behzad.rayegani@gmail - JDMAL

24
www.isadmc.ir [email protected]

Transcript of behzad.rayegani@gmail - JDMAL

Page 2: behzad.rayegani@gmail - JDMAL

de Paz et al., 2006; McDonagh

& Bunning, 2009a

McDonagh & Bunning, 2009b

de Paz et al., 2006

Diodato &

Ceccarelli, 2004

Snakin et al., 1996

Cammeraat & Imeson, 1998; de Paz et al., 2006; Diodato & Ceccarelli, 2004; McDonagh & Bunning, 2009a; Omuto, 2008; Rodríguez Rodríguez et al., 2005; Ruiz-Sinoga & Diaz, 2010; Sha-Sha et al., 2011; Snakin et al., 1996; Stocking & Murnaghan, 2000; Yanda, 2000

FAO/UNEP, 1983

ESAs

MEDALUS

ESAs

ESA

Kosmas et al.,

1999

LADAFAO

McDonagh & Bunning, 2009a, b

IMDPA

Provisional methodology for assessment and mapping of

desertification 2Methodology for mapping Environmentally Sensitive Areas(ESAs) to Desertification 3 Mediterranean Desertification and Land Use 4 Land Degradation Assessment in Aridlands 5 Food and Agricultural Organization 6 Iranian Model of Desertification Potential Assessment

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ESAsAhmadi, 2005

Pinet et al., 2006

Yang et al., 2005

Geist & Lambin, 2004; Ladisa et al., 2012; Liu et al.,

2003; Yang et al., 2005

Haijiang et al., 2008; Helldén & Tottrup, 2008; Hill

et al., 2008; Rasmussen et al., 2001

Feoli et al., 2002; Ibáñez et al., 2008; Jauffret & Visser, 2003; Okin et al., 2009; Ravi et al., 2010; Salvati & Zitti, 2009; Santini et al., 2010; Zucca et

al., 2009

Wang et al., 2010

Douaoui et al., 2006; Gutierrez & Johnson, 2010; Masoud, 2014;

Metternicht & Zinck, 2008; Wang et al., 2013

Bouaziz et al., 2011

Huang et al., 2007Dogan, 2009;

Wang et al., 2013

Zhao & Chen, 2005Goodwin et al.,

2008

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Ahmadi, 2004

Ahmadi, 2005

McDonagh & Bunning, 2009a, b

Ravi et al., 2010; Salvati & Zitti, 2009

FAO

Stratified Random Sampling

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ECpH

Ahmadi, 2005; McDonagh & Bunning, 2009a, b

IMD

PA

LAD

A

pH

mlcm

EC

Gutierrez & Johnson, 2010; Li & Chen, 2014; Masoud, 2014; Metternicht &

Zinck, 2008; Wang et al., 2013; Zhao & Chen, 2005

TM

Jensen, 2005

Banding Bad Pixel shot noise

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Jensen, 2005,

2007; Liang, 2004; Mather & Koch, 2011

Jensen, 2005; Liang, 2004; Mather &

Koch, 2011

Jensen, 2005; Liang, 2004; Mather & Koch, 2011

TM

GPS

Jensen, 2005

Li & Chen, 2014; Masoud, 2014

Zhao & Chen, 2005

Wang et al., 2013

Dogan, 2009

Huang et al., 2007Goodwin et al.,

2008

Digital Number(Brightness Value)

2 Dark Object 3 Tasselled Cap Coefficients 4 normalized difference bareness index5 Salinity Index 6 Chemical & Mineral Composition

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Brightness=(0.3037*TM1)+(0.2793*TM2)+(0.4343*TM3)+(0.5585*TM4)+(0.5082*TM5)+(0.1863*TM7) Greenness=(-0.2848*TM1)+(-0.2435*TM2)+(-0.5436*TM3)+(0.7243*TM4)+(0.084*TM5)+(-0.18*TM7) Wetness=(0.1509*TM1)+(0.1793*TM2)+(0.3299*TM3)+(0.3406*TM4)+(-0.7112*TM5)+(-0.4572*TM7)

Li & Chen, 2014; Masoud, 2014

Zhao & Chen, 2005

Wang et al., 2013

Dogan, 2009

Goodwin et al., 2008

Jensen, 2005

Jensen, 2005

Stepwise Artificial neural network

Hill

& Lewicki, 2006

Neuron Node Hidden Unit Fire

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Hill & Lewicki, 2006

Ivancevic & Ivancevic, 2005

Jensen, 2005

Ivancevic & Ivancevic, 2005

Multilayer perceptrons

(MLPs)

