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利用GIS对吉林针阔混交林TM遥感图像分类方法的初探

王立海;赵正勇;杨旗   

  1. 东北林业大学森林作业与环境中心,哈尔滨 150040
  • 收稿日期:2005-04-18 修回日期:2005-09-20 出版日期:2006-04-18 发布日期:2006-04-18

Classification method of deciduous-conifer mixed forest in Jilin Province based on GIS-TM remote sensing image

WANG Lihai;ZHAO Zhengyong;YANG Qi   

  1. Center of Forest Operations and Environment,Northeast Forestry University,Harbin 150040,China

  • Received:2005-04-18 Revised:2005-09-20 Online:2006-04-18 Published:2006-04-18

摘要: 为提高林区TM遥感图像自动分类识别精度,在GIS技术辅助下,以吉林省汪清林业局针阔混交林TM遥感图像为例,对研究区DEM、坡向等地理因子和土壤类型等环境因子与森林植被分布之间的内在规律进行了定量分析,并结合对遥感图像预分类的定性分析,形成分类知识库,建立了适用于针阔混交林的自动分类识别专家系统.分类试验证明,该系统能比较明显地削弱混合像元和地形阴影的影响,分类精度较无监督分类法提高了14.22%,Kappa指数为0.7556,达到区别森林类型的分类目的.将GIS数据引入专家系统,应用先验知识建立推理机制,可以解决遥感图像中云区和云阴影区由于不能接收到正确的光谱值而无法进行分类的问题.

关键词: 遥感, 地理信息系统, 土地利用, 动态监测

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 preclassification 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.

Key words: Remote sensing, GIS, Land use, Dynamic monitoring