A Novel GP Approach to Synthesize Vegetation Indices for Soil Erosion Assessment

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Cesar Puente and Gustavo Olague and 
                 Stephen V. Smith and Stephen Bullock and Miguel Gonzalez-Botello and 
                 Alejandro Hinojosa-Corona",
  title =        "A Novel GP Approach to Synthesize Vegetation Indices
                 for Soil Erosion Assessment",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2009: {EvoCOMNET}, {EvoENVIRONMENT},
                 {EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP},
                 {EvoINTERACTION}, {EvoMUSART}, {EvoNUM}, {EvoPhD},
                 {EvoSTOC}, {EvoTRANSLOG}",
  year =         "2009",
  month =        "15-17 " # apr,
  editor =       "Mario Giacobini and Ivanoe {De Falco} and Marc Ebner",
  series =       "LNCS",
  volume =       "5484",
  publisher =    "Springer Verlag",
  address =      "Tubingen, Germany",
  pages =        "375--384",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01128-3",
  DOI =          "doi:10.1007/978-3-642-01129-0_42",
  abstract =     "Today the most popular method for the extraction of
                 vegetation information from remote sensing data is
                 through vegetation indices. In particular, erosion
                 models are based on vegetation indices that are used to
                 estimate the cover factor (C) defined by healthy, dry,
                 or dead vegetation in a popular soil erosion model
                 named RUSLE, (Revised Universal Soil Loss Equation).
                 Several works correlate vegetation indices with C in
                 order to characterise a broad area. However, the
                 results are in general not suitable because most
                 indices focus only on healthy vegetation. The aim of
                 this study is to devise a new approach that
                 automatically creates vegetation indices that include
                 dry and dead plants besides healthy vegetation. For
                 this task we propose a novel methodology based on
                 Genetic Programming (GP) as summarised below. First,
                 the problem is posed as a search problem where the
                 objective is to find the index that correlates best
                 with on field C factor data. Then, new indices are
                 built using GP working on a set of numerical operators
                 and bands until the best composite index is found. In
                 this way, GP was able to develop several new indices
                 that are better correlated compared to traditional
                 indices such as NDVI and SAVI family. It is concluded
                 with a real world example that it is viable to
                 synthesise indices that are optimally correlated with
                 the C factor using this methodology. This gives us
                 confidence that the method could be applied in soil
                 erosion assessment.",
  notes =        "EvoWorkshops2009",

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