科研之友
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摘要
Understanding the soil attributes and types occurring within a region is critical for providing the best land-use decisions. Soils vary in their ability to clean and store water, provide water for plant growth, and many other ecosystem services. Soil variability is dependent on climate, parent material, organisms, time, and topography. When only topography varies within an area, the topography and redistribution of water should be the main drivers for soils differentiation. Digital soil mapping (DSM) has advantages due to computational tools and easily accessible digital elevation models (DEMs) at multiple resolutions. Terrain attributes (e. g., slope, wetness index, and profile curvature) are derived from the DEM and, in association with a soil expert, knowledge-based models can be applied to predict soil variability. The objective of this study was to create and validate a predicted Cambisol (Inceptisol) solum depth map for Lavrinha Creek Watershed (LCW) in Minas Gerais, Brazil, by applying DSM techniques for the Brazilian soil landscapes. The best available 30-m DEM was used to derive the terrain derivatives. A set of rules were formulated according to the terrain attributes, limited data, and expert knowledge to predict the solum depth behavior throughout the watershed. Conditioned Latin hypercube sampling scheme was used for allocating the validation points. In this study, 20 out of the 25 validating samples were correctly classified yielding a Kappa index of 0.616. Soil expert knowledge and Digital Soil Mapping techniques can be employed for mapping areas, especially in countries where there is limited data available, which will provide a useful soil map for planning while saving time and investments.
- 出版日期2014-8
