中文摘要：

以内蒙古呼伦贝尔地区为例，基于遥感数据获取区域7种典型植被的NDVI时间序列曲线。在此基础之上，分析曲线趋势及其特征值，研究基于曲线差异的植被分类信息提取方法。同时，以国产环境减灾卫星CCD数据作为主要遥感数据源，提取研究区5月上句与8月上旬两期NDVI数据及其比值，采用决策树分类方法研究得到区域30m空间分辨率植被分类结果。经实地验证，一级类型总体分类精度为83．64％，二级类型为70．91％，其中乔木林的分类精度最高，然后是农田与草地，灌丛的分类精度相对最低。结果表明该方法能够快速、准确据提取植被分类信息，为国产环境减灾卫星CCD数据的广泛深入应用提供理论与数据支持。

英文摘要：

Vegetation is critical for researches about global environmental change and regional sustainable de- velopment, and remote sensing is an important method for obtaining classification result. However, the Nor- malized Difference Vegetation Index （NDVI） time series method classification was limited by the coarse spa- tial resolution, and application of the medium high data such as Landsat TM was limited by the coverage and accessibility of remote sensing data. The Chinese environmental mitigation HJ satellite CCD sensors are capa- ble of large area, all-time monitoring, and have a great advantage in coverage and frequency of repeated obser- vations. A case study of Hulunbuir, Inner Mongolia was carried out in this paper. The NDVI time series curve of 7 vegetation types were extracted from both MODIS and HJ CCD data. Then, the curves and eigenvalue were analyzed. The result showed that between the 7 vegetation types, there was significant differences in the value range of early May NDVI, early August NDVI and the ratio of the two NDVI image. The vegetation clas- sification rules were extracted based on these differences. The HJ-CCD was used as the main data sources in this paper. Three images including two NDVI and one ratio were extracted and the decision tree method was applied. Based on the result, 30 m spatial resolution vegetation classification result was carried out. By field verification, the result shows a 83.64% overall accuracy in the level one classification, and 70.91% in the level two classification. The cartographic accuracy of evergreen coniferous forest can achieve 100%, followed by cropland 82.61%, mixed forest 76.19% and desert steppe 75%. The＇ accuracy of shrub is relatively low to 50%. This result proved a fast, simple and accurate method for vegetation classification, arjd provided the theory and data support for application of the Chinese HJ satellites.