[Classification method of deciduous-conifer mixed forest in jilin province based on GIS-TM remote sensing image].
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.
Wang
Lihai
L
Center of Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China. lihaiwg@public.hr.hl.cn
Zhao
Zhengyong
Z
Yang
Qi
Q
D002681:China; D017753:Ecosystem; D005103:Expert Systems; D040362:Geographic Information Systems; D017215:Satellite Communications; D064028:Tracheophyta / Q000145:classification* / Q000254:growth & development; D014197:Trees / Q000145:classification* / Q000254:growth & development