Proximal Soil Sensing (eBook)

Raphael A. Viscarra Rossel, Alex B. McBratney, Budiman Minasny (Herausgeber)

eBook Download: PDF

2010 | 2010
XXIV, 448 Seiten
Springer Netherland (Verlag)
978-90-481-8859-8 (ISBN)

Infos

This book reports on developments in Proximal Soil Sensing (PSS) and high resolution digital soil mapping. PSS has become a multidisciplinary area of study that aims to develop field-based techniques for collecting information on the soil from close by, or within, the soil. Amongst others, PSS involves the use of optical, geophysical, electrochemical, mathematical and statistical methods. This volume, suitable for undergraduate course material and postgraduate research, brings together ideas and examples from those developing and using proximal sensors and high resolution digital soil maps for applications such as precision agriculture, soil contamination, archaeology, peri-urban design and high land-value applications, where there is a particular need for high spatial resolution information. The book in particular covers soil sensor sampling, proximal soil sensor development and use, sensor calibrations, prediction methods for large data sets, applications of proximal soil sensing, and high-resolution digital soil mapping.

Key themes: soil sensor sampling - soil sensor calibrations - spatial prediction methods - reflectance spectroscopy - electromagnetic induction and electrical resistivity - radar and gamma radiometrics - multi-sensor platforms - high resolution digital soil mapping - applications

Raphael A. Viscarra Rossel is a scientist at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) of Australia.

Alex McBratney is Pro-Dean and Professor of Soil Science in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia.

Budiman Minasny is a Senior Research Fellow in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia.

