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Journal of Applied Remote Sensing

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida
Author(s): Ni-Bin Chang; Ammarin Daranpob; Y. Jeffrey Yang; Kang-Ren Jin
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Paper Abstract

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

Paper Details

Date Published: 1 September 2009
PDF: 19 pages
J. Appl. Rem. Sens. 3(1) 033549 doi: 10.1117/1.3244644
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Ni-Bin Chang, Univ. of Central Florida (United States)
Ammarin Daranpob, Univ. of Central Florida (United States)
Y. Jeffrey Yang, U.S. Environmental Protection Agency (United States)
Kang-Ren Jin, South Florida Water Management District (United States)

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