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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Puente:evows09,

• 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

Citations