Inhalt

Foreword 6
Proximal Soil Sensing: Looking, Touching, Feeling 6
Preface 8
Acknowledgements 10
Contents 11
About the Editors 15
Contributors 16
Part I Overview 24
1 Sampling for High-Resolution Soil Mapping 25
1.1 Introduction 25
1.2 Materials and Methods 27
1.2.1 Sensor Sampling: Some Theory 27
1.2.1.1 Optimisation for Equipment Type A 27
1.2.1.2 Optimisation for Equipment Type B 29
1.2.2 Sensor Sampling: Some Experiments 30
1.2.3 Calibration Sampling 31
1.2.3.1 Latin Hypercube Sampling 32
1.2.3.2 Sampling by Response Surface Methodology 33
1.2.3.3 Model-Based Sampling for Universal Kriging 33
1.2.3.4 Sampling by Fuzzy Cluster Analysis 33
1.3 Results and Discussion 35
1.4 Conclusions 35
References 36
2 Development of On-the-Go Proximal Soil Sensor Systems 37
2.1 Introduction 37
2.2 Sensor Development Review 38
2.2.1 Electrical and Electromagnetic Sensors 39
2.2.2 Optical and Radiometric Sensors 40
2.2.3 Mechanical Sensors 42
2.2.4 Acoustic and Pneumatic Sensors 44
2.2.5 Electrochemical Sensors 44
2.3 Sensor Applications 46
2.3.1 Multisensor Data Fusion 46
2.3.2 Sensor Deployment 48
2.4 Conclusions 49
References 49
3 Diffuse Reflectance Spectroscopy for High-Resolution Soil Sensing 51
3.1 Introduction 51
3.2 Fundamentals of Diffuse Reflectance Spectroscopy 52
3.3 Soil Diffuse Reflectance Spectra 53
3.3.1 Vis--NIR 53
3.3.2 Mid-IR 54
3.4 Mathematical Preprocessing of Spectra 55
3.5 Spectroscopic Multivariate Calibrations 57
3.6 Spectroscopic Calibrations for Predictions of Soil Properties 58
3.6.1 Visible--Near-Infrared (Vis--NIR) Calibrations 58
3.6.2 Mid-Infrared (Mid-IR) Calibrations 61
3.6.3 Generalisation and Limitations of Spectroscopic Calibrations 61
3.7 Proximal Soil Sensing Using Portable Spectrometers 64
3.8 Conclusions 65
References 66
4 High-Resolution Digital Soil Mapping: Kriging for Very Large Datasets 70
4.1 Introduction 70
4.2 Spatial Covariance Function 73
4.3 Kriging: Optimal Linear Spatial Prediction 73
4.4 Soil Properties on a Portion of Nowley Farm, New South Wales, Australia 76
4.5 Conclusions 82
References 83
Part II Soil Sensing and Sampling 85
5 The Sun Has Shone Here Antecedently 86
5.1 Introduction 87
5.2 High-Resolution Digital Soil Sensing and Mapping 87
5.3 The Precocious and Prescient Contribution of Haines and Keen 88
5.3.1 The Rationale: Cultivation and Soil Strength 88
5.3.2 The Sensor: A (Pre-electronic) Design for a Soil Draught Force Sensor 88
5.3.3 Data Logging: Analogue Data Recording 89
5.3.4 Data Analysis: Spatial Variation and Data Filtering 89
5.3.5 The Product: The First High-Resolution Digital Soil Map 90
5.4 Degrees of Separation 91
5.5 Conclusions 93
References 93
6 Proximal Soil Nutrient Sensing Using Electrochemical Sensors 95
6.1 Introduction 95
6.2 Proximal Soil Sensing Using Electrochemical Sensors 96
6.2.1 Electrochemical Sensors 96
6.2.2 Soil Nutrient Analysis Using Electrochemical Sensors 98
6.2.3 PSS: Stationary In Situ Analysis 99
6.2.4 PSS: On-the-Go 100
6.2.4.1 Direct Soil Measurement 100
6.2.4.2 Agitated Soil Measurement (ASM) 101
6.2.4.3 Batch/Chamber-Based Methods 101
6.2.4.4 Flow Injection Analysis (FIA) 102
6.2.5 Addressing Limitations of Electrochemical Sensors for Proximal Soil Sensing 103
6.3 Conclusions 104
References 105
7 DIGISOIL: An Integrated System of Data Collection Technologies for Mapping Soil Properties 107
7.1 Introduction 107
7.2 Objectives 108
7.3 Strategy and Workplan 111
7.4 From Soil Threats to Geophysical Properties 112
7.5 Conclusions 118
References 118
8 iSOIL: An EU Project to Integrate Geophysics, Digital Soil Mapping, and Soil Science 120
8.1 Introduction 121
8.2 General Objectives 122
8.3 Motivation of the Project 122
8.3.1 Development of Geophysical Techniques 123
8.3.2 Development of Geophysical Transfer Functions 123
8.3.3 Digital Soil Mapping 124
8.4 Structure of the Project 125
