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'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 maximise 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 index.
Experimental results demonstrate through a real world
example using a survey at Todos Santos Watershed, that
it is viable to design novel indexes that achieve a
much better performance than common indices such as
NDVI, EVI, and SAVI.",
notes = "GECCO-2009 A joint meeting of the eighteenth
international conference on genetic algorithms
(ICGA-2009) and the fourteenth annual genetic
programming conference (GP-2009).