Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images

Abstract

The universal soil loss equation (USLE) is a product of six factors: (1) rainfall erosivity, (2) soil erodibility, (3) slope length, (4) slope steepness, (5) cover and management, and (6) support practice, and is widely used to estimate average annual soil loss. The cover and management variable, called the C factor, represents the effect of cropping and management practices on erosion rates in agriculture, and the effect of ground, tree and grass canopy covers on reduction of soil loss in non-agriculture situation. This study compared three traditional and three geostatistical methods for mapping the C factor. They included vegetation classification with average, linear and log-linear regression for C factor assignment, sequential Gaussian cosimulations with and without Thematic Mapper (TM) images, and colocated cokriging with TM images. The coefficient of correlation between estimates and observations varied from 0.4888 to 0.7317, and the root mean square error (RMSE) from 0.0159 to 0.0203. The sequential Gaussian cosimulation with a TM ratio image resulted in the highest correlation and the smallest RMSE, and reproduced the best and most detailed spatial variability of the C factor. This method may thus be recommended for mapping the C factor. It is also expected that this method could be applied to image-based mapping in other disciplines.