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The Resource Digital Soil Mapping Across Paradigms, Scales and Boundaries

Digital Soil Mapping Across Paradigms, Scales and Boundaries

Label
Digital Soil Mapping Across Paradigms, Scales and Boundaries
Title
Digital Soil Mapping Across Paradigms, Scales and Boundaries
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Language
eng
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MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Springer Environmental Science and Engineering Ser
Digital Soil Mapping Across Paradigms, Scales and Boundaries
Label
Digital Soil Mapping Across Paradigms, Scales and Boundaries
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4420247
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Foreword -- References -- Preface -- Contents -- Part I Digital Soil Modelling -- 1 Digital Soil Mapping Across Paradigms, Scales, and Boundaries: A Review -- Abstract -- 1.1 Introduction -- 1.2 Soil Mapping Paradigms -- 1.3 Soil Mapping Scales -- 1.4 Soil Mapping Boundaries -- 1.5 Current Challenges -- 1.5.1 3D Digital Soil Mapping of Soil Properties -- 1.5.2 Soil Mapping in Areas with Intensive Human Activities -- 1.5.3 Multi-Source Data Integration for Soil Mapping -- Acknowledgements -- References -- 2 Spatial Prediction of Soil Antibiotics Based on High-Accuracy Surface Modeling -- Abstract -- 2.1 Introduction -- 2.2 Data and Methods -- 2.2.1 Data -- 2.2.2 Methods -- 2.3 Results and Analysis -- 2.3.1 Distributions of Soil Antibiotics in Different Types of Vegetable Areas -- 2.3.2 Comparisons of the Performance of HASM and Splines -- 2.3.3 The Interpolated Maps by HASM Methods -- 2.4 Conclusions -- Acknowledgements -- References -- 3 Incorporating Probability Density Functions of Environmental Covariates Related to Soil Class Predictions -- Abstract -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Site and Data Description -- 3.2.2 Landform Classification -- 3.2.3 Curve Fitting -- 3.2.4 Goodness-of-Fit Assessment -- 3.3 Results and Discussion -- 3.3.1 Landform Classification -- 3.3.2 Curve Fitting -- 3.4 Conclusions -- References -- 4 Mapping Horizontal and Vertical Spatial Variability of Soil Salinity in Reclaimed Areas -- Abstract -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Study Area -- 4.2.2 Data Collection and Processing -- 4.2.3 Mapping Horizontal Spatiotemporal Variability of Salinity Using Geostatistical Approaches -- 4.2.4 Mapping Vertical Spatiotemporal Variation of Salinity Using Quasi-3D Inversion -- 4.3 Results and Discussion -- 4.3.1 Statistical Analysis of Multitemporal EM38 Data
  • 4.3.2 Horizontal Spatiotemporal Variability of EM38-Directed Soil Salinity with Geostatistical Approaches -- 4.3.3 Vertical Spatiotemporal Variability of Soil Salinity With Quasi-3D Inversion -- 4.4 Conclusions -- Acknowledgements -- References -- 5 Mapping Soil Organic Matter in Low-Relief Areas Based on Time Series Land Surface Diurnal Temperature Difference -- Abstract -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.2.1 Description of the Study Area -- 5.2.2 Soil Samples and Analysis -- 5.2.3 Acquisition of DTD and Processing -- 5.2.4 Linear Mixed Model -- 5.2.5 Data Processing and Analyzing -- 5.3 Results and Discussion -- 5.3.1 Exploratory Data Analysis -- 5.3.2 Relationship Between DTD and SOM -- 5.3.3 Mapping of SOM Based on DTD and Linear Mixed Model -- 5.3.4 Comparison of Spatial