8.5 Conclusions 126
References 127
9 Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties 128
9.1 Introduction 128
9.2 Theory 130
9.3 Applications 132
9.4 Results and Discussion 134
References 136
10 Response Surface Sampling of Remotely Sensed Imagery for Precision Agriculture 137
10.1 Introduction 138
10.2 Material and Methods 139
10.2.1 Remote Sensing and Image Processing 139
10.2.2 Directed Sampling 139
10.2.3 Ground Sampling 140
10.2.4 Statistical Analysis and Mapping 141
10.3 Results and Discussion 141
10.4 Conclusions 144
References 145
Part III Soil UV, Visible, and Infrared Spectral Sensing 146
11 Mid- Versus Near-Infrared Spectroscopy for On-Site Analysis of Soil 147
11.1 Introduction 148
11.2 Materials and Methods 148
11.2.1 Soil Samples 148
11.2.2 Compositional Determination 149
11.2.3 Fourier Transform Spectrometer (FTS) 149
11.2.4 Non-FTS NIR Spectroscopy 150
11.2.5 Chemometrics 150
11.3 Results and Discussion 151
11.4 Conclusions 155
References 156
12 Determination of Soil Nitrate and Organic Matter Content Using Portable, Filter-Based Mid-Infrared Spectroscopy 157
12.1 Introduction 158
12.2 Materials and Methods 159
12.2.1 FTIR/ATR Spectrometer Tests 159
12.2.2 Portable Filter-Based Spectrometer Tests 160
12.2.2.1 Nitrate Experiments 160
12.2.2.2 Organic Matter Experiments 161
12.3 Results and Discussion 161
12.3.1 FTIR/ATR Spectrometer Test Results 161
12.3.2 Filter-Based Spectrometer Test Results 162
12.3.2.1 Nitrate Results 163
12.3.2.2 Organic Matter Results 165
12.4 Conclusions 165
References 166
13 VNIR Spectroscopy Estimates of Within-Field Variability in Soil Properties 167
13.1 Introduction 168
13.2 Materials and Methods 169
13.2.1 Study Site and Soil Sampling 169
13.2.2 Spectral Data Acquisition 169
13.2.3 Analysis Procedures 170
13.3 Results and Discussion 171
13.3.1 Variability in Soil Properties 171
13.3.2 Predictive Capability of Spectral Regions 171
13.3.3 Regression Kriging 174
13.4 Conclusions 176
References 177
14 Infrared Sensors to Map Soil Carbon in Agricultural Ecosystems 178
14.1 Introduction 179
14.2 Materials and Methods 180
14.2.1 Soil Samples 180
14.2.2 Spectral Measurements 180
14.2.2.1 Airborne Measurements 180
14.2.2.2 Proximal Measurements 181
14.2.2.3 Laboratory Measurements 182
14.2.3 Chemometrics 182
14.3 Results and Discussion 183
14.3.1 Calibration and Validation 183
14.3.2 Potential for Optimising Sampling Design 186
14.3.3 Remote Sensing of Soil Carbon 186
14.4 Conclusions 188
References 189
15 Predicting Soil Carbon and Nitrogen Concentrations and Pasture Root Densities from Proximally Sensed Soil Spectral Reflectance 190
15.1 Introduction 191
15.2 Materials and Methods 191
15.2.1 Contact Probe Modification and Measurement Techniques 191
15.2.2 Site Locations and Sample Collection 192
15.2.3 Measurement of Soil Properties 193
15.2.4 Spectral Pre-processing and Data Analysis 193
15.2.5 Regression Model Accuracy 194
15.3 Results and Discussion 195
15.3.1 C and N Prediction of Taupo--Rotorua Allophanic, Pumice, and Tephric Recent Soil 195
15.3.2 Comparison Between H and V Method for Fluvial Recent Soil 197
15.3.3 Vertical Method on Fluvial Recent Soil Collected in Autumn (May) 199
15.3.4 Is the Calibration Model Influenced by Temporal Variations in the Soil? 199
15.3.5 Root Density Prediction on Ramiha and Manawatu Soil 201
15.4 Conclusions 202
References 203
16 Diagnostic Screening of Urban Soil Contaminants Using Diffuse Reflectance Spectroscopy 204
16.1 Introduction 204
16.2 Materials and Methods 205
16.2.1 Location 205
16.2.2 Diffuse Spectral Reflectance Measurements 206
16.2.3 Statistical Analysis 206
16.2.4 Diagnostic Screening of Soil Contaminants 206
16.3 Results and Discussion 207
16.3.1 Exploratory Data Analysis 207
16.3.2 Spectroscopic Analysis 207
16.3.3 Diagnostic Screening Using Ordinal Logistic Regression 209
