期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B2
页码：790-795
出版社：Copernicus Publications
摘要：The land cover mosaic (LCM) classification concept, which is based on the aggregate-mosaic theory, improves geo-information on tropical deforestation for decision-makers. Land cover mosaics are spatial units constituting mixtures of different land cover types. They are defined by two parameters, that is the mix of different land cover types and the spatial size of these land cover types. In this paper, a sensitivity analysis was performed to test the impact of the parameter spatial size on LCM classification results using two Landsat TM images of a peatswamp forest in Kalimantan, Indonesia. Five methods were selected to evaluate the LCM -classification re sults, the standard remote sensing accuracy method KHAT, and four Landscape Pattern Metrics as applied in landscape ecology. Results showed that for spatial sizes up to 150 ha, the total area of forest cover remained constant for both the 1990 image and th e more fragmented 1996 image. This finding is very useful for assessing the area of forest cover, which often differ widely between various sources. From a decision-making point of view it is important that maps are produced with identical parameter settin gs, when comparing temporally different images, because spatial size has effect on the spatial arrangement of forest cover. Combining results of KHAT and Landscape Pattern Metrics thematic classes causing significant differences could be indicated. Finally , based on variations in LCM classifications, it can be concluded that between 1990 and 1996 forest was depleted not due to logging practices, but due to agricultural practices. Such a finding could be very useful to support planning and development strategies, and to improve governmental policies to manage tropical rainforests in a sustainable way
关键词：Theory; Segmentation; Forestry; Land Cover; Mapping; Vegetation heterogeneity
