Resource management information systems : remote sensing, GIS and modelling

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Resource Management Information Systems: Remote Sensing, GIS and Modelling, Second Edition provides you with the knowledge and skill necessary to design, build, implement, and operate spatial resource management information systems for the management of physical resources. This volume promotes the use of these technologies in a spatial context, enabling you to apply information systems toward the management of resources in agriculture, forestry, land use planning, valuation, engineering, and many additional fields.

A follow-up to the first edition, Resource Management Information Systems: Process and Practice , this book offers extensive revisions, reflecting the rapidly evolving nature of the technologies needed to manage spatial resources.

Author Notes

McCloy, Keith R.

Chapter 1 Introduction	p. 1
1.1 Goals of this Book	p. 1
1.2 Current Status of Resources	p. 2
1.2.1 Ozone Hole	p. 2
1.2.2 Water-Borne Soil Erosion	p. 3
1.2.3 Loss of Biodiversity	p. 5
1.3 Impact of Resource Degradation	p. 7
1.4 Nature of Resource Degradation	p. 10
1.5 Nature of Resource Management	p. 12
1.5.1 Strategic Management	p. 12
1.5.2 Process or Regional Management	p. 13
1.5.3 Operational Management	p. 15
1.5.4 Relationship between These Levels of Management	p. 15
1.6 Nature of Regional Resource Management Information Systems	p. 16
1.7 Geographic Information in Resource Management	p. 20
1.8 Structure of this Book	p. 24
Reference	p. 25
Chapter 2 Physical Principles of Remote Sensing	p. 27
2.1 Introduction	p. 27
2.2 Electromagnetic Radiation	p. 31
2.2.1 Nature of Electromagnetic Radiation	p. 31
2.2.2 Radiometric Terms and Definitions	p. 34
2.2.3 Energy Radiated by the Sun and the Earth	p. 35
2.2.4 Effects of the Atmosphere	p. 36
2.2.5 Correction of Remotely Sensed Data for Attenuation through the Atmosphere	p. 40
2.2.6 Measurement of Radiance and Irradiance	p. 44
2.3 Interaction of Radiation with Matter	p. 48
2.3.1 Nature of Reflectance	p. 48
2.3.2 Reflectance of Water Surfaces	p. 54
2.3.3 Reflectance Characteristics of Soils	p. 55
2.3.4 Reflectance of Vegetation	p. 58
2.3.5 Reflectance Characteristics of Green Leaves	p. 62
2.3.6 Reflectance Characteristics of Dead Leaves	p. 66
2.3.7 Vegetative Canopy Reflectance	p. 66
2.3.8 Bi-Directional Reflectance Distribution Function of Surfaces	p. 71
2.4 Passive Sensing Systems	p. 72
2.4.1 The Camera	p. 74
2.4.2 Acquisition of Aerial Photography with a Framing Camera	p. 84
2.4.3 The Scanner	p. 89
2.4.4 The Moving Mirror Scanner	p. 89
2.4.5 Pushbroom Scanners	p. 96
2.5 Active Sensing Systems	p. 97
2.5.1 Introduction	p. 97
2.5.2 The Geometry of Radar Systems	p. 98
2.5.3 The Attenuation and Scattering of Radar in the Atmosphere	p. 102
2.5.4 The Information Content of Radar Imagery	p. 102
2.5.5 Radar Interferometry	p. 109
2.5.6 Summary	p. 111
2.6 Hyperspectral Image Data	p. 111
2.6.1 Definition	p. 111
2.6.2 Applications of Hyperspectral Image Data	p. 112
2.7 Hypertemporal Image Data	p. 112
2.7.1 Introduction	p. 112
2.8 Platforms	p. 113
2.8.1 Terrestrial Platforms	p. 113
2.8.2 Balloon	p. 114
2.8.3 Helicopter or Boat	p. 114
2.8.4 Manned and Unmanned Aircraft	p. 114
2.8.5 Planning an Aerial Sortie	p. 116
2.8.6 Satellite Platform	p. 117
2.9 Satellite Sensor Systems	p. 119
Additional Reading	p. 119
References	p. 120
Chapter 3 Visual Interpretation and Map Reading	p. 123
3.1 Overview	p. 123
3.1.1 Remotely Sensed Data and Visual Interpretation	p. 123