16.4 Conclusions 211
References 211
17 Using Wavelets to Analyse Proximally Sensed Vis--NIRSoil Spectra 213
17.1 Introduction 213
17.2 Materials and Methods 214
17.2.1 The Soil Spectral Library 214
17.2.2 Proximal Vis--NIR Sensing of Soil Profiles 215
17.2.3 The Wavelet Transform 216
17.2.4 Multivariate Calibrations 216
17.3 Results 217
17.3.1 The Soil Vis--NIR Spectral Library and Validation Samples 217
17.3.2 A Multiresolution Analysis (MRA) 218
17.3.3 The Wavelet Transform for Data Compression and Multivariate Calibrations 219
17.3.4 Denoising by Back-Transforming the Wavelet Coefficients 220
17.4 Discussion 221
17.5 Conclusions 221
References 222
18 Mapping Soil Surface Mineralogy at Tick Hill, North-Western Queensland, Australia, Using Airborne Hyperspectral Imagery 223
18.1 Introduction 224
18.2 Tick Hill Study Area 226
18.3 Materials and Methods 228
18.3.1 Geoscience Mapping Data and Processing 228
18.3.2 Airborne HyMap Data Processing for Mineral Mapping 229
18.3.3 Field Samples and Related Laboratory Analyses 230
18.3.4 Field Validation of Airborne Mineral-Mapping Results 231
18.4 Results and Discussion 231
18.4.1 Field Samples 231
18.4.2 Airborne Versus Field Spectra 232
18.4.3 Mineral Group Abundances 232
18.4.4 Clay Mineral Abundances 235
18.4.5 Clay Mineral Physicochemistry 236
18.4.6 Other Products 237
18.4.7 Integrated Mineral Analysis 238
18.5 Conclusions 239
References 239
Part IV Soil Sensing by Electromagnetic Induction and Electrical Resistivity 242
19 Combining Proximal and Penetrating Soil Electrical Conductivity Sensors for High-Resolution Digital Soil Mapping 243
19.1 Introduction 244
19.2 Materials and Methods 245
19.2.1 Soil Landscapes, Measurements, and Observations 245
19.2.2 ECa--P Measurement 245
19.2.3 ECa--M Measurement 246
19.2.4 Proximal ECa Measurement 246
19.3 Results and Discussion 247
19.3.1 Soil Profile ECa 247
19.3.2 ECa--P Predicted Depth to Claypan 249
19.3.3 Calibrating ECa to ECa--P Features 249
19.3.4 Profile Sources of Proximal ECa 251
19.4 Conclusions 252
References 253
20 A Neural Network Approach to Topsoil Clay Prediction Using an EMI-Based Soil Sensor 254
20.1 Introduction 255
20.2 Materials and Methods 255
20.2.1 Study Site and Data Collection 255
20.2.2 Neural Network Analysis 256
20.2.3 Multivariate Linear Regression 257
20.3 Results and Discussion 258
20.4 Conclusions 262
References 262
21 Field Determination of Soil Moisture in the Root Zone of Deep Vertosols Using EM38 Measurements: Calibration and Application Issues 264
21.1 Introduction 265
21.2 Materials and Methods 266
21.2.1 Study Area 266
21.2.2 EM38 Depth--Response Function 266
21.2.3 Field Calibration and Prediction of Average Moisture Content at Depth 267
21.3 Results and Discussion 268
21.4 Conclusions 271
References 272
22 Can the EM38 Probe Detect Spatial Patterns of Subsoil Compaction? 273
22.1 Introduction 273
22.2 Materials and Methods 276
22.2.1 Measurement of Penetration Resistance (PR) 276
22.2.2 Measurement of Apparent Electrical Conductivity (ECa) 276
22.2.3 Study Sites 276
22.3 Results and Discussion 277
22.4 Conclusions 280
References 281
23 Changes in Field Soil Water Tracked by Electrical Resistivity 282
23.1 Introduction 283
23.2 Materials and Methods 283
23.2.1 Characteristics of the Soils Studied 283
23.2.2 Soil Water Content Monitoring at the Field Scale 283
23.2.3 Electrical Monitoring Over Time 284
23.2.4 Spatial and Temporal Variability Analysis 285
23.3 Results and Discussion 286
23.3.1 Statistical Relationship Between Electrical Resistivity and Soil Water Content 286
23.3.2 Spatial Analysis of the Experimental Data 286
23.3.3 Temporal Analysis of the Experimental Data 288
23.4 Conclusion 289
References 289
24 Is a Systematic Two-Dimensional EMI Soil Survey Always Relevant for Vineyard Production Management? A Test on Two Pedologically Contrasting Mediterranean Vineyards 290
