中文摘要：

在这份报纸，为山区的自然的带的高度的带系列的抽取的一个数字鉴定方法被介绍。如果地调查的传统的方法将被过去常，在可接受的时间的频率并且在一个大区域上在山获得数字高度的带的顺序的系列要求广泛的时间和工作。如此的是盒子，经常，一座整个山的高度的带或在地区性的规模的带被单个点代表。然而，单个点不能精确地显然反映高度的带的空间变化。在这上下文，一个数字方法被开发在西方 Kunlun 山从遥感数据和 SRTM DEM 提取高度的带的系列。借助于 1km 决定 SPOT-4 植被 10 天的合成 NDVI，高度的带的水平分发通过监督分类被提取，与 72.23% 的全部的分类精确性。然后，两次扫描的一个方法被用来认识到水平地图的自动转变到垂直的带。遥远察觉到的数据的分类结果能因此自动地被转变到数字高度的带的顺序的系列。高度的带的上面、更低的线然后被带的垂直扫描提取。在基于有关植被类型和 geomorphological 的山区的自然地区，高度的带也是的高度的带之间的关系讨论了。作为一试验方法，数字抽取方法这里介绍了在数字地识别高度的带是有效的，并且能在在大规模区域上的快速的信息抽取有用。关键词高度的带系列 - Kunlun 山 - NDVI - 数字抽取

英文摘要：

In this paper, a digital identification method for the extraction of altitudinal belt spectra of montane natural belts is presented. Acquiring the sequential spectra of digital altitudinal belts in mountains at an acceptable temporal frequency and over a large area requires extensive time and work if traditional methods of field investigation are to be used. Such being the case, often the altitudinal belts of a whole mountain or the belts at a regional scale are represented by single points. However, single points obviously cannot accurately reflect the spatial variety of altitudinal belts. In this context, a digital method was developed to extract the spectra of altitudinal belts from remote sensing data and SRTM DEM in the We.st Kunlun Mountains. By means of the 4km resolution SPOT-4 vegetation 10-day composite NDVI, the horizontal distribution of altitudinal belts were extracted through supervised classification, with a total classification accuracy of 72.23%. Then, a way of twice-scan was used to realize the automatic transition of horizontal maps to vertical belts. The classification results of remote-sensing data could thus be transformed automatically to sequential spectra of digital altitudinal belts. The upper and lower lines of the altitudinal belts were then extracted by vertical scanning of the belts. Relationships between the altitudinal belts based on the montane natural zones concerning vegetation types and the geomorphological altitudinal belts discussed. As a tentative method, were also the digital extraction method presented here is effective at digitally identifying altitudinal belts, and could be helpful in rapid information extraction over large-scale areas.