Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover

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@InProceedings{DBLP:conf/gecco/PuenteOSBGH09,
  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",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570114",
  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.",
}

Genetic Programming entries for Cesar Puente Gustavo Olague Stephen V Smith Stephen H Bullock Miguel A Gonzalez-Botello Alejandro Hinojosa Corona

Citations