Predictions and Validation -- 5.4 Conclusions -- Acknowledgments -- References -- 6 Mapping Soil Thickness by Integrating Fuzzy C-Means with Decision Tree Approaches in a Complex Landscape Environment -- Abstract -- 6.1 Introduction -- 6.2 Materials and Methods -- 6.2.1 Study Area -- 6.2.2 Data Sources -- 6.2.3 Methodology -- 6.2.3.1 Construction of the Environmental Factors Database -- 6.2.3.2 Combinations Obtainment Using FCM Method -- 6.2.3.3 Extraction of the Training Data Set -- 6.2.3.4 Obtainment of Soil--Environment Relationships Using DT Method -- 6.2.3.5 Evaluation of the Soil Thickness Map -- 6.3 Results and Discussion -- 6.3.1 Extraction of the Training Data Set -- 6.3.2 Knowledge Obtainment of Soil--Environment Relationships -- 6.3.3 Validation of the Methods -- 6.3.3.1 Spatial Distribution of Soil Thickness -- 6.3.3.2 Evaluation of the Predictive Map -- 6.4 Conclusions -- Acknowledgements -- References -- 7 Multivariate Sampling Design Optimization for Digital Soil Mapping -- Abstract -- 7.1 Introduction -- 7.2 Materials and Methods
  • 7.2.1 Study Site and Legacy Soil Data -- 7.2.2 Auxiliary Data -- 7.2.3 Regression Kriging -- 7.2.4 Extended SSA Methodology and Its Settings -- 7.2.5 Evaluation of the Optimized Sampling Design -- 7.3 Results and Discussion -- 7.3.1 Regression and Variogram Models -- 7.3.2 Sampling Optimization by the Extended SSA Methodology -- 7.3.3 Performance of the Sampling Configuration -- 7.4 Conclusions -- Acknowledgements -- References -- 8 Applying Artificial Neural Networks Utilizing Geomorphons to Predict Soil Classes in a Brazilian Watershed -- Abstract -- 8.1 Introduction -- 8.2 Materials and Methods -- 8.2.1 Characterization of the Area -- 8.2.2 Terrain Attributes (Input Variables) -- 8.2.3 Soil Sampling and Profile Description -- 8.2.4 Classification by Neural Networks -- 8.3 Results and Discussion -- 8.3.1 Characterization of Soil Types and Occurrence Conditions -- 8.3.2 Inferred Classification Using Geomorphons as Discriminant Variable -- 8.4 Conclusion -- Acknowledgements -- References -- 9 Comparison of Traditional and Geostatistical Methods to Estimate and Map the Carbon Content of Scottish Soils -- Abstract -- 9.1 Introduction -- 9.2 Methods -- 9.3 Results -- 9.4 Discussion -- 9.5 Preliminary Conclusions -- Acknowledgements -- References -- Part II Environmental Application and Assessment -- 10 Digital Soil Mapping for Hydrological Modelling -- Abstract -- 10.1 Introduction -- 10.2 Site Description -- 10.3 Materials and Methods -- 10.4 Results and Discussion -- 10.5 Conclusions -- Acknowledgements -- References -- 11 Some Challenges on Quantifying Soil Property Predictions Uncertainty for the GlobalSoilMap Using Legacy Data -- Abstract -- 11.1 Introduction -- 11.2 Materials and Methods -- 11.2.1 US Case Study -- 11.2.2 Colombia Case Study -- 11.3 Results and Discussion -- 11.3.1 US Case Study -- 11.3.2 Colombia Case Study -- 11.4 Conclusions
