NDLI: A Two-Level Approach for Species Identification of Coniferous Trees in Central Ontario Forests Based on Multispectral Images

Content Provider	IEEE Xplore Digital Library
Author	Jili Li Baoxin Hu Woods, M.
Copyright Year	2008
Abstract	This study aims to provide detailed spatial information of valuable tree species to support improved management of winter habitat of white-tailed deer. To achieve this, we proposed a novel approach using information from two spatial scales and a suite of methods for analysis and classification of remotely sensed data. High-spatial resolution, multispectral images were employed to test the proposed method. A new structure-based remote sensing feature [local binary pattern (LBP) index] was developed and proved to be effective for species classification. A simple but effective fusion approach based on information entropy theory was proposed to incorporate features derived from different methods and their uncertainties. Based on tenfold cross validation, an overall accuracy (OA) of 77% was obtained for the classification of three tree species groups. The proposed approach has high potential to improve species mapping for operational ecological modeling.
Starting Page	1487
Ending Page	1497
Page Count	11
File Size	1209604
File Format	PDF
ISSN	19391404
Volume Number	8
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation Spatial resolution Remote sensing Feature extraction Earth Image segmentation image processing Entropy forestry
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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