NDLI: Non-wood forest information extraction based on ALOS data

Content Provider	IEEE Xplore Digital Library
Author	Enping Yan Hui Lin Dengkui Mo Liming Bai Hua Sun
Copyright Year	2010
Description	Author affiliation: Research Center of Forest Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha, Hunan, China (Enping Yan; Hui Lin; Dengkui Mo; Liming Bai; Hua Sun)
Abstract	Non-wood forest is a kind of important forest resource. This paper focused on the information extraction of non-wood forest based on Advanced Land Observation Satellite (ALOS) data. Band characteristics were analyzed to get understanding of this data wholly by information content, correlation coefficient and Optimum Index Factor (OIF). A new set of data with eight bands were obtained by the fusion of Normalized Difference Vegetation Index (NDVI), the first three components of Principal Component Analysis (PCA1, PCA2, PCA3) and the four bands of ALOS data. Various kinds of vegetations, especially non-wood forest was analyzed through the Spectral Feature Model (SFM) and Maximum Likelihood (ML) with association of topographical map and field investigation data. Results show that NDVI and PCA can improve the extraction accuracy of non-wood forest. In addition, SFM reduces the phenomenon of mixed classification and improves the information extraction accuracy of non-wood forest, which will provide reference for the classification of vegetation.
Starting Page	2037
Ending Page	2041
File Size	397772
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Presses Accuracy Correlation information extraction ALOS data Vegetation mapping Feature extraction non-wood forest remote sensing Data mining Remote sensing
Content Type	Text
Resource Type	Article

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