Title :
Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling
Author :
Li, Lin ; Ustin, Susan L. ; Riano, David
Author_Institution :
Dept. of Earth Sci., Indiana Univ.-Purdue Univ., Indianapolis, IN
fDate :
4/1/2007 12:00:00 AM
Abstract :
Fuel moisture content (FMC) is an important parameter in forest fire modeling. We investigated the performance of genetic algorithms with partial least squares (GA-PLS) modeling to retrieve live FMC and its components, equivalent water thickness (EWT) and dry matter content (DM), from fresh leaf reflectance in the leaf optical properties experiment dataset. The results show that GA-PLS achieved a good estimation of FMC directly (R2=0.878-0.893) or indirectly (R 2=0.815-0.862) through the joint retrieval of EWT and DM; future work is required to assess the effectiveness of GA-PLS when applied to datasets that consist of low FMC values
Keywords :
data acquisition; forestry; genetic algorithms; least squares approximations; moisture measurement; vegetation mapping; Leaf Optical Properties Experiment; dry matter content; equivalent water thickness; forest fire modeling; fresh leaf fuel moisture content retrieval; fresh leaf reflectance; genetic algorithm partial least squares modeling; hyperspectral reflectance; Content based retrieval; Delta modulation; Fires; Fuels; Genetic algorithms; Information retrieval; Least squares methods; Moisture; Reflectivity; Water; Dry matter (DM); Leaf Optical Properties Experiment (LOPEX); equivalent water thickness (EWT); fuel moisture content (FMC); genetic algorithm (GA); hyperspectral reflectance; partial least squares (PLS);
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2006.888847
