Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation
Created by W.Langdon from
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- @Article{Puente:2019:RS,
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author = "Cesar Puente and Gustavo Olague and
Mattia Trabucchi and Pedro David Arjona-Villicana and
Carlos Soubervielle-Montalvo",
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title = "Synthesis of Vegetation Indices Using Genetic
Programming for Soil Erosion Estimation",
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journal = "Remote Sensing",
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year = "2019",
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volume = "11",
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number = "2",
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pages = "156",
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keywords = "genetic algorithms, genetic programming, vegetation
indices, RUSLE, image synthesis, c factor, evolutionary
computation",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/remotesensing/remotesensing11.html#PuenteOTAS19",
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URL = "https://www.mdpi.com/2072-4292/11/2/156/pdf",
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DOI = "doi:10.3390/rs11020156",
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size = "25 pages",
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abstract = "Vegetation Indices (VIs) represent a useful method for
extracting vegetation information from satellite
images. Erosion models like the Revised Universal Soil
Loss Equation (RUSLE), employ VIs as an input to
determine the RUSLE soil Cover factor (C). From the
standpoint of soil conservation planning, the C factor
is one of the most important RUSLE parameters because
it measures the combined effect of all interrelated
cover and management variables. Despite its importance,
the results are generally incomplete because most
indices recognise healthy or green vegetation, but not
senescent, dry or dead vegetation, which can also be an
important contributor to C. The aim of this research is
to propose a novel approach for calculating new VIs
that are better correlated with C, using field and
satellite information. The approach followed by this
research is to state the generation of new VIs in terms
of a computer optimisation problem and then applying a
machine learning technique, named Genetic Programming
(GP), which builds new indices by iteratively
recombining a set of numerical operators and spectral
channels until the best composite operator is found.
Experimental results illustrate the efficiency and
reliability of this approach to estimate the C factor
and the erosion rates for two watersheds in Baja
California, Mexico, and Zaragoza, Spain. The synthetic
indices calculated using this methodology produce
better approximation to the C factor from field data,
when compared with state-of-the-art indices, like NDVI
and EVI.",
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notes = "journals/remotesensing/PuenteOTAS19",
- }
Genetic Programming entries for
Cesar Puente
Gustavo Olague
Mattia Trabucchi
Pedro David Arjona-Villicana
Carlos Soubervielle-Montalvo
Citations