- author = "Cesar Puente and Gustavo Olague and Stephen V. Smith and Stephen H. Bullock and Miguel A. Gonzalez-Botello and Alejandro Hinojosa-Corona",
- title = "Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover",
- booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation",
- year = "2009",
- editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba",
- pages = "1593--1600",
- address = "Montreal",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "8-12 " # jul,
- organisation = "SigEvo",
- keywords = "genetic algorithms, genetic programming, Vegetation index, remote sensing, soil erosion, RUSLE",
- isbn13 = "978-1-60558-325-9",
- bibsource = "DBLP, http://dblp.uni-trier.de",
-
DOI = "
doi:10.1145/1569901.1570114",
- size = "8 pages",
- 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).
ACM Order Number 910092.",
