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:1.8051

@Article{Chang:2013:RSE,

• 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

Citations