NOAA/AVHRR由于运行周期短、覆盖范围大、成本低、波段宽等特点, 目前正越来越广泛地受到人们的普遍关注。在大尺度、中尺度植被遥感上, NOAA/AVHRR具有陆地卫星无法比拟的优势, 但在另一方面, NOAAAVHRR也存在分辨率低、数据变形较大和几何畸变较严重等问题。这样, 在应用NOAAAVHRR数据进行大区域植被制图时, 植被分类的精度仍待提高。本文从理论上探讨了将地理信息系统提供的地理数据与遥感数据复合的可行性；尝试在GIS环境下, 将气温、降水、高程3个影响区域植被覆盖的主要指标, 按一定

As the satellite remote sensing data have been available since early 1990s, these data are being employed towards the improvement of vegetation classification. On macro and middle scale of vegetation remote sensing, NOAA AVHRR possesses an advantage when compared to other satellite data On the other hand, because the scanning width of NOAA AVHRR is so large (2800km), the earth's curvature, characteristics, the angle of reflection from earth's object and atmosphere as well as the angle of scanner and deviation of sun's height cause a serious effect on the data. Therefore, NOAA AVHRR also has problems of low resolution, data distortion and geometrical distortion. AS a result, applying NOAA AVHRR to large scale vegetation-mapping, the accuracy of vegetation classification should be increased. This paper discusses the feasibility of integrating the geographic and remotely sensed data in GIS. Under the GIS environment,temperature, precipitation and elevation, which serve as main factors affecting vegetation growth, were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographic image were overlaid to the NOAA AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification,the area under study was digitally classified by two interpreter methods. A maximum likelihood classification assisted by the geographic database, and a conventional maximum likelihood classification only.Both results were compared using Kappa statistics. The indices to both the proposed and the conventional digital classification methodology were 0.668(very good) and 0.563(good), respectively. The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study, some problems related to multi-sources data integration are discussed.