A Geoinformatics Approach to Water Erosion: Soil Loss and Beyond

✍ Scribed by Tal Svoray

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 364
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

Degradation of agricultural catchments due to water erosion is a major environmental threat at the global scale, with long-lasting destructive consequences valued at tens of billions of dollars per annum. Eroded soils lead to reduced crop yields and deprived agroecosystem’s functioning through, for example, decreased water holding capacity, poor aeration, scarce microbial activity, and loose soil structure. This can result in reduced carbon sequestration, limited nutrient cycling, contamination of water bodies due to eutrophication, low protection from floods and poor attention restoration―consequences that go far beyond the commonly modelled soil loss and deposition budgets.

This book demonstrates, using data from the Harod catchment in northern Israel, how cutting-edge geoinformatics, data science methodologies and soil health indicators can be used to measure, predict, and regulate these major environmental hazards. It shows how these approaches are used to quantify―in time and space―the effect of water erosion not only on the soil layer, soil minerals, and soil loss, but also on the wide-range of services that agricultural ecosystems might supply for the benefit and well-being of humans. The algorithms described in this book play a major role in this paradigm shift and they include, for example, extraction of photogrammetric DEMs from drone's data, advanced drainage structure calculations, fuzzy process-based modelling and spatial topographic threshold computations, multicriteria analyses and expert-based systems development using analytic hierarchal processes, innovative data-mining and machine learning tools, autocorrelation and interpolation of soil health, physically-based soil evolution models, spatial decision support systems and many more.

