.

0 (hodnocen0 x )

BK

New York : John Wiley & Sons, c2000

xxiii, 479 s., [8] s. obr. příl. : il., mapy, grafy ; 24 cm

ISBN 0-471-32188-5 (váz.)

Obsahuje bibliografii na s. 429-468, bibliografické odkazy a rejstřík

000069745

Preface xvii // Acknowledgments xix // List of Contributors xxiii // 1. Digital Terrain Analysis 1 by John P. Wilson and John C. Gallant // 1.1 Principles and Applications 1 // 1.1.1 Digital Elevation Data Sources and Structures 3 // 1.1.2 Calculation and Use of Topographic Attributes in // Hydrological, Geomorphological, and Biological Applications 5 // 1.1.3 Identification and Treatment of Error and Uncertainty 15 // 1.2 The Purpose of This Book 20 // 1.3 Overview 22 // 1.3.1 Digital Terrain Analysis Methods 22 // 1.3.2 Hydrological Applications 24 // 1.3.3 Geomorphological Applications 25 // 1.3.4 Biological Applications 26 // 2. Digital Elevation Models and Representation of Terrain Shape 29 by Michael F Hutchinson and John C. Gallant // 2.1 Introduction 29 // 2.2 Sources of Topographic Data 32 // 2.2.1 Surface-Specific Point Elevation Data 32 // 2.2.2 Contour and Stream-Line Data 32 // 2.2.3 Remotely Sensed Elevation Data 33 // 2.2.4 Scales of Source Topographic Data 34 // 2.3 DEM Interpolation Methods 34 // 2.3.1 Triangulation 35 // 2.3.2 Local Surface Patches 35 // 2.3.3 Locally Adaptive Gridding 36 // 2.4 Filtering of Remotely Sensed Gridded DEMs 37 // 2.5 Quality Assessment of DEMs 38 // 2.5.1 Spurious Sinks and Drainage Analysis 38 // 2.5.2 Views of Shaded Relief and Other Terrain Attributes 39 // 2.5.3 Derived Elevation Contours 39 // 2.5.4 Frequency Histograms of Primary Terrain Attributes 39 // 2.6 Optimization of DEM Resolution 39 // 2.7 Interpolation of the Cottonwood DEM Using ANUDEM 41 // 2.7.1 Specification of ANUDEM Options 43 // 2.7.2 Elevation Units and Vertical Precision 43 // 2.8 Assessment of Resolution and Quality of the Cottonwood DEM 44 // 2.8.1 Optimization of Resolution Using the Root Mean Square // Slope Criterion 44 // 2.8.2 Comparison of Data Contours With Derived Contours 45 // 2.8.3 Views of Slope and Profile Curvature 45 //

2.8.4 Histograms of Elevation and Aspect 46 // 2.8.5 Summary Recommendation 49 // 2.9 Conclusions 49 // 3. Primary Topographic Attributes 51 by John C. Gallant and John P Wilson // 3.1 TAPES-G: Terrain Analysis on Gridded DEMs 51 // 3.1.1 Surface Derivatives 51 // 3.1.2 Slope 53 // 3.1.3 Aspect and Primary Flow Direction 54 // 3.1.4 Curvature 56 // 3.1.5 Upslope Contributing Area and Specific // Catchment Area 58 // 3.1.6 Flow Width 69 // 3.1.7 Maximum Flow-Path Length 70 // 3.1.8 Downslope Attributes 70 // 3.1.9 Upslope Averages of Terrain Attributes 71 // 3.1.10 Other Terrain Attributes 71 // 3.1.11 Inputs 71 // 3.1.12 Outputs 72 // 3.2 Elevation Residual Analysis 73 // 3.2.1 Mean Elevation 74 // 3.2.2 Difference From Mean Elevation 74 // 3.2.3 Standard Deviation of Elevation 74 // 3.2.4 Elevation Range 75 // 3.2.5 Deviation From Mean Elevation 75 // 3.2.6 Percentile 75 // 3.2.7 Other Attributes 75 // 3.2.8 ELEVRESIDGRID Examples 76 // 3.3 TAPES-C: Terrain Analysis on Contour DEMs 77 // 3.3.1 Element Construction 79 // 3.3.2 Computed Terrain Attributes 82 // 3.3.3 Differences Between TAPES-C and TOPOG Element // Networks 83 // 3.3.4 Inputs 84 // 3.3.5 Outputs 85 // 3.4 Conclusions 85 // 4. Secondary Topographic Attributes 87 by John P Wilson and John C. Gallant // 4.1 Introduction 87 // 4.2 EROS 88 // 4.2.1 Estimation Methods 88 // 4.2.2 Inputs 90 // 4.2.3 Outputs 91 // 4.3 SRAD 91 // 4.3.1 Estimation Methods 91 // 4.3.2 Inputs 100 // 4.3.3 Outputs 106 // 4.4 WET 106 // 4.4.1 Estimation Methods 107 // 4.4.2 Inputs 110 // 4.4.3 Outputs 113 // 4.5 DYNWET-G 113 // 4.5.1 Estimation Methods 115 // 4.5.2 Inputs 117 // 4.5.3 Outputs 118 // 4.6 Sample Application 118 // 4.7 Conclusions 131 //

