Spatial Modeling in GIS and R for Earth and Environmental Sciences

Cover......Page 1
Spatial Modeling in GIS and R for Earth and Environmental Sciences......Page 3
Copyright......Page 4
Dedication......Page 5
List of Contributors......Page 6
1.1 Introduction......Page 14
1.2 The Study Area......Page 17
1.3 Methodology and Data......Page 18
1.4 Results......Page 23
1.5 Performance Criteria......Page 25
1.6 Discussion......Page 27
1.7 Conclusions......Page 28
References......Page 29
2.1 Introduction......Page 33
2.2.1 Study Area......Page 35
2.2.2 Data......Page 36
2.2.3 Vegetation Indices......Page 39
2.3.1.1 Normalized Difference Vegetation Index......Page 40
2.3.1.3 Green Normalized Difference Vegetation Index......Page 44
2.3.1.4 Global Environmental Monitoring Index......Page 47
2.3.2 Spatial Normalized Differential Reflectance and Shortwave Crop Reflectance Index......Page 49
2.3.3.1 Prespring Rainfall......Page 55
2.3.3.2 Cumulative Rainfall......Page 56
2.3.3.3 Rainfall Distribution......Page 58
References......Page 60
Further Reading......Page 64
3.1 Introduction......Page 66
3.2.1 Multiple Occurrence Regional Landslide Event, Messina, 2009......Page 68
3.2.2 Computing Slope Units in GIS......Page 71
3.3.1 Preprocessing......Page 72
3.3.2 Fitting a Cox Point Process Model Using R-INLA......Page 74
3.4.1 Estimated Fixed and Random Effects......Page 77
3.4.2 Estimated Landslide Intensity at Various Spatial Resolutions......Page 82
3.4.3 Model Checking and Goodness-of-Fit Assessment......Page 83
3.4.4 Cross-Validation Study and Out-of-Sample Predictive Skill......Page 85
3.5 Discussion......Page 87
3.6 Conclusion......Page 90
References......Page 91
4.1 Introduction......Page 95
Criteria and Subcriteria......Page 96
4.1.1.4 Interpretation Findings......Page 97
4.1.2 Classification of Spatial Decision Support System......Page 98
4.1.2.1 Geostatistical Analysis With R packages......Page 99
Tactical Planning......Page 100
Operational Planning......Page 101
4.1.3.1 General Information......Page 102
4.2 Planning Problems......Page 103
4.3.1 Multicriteria Decision Analysis......Page 105
4.3.2 Geostatistical Analysis......Page 107
4.3.3 Spatial Modeling Procedure......Page 108
4.4.1 The Current Conditions of the Terrain......Page 111
4.5 Discussion......Page 116
4.6 Conclusions......Page 121
References......Page 122
Further Reading......Page 126
5.1 Introduction......Page 127
5.2.1 Study Area......Page 131
5.2.3.1 KINEROS......Page 132
5.2.3.2 Optimization Algorithm......Page 135
5.2.3.3 Model Evaluation......Page 136
5.2.3.4 Parameters of Model in Optimization Process......Page 137
5.3 Results and Discussion......Page 138
5.4 Conclusion......Page 149
References......Page 151
Further Reading......Page 156
6.1 Introduction......Page 157
6.3.1 Land-Subsidence Inventory Mapping......Page 158
6.3.2 Effective Factors on Land Subsidence......Page 159
6.3.3 Spatial Relationship Between Land-Subsidence Locations and Different Effective Factors......Page 160
6.4.1 Investigating the Spatial Relationship Between Effective Factors and the Occurrence of Land Subsidence Using the FR Model......Page 161
6.4.3 Preparing the Land-Subsidence Susceptibility Map Using an RF Model......Page 164
References......Page 167
7.1 Introduction......Page 170
7.3 Methodology......Page 172
7.3.1 Multicriteria Decision-Making Drawbacks......Page 177
7.3.2 Radial Basic Function......Page 178
7.3.3.1 Initial Empires Creation......Page 179
7.3.3.2 Assimilation Policy......Page 180
7.3.3.5 The Calculation of Empires’ Power......Page 181
7.3.3.6 Empires’ Competition......Page 182
7.3.4 Combination of SWARA, RBF, and ICA......Page 183
7.4 Results......Page 184
7.4.2 The Location of Schools Regarding the Population Density and Proximity to Residential Areas......Page 186
7.4.3 The Location of Schools Regarding Accessibility to the Urban Road Network......Page 189
7.4.5 The Location of Schools Regarding Cultural and Recreational Centers......Page 190
7.5 Discussions......Page 191
7.6 Conclusions......Page 192
References......Page 193
8.1 Introduction......Page 198