Generalized feedforward

networks

MLPMLPGFNGFN

Modular feedforward

networks

MLPMLPMLPMLP

Jordan and Elman

networks

MLPprocessing elementscontext unitsElman

JordanPrincipal

component analysis networks

MLP

Radial basis function (RBF)

Ssigmoidal functionsMLPMLP

Self-

organizing feature maps

(SOFMs

feature mapsKohonenMLP

Time lagged

recurrent networks (TLRNs)

TLRNMLPTLRN

Fully recurrent networks

The CANFIS (Co-Active

Neuro-Fuzzy Inference System)

The Support Vector

Machine (SVM)

kernel Adatron

SVM

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MLP

RBF

CANFISSVM

CANFISSVM

LevenbergMarquardt

Genetic Optimization Algoritm momentum values processing elements Hidden Layers exemplar The Maximum Epochs Learning Rule test train

r

rtotal

Jensen, 2005, 2007; Liang,

2004; Mather & Koch, 2011

enter

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pH

LADA

ECpH

ttrain

ttest

Modular feedforward networksSOFMs

CANFISRBF

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total r

total r

total r

total r

total r

total r

total r

total r

IMDPA

0.623 0.708

0.623

0.708

0.605 0.681

0.628

0.680

0.639

0.698

0.59

0.677

0.591

0.690

0.624 0.680

0.732 0.798

0.732

0.798

0.753 0.819

0.748

0.821

0.770

0.821

0.763

0.809

0.749

0.804

0.751 0.811

0.366 0.411

0.366

0.411

0.366 0.411

0.367

0.413

0.365

0.412

0.293

0.293

0.292

0.292

0.364 0.410

0.552 0.570

0.552

0.570

0.552 0.570

0.552

0.569

0.551

0.568

0.566

0.566

0.571

0.571

0.551 0.569

LADA

0.414 0.414

0.414

0.414

0.414 0.414

0.415

0.415

0.415

0.415

0.408

0.408

0.408

0.408

0.414 0.414

0.437 0.437

0.437

0.437

0.437 0.437

0.438

0.438

0.438

0.438

0.435

0.435

0.435

0.435

0.437 0.437

0.523 0.590

0.523

0.590

0.523 0.590

0.528

0.592

0.528

0.592

0.517

0.540

0.517

0.540

0.523 0.590

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0.381 0.393

0.381

0.393

0.381 0.393

0.383

0.395

0.383

0.395

0.352

0.352

0.361

0.361

0.381 0.393

0.496 0.496

0.496

0.496

0.496 0.496

0.477

0.549

0.477

0.550

0.378

0.513

0.349

0.466

0.496 0.496

0.456 0.456

0.456

0.456

0.456 0.456

0.463

0.463

0.463

0.463

0.402

0.402

0.402

0.402

0.456 0.456

0.555 0.575

0.555

0.575

0.555 0.575

0.573

0.594

0.573

0.594

0.536

0.536

0.535

0.535

0.555 0.575

0.614 0.614

0.614

0.614

0.614 0.614

0.611

0.611

0.611

0.611

0.478

0.478

0.478

0.478

0.615 0.615

VS-Fast

0.543 0.543

0.543

0.543

0.543 0.543

0.538

0.538

0.538

0.538

0.473

0.473

0.474

0.474

0.543 0.543

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rbs risrbts ritsrbs risrbts rits

