[Classification method of deciduous-conifer mixed forest in jilin province based on GIS-TM remote sensing image]

Full Abstract

To improve the accuracy of automatic classification and identification of TM remote sensing images in forest area, an expert system for automatically classifying and identifying deciduous-conifer mixed forest was built up, based on the GIS technique, quantitative analysis on the internal relations between geographic factors such as DEM and slope aspect and environment factors like soil type, and qualitative analysis on the spectrum information and preclassification information of sensing images, aimed to build a classification knowledge system. Taking the TM remote sensing image of Wangqing Forest Bureau in Jilin Province as an example, the study showed that this expert system could obviously reduce the influence of mixed pixel and terrain shadow. The classification precision of this system was increased by 14.22%, compared with that of Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised classification, and the Kappa index was 0.7556, which could help to classify needle, deciduous and mixed forests. Introducing GIS data into the expert system could also solve the problem that TM remote sensing image could not do, due to the loss of correct spectrum value in cloudy and shady area.

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Author information

Author/s: Wang, Lihai (L); Zhao, Zhengyong (Z); Yang, Qi (Q);

Affiliation: Center of Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China. lihaiwg(-atsign-)public.hr.hl.cn

Journal and publication information

Publication Type: English Abstract; Journal Article; Research Support, Non-U.S. Gov't

Journal: Ying yong sheng tai xue bao = The journal of applied ecology / Zhongguo sheng tai xue xue hui, Zhongguo ke xue yuan Shenyang ying yong sheng tai yan jiu suo zhu ban (Ying Yong Sheng Tai Xue Bao), published in China. (Language: chi)

Reference: 2006-Apr; vol 17 (issue 4) : pp 577-82

Dates: Created 2006/07/13; Completed 2007/02/16;

PMID: 16836081, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

China
Coniferophyta - classification; growth & development
Ecosystem
Expert Systems

Expert Systems
Geographic Information Systems
Satellite Communications
Trees - classification; growth & development

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