NDLI: High-resolution tropical forest mapping of the Amazon basin: a novel classification approach for the GRFM radar mosaic

Content Provider	IEEE Xplore Digital Library
Author	Sgrenzaroli, M. Baraldi, A. De Grandi, G.F. Achard, F. Eva, H.
Copyright Year	2001
Description	Author affiliation: Space Applications Inst., Eur. Comm. Joint Res. Centre, Ispra, Italy (Sgrenzaroli, M.)
Abstract	The high resolution (100 m) Global Rain Forest Mapping (GRFM) radar mosaics, providing a spatially continuous coverage of entire ecosystems, pave the way to improved estimates of bio-physical parameters related to the tropical vegetation. A new classification scheme for producing a high-resolution regional scale forest/non-forest thematic map of the Amazon is proposed. First, a new wavelet multi-resolution decomposition/reconstruction technique is employed to generate an edge-preserving piecewise constant approximation of the original radar image. Second, a two-stage hybrid learning Nearest Multiple-Prototype (NMP) classifier is applied to the reconstructed radar image. The NMP first stage employs a near-optimal vector quantization algorithm called Enhanced Linde-Buzo-Gray (ELBG). During the training phase, ELBG employs only 1% of the whole data set. At the second stage of NMP, vector prototypes are combined into land cover classes of interest by an expert photo-interpreter. In the pattern recognition phase, each pixel is labeled according to the minimum-distance-to-prototype criterion. Experimental results are reported for a thematic problem involving classes: primary forest, degraded forest, non-forest, and water bodies. Validation is performed using land cover maps provided by the Tropical Rain Forest Information Center (TRIFIC) over three test sites featuring different forest cover disturbance patterns. Main results are that the proposed classifier (i) provides a classification accuracy of 87% in forest/non-forest mapping, (ii) is capable of generalizing over the entire data set, and (iii) requires minor user interaction. It is concluded that the proposed approach responds adequately to the requirements of regional scale high-resolution vegetation mapping.
Starting Page	1356
Ending Page	1358
File Size	610343
Page Count	3
File Format	PDF
ISBN	0780370317
DOI	10.1109/IGARSS.2001.976843
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation mapping Rain Image reconstruction Radar imaging Spatial resolution Ecosystems Parameter estimation Vector quantization Prototypes Pattern recognition
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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