8.2 Study Area and Data Analysis......Page 200
8.2.1 Data......Page 201
8.2.2 Methodology......Page 204
8.2.2.2 Fault Criteria......Page 205
8.2.2.4 Geology......Page 206
8.2.3 Nomination Criteria for Evaluating and Ranking Suitable Sites......Page 207
Analytic Hierarchy Process......Page 209
Technique for Order Performance by Similarity to Ideal Solution......Page 211
8.3.1 First Step (Boolean Algorithm)......Page 213
8.3.2 Secondary Step......Page 214
8.5 Conclusions......Page 216
References......Page 217
Further Reading......Page 220
9.1 Introduction......Page 221
9.2.1 Study Area......Page 222
9.2.3 Geo-Environmental Variables......Page 223
9.2.4.1 Generalized Linear Model (GLM)......Page 225
9.2.5 Model Validation......Page 227
9.3.1 Application of GLM......Page 228
9.3.2 Validation of the Habitat Suitability Map......Page 230
9.4 Conclusion......Page 231
References......Page 232
10.1 Introduction......Page 236
10.2 Study Area......Page 238
10.4 Data Used and Methodology......Page 241
10.4.1 Flood-Related Factors......Page 243
10.4.2 Application of AHP Approach......Page 248
10.4.3 Application of Remote Sensing to Establish a Flood Inventory Map......Page 249
10.5.1 Drainage Networks and Their Characteristics......Page 250
10.5.2 Flash Flood Susceptibility Map......Page 251
10.5.3 Real Flood Area Extraction From Satellite Images......Page 254
10.6 Model Validation......Page 255
References......Page 258
Further Reading......Page 264
11.1 Introduction......Page 265
11.2.1 Case Study......Page 266
11.2.2 Methodology......Page 267
11.2.2.1 Binary Logistic Regression......Page 268
11.2.2.2 Bayesian Theory......Page 269
11.2.2.3 SINMAP Model......Page 270
11.2.2.5 Random Forest Algorithm......Page 271
11.3.1 Binary Logistic Regression......Page 272
11.3.2 Landslide Susceptibility Map Using ANFIS......Page 274
11.4 Conclusion......Page 278
References......Page 279
Further Reading......Page 281
12.1 Introduction......Page 282
12.2 Study Area......Page 283
12.3 Data Collection......Page 285
12.4.1 Logistic Regression......Page 289
12.4.2 General Additive Model......Page 290
12.5 Spatial Prediction......Page 291
12.6 Results......Page 292
12.7 Discussion......Page 294
References......Page 299
13.1 Introduction......Page 303
13.2 Study Area......Page 305
13.3.1 Gully Erosion Inventory Map......Page 306
13.3.2.4 Plan Curvature......Page 307
13.3.2.8 Topography Wetness Index......Page 310
13.3.2.11 Normalized Difference Vegetation Index......Page 311
13.3.3 Multicollinearity Test......Page 312
13.3.5 Validation of Models......Page 313
13.4.1 Multicollinearity......Page 314
13.4.2 Applying the Linear Discriminant Analysis Model......Page 315
13.4.3 Applying the Quadratic Discriminant Analysis Model......Page 317
13.4.4 Validation of Models......Page 318
13.5 Conclusion......Page 320
References......Page 321
Further Reading......Page 325
14.1 Introduction......Page 326
14.2.2 Methodology......Page 327
14.2.2.2 Flood Conditioning Factors......Page 329
14.2.2.4 Variable Contribution Analysis......Page 331
14.3 Results and Discussion......Page 332
References......Page 335
Further Reading......Page 339
15.1 Introduction......Page 340
15.2.1 Study Area......Page 343
15.2.2 Used Data......Page 344
15.3.1 Interval Rough Numbers......Page 346
15.3.2 Rough Analytical Hierarchy Process Method......Page 348
15.4 Results......Page 352
15.4.2 The Application of the IR’AHP Model......Page 353
15.4.3 Weighted Linear Combination Aggregation......Page 360
15.4.4 Validation and Final Results......Page 363
15.5 Discussion......Page 364
15.6 Conclusion......Page 366
References......Page 367
Further Reading......Page 372
16.1 Introduction......Page 373
16.1.1 Background Research......Page 374
16.2.2 Research Methodology......Page 376
16.2.2.1 AHP Model......Page 378
16.2.2.2 Technique for Order of Preference by Similarity to the Ideal Solution Model......Page 380
16.3 Results and Discussion......Page 382
References......Page 389
Further Reading......Page 392
17.1 Introduction......Page 393
17.2 Study Area......Page 396
17.3.1 Data......Page 398
17.3.2 Methodology......Page 401
