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

Volume

4

Issue

2

fYear

2007

fDate

4/1/2007 12:00:00 AM

Firstpage

216

Lastpage

220

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);

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2006.888847

Filename

4156162