24.1 Introduction 291
24.2 Materials and Methods 291
24.2.1 Location, Geology, and Pedology 291
24.2.2 Geophysical Surveys 292
24.2.3 Soil Survey 293
24.2.4 NDVI Maps of Vine Vigour and Map Comparison 294
24.3 Results 295
24.3.1 Relations Between NDVI, Soil, and ECa 295
24.3.2 Differences Between Different ECa Measurements 296
24.3.2.1 Erratic Shifting with Mobile EMI 296
24.3.2.2 Comparison Between R--ECa and I--ECa 297
24.3.3 Electrical Conductivity of Different Soils and Materials in the Two Blocks 298
24.3.4 Soil Type Detection with R--ECa Data 299
24.4 Discussion and Conclusions 301
References 302
Part V Radar and Gamma Radiometric Sensors 303
25 Full-Waveform Modelling and Inversion of Ground-Penetrating Radar Data for Non-invasive Characterisation of Soil Hydrogeophysical Properties 304
25.1 Introduction 305
25.2 Ground-Penetrating Radar 306
25.3 Full-Waveform Analysis of Proximal GPR Data 308
25.3.1 GPR Forward Modelling 308
25.3.1.1 Antenna Equation in the Frequency Domain 308
25.3.1.2 Zero-Offset Green's Function for Multilayered Media 309
25.3.2 Model Inversion 311
25.3.3 Model Validation and Applications 311
25.4 Conclusions 314
References 314
26 Using Proximal Sensors to Continuously Monitor Agricultural Soil Physical Conditions for Tillage Management 317
26.1 Introduction 318
26.2 Materials and Methods 318
26.2.1 Description of the Sensors 318
26.2.2 Field Experiment 320
26.2.3 Data Acquisition 320
26.3 Results and Discussion 321
26.3.1 Soil Physical Conditions 321
26.3.2 Radar Data 321
26.3.3 Capacitance Probe 322
26.3.4 Mechanical Resistance Probe 323
26.3.5 Tillage Effects on Seedling Emergence 323
26.4 Conclusions 325
References 325
27 Gamma Ray Sensor for Topsoil Mapping: The Mole 326
27.1 Introduction 326
27.2 Equipment and Data Analysis Methods 328
27.2.1 Hardware 328
27.2.2 Spectral Data Analysis 328
27.2.3 Fingerprinting and Soil Sampling 330
27.3 Applications 333
27.4 Future Developments 333
27.5 Conclusions 335
References 335
28 Gamma Ray Sensing for Cadmium Risk Assessment in Agricultural Soil and Grain: A Case Study in Southern Sweden 336
28.1 Introduction 336
28.2 Materials and Methods 337
28.3 Results and Discussion 339
28.4 Conclusions 344
References 345
29 Use of EM38 and Gamma Ray Spectrometry as Complementary Sensors for High-Resolution Soil Property Mapping 346
29.1 Introduction 347
29.2 Materials and Methods 348
29.2.1 Location and Soil 348
29.2.2 EM38 and .-Radiometric Survey 348
29.2.3 Sensor Response and Interpretation 349
29.3 Results and Discussion 349
29.4 Conclusions 351
References 352
Part VI Multisensor Systems and Other Sensors 353
30 Field-Scale Draught Resistance and Soil Moisture Measurement in Australia Using a Tine-Based ForceCapacitance Sensing System 354
30.1 Introduction 354
30.2 Materials and Methods 356
30.3 Results and Discussion 358
30.3.1 Transect 358
30.3.2 Whole Paddock 361
30.4 Conclusions 362
References 363
31 Sensor-Based Mapping of Soil Quality on Degraded Claypan Landscapes of the Central United States 364
31.1 Introduction 364
31.2 Materials and Methods 365
31.2.1 Soil ECa 366
31.2.2 Yield Mapping 366
31.2.3 Claypan Hydraulic Properties 367
31.2.4 Soil Compaction 367
31.3 Results and Discussion 367
31.3.1 Claypan Topsoil Depth 367
31.3.2 Claypan Hydraulic Properties 369
31.3.3 Soil Organic Carbon 370
31.3.4 Nutrients 371
31.3.5 Soil Compaction 372
31.4 Conclusions 374
References 374
32 Proximal Sensing Methods for Mapping Soil Water Status in an Irrigated Maize Field 375
32.1 Introduction 376
32.2 Materials and Methods 377
32.2.1 Study Site 377
32.2.2 Electromagnetic Induction Mapping and Soil AWC 378
32.2.3 Soil Moisture Measurement 379
32.2.3.1 Time Domain Reflectometry (TDR) 379
32.2.3.2 Collection of Vis--NIR Soil Reflectance Spectra 379
32.2.3.3 Spectral Data Pre-processing 380