17.4 Results and Discussion......Page 406
References......Page 409
Further Reading......Page 412
18.1 Introduction......Page 413
18.2.1 Study Area......Page 414
18.3.1 Dataset Preparation for Habitat Suitability Modeling......Page 415
18.3.3 Modeling of Habitat Suitability of Zataria multiflora Using the Support Vector Machine Model......Page 420
18.3.5 Accuracy Assessment......Page 421
18.4.2 Results of Variable Importance of Effective Factors......Page 422
18.4.3 Habitat Suitability Using the S V M Model......Page 423
18.4.4 Validation of Habitat Suitability Map of Zataria multiflora......Page 425
References......Page 426
Further Reading......Page 429
19.1 Introduction......Page 430
19.2.1 Study Area......Page 431
19.2.2 Soil Organic Carbon Analysis From Field Data......Page 433
19.2.3 Data Collection and Processing......Page 434
19.2.4 The Predicted Variables (NDVI, MSAVI, RDVI, and MNLI)......Page 435
19.2.6 Model Validation......Page 437
19.3.2 Predictor Variables......Page 438
19.3.3 Analysis of Predictor Variables and Soil Organic Carbon Relationship......Page 440
19.3.4.1 Validation of Results......Page 442
19.4 Discussion......Page 445
References......Page 447
20.1 Introduction......Page 452
20.2 Study Area......Page 453
20.3 Methodology......Page 454
20.3.1 Aerial Image Acquisition Using Unmanned Aerial Vehicle Technology......Page 455
20.3.2 The Processing Stage of Aerial Images......Page 457
20.4 Results......Page 460
20.5 Conclusions......Page 463
References......Page 464
Further Reading......Page 466
21.1 Introduction......Page 467
21.3.1 Landslide Inventory Mapping......Page 468
21.3.2 Landslide Conditioning Factors......Page 470
21.3.3.1 Functional Data Analysis......Page 471
21.3.3.2 Generalized Linear Model......Page 474
21.4.1 Variable Importance......Page 475
21.4.2 Application of Functional Data Analysis......Page 476
21.4.4 Validation of Landslide Susceptibility Models......Page 477
21.5 Discussion......Page 478
References......Page 481
22.1 Introduction......Page 485
22.3 Methodology......Page 486
Topographic Factors......Page 488
Geological Factors......Page 491
22.3.3 Sensitivity Analysis and Model Performance......Page 492
22.4.1 Application of the Classification and Regression Tree Model for Groundwater Potentiality Mapping......Page 493
22.4.3 Sensitivity Analysis......Page 494
References......Page 496
Further Reading......Page 498
23.1 Introduction......Page 499
23.2 Study Area and Data......Page 501
23.3.1 Creation of Image Objects......Page 503
23.3.2 Selection of the Most Effective Object Features......Page 505
23.3.5 Canonical Correlation Forest Classifier......Page 506
23.4 Results and Discussion......Page 508
23.5 Conclusions......Page 513
References......Page 515
24.1 Introduction......Page 518
24.2.1 Theoretical Background and Practical Implementation......Page 520
24.2.2 Preparation and Selection of Spatial Data......Page 522
24.2.3 Modeling Algorithms......Page 526
24.3 Results: How to Evaluate a Statistical Landslide Susceptibility Model......Page 528
24.4.1 Challenges in Statistical Landslide Susceptibility Modeling......Page 531
24.4.2 Beyond a Data-Driven Identification of Landslide-Prone Zones......Page 533
24.5 Conclusion: A Word of Caution......Page 536
References......Page 537
25.1 Introduction......Page 546
25.2.1 Study Area......Page 547
25.2.2 Data Collection......Page 548
25.2.5.1 Groundwater Occurrence......Page 549
25.2.5.3 Groundwater Table Drawdown......Page 550
25.2.5.8 Bedrock Depth......Page 553
25.2.6.1 Generalized Linear Model......Page 554
25.2.6.3 Support Vector Machine......Page 555
25.3.1 Training/Calibration of Data-Mining Models......Page 556
25.3.2 Vulnerability Map of Data-Mining Models......Page 557
25.3.3 Importance of Factors by Learning Vector Quantization......Page 558
25.3.4 Validation Map of Data-Mining Models......Page 560
25.4 Discussion......Page 561
25.5 Conclusions......Page 562
References......Page 563
Further Reading......Page 570
26.1 Introduction......Page 571
26.2.1 Study Area and Dataset......Page 573
26.2.2.1 Segmentation and Region Merging......Page 574