5. Effect of Data Source, Grid Resolution, and Flow-Routing Method on Computed Topographic Attributes 133 by John P. Wilson, Philip L. Repetto, and Robert D. Snyder // 5.1 Introduction 133 // 5.2 Squaw Creek, Montana Sensitivity Analysis 135 // 5.2.1 Methods and Data Sources 135 // 5.2.2 Results and Discussion 137 // 5.3 Idaho Farm Field Model Validation Field Experiment 150 // 5.3.1 Methods and Data Sources 152 // 5.3.2 Results and Discussion 155 // 5.4 Conclusions 160 // 6. Spatial Analysis of Soil-Moisture Deficit and Potential Soil Loss // in the Elbe River Basin 163 by Valentina Krysanava, Dirk-Ingmar Müller-Wohlfeil, Wolfgang Cramer, and Alfred Becker // 6.1 Introduction 163 // 6.1.1 Background 163 // 6.1.2 Study Objectives and Approach 165 // 6.1.3 The Study Region 166 // 6.2 Freshwater Availability 166 // 6.2.1 Method 166 // 6.2.2 Large-Scale Applications of Topography-Based Models 168 // 6.2.3 Application in the Elbe Basin 170 // 6.2.4 Results and Comparisons With Previous Studies 172 // 6.3 Erosion 177 // 6.3.1 GIS-Based Approaches for the Analysis of Pollutant Yield // in Large Basins 177 // 6.3.2 Methods and Results 178 // 6.4 Conclusions 178 // 7. Mapping Contributing Areas for Stormwater Discharge to // Streams Using Terrain Analysis 183 by Jeremy S. Fried, Daniel G. Brown, Mark O. Zweifler, and Michael A. Gold // 7.1 Introduction 183 // 7.1.1 Implications of Sediment for Water Quality 185 // 7.1.2 Sediment Management With Riparian Buffer Strips 185 // 7.1.3 Hydrological Principles 185 // 7.2 Description of Study Area 188 // 7.3 Methods 188 // 7.3.1 GIS Database and Terrain Model Creation 188 // 7.3.2 Generation of Terrain-Analysis Indices 190 // 7.3.3 Formulation of Investigative Buffer Model 191 // 7.3.4 Collection of Validation Data 192 // 7.4 Results 193 // 7.4.1 Comparison of Indices 194 //

