engineering activities. Supported by over 100 illustrations, it puts today's cutting-edge DTM techniques and applications at your fingertips.
Introduction ë What is DTM. Terminology. The Importance and Need for Digital Terrain Models. Elements of Digital Terrain Modeling. Data Models in DTM. ; DTM Generation Techniques ë Ground Surveys. Digitized Cartographic Data Sources. Photogrammetric Data Capture. Direct Georeferencing Systems. IFSAR, and Laser Altimeters. ; DTM Data Structures ë Data Structure in Gridded Data. Storage and Compression Techniques for Gridded Data. Filtering and Smoothing of Gridded Data. TIN Data Structure. ; DTM Manipulation ëIntroduction. Interpolation Methods. ; Kriging: Closer Look ë KRIGING from the Least Squares Adjustment Principles. KRIGING as the Best Linear Estimates (BLE). Empirical Determination of the Covariance Function. KRIGING as the Best Linear Unbiased Estimates (BLUE). KRIGING Using Semi-Variance Analysis. Directional Variograms/Variance-Covariance Functions. Universal KRIGING. KRIGING: Final Remarks.; DTM Generalization and Quality Control ë DTM Generalization. Errors in DTM. Why Quality Control? Quality Control in the Spatial Domain. Quality Control in the Frequency Domain. Quality Control Using Ortho-Photography. Automatic Surface Matching for Quality Control Purposes. ; Mapping and Engineering Applications ë DTM Visualization. DTMs and Data Fusion. First Order Derivatives. Second Order Derivatives. Volume Computations. Transportation Applications. Military Applications. Wireless Civilian Applications - Visibility. ; Applications in Environmental Modeling ë Drainage Modeling. Issues of Scale. Surface Water Modeling. Groundwater Modeling. Waterworks and Municipal Applications. Indicators of Environmental Degradation. Agricultural Applications. Habitat and Biodiversity Modeling. Health and Disease. Meteorological Parameterization. Earth System Modeling. ;
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Naser El-Sheimy
Naser El-Sheimy is professor in Geomatics Engineering at the University of Calgary, Canada, and is the Canada Research Chair in mobile multi-sensor geomatics (M2G) Systems. He received his Ph.D. in geomatics engineering from the University of Calgary.
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Ayman Habib
Ayman Habib is an associate professor in Geomatics Engineering, University of Calgary. He holds a Ph.D. in Geodetic Science from Ohio State University.
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Caterina Valeo
Caterina Valeo is associate professor in Geomatics Engineering, University of Calgary. She received her Ph.D. in Civil Engineering from McMaster University.