Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover
Conference Paper
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
Remote sensing has become a powerful tool to derive biophysical properties of plants. One of the most popular methods for extracting vegetation information from remote sensing data is through vegetation indices. Models to predict soil erosion like the Revised Universal Soil Loss Equation (RUSLE) can use vegetation indices as input to measure the effects of soil cover. Several studies correlate vegetation indices with RUSLE%27s cover factor to get a linear mapping that describes a broad area. The results are considered as incomplete because most indices only detect healthy vegetation. The aim of this study is to devise a genetic programming approach to synthetically create vegetation indices that detect healthy, dry, and dead vegetation. In this work, the problem is posed as a search problem where the objective is to find the best indices that maximize the correlation of field data with Landsat5-TM imagery. Thus, the algorithm builds new indices by iteratively recombining primitive-operators until the best indices are found. This article outlines a GP methodology that was able to design new vegetation indices that are better correlated than traditional man-made indices. Experimental results demonstrate through a real world example using a survey at Todos Santos Watershed, that it is viable to design novel indices that achieve a much better performance than common indices such as NDVI, EVI, and SAVI. Copyright 2009 ACM.
Remote sensing has become a powerful tool to derive biophysical properties of plants. One of the most popular methods for extracting vegetation information from remote sensing data is through vegetation indices. Models to predict soil erosion like the Revised Universal Soil Loss Equation (RUSLE) can use vegetation indices as input to measure the effects of soil cover. Several studies correlate vegetation indices with RUSLE's cover factor to get a linear mapping that describes a broad area. The results are considered as incomplete because most indices only detect healthy vegetation. The aim of this study is to devise a genetic programming approach to synthetically create vegetation indices that detect healthy, dry, and dead vegetation. In this work, the problem is posed as a search problem where the objective is to find the best indices that maximize the correlation of field data with Landsat5-TM imagery. Thus, the algorithm builds new indices by iteratively recombining primitive-operators until the best indices are found. This article outlines a GP methodology that was able to design new vegetation indices that are better correlated than traditional man-made indices. Experimental results demonstrate through a real world example using a survey at Todos Santos Watershed, that it is viable to design novel indices that achieve a much better performance than common indices such as NDVI, EVI, and SAVI. Copyright 2009 ACM.