IMD

PA

.345.377.68.716

LAD

A

.00 .00 .00 .00

.699.699.811 .829 .244 .340 .393 .394

.188.188.410 .417 .00 .00 .496 .560

.524.527.569 .567 pH.00 .162 .456 .555

.281.281.362 .362 .00 .00 .00 .00

LAD

A

.352.352.414.428 .438 .624 .575 .608

.287.409.437 .470 .433 .433 .615 .694

.366.366.590 .590

VS-Fast.419 .419 .543 .546

0.00 0.00 .00 0.00

itsr

btsr

isr

bsr

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IMDPA

.42

0.978

0.404

0.97

0.422

0.998

-0.355

0.92

0.56

0.84

0.283

0.7

0.211

0.98

0.322

0.8

-0.244

0.6

0.467

0.5

0.4

0.5

0.2

.65

0.87

0.60

0.89

0.69

0.99

0.64

0.92

0.64

0.88

0.64

0.82

0.62

0.99

0.57

0.96

0.5

0.82

0.58

0.79

0.5

0.72

0.39

.23

0.64

0.35

0.67

0.3

0.95

0.145

0.78

0.197

0.62

0.35

0.46

0.27

0.82

0.254

0.85

0.23

0.29

0.331

0.47

0.261

0.49

-0.11

..52

0.83

0.315

0.86

0.336

0.995

0.213

0.93

-0.207

0.8

0.207

0.69

0.354

0.98

0.309

0.92

0.323

0.74

0.473

0.61

0.455

0.81

0.254

.292

LADA

.352

.409

0.72

0.330

0.74

0.385

0.997

0.311

0.753

0.277

0.722

0.344

0.56

0.358

0.896

0.320

0.7

0.308

0.47

0.328

0.65

0.333

0.88

0.232

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.366

0.0 .202

.340

0.67

0.349

0.72

0.268

0.986

0.328

0.788

0.299

0.71

0.349

0.53

0.336

0.88

0.263

0.66

0.331

0.44

0.192

0.486

0.302

0.69

0.315

0.0 .486

0.8

0.144

0.81

0.510

0.991

0.266

0.857

0.396

0.789

0.371

0.67

0.335

0.973

0.360

0.844

0.407

0.63

0.371

0.62

0.280

0.776

0.377

.493

0.7

0.404

0.73

0.560

0.987

0.455

0.725

0.490

0.695

0.465

0.57

0.549

0.81

0.545

0.737

0.489

0.358

0.613

0.467

0.556

0.6

-0.119

.433

VS-Fast

.507

0.735

0.329

0.757

0.351

0.949

0.369

0.81

0.270

0.73

0.278

0.68

0.374

0.859

0.186

0.817

0.403

0.63

0.224

0.707

0.336

0.73

0.261

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IMDPA

0.625

0.89

0.555

0.896

0.581

0.998

0.357

0.948

0.649

0.882

0.554

0.85

0.651

0.988

0.576

0.915

0.607

0.6

0.624

0.653

0.603

0.61

0.290

0.773

0.903

0.716

0.92

0.72

0.999

0.5

0.922

0.652

0.912

0.732

0.877

0.735

0.996

0.563

0.964

0.698

0.83

0.639

0.83

0.667

0.73

0.323

0.364

0.73

0.223

0.765

-0.247

0.989

0.499

0.847

0.341

0.74

0.239

0.479

0.289

0.987

-0.312

0.923

-0.343

0.499

0.389

0.458

0.348

0.51

0.136

0.549

0.809

0.885

0.804

0.435

0.998

0.338

0.926

0.096

0.77

0.446

0.683

0.497

0.97

0.470

0.98

0.370

0.783

0.505

0.642

0.524

0.8

0.233

.35

LADA

0.400

0.63

0.281

0.69

0.244

0.997

0.184

0.8

0.344

0.63

0.281

0.5

0.235

0.899

0.171

0.78

0.483

0.5

0.262

0.57

0.286

0.938

0.223

0.470

0.77

0.454

0.786

0.495

0.998

0.413

0.84

0.374

0.764

0.453

0.66

0.425

0.963

0.297

0.69

0.445

0.47

0.313

0.643

0.508

0.876

0.459

0.522

0.797

0.546

0.834

0.546

0.986

0.351

0.845

0.545

0.8

0.548

0.726

0.649

0.953

0.220

0.898

0.389

0.551

0.592

0.667

0.575

0.87

0.638

Page 17: behzad.rayegani@gmail - JDMAL

0 0.228

0.394

0.72

0.283

0.71

0.237

0.985

0.224

0.784

0.297

0.73

0.207

0.55

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0.315

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0.516

0.86

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0.992

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0.150

0.7

0.337

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0.187

0.926

0.287

0.647

0.317

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0.431

0.830

0.241

0.519

0.847

0.229

0.849

0.311

0.994

0.263

0.895

0.390

0.847

0.348

0.774

0.415

0.979

0.152

0.845

0.290

0.69

0.356

0.740

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0.794

0.313

0 0.567

0.876

0.589

0.864

0.596

0.997

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0.878

0.516

0.798

0.588

0.7

0.536

0.978

0.368

0.845

0.616

0.57

0.618

0.674

0.670

0.63

0.283

0.620

0.793

0.364

0.824

0.363

0.992

0.222

0.852

0.433

0.8

0.359

0.68

0.494

0.960

0.268

0.842

0.517

0.69

0.541

0.715

0.553

0.753

0.315

VS-Fast

0.546

0.732

0.370

0.759

0.352

0.958

0.273

0.86

0.301

0.837

0.322

0.7

0.403

0.817

0.509

0.824

0.273

0.7

0.171

0.71

0.378

0.75

0.454

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Reflectance by Origin RadiaceReflectance by Origin Radiace trainrtestr trainrtestr

IMD

PA

0.42

2

0.2

LAD

A

Iron Oxide

0.42

0

0.36

4

NDBS2 Wetness

0.31

8 0.

