Digital Soil Mapping Management
This online engineering CPD course examines the quantitative revolution in soil survey through the lens of digital soil mapping (DSM).
This course explores the transition from conventional qualitative models to spatially explicit, raster-based soil databases that document the uncertainty of soil predictions. It investigates the SCORPAN framework, which controls environmental covariates, such as climate, relief, and parent material, to predict soil classes and properties at varying spatial scales.
Furthermore, this course describes the technical stages of data preprocessing, covariate selection, and the application of machine learning to generate high-fidelity geospatial products.
This 6 CPD online course is applicable to civil engineers, surveyors, as well as other technical professionals who are interested in learning more about digital soil mapping principles.
This PE continuing education course is intended to provide you with the following specific knowledge and skills:
- Recognizing the functional relationships between soil attributes and environmental covariates using the spatially explicit SCORPAN model
- Explaining terrain and spectral attributes from digital elevation models (DEMs) and remote sensing platforms to identify physical soil-forming factors
- Implementing advanced sampling strategies, particularly conditioned Latin hypercube sampling (cLHS), to capture the multivariate distribution of covariate data
- Exploring diverse prediction methodologies, including fuzzy classification, knowledge-based expert systems, and machine learning algorithms like random forests
- Assessing prediction quality through quantitative metrics such as user/producer accuracy, root mean square error (RMSE), and confusion indices
- Utilizing digital disaggregation and data mining techniques to refine legacy soil maps and produce continuous property models for modern land management
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