✦ Table of Contents

Preface
The Challenges
The Questions
Book Structure
Whom is this Book for?
Looking Ahead
References
Acknowledgments
Contents
1 Soil Erosion: The General Problem
Abstract
1.1 The Soil and Erosion
1.1.1 The Soil Layer
1.1.2 Extrinsic and Intrinsic Factors
1.1.2.1 Extrinsic Factors
1.1.2.2 Intrinsic Factors
1.1.3 Basic Terms in Soil Erosion Studies
1.1.3.1 Soil Erosion
1.1.3.2 Soil Loss
1.1.3.3 Soil Quality/Health
1.1.3.4 Soil Degradation
1.2 Scope of Soil Erosion
1.2.1 The Monetary Cost
1.2.2 Geographical Extent
1.2.3 Implications for Food Supply
1.3 A Brief History of Soil Loss
1.3.1 Hunters-Gatherers
1.3.2 Agricultural Societies
1.3.3 Modern Farmers
1.4 Soil as a Finite Resource
1.5 Summary
References
2 The Case of Agricultural Catchments
Abstract
2.1 Erosion Factors in a Distinct Landform
2.1.1 Human Factors
2.1.1.1 Cultivation Method
2.1.1.2 Tillage Direction
2.1.1.3 Unpaved Roads
2.1.1.4 Cropping System
2.1.2 Environmental Factors
2.1.2.1 Rainfall Characteristics
2.1.2.2 Topography
2.1.2.3 Vegetation Cover
2.1.2.4 Parent Material
2.1.2.5 Bioturbation
2.2 Processes and Forms of Water Erosion in Agricultural Catchments
2.2.1 Splash Erosion
2.2.2 Sheet Erosion
2.2.3 Rill Erosion
2.2.4 Gully Erosion
2.2.5 Piping Erosion
2.3 The Damage: On-Site and Off-Site Consequences
2.3.1 On-Site Consequences
2.3.2 Off-Site Consequences
2.4 The Human Agent
2.4.1 Agricultural Soils as Social Traps
2.4.2 The Psychological Barriers
2.4.2.1 Limited Cognition
2.4.2.2 Ideologies
2.4.2.3 Comparisons with Others
2.4.2.4 Sunk Costs
2.4.2.5 Discredence
2.4.2.6 Perceived Risk
2.4.2.7 Limited Behavior
2.4.3 Evidence for Farmer’s Conservation Actions
2.5 Summary
References
3 Modeling the Erosion Process
Abstract
3.1 Basics of a Hillslope—The Regolith Profile
3.2 Landscape Evolution Models
3.2.1 Background
3.2.2 The CAESAR-Lisflood
3.2.3 The Model Operation
3.2.4 The Model Output
3.3 Water Erosion Prediction Project (WEPP)
3.3.1 Background
3.3.2 Model Operation
3.3.3 The Model Output
3.4 Morgan–Morgan–Finney (MMF)
3.4.1 Background
3.4.2 Model Operation
3.4.3 Further Development and Model Application
3.5 Summary
References
4 Spatial Variation in Soils
Abstract
4.1 Discrete Spatial Units
4.1.1 The Topographic Approach
4.1.1.1 The Hillslope Catena
4.1.1.2 Catchment Variables
4.1.2 The Field-Based Approach
4.1.3 The Image Pixel Approach
4.2 A Suite of Continuous Variables
4.2.1 Spatial Sampling
4.2.1.1 Sample Size
4.2.1.2 The Location of Points
4.2.2 Autocorrelation in Soil Properties
4.2.3 Interpolation of Soil Properties
4.2.3.1 Triangulation and Thiessen Polygons
4.2.3.2 Inverse-Distance Weighting (IDW)
4.2.3.3 Kriging
4.3 Summary
References
5 Earth Observations
Abstract
5.1 Spectral Indices: Spectral Signatures, and Algebraic Expressions
5.1.1 Vegetation Indices
5.1.2 Soil Indices
5.2 Image Classification Techniques
5.2.1 Hard Classification
5.2.1.1 Unsupervised Classification
5.2.1.2 Supervised Classification
5.2.1.3 Object-Based Classification
5.2.2 Soft Classification—Spectral Mixture Modeling
5.2.2.1 Theoretical Basis
5.2.2.2 Procedure and Results
5.3 Synergy of RS Data in Catchment Models
5.3.1 Background
5.3.2 The Procedure
5.3.2.1 Data
5.3.2.2 Image Processing Procedure
5.3.2.3 The DEM-Based Algorithm and Data Fusion
5.3.2.4 Weighting Flowaccumulation
5.3.2.5 Error Assessment
5.3.3 Results
5.4 Drone Remote Sensing
5.4.1 DEM Extraction
5.4.1.1 High-Resolution Topographic Threshold
5.4.1.2 Change Detection
5.5 Summary
References
6 Assessments of Erosion Risk
Abstract
6.1 Topographic Threshold
6.1.1 The Approach
6.1.2 Procedure
6.1.3 Results and Discussion
6.1.3.1 Application to the Yehezkel Catchment
6.1.3.2 Application to a Larger Catchment
6.1.3.3 Aggregation at the Field Scale
6.1.3.4 Sensitivity Analysis
6.2 Expert-Based Systems
6.2.1 The Approach
6.2.2 Weighting
6.2.3 Decision Rules
6.2.4 Procedure for Estimating Risk Levels
6.2.5 Simulations
6.2.6 Results and Discussion
6.2.6.1 A Single Expert or Several Experts?
6.2.6.2 A Complex or a Simple System?
6.2.6.3 Which are the Most Important Clorpt Factors?
6.2.6.4 Risk Maps
6.2.6.5 Scenarios
6.3 Data Mining (DM)
6.3.1 Theoretical Basis
6.3.1.1 Decision trees
6.3.1.2 Artificial Neural Networks (ANN)
6.3.1.3 The Logistic regression
6.3.1.4 Support Vector Machine (SVM)
6.3.2 Procedures
6.3.3 Results and Discussion
6.3.4 Summary
6.4 Fuzzy Logic
6.4.1 Theoretical Background
6.4.2 Procedure
6.4.2.1 Modeling Approach
6.4.2.2 Model Computation
6.4.3 Results and Discussion
6.4.4 Summary
6.5 Summary
References
7 The Health of the Remaining Soil
Abstract
7.1 Soil Health Indicators
7.1.1 Selection of Soil Properties
7.1.2 Scoring Functions
7.1.3 Composite Soil Health Index
7.1.4 Summary
7.2 Autocorrelation in Space
7.2.1 Spatial Variation in Soil Properties
7.2.2 Autocorrelation of Soil Properties in the Harod Catchment—Procedure
7.2.3 Autocorrelation of Soil Properties of the Harod—Results
7.2.4 Summary
7.3 Soil Health Maps
7.3.1 Procedure
7.3.2 Results and Discussion
7.3.2.1 RMSE Error
7.3.2.2 Output Layers
7.3.3 Summary
7.4 Summary
References
8 Spatial Decision Support Systems
Abstract
8.1 Introduction
8.1.1 The Decision-Making Process
8.1.2 Basic Terms in SDSS
8.1.3 The Single-Criterion System
8.2 SDSS in Soil Conservation
8.2.1 Applications of SDSS in Water Erosion Studies
8.2.2 Beyond Soil Loss
8.3 MCDM
8.3.1 Background
8.3.2 Procedure
8.3.3 Decision Rules
8.3.3.1 Weighted Summation (WS)
8.3.3.2 Concordance–Discordance Analysis (CA)
8.4 GISCAME
8.4.1 Background
8.4.2 Application of GISCAME to the Harod Catchment
8.4.2.1 Method
8.4.2.2 Results
8.5 Summary
References
9 Final Thoughts
Abstract
9.1 The General Framework
9.2 Usage of Geoinformatics to Study Water Erosion
9.2.1 Known Knowns
9.2.2 Known Unknowns
9.2.3 Unknown Knowns
9.2.4 Unknown Unknowns
9.3 Summary
References
Index