Land-cover classification in the Andes of southern Ecuador using Landsat ETM plus data as a basis for SVAT modelling
Land-cover classification in the Andes of southern Ecuador using Landsat ETM plus data as a basis for SVAT modelling
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DOI：
10.1080/01431160802541531
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发表时间：
2009-01-01
影响因子：
3.4
通讯作者：
Bendix, J.
中科院分区：
文献类型：
--
作者：
Goettlicher, D.;Obregon, A.;Bendix, J.
土地覆被分类是需要推导出土壤-植被-大气传输（SVAT）计划，这是由一个地质生态研究单位在厄瓜多尔南部的安第斯山脉工作的表面边界条件。大地卫星增强型专题制图仪（ETM+）数据被用于对热带山区森林的不同植被类型进行分类。除了硬分类之外，还应用软分类技术。Dempster-Shafer证据理论用于分析光谱训练点的质量，并选择一种改进的线性光谱解混技术来产生光谱端元的丰度。硬分类提供了非常好的结果，Kappa值为0.86。Dempster-Shafer模糊性强调了训练场地的良好质量，并选择概率引导的光谱分解来确定土地模型的植物功能类型。一个类似的模型运行的土地覆盖的空间分布从硬和软分类过程清楚地指向更现实的模型结果，通过使用基于概率引导的光谱分解技术的土地表面。
A land-cover classification is needed to deduce surface boundary conditions for a soil-vegetation-atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster-Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster-Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.