Title page for ETD etd-03302006-122704

Type of Document Dissertation
Author Liwa, Evaristo Joseph
Author's Email Address eliwa1@lsu.edu
URN etd-03302006-122704
Title A Neural Network Model for Classification of Coastal Wetlands Vegetation Structure with Moderate Resolution Imaging Spectro-Radiometer (MODIS) Data
Degree Doctor of Philosophy (Ph.D.)
Department Oceanography & Coastal Sciences
Advisory Committee
Advisor Name Title
Lawrence Rouse Committee Chair
Charles Wilson Committee Member
Irving Mendelssohn Committee Member
Kenneth Rose Committee Member
Nan Walker Committee Member
Andrew Curtis Dean's Representative
  • classification
  • neural networks
  • coastal wetlands
Date of Defense 2006-02-16
Availability unrestricted
Mapping coastal marshes is an important component in the management of coastal environments. Classification of marshes using remote sensing data has traditionally been performed by employing either parametric supervised classification algorithms or unsupervised classification algorithms. The implementation of these conversional classification methods is based on the underlying distributions concerning the probability density functions (PDF). Neural networks provide a practical approach to this classification because they are essentially non-parametric data transformations that are not restricted by any underlying assumptions.

The major objective of this study was to evaluate the ability of neural networks using Moderate Resolution Imaging Spectro-radiometer (MODIS) data to classify coastal marshes based on the phenelogical stages of plants. The first stage of the study was to develop a neural network model. The analysis has shown that six day images with eight input variables each are required to perform the classification. The variables are: MODIS bands - the near infrared and the near infrared composite bands, biophysical variables the leaf area index (LAI) and the fraction of photosynthetically active radiation (fPAR). Other variables are vegetation indices the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the wetness index (WI), and, the day time land surface temperature. The near infrared and the wetness index were found to be the strongest predictor variables in the classification. Six hidden neurons and one output neuron were required in the neural network model for the output of six classes.

The second stage of the dissertation was the model application. Images from four years: 2001, 2002, 2003, and 2004 were classified using the model. Accuracy assessment of the classification indicated that neural network techniques using MODIS data could achieve an accuracy of over 80% (at 0.95 confidence level). Using the classified images change detection was performed to determine the loss and gain of four marsh types; saline marsh, brackish marsh, intermediate marsh, and, fresh water marsh found in the south eastern coastal areas of Louisiana. The greatest gain was in the intermediate marsh, 3.0% of the study area, and the greatest loss was in the saline marsh, 3.8% of the study area.

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