Research Papers

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida

[+] Author Affiliations
Ni-Bin Chang, Ammarin Daranpob

Department of Civil and Environmental Engineering, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816

Y. Jeffrey Yang

U.S. EPA, National Risk Management Research Laboratory, Water Supply and Water Resources Division, Cincinatti, OH 45268

Kang-Ren Jin

Hydrologic & Environmental Systems Modeling Division, South Florida Water Management District, West Palm Beach, FL 33416

J. Appl. Remote Sens. 3(1), 033549 (September 17, 2009). doi:10.1117/1.3244644
History: Received June 13, 2009; Revised September 7, 2009; Accepted September 14, 2009; September 17, 2009; Online September 17, 2009
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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.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Ni-Bin Chang ; Ammarin Daranpob ; Y. Jeffrey Yang and Kang-Ren Jin
"Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida", J. Appl. Remote Sens. 3(1), 033549 (September 17, 2009). ; http://dx.doi.org/10.1117/1.3244644


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