7.4.2 Comparison of Variable-Width Investigative Buffers 196 // 7.4.3 Concordance With Validation Data Set 198 // 7.5 Discussion 200 // 7.6 Conclusions 203 // 8. Soil-Moisture Modeling in Humid Mountainous Landscapes 205 by J. Alan Yeakley, George M. Hornberger, Wayne T Swank, Paul V Bolstad, and James M. Vose // 8.1 Introduction // 8.2 Study Site // 8.3 Modeling Approach 207 // 8.3.1 Terrain Analysis 207 // 8.3.2 Canopy Interception Modeling 210 // 8.3.3 Hillslope Hydrology Model 210 // 8.4 Parameterization and Calibration 212 // 8.4.1 Structural Parameters 212 // 8.4.2 Above-Ground Parameters 213 // 8.4.3 Below-Ground Parameters 215 // 8.4.4 Storm-Scale Calibration 216 // 8.5 Validation 217 // 8.5.1 Storm Scale 217 // 8.5.1.2 Soil-Moisture Response 218 // 8.5.2 Seasonal Scale 219 // 8.5.3 Model Comparisons 220 // 8.6 Discussion 221 // 8.7 Conclusions 223 // 9. Stochastic Analysis of a Coupled Surface/Subsurface // Hydrologic Model 225 by Gregory M. Pohli and John J. Warwick // 9.1 Introduction 225 // 9.1.1 Statement of the Problem 225 // 9.1.2 Review of Existing Coupled Modeling Approaches 226 // 9.1.3 Stochastic Model 227 // 9.1.4 Study Site 227 // 9.2 Description of Numerical Models 229 // 9.2.1 Vadose Zone Model 229 // 9.2.2 Coupled Surface/Subsurface Model 231 // 9.2.3 Stochastic Model 235 // 9.3 Simulation Results 237 // 9.3.1 Coupled Model Calibration 237 // 9.3.2 Model Comparison (Vadose Zone Versus Coupled // Model) 238 // 9.3.3 Long-Term Simulation Comparison of Stochastic // Structures and the Vadose Zone Model 240 // 9.4 Discussion and Conclusions 241 // 9.4.1 Coupled Surface/Subsurface Model 241 // 9.4.2 Coupled Surface/Subsurface Model Versus Vadose // Zone Model 243 // 9.4.3 Stochastic Model 244 //

10. The Role of Terrain Analysis in Soil Mapping 245 by Neil J. McKenzie, Paul E. Gessler, Philip J. Ryan, and Deborah O’Connell // 10.1 The Potential of Terrain Analysis 245 // Theories of Pedogenesis and Modeling for Prediction 246 // 10.2.1 The Functional Factorial Approach 246 // 10.2.2 Contemporary Views 247 // 10.2.3 Environmental Change in Ancient Landscapes 248 // 10.2.4 The Role of Landform 250 // 10.3 Examples of the Use of Terrain Analysis in Australian Soil Survey // Research 251 // 10.3.1 Improved Environmental Information 254 // 10.3.2 Explicit Survey Design 256 // 10.3.3 Quantitative Spatial Prediction 259 // 10.4 Factors Affecting the Utility of Terrain Analysis 263 // 10.4.1 Landscape Complexity 263 // 10.4.2 Issues of Scale 263 // 10.4.3 Technology 264 // 10.4.4 Quantitative Versus Intuitive Mental Models 265 // 10.5 Conclusions 265 // 11. Automated Landform Classification Methods for Soil-Landscape // Studies 267 by Stephen J. Ventura and Barbara J. Irvin // 11.1 Introduction 267 // 11.2 Role of Landform Classification in Modem Soil Survey and // Soil-Landscape Studies 268 // 11.3 Pleasant Valley Study 274 // 11.4 Methods 275 // 11.4.1 Calculation of Topographic Attributes From a DEM 277 // 11.4.2 ISODATA Unsupervised Classification 281 // 11.4.3 Continuous Classification Overview 282 // 11.5 Results and Discussion 286 // 11.6 Conclusions 290 // 12. A Soil-Terrain Model for Estimating Spatial Patterns of Soil // Organic Carbon 295 by Jay C. Bell, David F Grigal, and Peter C. Bates // 12.1 Introduction 295 // 12.2 Methods 297 // 12.2.1 Study Site 297 // 12.2.2 Field Sampling 298 // 12.2.3 Laboratory Methods 299 // 12.2.4 Spatial Data 299 // 12.2.5 Spatial Modeling 301 // 12.3 Results and Discussion 302 // 12.3.1 Mineral Soils 303 // 12.3.2 Peatlands 304 // 12.3.3 Estimating Spatial Patterns of SOC 305 // 12.4 Conclusions 309 //

