A Genetic Programming Approach to Estimate Vegetation Cover in the Context of Soil Erosion Assessment

This work describes a genetic programming (GP) approach that creates vegetation indices (VI’s) to automatically detect the sum of healthy, dry, and dead vegetation. Nowadays, it is acknowledged that VI’s are the most popular method for extracting vegetation information from satellite imagery. In particular, erosion models like the “Revised Universal Soil Loss Equation ” (RUSLE) can use VI’s as input to measure the effects of the RUSLE soil cover factor (C). However, the results are generally incomplete, because most indices recognize only healthy vegetation. The aim of this study is to devise a novel approach for designing new VI’s that are better-correlated with C, using field and satellite information. Our approach consists on stating the problem in terms of optimization through GP learning, building novel indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of our approach in contrast with traditional indices like those of the NDVI and SAVI family. This study provides evidence that similar problems related to soil erosion assessment could be analyzed with our proposed methodology.