Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models

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

  author =       "Ni-Bin Chang and Zhemin Xuan and Y. Jeffrey Yang",
  title =        "Exploring spatiotemporal patterns of phosphorus
                 concentrations in a coastal bay with {MODIS} images and
                 machine learning models",
  journal =      "Remote Sensing of Environment",
  volume =       "134",
  pages =        "100--110",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Remote
                 sensing, Coastal bay, Nutrient monitoring, MODIS",
  ISSN =         "0034-4257",
  DOI =          "doi:10.1016/j.rse.2013.03.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0034425713000746",
  abstract =     "This paper explores the spatiotemporal patterns of
                 total phosphorus (TP) in Tampa Bay (Bay), Florida, with
                 the aid of Moderate Resolution Imaging
                 Spectroradiometer (MODIS) images and genetic
                 programming (GP) models. The study was designed to link
                 TP concentrations with relevant water quality
                 parameters and remote sensing reflectance bands in
                 aquatic environments using in-situ data from a local
                 database to support the calibration and validation of
                 the GP model. The GP models show the effective capacity
                 to demonstrate snapshots of spatiotemporal
                 distributions of TP across the Bay, which helps to
                 delineate the short-term seasonality effects and the
                 decadal trends of TP in an environmentally sensitive
                 coastal bay area. In the past decade, urban development
                 and agricultural activities in the Bay area have
                 substantially increased the use of fertilisers.
                 Landfall hurricanes, including Frances and Jeanne in
                 2004 and Wilma in 2005, followed by continuous droughts
                 from 2006 to 2008 in South Florida, made the Bay area
                 an ideal place for a remote sensing impact assessment.
                 A changing hydrological cycle, triggered by climate
                 variations, exhibited unique regional patterns of
                 varying TP waste loads into the Bay over different time
                 scales ranging from seasons to years. With the aid of
                 the derived GP model in this study, we were able to
                 explore these multiple spatiotemporal distributions of
                 TP concentrations in the Tampa Bay area aquatic
                 environment and to elucidate these coupled dynamic
                 impacts induced by both natural hazards and
                 anthropogenic perturbations. This advancement enables
                 us to identify the hot moments and hot spots of TP
                 concentrations in the Tampa Bay region.",

Genetic Programming entries for Ni-Bin Chang Zhemin Xuan Y Jeffrey Yang