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Mountain vegetation mapping in Dovre area, Norway, using Landsat TM data and GIS

机译:山区植被映射在挪威徒步旅行区,使用Landsat TM数据和GIS

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Vegetation mapping by use of satellite data are often divided into two main operations, the pre- and post-classification processes. Experience from producing vegetation maps based on spectral-only classifications, has shown that misclassifications occurs. The aim of the post-classification process is to improve the pre-classified product by use of ancillary data. The mountain areas of Norway are characterized by complex topography. Vegetation maps are though difficult to produce for these areas. In this study two Landsat 5/TM image from 1986 and 1998, covering parts of the Dovre mountain massif in Norway, are classified using unsupervised classification methods. The spectrally classified product is thereafter corrected using several ancillary data layers. Based on the ancillary data the delineation of forest vegetation and the heather vegetation above the woodland limit is more precisely defined. Bogs and mires are easily differentiated from snow-bed communities. The grass- and herb-rich communities in the mountain areas are spectrally much similar to agricultural areas in the lowland; even the floristical composition and content are totally different. By use of digital elevation models the alpine meadows and cultivated land in the lowland are separated into different classes by the use of an altitude threshold. The cost of, and types of corrections we can do in the post-classification process, largely depends on what additional information is available and the quality of this information.
机译:通过使用卫星数据的植被映射通常分为两个主要操作,并且分类前的过程。基于仅限光谱分类,从生产植被地图的经验表明发生了错误分类。分类后进程的目的是通过使用辅助数据来改进预分类产品。挪威的山区的特点是复杂的地形。植被地图虽然难以为这些领域生产。在本研究中,来自1986年和1998年的两个Landsat 5 / TM图像,覆盖了挪威Dovre Mountain Massif的部分,使用无监督的分类方法进行分类。此后使用若干辅助数据层校正光谱级产品。基于辅助数据,更精确地定义了森林植被的描绘和林地极限上方的石兰植被。 Bogs和Mires很容易被雪床社区区分开来。山地地区的草地和草本植物的社区与低地的农业领域的光谱相似;即使是植物组成和含量也完全不同。通过使用数字高度模型,利用高度阈值,低地的高山草甸和耕地被分成不同的课程。我们可以在分类后流程中进行的成本和更正类型的成本在很大程度上取决于可用的附加信息以及此信息的质量。

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