3.1.2 Effects of Height Differences on Remotely Sensed Images	p. 124
3.2 Stereoscopy	p. 124
3.2.1 Introduction	p. 124
3.2.2 Monocular Vision	p. 125
3.2.3 Binocular Vision	p. 126
3.2.4 Binocular Perception of Colour	p. 128
3.2.5 General Principles of Stereoscopic Vision	p. 129
3.2.6 Methods of Stereoscopic Viewing	p. 130
3.2.7 Physical Methods of Separation Using Stereoscopes	p. 130
3.2.8 Viewing with a Stereoscope	p. 131
3.2.9 Optical Methods of Separation	p. 132
3.2.10 Construction of a Stereo-Triplet	p. 133
3.3 Measuring Height Differences in a Stereoscopic Pair of Photographs	p. 134
3.3.1 Principle of the Floating Mark	p. 134
3.3.2 Parallax Bar	p. 135
3.3.3 Vertical Exaggeration	p. 135
3.3.4 Displacements due to Height Differences in an Aerial Photograph	p. 135
3.3.5 Derivation of the Parallax Bar Formulae	p. 136
3.3.6 Characteristics of the Parallax Bar Equation	p. 138
3.4 Planimetric Measurements on Aerial Photographs	p. 139
3.4.1 Introduction	p. 139
3.4.2 Determination of Scale	p. 139
3.4.3 Measurement of Distances	p. 140
3.4.4 Measurement of Areas	p. 141
3.4.5 Transfer of Planimetric Detail by the Use of the Anharmonic Ratio	p. 141
3.4.6 Proportional Dividers	p. 143
3.5 Perception of Colour	p. 144
3.6 Principles of Photographic Interpretation	p. 146
3.6.1 Introduction	p. 146
3.6.2 Levels of Interpretation	p. 147
3.6.3 Principles of Object Recognition	p. 148
3.6.4 Interpretation Strategies	p. 150
3.6.5 Interpretation Procedure	p. 151
3.7 Visual Interpretation of Images	p. 152
3.7.1 Visual Interpretation of Thermal Image Data	p. 154
3.7.2 Visual Interpretation of Radar Image Data	p. 155
3.8 Maps and Map Reading	p. 157
3.8.1 Map Projections	p. 157
3.8.2 Mapping Systems and Map Types	p. 163
3.8.3 Map Co-ordinates and Bearings	p. 165
3.8.4 Establishing One's Location on a Map	p. 167
3.8.5 Map Reading on a Topographic Map	p. 168
3.8.6 Terrain Classification	p. 170
Further Reading	p. 171
References	p. 171
Chapter 4 Image Processing	p. 173
4.1 Overview	p. 173
4.1.1 Pre-Processing	p. 174
4.1.2 Enhancement	p. 175
4.1.3 Classification	p. 175
4.1.4 Estimation	p. 176
4.1.5 Temporal Analysis	p. 177
4.2 Statistical Considerations	p. 179
4.2.1 Probability Density Functions	p. 180
4.2.2 Correlation	p. 183
4.2.3 Statistical Characteristics of Satellite Scanner Data	p. 185
4.2.4 Measures of Distance	p. 189
4.2.5 Shannon's Sampling Theorem	p. 190
4.2.6 Autocorrelation and Variograms	p. 191
4.2.7 Frequency Domain	p. 193
4.2.8 Least Squares Method of Fitting	p. 196
4.3 Pre-Processing of Image Data	p. 201
4.3.1 Introduction	p. 201
4.3.2 Rectification	p. 203
4.3.3 Radiometric Calibration	p. 215
4.3.4 Atmospheric Correction	p. 216
4.4 The Enhancement of Image Data	p. 221
4.4.1 Radiometric Enhancement	p. 221
4.4.2 Spectral Enhancements	p. 228
4.4.3 Spatial Transformations of Image Data	p. 238
4.4.4 Temporal Enhancements	p. 255
4.5 Analysis of Mixtures or End Member Analysis	p. 258
4.5.1 Linear End Member Model	p. 259
4.5.2 Characteristics of the Linear End Member Model	p. 263
4.5.3 Identification of End Members	p. 264
4.5.4 Implementation of the Linear End Member Algorithm	p. 265
4.6 Image Classification	p. 265
4.6.1 Principles of Classification	p. 265
4.6.2 Discriminant Function Classifiers	p. 270
4.6.3 Fuzzy Classifiers	p. 277
4.6.4 Neural Network Classifiers	p. 278
4.6.5 Hierarchical Classifiers	p. 281
4.6.6 Classification Strategies	p. 282
4.7 Clustering	p. 288
4.7.1 Clustering Criteria	p. 288
4.7.2 Clustering of Training Data	p. 290
4.7.3 Strategies for Clustering	p. 290
