天山北坡草地遥感分类及其精度分析

基于遥感资料的植被类型划分能快速获得大尺度的植被覆盖变化数据。采用遥感和GIS技术，利用天山北坡典型草地2008年Landsat 5 TM遥感影像和1999年Landsat 7 ETM+全色波段数据，在对遥感影像预处理的基础上，进行了数据融合处理。根据融合影像的纹理特征进行监督分类，将研究区域的植被初步分为8种主要覆盖类型，在监督分类的基础上借助专家知识系统构建决策树，进一步将草地分为5类，包括平原荒漠、平原沙漠、低山荒漠、温性草甸和高寒草甸，最后对分类结果进行精度评价，总精度在95%以上，总Kappa系数为0.939 6，间隔9年的影像融合和决策树分类方法在研究区植被分类中具有较高的可行性。

Typical grassland classification and precision evaluation based on remote sensing data in the northern slope of Tianshan Mountain

Vegetation classification based on remote sensing data can quickly get vegetation change information at large scale. This paper chose the Landsat TM data in 2008 and Landsat ETM+ Pan data in 1999 of the typical grassland in the northern slope of Tianshan Mountain. The data used in this paper were pre processed firstly, then the two temporal remote sensing data were fused by means of remote sensing and GIS (Geographic Information System) technologies. The vegetation in the study area was classified into eight types by visual interpretation based on the texture feature of the fused remote sensing image. Besides, we established an expert knowledge decision tree system to make a further classification combining the preliminary classification results of the visual interpretation. At last the grasslands in the study area were classified into five types, including plain desert, desert plants, low mountain desert, temperate meadow and alpine meadow. The results of accuracy evaluation indicated that the overall classification accuracy reaches to 95%, and the overall Kappa coefficient is 0.9396, which suggests that the vegetation classification methods based on the fused image generated by nine year interval TM and ETM+ Pan images and decision tree classification in the study area have higher feasibility. The classification effect is good and consistent with the actual vegetation cover situation.