Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

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@Article{Makkeasorn20091069,
  author =       "Ammarin Makkeasorn and Ni-Bin Chang and Jiahong Li",
  title =        "Seasonal change detection of riparian zones with
                 remote sensing images and genetic programming in a
                 semi-arid watershed",
  journal =      "Journal of Environmental Management",
  volume =       "90",
  number =       "2",
  pages =        "1069--1080",
  year =         "2009",
  ISSN =         "0301-4797",
  DOI =          "DOI:10.1016/j.jenvman.2008.04.004",
  URL =          "http://www.sciencedirect.com/science/article/B6WJ7-4SNGRR7-1/2/952c6978ecce3d3e3e5a40f16f9ad11b",
  keywords =     "genetic algorithms, genetic programming, Riparian
                 classification, Soil moisture, RADARSAT-1, LANDSAT,
                 Vegetation index, Ecohydrology",
  abstract =     "Riparian zones are deemed significant due to their
                 interception capability of non-point source impacts and
                 the maintenance of ecosystem integrity region wide. To
                 improve classification and change detection of riparian
                 buffers, this paper developed an evolutionary
                 computational, supervised classification method - the
                 RIparian Classification Algorithm (RICAL) - to conduct
                 the seasonal change detection of riparian zones in a
                 vast semi-arid watershed, South Texas. RICAL uniquely
                 demonstrates an integrative effort to incorporate both
                 vegetation indices and soil moisture images derived
                 from LANDSAT 5 TM and RADARSAT-1 satellite images,
                 respectively. First, an estimation of soil moisture
                 based on RADARSAT-1 Synthetic Aperture Radar (SAR)
                 images was conducted via the first-stage genetic
                 programming (GP) practice. Second, for the statistical
                 analyses and image classification, eight vegetation
                 indices were prepared based on reflectance factors that
                 were calculated as the response of the instrument on
                 LANDSAT. These spectral vegetation indices were then
                 independently used for discriminate analysis along with
                 soil moisture images to classify the riparian zones via
                 the second-stage GP practice. The practical
                 implementation was assessed by a case study in the
                 Choke Canyon Reservoir Watershed (CCRW), South Texas,
                 which is mostly agricultural and range land in a
                 semi-arid coastal environment. To enhance the
                 application potential, a combination of Iterative
                 Self-Organizing Data Analysis Techniques (ISODATA) and
                 maximum likelihood supervised classification was also
                 performed for spectral discrimination and
                 classification of riparian varieties comparatively.
                 Research findings show that the RICAL algorithm may
                 yield around 90percent accuracy based on the unseen
                 ground data. But using different vegetation indices
                 would not significantly improve the final quality of
                 the spectral discrimination and classification. Such
                 practices may lead to the formulation of more effective
                 management strategies for the handling of non-point
                 source pollution, bird habitat monitoring, and grazing
                 and live stock management in the future.",
}

Genetic Programming entries for Ammarin Makkeasorn Ni-Bin Chang Jiahong Li

Citations