4.8 Estimation	p. 291
4.8.1 Introduction	p. 291
4.8.2 Development of Regression Estimation Models	p. 292
4.8.3 Application of Regression Estimation Models	p. 292
4.8.4 Development of Interpolation Estimation Models	p. 294
4.8.5 Estimation Based on Physical Models	p. 295
4.9 Analysis of Hyper-Spectral Image Data	p. 296
4.9.1 Introduction	p. 296
4.9.2 Vegetation Mapping	p. 296
4.9.3 Fitting of Spectra	p. 297
4.10 Analysis of Dynamic Processes	p. 297
4.10.1 Introduction	p. 297
4.10.2 Time Series Analysis of Image Data	p. 299
4.10.3 Comparison of Two Time Series	p. 303
4.10.4 Analysis of Spatio-Temporal Dynamic Processes	p. 305
4.11 Summary	p. 305
Further Reading	p. 307
References	p. 307
Chapter 5 Use of Field Data	p. 309
5.1 The Purpose of Field Data	p. 309
5.1.1 Definition and Description of Field Data	p. 309
5.1.2 Role and Types of Field Data	p. 310
5.1.3 Accuracy and Reliability	p. 313
5.2 Collection of Field Spectral Data	p. 315
5.2.1 Purpose of Collecting Field Spectral Data	p. 315
5.2.2 Measurement of Field Spectral Data	p. 315
5.2.3 Considerations in Collecting Field Spectral Data	p. 320
5.2.4 Collection of Other Data With Spectral Data	p. 322
5.2.5 Construction of Models from a Set of Related Spectral and Field Data	p. 326
5.3 Use of Field Data in Visual Interpretation	p. 330
5.3.1 Identification	p. 330
5.3.2 Interpretation	p. 331
5.4 Use of Field Data in the Classification of Digital Image Data	p. 333
5.4.1 Classification with the Normal Classifiers	p. 333
5.4.2 Classification Using the Mixture Model	p. 335
5.5 Stratified Random Sampling Method	p. 335
5.5.1 Delineation of Strata	p. 336
5.5.2 Determination of the Sampling Fraction and the Number of Samples	p. 336
5.5.3 Selection of Samples	p. 342
5.5.4 Measurement of Samples	p. 344
5.6 Accuracy Assessment	p. 345
5.6.1 Role of Accuracy Assessment	p. 345
5.6.2 Comparison of Field Data with Classification Results	p. 347
5.6.3 Use of Field Data in Estimation	p. 353
5.7 Summary	p. 353
Further Reading	p. 354
References	p. 354
Chapter 6 Geographic Information Systems	p. 355
6.1 Introduction to Geographic Information Systems	p. 355
6.2 Data Input	p. 360
6.2.1 Databases and Attribute Data	p. 360
6.2.2 Creation of Spatial GIS Layers by Digitising from an Existing Map or Image	p. 363
6.2.3 Field Collected Data and Observations	p. 370
6.2.4 Information from Image Data	p. 372
6.3 Simple Raster Data Analysis in a GIS	p. 373
6.3.1 Displaying Data	p. 374
6.3.2 Overlaying Layers of Data on the Screen	p. 376
6.3.3 Combining Layers, Numerically and Logically	p. 377
6.3.4 Filtering and Neighbourhood Analyses	p. 381
6.3.5 Distances, Cost Surfaces, Least Cost Pathways and Contextual Analysis	p. 383
6.3.6 Statistical Analysis	p. 386
6.4 Vector GIS Data Analysis Functions (Susanne Kickner)	p. 388
6.4.1 Selection	p. 388
6.4.2 Dividing and Joining Areas	p. 394
6.4.3 Types of Spatial Overlay	p. 395
6.4.4 Proximity Analysis	p. 399
6.5 Data Management in a GIS	p. 400
6.6 Advanced Analysis Techniques in a Vector GIS - Network Modelling (Susanne Kickner)	p. 403
6.6.1 Topology in a Network	p. 404
6.6.2 Address Geocoding	p. 405
6.6.3 Path Finding in Vector GIS	p. 405
6.6.4 Location-Allocation	p. 407
6.6.5 Gravity Models	p. 408
6.7 Advanced Raster Analysis Techniques in a GIS	p. 409
6.7.1 Geostatistics	p. 409
6.7.2 Map Algebra and Script Languages	p. 410
6.7.3 Surface Analysis	p. 414
6.7.4 Analyses of Cross-Tabulation Tables	p. 425
6.7.5 Clump Analysis	p. 426
6.7.6 Zonal Analysis	p. 426
6.8 Modelling in a GIS	p. 426
6.8.1 What is Modelling?	p. 426
