Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models
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gp-bibliography.bib Revision:1.8010
- @Article{Chang:2013:RSE,
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author = "Ni-Bin Chang and Zhemin Xuan and Y. Jeffrey Yang",
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title = "Exploring spatiotemporal patterns of phosphorus
concentrations in a coastal bay with {MODIS} images and
machine learning models",
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journal = "Remote Sensing of Environment",
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volume = "134",
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pages = "100--110",
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year = "2013",
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keywords = "genetic algorithms, genetic programming, Remote
sensing, Coastal bay, Nutrient monitoring, MODIS",
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ISSN = "0034-4257",
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DOI = "doi:10.1016/j.rse.2013.03.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S0034425713000746",
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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