NDLI: Retrieval of Fuel Moisture Content from hyperspectral data via Partial Least Square

Content Provider	IEEE Xplore Digital Library
Author	Jie Zhang Jianjun Wu Lei Zhou
Copyright Year	2010
Description	Author affiliation: State Key Laboratory of Earth Surface Process and Resource Ecology, Beijing Normal University, 100875, China (Jie Zhang; Jianjun Wu; Lei Zhou)
Abstract	As an important indicator of vegetation moisture status, Fuel Moisture Content (FMC) is commonly used for predicting vulnerability to wild fire. Currently, the FMC estimation using spectral data is mainly based on spectral indices derived from several bands and these methods do not make full use of the entire spectrum. Partial Least Square (PLS) is a new multivariate statistical method which can effectively reduce collinearity. In this paper, using LOPEX dataset, we mainly explored the performance of PLS coupled with different feature selection methods for FMC retrieval. According to the results, PLS shows great potential to extract FMC from spectral data; when coupled with different band selection approaches, the models also generate high estimation precision; with band selection, the PLS coupled models involved fewer bands, lowering the model complexity. Thus, the high estimation precision and much simpler modeling make band selection-PLS coupled methods superior to original PLS for FMC retrieval.
Starting Page	2676
Ending Page	2679
File Size	304048
Page Count	4
File Format	PDF
ISBN	9781424495658
ISSN	21537003
e-ISBN	9781424495665
e-ISBN	9781424495641
DOI	10.1109/IGARSS.2010.5652617
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Calibration Moisture Correlation Reflectivity Fuels Vegetation mapping PLS Retrieval hyperspectral
Content Type	Text
Resource Type	Article

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