  • References -- 12 Spatial Assessment of Soil Organic Carbon Using Bayesian Maximum Entropy and Partial Least Square Regression Model -- Abstract -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 Study Area -- 12.2.2 Data Preparation -- 12.2.3 Data Analyses -- 12.3 Results and Discussion -- 12.4 Conclusions -- Acknowledgements -- References -- 13 Estimation of the Actual and Attainable Terrestrial Carbon Budget -- Abstract -- 13.1 Introduction -- 13.2 Study Area -- 13.3 Materials and Methods -- 13.3.1 Above-Ground and Below-Ground Carbon Data -- 13.3.2 Environmental and Anthropogenic Covariates -- 13.3.3 Modeling the Relationships Between SOC and STEP-AWBH Factors -- 13.3.4 Assessing Terrestrial Carbon Stocks -- 13.4 Results -- 13.4.1 Variable Importance and Spatial Variation in Soil Organic Carbon -- 13.4.2 Model Performance -- 13.4.3 Estimates of the Terrestrial Carbon Stocks -- 13.5 Conclusions -- Acknowledgements -- References -- 14 The Meta Soil Model---An Integrative Framework to Model Soil Carbon Across Various Ecosystems and Scales -- Abstract -- 14.1 Introduction -- 14.2 Soil Carbon Modeling Paradigms -- 14.3 The Meta Soil Model---Integrative Modeling of Soil Carbon -- 14.4 Moving Toward a Meta Soil Carbon Model -- 14.4.1 Meta Soil Carbon Modeling Based on Data Mining and Ensemble Modeling of SOC Stocks in Florida (Fig. 14.2) -- 14.4.2 Meta Soil Carbon Modeling Focused on Ecosystem Services in a Basin in North-Central Florida (Fig. 14.3) -- 14.5 Final Remarks -- Acknowledgements -- References -- 15 Example of Bayesian Uncertainty for Digital Soil Mapping -- Abstract -- 15.1 Introduction -- 15.2 Data -- 15.2.1 Test Area -- 15.2.2 Response Variable -- 15.2.3 Covariates -- 15.3 Methods -- 15.3.1 Validation -- 15.3.2 Software Used -- 15.4 Results -- 15.5 Discussion, Conclusions, and Future Work -- Acknowledgements -- References
  • 16 An Unsupervised Fuzzy Clustering Approach for the Digital Mapping of Soil Organic Carbon in a Montaneous Region of China -- Abstract -- 16.1 Introduction -- 16.2 Study Area -- 16.3 Methods -- 16.3.1 Establish Environmental Factors Database -- 16.3.2 Cluster Combination Based on Fuzzy Logic -- 16.3.3 Obtain Surface SOM of Typical Points for Each Combination -- 16.3.4 Simulate Surface SOM -- 16.4 Results -- 16.4.1 Simulation of Surface SOM -- 16.4.2 Accuracy Measure -- 16.5 Conlusions -- Acknowledgements -- References -- 17 Application of Digital Soil Mapping Techniques to Refine Soil Map of Baringo District, Rift Valley Province, Kenya -- Abstract -- 17.1 Introduction -- 17.2 Materials and Methods -- 17.2.1 Study Area -- 17.2.2 Data Collection -- 17.2.3 Data Evaluation: Extracting Relationships from Existing Databases -- 17.2.4 Data Evaluation: Prediction -- 17.3 Results -- 17.4 Discussion -- 17.5 Conclusion -- Acknowledgements -- References -- 18 Predictive Mapping of Soil Organic Matter at a Regional Scale Using Local Topographic Variables: A Comparison of Different Polynomial Models -- Abstract -- 18.1 Introduction -- 18.2 Materials and Methods -- 18.2.1 Data -- 18.2.2 Calculation of Local Terrain Variables and Other Related Terrain Variables -- 18.2.3 Methods -- 18.3 Results -- 18.3.1 Exploratory Data Analysis -- 18.3.2 Prediction Accuracy of Different Kriging Methods -- 18.4 Conclusions -- Acknowledgments -- References -- 19 Estimating Soil Carbon Sequestration Potential in Fine Particles of Top Soils in Hebei Province, China -- Abstract -- 19.1 Introduction -- 19.2 Materials and Methods -- 19.2.1 Study Area -- 19.2.2 Data Source -- 19.2.3 Calculation Methods -- 19.2.3.1 Calculation of SOC Content and SOC Density in Topsoil (0--30 cm) -- 19.2.3.2 Calculation of Carbon Sequestration Potential -- 19.2.3.3 Calculation of Soil Bulk Density
  • 19.3 Results and Discussion
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9789811004155
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rdamedia
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