13. Shallow Landslide Delineation for Steep Forest Watersheds Based on Topographic Attributes and Probability Analysis 311 by Jinfan Duan and Gordon E. Grant // 13.1 Introduction 311 // 13.2 Study Area Description 313 // 13.3 Methods and Data Sources 315 // 13.3.1 Infinite Slope Model 316 // 13.3.2 Parameter Estimation From Probability Analysis 317 // 13.3.3 Effect of Vegetation 319 // 13.3.4 Terrain Analysis and Climate Data Acquisition 320 // 13.3.5 Monte Carlo Simulation and Probability Derivation 323 // 13.4 Results and Discussion 324 // 13.5 Implications for Management and Future Modeling 328 // 13.6 Conclusions 329 // 14 Terrain Variables Used for Predictive Mapping of Vegetation // Communities in Southern California 331 by Janet Franklin, Paul McCullough, and Curtis Gray // 14.1 Introduction 331 // 14.1.1 Species Distributions and Environmental Gradients 332 // 14.1.2 Ecological Field Data in Geographical Databases 332 // 14.1.3 Error in Digital Elevation Models 333 // 14.1.4 Modeling Methods 333 // 14.2 Case Studies 334 // 14.2.1 Study Area 335 // 14.2.2 Materials and Methods 335 // 14.2.3 Results 346 // 14.2.4 Discussion 347 // 14.3 Conclusions 352 // 15. Automated Land Cover Mapping Using Landsat Thematic Mapper Images and Topographic Attributes 355 by Jonathan M. Wheatley, John R Wilson, Roland L Redmond, Zhenkui Ma, and Jeff DiBenedetto // 15.1 Introduction 355 // 15.2 Description of Study Area 358 // 15.3 Methods and Data Sources 362 // 15.3.1 TM Image Classification 362 // 15.3.2 Digital Elevation Models 363 // 15.3.3 Terrain Analysis 364 // 15.3.4 Ground-Truth Data 366 // 15.3.5 Performance Evaluation 366 // 15.4 Results 367 // 15.4.1 Terrain Attribute Maps 367 // 15.4.2 Land Cover Classification Without Topographic Attributes 370 // 15.4.3 Adding Topographic Attributes 377 //

15.4.4 Use of Stream Buffers to Delineate Riparian and Upland Cover Classes 377 // 15.4.5 Evaluation of Land Cover Maps 381 // 15.5 Discussion 384 // 15.6 Conclusions 389 // 16. Toward a Spatial Model of Boreal Forest Ecosystems: The Role of Digital Terrain Analysis 391 by Brendan G. Mackey, Ian C. Mullen, Kenneth A. Baldwin, John C. Gallant, Richard A. Sims, and Daniel W. McKenney // 16.1 Introduction 391 // 16.1.1 The Niche Hypothesis 392 // 16.1.2 The Disturbance Hypothesis 393 // 16.2 The Study Area 394 // 16.3 Why Digital Terrain Analysis? 396 // 16.4 Methods 397 // 16.4.1 Ecology of the Target Tree Species 398 // 16.4.2 Data Sources 399 // 16.4.3 Analytical Techniques 403 // 16.5 Results 409 // 16.5.1 AMF Analyses 409 // 16.5.2 Domain Analyses 410 // 16.6 Discussion 415 // 16.6.1 The Domain Hypothesis 420 // 16.7 Conclusions 421 // 17. Future Directions for Terrain Analysis 423 by John C. Gallant, Michael F Hutchinson, and John P Wilson // 17.1 Introduction 423 // 17.2 Methodology and Data 423 // 17.3 Knowledge of Relationships 425 // 17.4 Scaling 426 // 17.5 Concluding Remarks 426 // References 429 // Index 469