32.3 Results and Discussion 380
32.3.1 Electromagnetic Induction Mapping and Soil AWC 380
32.3.2 Soil Moisture Measurements 382
32.3.3 Vis--NIR Soil Reflectance Spectra 382
32.4 Conclusions 384
References 384
33 Comparing the Ability of Multiple Soil Sensors to Predict Soil Properties in a Scottish Potato Production System 386
33.1 Introduction 386
33.2 Materials and Methods 388
33.2.1 On-the-Go Soil Survey 388
33.2.2 Manual Soil Sampling 388
33.2.3 Multivariate Data Analysis 389
33.3 Results and Discussion 389
33.3.1 Comparison of the Usefulness of Individual Sensors 389
33.3.2 Multi-sensors vs. Single Sensor 393
33.3.3 Discussion 393
33.3.4 Other Considerations 394
33.4 Conclusions 394
References 395
34 Spatial Variability and Pattern of Selected Properties of Agricultural Soils in the Czech Republic Measured by Indirect Proximal and Remote Sensing 396
34.1 Introduction 396
34.2 Materials and Methods 397
34.2.1 Experimental Field Description 397
34.2.2 Soil Sampling and Soil Property Determination 397
34.2.3 Proximal and Remote Measurement Methods 397
34.2.4 Data Evaluation, Statistical, and Geostatistical Analyses 398
34.3 Results and Discussion 398
34.3.1 Geostatistical Analysis 399
34.3.2 Discussion 404
34.4 Conclusions 407
References 408
Part VII Applications 409
35 Inverse Meta-modelling of Yield-Monitor Data for Estimating Soil-Available Water-Holding Capacities at a Farm Resolution of 10 m 410
35.1 Introduction 411
35.2 Materials and Methods 412
35.2.1 Key Assumptions 412
35.2.2 Study Site and Available Data 413
35.2.3 Creating a Meta-model 413
35.2.4 Estimating 'Effective' Hydraulic Properties 414
35.2.5 Validating 'Effective' AWCs 414
35.3 Results and Discussion 414
35.4 Conclusions 417
References 418
36 Reconstructing Palaeotopography at the Beginning of the Weichselian Glacial Stage Using an Electromagnetic Induction Sensor 419
36.1 Introduction 420
36.2 Materials and Methods 420
36.2.1 Study Site 420
36.2.2 Electromagnetic Induction Sensing 420
36.2.3 Mobile ECa Measurement Equipment and ECa Mapping 421
36.2.4 Depth to Tertiary Clay Observations 422
36.2.5 Relationship Between ECa-V and Depth to Tertiary Clay 422
36.2.6 Relationship Between the Combined ECa-V and ECa-H and Depth to Tertiary Clay 425
36.3 Results and Discussion 426
36.3.1 Relationship Between ECa-V and Depth to Tertiary Clay 426
36.3.2 Relationship Between the Combined ECa-V and ECa-H and Depth to Tertiary Clay 427
36.3.3 Validation of Predicted Depth of Tertiary Clay 427
36.3.4 Palaeotopography Beneath the Loess Cover 428
36.4 Conclusions 429
References 430
Postscript: Where to from Here? 431
Soil Sensing and Sampling 431
Future Work 431
Soil UV, Visible, and Infrared Spectral Sensing 431
Future Work 432
Soil Sensing by Electromagnetic Induction and Electrical Resistivity 432
Future Work 432
Radar and Gamma Radiometric Sensors 433
Future Work 433
Multi-sensor Systems and Other Sensors 433
Future Work 433
Applications 433
Future Work 434
Initiatives 434
FP7 Projects iSoil and Digisoil 434
IUSS WG-PSS 434
Global Soil Spectral Library 434
Index 436

Produkt-Details

• Erscheint lt. Verlag: 25.7.2010
• Reihe/Serie: Progress in Soil Science
• Reihe/Serie: Progress in Soil Science
• Zusatzinfo: XXIV, 448 p.
• Verlagsort: Dordrecht
• Sprache: englisch
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geologie
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geophysik
• Themenwelt: Naturwissenschaften ► Physik / Astronomie
• Themenwelt: Sozialwissenschaften ► Politik / Verwaltung
• Themenwelt: Technik
• Schlagworte: contaminated sites • digital soil mapping • Geophysical sensing • High resolution soil mapping • Multi sensor platform • Precision Agriculture • Proximal soil sensing • Remote Sensing • Soil • Soil spectroscopy
• ISBN-10: 90-481-8859-8 / 9048188598
• ISBN-13: 978-90-481-8859-8 / 9789048188598