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Proceedings Paper

A multi-characteristic based algorithm for classifying vegetation in a plateau area: Qinghai Lake watershed, northwestern China
Author(s): Weiwei Ma; Cailan Gong; Yong Hu; Long Li; Peng Meng
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Paper Abstract

Remote sensing technology has been broadly recognized for its convenience and efficiency in mapping vegetation, particularly in high-altitude and inaccessible areas where there are lack of in-situ observations. In this study, Landsat Thematic Mapper (TM) images and Chinese environmental mitigation satellite CCD sensor (HJ-1 CCD) images, both of which are at 30m spatial resolution were employed for identifying and monitoring of vegetation types in a area of Western China——Qinghai Lake Watershed(QHLW). A decision classification tree (DCT) algorithm using multi-characteristic including seasonal TM/HJ-1 CCD time series data combined with digital elevation models (DEMs) dataset, and a supervised maximum likelihood classification (MLC) algorithm with single-data TM image were applied vegetation classification. Accuracy of the two algorithms was assessed using field observation data. Based on produced vegetation classification maps, it was found that the DCT using multi-season data and geomorphologic parameters was superior to the MLC algorithm using single-data image, improving the overall accuracy by 11.86% at second class level and significantly reducing the “salt and pepper” noise. The DCT algorithm applied to TM /HJ-1 CCD time series data geomorphologic parameters appeared as a valuable and reliable tool for monitoring vegetation at first class level (5 vegetation classes) and second class level(8 vegetation subclasses). The DCT algorithm using multi-characteristic might provide a theoretical basis and general approach to automatic extraction of vegetation types from remote sensing imagery over plateau areas.

Paper Details

Date Published: 8 October 2015
PDF: 17 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750L (8 October 2015); doi: 10.1117/12.2197777
Show Author Affiliations
Weiwei Ma, Shanghai Institute of Technical Physics (China)
Cailan Gong, Shanghai Institute of Technical Physics (China)
Yong Hu, Shanghai Institute of Technical Physics (China)
Long Li, Vrije Univ. Brussel (Belgium)
Peng Meng, Shanghai Institute of Technical Physics (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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