6.8.2 Modifiable Areal Unit Problem	p. 432
6.8.3 Measures of Global and Local Homogeneity/Heterogeneity	p. 435
6.8.4 An Example of a "Top-Down," Regression Based Model - The Universal Soil Loss Equation	p. 435
6.8.5 Modelling of Ecological Systems	p. 441
6.9 Uncertainty in GIS Analysis	p. 443
6.9.1 Errors within a Layer and Error Statements	p. 444
6.9.2 Propagation of Error in Combining Layers	p. 446
6.9.3 Sensitivity Analyses	p. 449
6.9.4 Decision Making under Uncertainty	p. 450
6.10 Presentation in a GIS	p. 451
6.11 Three-Dimensional GIS	p. 453
Additional Reading	p. 453
References	p. 454
Chapter 7 The Analysis and Interpretation of Vegetation	p. 455
7.1 Introduction	p. 455
7.1.1 Energy Budget	p. 456
7.1.2 Hydrologic Cycle	p. 458
7.1.3 Carbon Cycle	p. 458
7.1.4 Nutrient Budgets	p. 459
7.2 Regional Vegetation Mapping and Monitoring	p. 463
7.2.1 Landcover	p. 464
7.2.2 Estimation	p. 465
7.3 Signatures of Vegetation	p. 465
7.3.1 Spectral Signatures ([lambda])	p. 466
7.3.2 Spatial Signatures (x, y, z)	p. 467
7.3.3 Temporal Signatures (t)	p. 467
7.3.4 Angular Signatures	p. 469
7.3.5 Polarisation Signatures	p. 469
7.4 Modelling Canopy Reflectance	p. 470
7.4.1 Introduction	p. 470
7.4.2 Leaf Reflectance Models	p. 475
7.4.3 Canopy Reflectance Models	p. 480
7.4.4 Empirical Models	p. 481
7.4.5 Turbid Medium Models	p. 481
7.4.6 Geometric-Optic (GO) Models	p. 491
7.4.7 Ray Tracing Models	p. 492
7.4.8 Radiosity or Computer Graphics Based Models	p. 493
7.4.9 Linear Semi-Empirical Approximations to the Physical Models	p. 493
7.5 Estimation of Vegetation Parameters and Status	p. 497
7.5.1 Introduction	p. 497
7.5.2 Regression Models	p. 498
7.5.3 Empirical Interpolation	p. 500
7.5.4 Inversion of Canopy Reflectance Models	p. 500
7.5.5 Estimation Based on Linear Approximations to the CR Models	p. 505
7.6 Classification of Vegetation	p. 505
7.6.1 Incorporation of Environmental Knowledge into the Classification Process	p. 507
7.6.2 Incorporation of Environmental Data into the Classification Process	p. 508
7.7 Analysis of Vegetation Phenology	p. 509
7.7.1 Germination	p. 510
7.7.2 Seedling Stage	p. 510
7.7.3 Tillering	p. 510
7.7.4 Stem Elongation	p. 510
7.7.5 Booting Stage	p. 510
7.7.6 Heading Stage	p. 510
7.7.7 Flowering or Anthesis Stage	p. 511
7.7.8 Milk Stage	p. 511
7.7.9 Dough Development Stage	p. 512
7.7.10 Ripening Stage	p. 512
7.8 Concluding Remarks	p. 517
Additional Reading	p. 518
References	p. 518
Chapter 8 The Management of Spatial Resources and Decision Support	p. 521
8.1 Introduction	p. 521
8.2 Nature of Management of Rural Physical Resources	p. 523
8.2.1 Introduction	p. 523
8.2.2 Levels of Management	p. 523
8.2.3 Role of Remote Sensing and GIS in Resource Management	p. 527
8.3 Process of Decision Making in Resource Management	p. 529
8.3.1 Selecting an Appropriate Model of Events	p. 529
8.3.2 Parameterise the Model	p. 529
8.3.3 Identify Decision Choices	p. 530
8.3.4 Making the Choice	p. 530
8.3.5 Types of Models and Decisions	p. 530
8.4 Decision Support Systems and Their Role in Decision Making	p. 531
8.4.1 Definitions	p. 533
8.4.2 Multi-Criteria Evaluation	p. 535
8.4.3 Multi-Objective Evaluation	p. 539
8.4.4 Use of Data Supplied by a Third Party for Use by the Analyst	p. 541
8.4.5 Protection of Confidentiality of Data about Third Parties	p. 543
8.4.6 Use of Information Derived from the Analysis of Data	p. 544
8.5 Other Project Management Tools	p. 546
8.6 Concluding Remarks	p. 546
Further Reading	p. 547
References	p. 547
Index	p. 549

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