Deriving vegetation indices for phenology analysis using genetic programming

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@Article{Almeida:2015:EI,
  author =       "Jurandy Almeida and Jefersson A. {dos Santos} and 
                 Waner O. Miranda and Bruna Alberton and 
                 Leonor Patricia C. Morellato and Ricardo {da S. Torres}",
  title =        "Deriving vegetation indices for phenology analysis
                 using genetic programming",
  journal =      "Ecological Informatics",
  volume =       "26, Part 3",
  pages =        "61--69",
  year =         "2015",
  keywords =     "genetic algorithms, genetic programming, Remote
                 phenology, Digital cameras, Image analysis, Vegetation
                 indices",
  ISSN =         "1574-9541",
  DOI =          "doi:10.1016/j.ecoinf.2015.01.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1574954115000114",
  size =         "9 pages",
  abstract =     "Plant phenology studies recurrent plant life cycle
                 events and is a key component for understanding the
                 impact of climate change. To increase accuracy of
                 observations, new technologies have been applied for
                 phenological observation, and one of the most
                 successful strategies relies on the use of digital
                 cameras, which are used as multi-channel imaging
                 sensors to estimate colour changes that are related to
                 phenological events. We monitor leaf-changing patterns
                 of a cerrado-savanna vegetation by taking daily digital
                 images. We extract individual plant color information
                 and correlate with leaf phenological changes. For that,
                 several vegetation indices associated with plant
                 species are exploited for both pattern analysis and
                 knowledge extraction. In this paper, we present a novel
                 approach for deriving appropriate vegetation indices
                 from vegetation digital images. The proposed method is
                 based on learning phenological patterns from plant
                 species through a genetic programming framework. A
                 comparative analysis of different vegetation indices is
                 conducted and discussed. Experimental results show that
                 our approach presents higher accuracy on characterising
                 plant species phenology.",
}

Genetic Programming entries for Jurandy G Almeida Jr Jefersson Alex dos Santos Waner O Miranda Bruna de Costa Alberton Leonor Patricia Cerdeira Morellato Ricardo da Silva Torres

Citations