Applied multivariate statistical analysis
Applied multivariate statistical analysis
Quantitative Remote Sensing of Land Surfaces
Quantitative Remote Sensing of Land Surfaces
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Physical degradation undermines soils' ability to perform their many biophysical functions. Currently, there is lack of rapid methods to facilitate timely large-area assessment for effective control of the degradation. This study tested the combined applications of point-measurements of physical properties, soil spectral reflectance, and remote sensing for prediction of the degradation in a large watershed. Infiltration and water retention measurements at selected sites were used to aid definition of the degradation classes. A tree classification was then developed with diffuse spectral reflectance to predict the degradation classes. 93% accuracy with holdout cross-validation was achieved and the tree used to predict the degradation at multiple points in the study area. In addition, standardized deviations of land surface temperature (LST) and normalized difference vegetation index (NDVI) from long-term Landsat scenes were used to study the thermal and vegetation conditions at the sampled points. The deviations of LST and NDVI were effectively incorporated in the prediction of the degradation at other places with 80% accuracy of ground reference data. This approach has the potential as a useful tool for guiding policy decision on sustainable land management.