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：2008
卷号：XXXVII Part B2
页码：209-214
出版社：Copernicus Publications
摘要：In this paper we attempt to isolate spatially those factors responsible for forest degradation in Brunei Darussalam. While human activities are one of the deforestation forces, the degradation of forest far from development corridors may implicate extra-human factors in its explanation. In the absence of apparent sources of pollution, mean temperature rise is a good candidate factor as heat stress is capable of reducing photosynthesis, which can manifest itself in a drop in canopy density. Considering that leaves are the most active scattering kernels of electromagnetic waves within tree canopies, especially in the C-band (λ=5.3cm), one can expect that the shuttle radar topography mission elevation data product (SRTM) developed for that band should exhibit a variable bias over forest depending on its density. This relationship, e.g., vegetation density versus elevation bias, is used to measure forest depletion. Based on data from a forest map, SRTM and a reference DTM we calculated a typical bias for seven forest types. A linear relationship was established between elevation bias and forest depletion level. The typical bias means zero depletion, and bias equal to zero means 100% depletion. A map of forest depletion was developed using that relationship. By excluding pixels most likely to be affected by human activities (2.5km buffer around settlements and 0.5km buffer around sealed roads), depletion levels for all remaining pixels were categorized by forest type. It was found that forest plots potentially free of direct human activities are also depleted to various degrees. It would indicate the presence of a forest depleting force, possibly an increase in the mean monthly average temperature, which has been observed in Brunei over the last 30 years
关键词：Forestry; GIS; Identification; Change Detection; Mapping; Data mining; SAR; DEM/DTM