270

0.70

8 0.

499

Iron OxideNDBS2 NDBS3

Clay Minerals pH

0.18

2 0.

164

0.23

5

0.23

0Ferrous MineralsNDBS3 Brightmess

0.54

0 0.

494

Greenness

0.38

7

0.56

5

Ferrous Minerals Chemical Soil Composition

VS- Fa

st

0.48

4 0.

223

Clay Minerals

trainrtestr trainrtestr trainrtestr

IMD

PA

0.69

1 0.

609

LA

DA

0.40

1 0.

336

LA

DA

pH

FerrousMinerals

0.27

8 0.

063

Iron Oxide

Salinity Index1 Iron Oxide

0.50

6 0.

439

WetnessGreenness

0.47

7 0.

572

0.73

4 0.

708

0.51

0 0.

481

NDBS1Ferrous

Minerals Iron

Oxide

0.46

3 0.

170

0.66

1 0.

576

0.56

3 0.

368

0.33

7 0.

336

Moisture Index

VS-F

ast

1

0.58

9 0.

432

0.59

6 0.

366

NDBS3Greenness

Page 19: behzad.rayegani@gmail - JDMAL

Yang et al, 2005

ECpH

pH

terrain effect

pH

Jensen, 2005

Ivancevic & Ivancevic, 2005

MLP

Ferrous Minerals Ratio Index

Page 20: behzad.rayegani@gmail - JDMAL

Modular feedforward

networks

SOFMs

SVM

CANFIS

RBFPCA

MPL

Jensen, 2005

ECpH

Jensen, 2007

pH

Page 21: behzad.rayegani@gmail - JDMAL

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2. Bouaziz, M., Matschullat, J., & Gloaguen, R. (2011). Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil. Comptes Rendus Geoscience, 343, 795-803

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4. de Paz, J.M., Sanchez, J., & Visconti, F. (2006). Combined use of GIS and environmental indicators for assessment of chemical, physical and biological soil degradation in a Spanish Mediterranean region. J Environ Manage, 79, 150-162

5. Diodato, N., & Ceccarelli, M. (2004). Multivariate indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands. Ecological Indicators, 4, 177-187

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7. Douaoui, A.E.K., Nicolas, H., & Walter, C. (2006). Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 134, 217-230

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9. Feoli, E., Vuerich, L.G., & Zerihun, W. (2002). Evaluation of environmental degradation in northern Ethiopia using GIS to integrate vegetation, geomorphological, erosion and socio-economic factors. Agriculture, Ecosystems and Environment, 91, 313 325

10. Geist, H.J., & Lambin, E.F. (2004). Dynamic Causal Patterns of Desertification. BioScience, 54, 817-829

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No. 6, Autumn & Winter, 2016, pp 54-77

Deser t Managementwww.isadmc.ir

Iranian Scientific Association of Desert Management and Control

Remote Sensed Data Capacities to Assess Soil Degradation B. Rayegani1*

1. Assistant Prof., College of Environment, Department of Environment, Karaj, Iran * Corresponding Author, Email: [email protected]

Received date: 31/10/2014 Accepted date: 08/03/2016

Abstract

This research has tried to take advantage of the two-field models in order to assess the remote sensing data capacities for modeling soil degradation. Based on the findings, pre-processing analysis types have not shown significant effect on accuracy of the model. Conversely type of field model used and its indicators and indices have a large impact on the accuracy of modeling. Also using some remote sensed indices such as Iron Oxide index and Ferrous Minerals index can help to improve the modeling accuracy of some field indices of soil condition assessment. According to the results, using time-series remote sensed data compared to the use of single date data can improve the model capacities significantly. Alse, if artificial neural networks used on single date remote sensed data instead of multivariate linear regression, accuracy of the model can be increased dramatically because it helps the model to take the form of nonlinear. However, if time series of remote sensed data used, the accuracy of the artificial neural network modeling is not much different than the accuracy of regression model. It turned out to be contrary to what is thought but according to the results, increasing the number of inputs to artificial neural network modeling in practice reduces the actual accuracy of the model.

Keywords: Soil Degradation, Iranian Model of Desertification Potential Assessment, LADA methodology of land degradation, Remote sensed time series, Multivariate linear regression, Artificial neural networks