26.2.2.2 Region Adjacency Graph Construction......Page 575
26.2.2.4 Multigraph Matching......Page 576
26.2.2.5 Correspondence Discovery......Page 577
26.2.2.6 Occlusion Detection......Page 578
26.2.2.8 Moving Object Detection......Page 579
26.3.2 Region Merging......Page 580
26.3.3 Multigraph Matching......Page 581
26.3.5 Moving Object Detection......Page 582
References......Page 584
27.1 Introduction......Page 587
27.2 Material and Methods......Page 589
27.2.2 Field Sampling......Page 590
27.2.3.2 Weather......Page 591
27.2.3.3 Vegetation......Page 592
27.2.4 Statistical Analysis......Page 593
27.3 Results......Page 594
27.4 Discussion......Page 597
References......Page 600
Further Reading......Page 604
28.1 Introduction......Page 605
28.3 Materials and Methods......Page 606
28.3.1 Data Collection and Processing......Page 607
28.3.3 Probability Modeling Using the Support Vector Machine Model......Page 609
28.4.1 Factor Importance......Page 610
28.4.2 Prediction Map......Page 612
References......Page 614
Further Reading......Page 617
29.1 Introduction......Page 618
29.1.2 Urban Growth Processes......Page 619
29.1.3 Geographic Information Systems and Remote Sensing Techniques in Urban Growth Analysis......Page 621
29.2.1 Study Area......Page 623
29.2.2 Satellite Image Selection and Preprocessing......Page 624
29.2.3.2 Normalized Difference Built-Up Index......Page 625
29.2.3.4 Normalized Difference Water Index......Page 626
29.2.4 Time-Series Image Classification......Page 627
29.2.4.1 Cross-Validation......Page 628
29.2.5 Measuring Urban Sprawl......Page 630
29.3.1 Time-Weighted Dynamic Time Warping Classification......Page 631
29.3.2 Time-Weighted Dynamic Time Warping Validation......Page 634
29.3.3 Urban Footprints......Page 637
29.3.4 Spatiotemporal Changes and Urban Sprawl......Page 638
Acknowledgments......Page 641
References......Page 642
30.1 Introduction......Page 649
30.2.1 Study Area......Page 650
30.2.2 Gully Erosion Inventory Mapping......Page 651
30.2.3.1 Primary Topographical Attributes Maps......Page 652
30.2.3.3 Linear Feature Maps......Page 653
30.3.1 Application of Classification and Regression Tree......Page 656
30.4.1 Gully Erosion Susceptibility models......Page 657
30.4.2 Validation of Machine Learning Models......Page 659
References......Page 661
Further Reading......Page 665
31.1 Introduction......Page 666
31.1.1 The State of the Art......Page 667
31.2 Valjevo Case Study......Page 668
31.2.1 Study Area Setting......Page 669
31.3 Materials and Methods......Page 671
31.3.1 Data Preparation......Page 672
31.3.2 Methodology......Page 673
31.3.2.2 Overlay Code......Page 676
31.4 Results and Discussion......Page 677
31.5 Conclusions......Page 680
References......Page 681
32.1 Introduction......Page 684
32.2 Study Area......Page 687
32.4 Landslide Inventory Mapping and Thematic Layers......Page 689
32.5 Analyzing Fractal Dimensions and Geomorphometric Indices......Page 697
32.5.1 Shannon’s Entropy......Page 699
32.6 Ensemble Modeling of Shannon’s Entropy and Fractal Dimension......Page 700
32.7 Intercomparison and Validation of Model Results......Page 701
32.8.1 Fractal Dimension and Geomorphometric Indices......Page 702
32.8.2 Shannon’s Entropy and the Fractal Dimensions......Page 709
32.8.3 Intercomparison and Validation of Models......Page 712
32.9 Discussion......Page 715
32.10 Limitations and Future Work......Page 719
32.11 Conclusion......Page 720
References......Page 721
Appendix I Different Landslide Types in the Study Area (the Rows Are in Line With Table 32-5)......Page 727
33.1 Introduction......Page 728
33.3.1 Field Measurements......Page 729
33.3.2 Spatial Analysis......Page 730
33.4.1 Ecological Attributes in Rangeland Ecosystems......Page 732
33.4.2 Diagnostic Dynamic Patterns of Desertification in Rangelands......Page 734
33.4.3 Statistical Analysis of Plant Spatial Patterns......Page 735
33.4.5 Introducing the Statistical Results to the New Monitoring Model......Page 736
33.5 Conclusion......Page 738
References......Page 739
Index......Page 742
Back Cover......Page 766