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Hydrologic land cover classification mapping at local level with the combined use of ASTER multispectral imagery and GPS measurements

机译:利用Aster多光谱图像和GPS测量的局部水能覆盖在地方一级的水文覆盖分类映射

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Digital Elevation Models (DEMs) and land cover products are primary inputs for hydrologic models of surface runoff that affects infiltration, erosion, and evapotranspiration. DEM and land cover play important role in determining the runoff characteristics of specific catchment areas. Recently, at local level, a number of data sources have been used to derive land cover products for high resolution studies. These studies have been carried out for a number of different applications, including estimation of biomass and vegetation mapping. A hydrologic land cover classification includes information not only about vegetation species, but also about the land surface and what classes are important hydrologically. This kind of classification must therefore incorporate information on elevation, slope, aspect, surface roughness, as well as vegetation species derived from satellite added-value products. The main problems when generating hydrologic land cover maps is the lack of accurate DEMs and the confusion of spectral responses from different features. In this study, a Terra/ASTER image acquired over the region of Heraklion, Crete, Greece was used. ASTER stereo imagery is used for DEM production because it gives a strong advantage in terms of radiometric variations versus the multi-date stereo-data acquisition with across-track stereo, which can then compensate for the weaker stereo geometry. GCPs (Ground Control Points) derived from differential GPS measurements were also used for absolute DEM production. A hydrologic land cover classification scheme was developed by combining ASTER multispectral imagery, ASTER DEM products and the spectral signatures derived from field observations at predefined training sites.
机译:数字高度模型(DEMS)和陆地覆盖产品是用于表面径流的水文模型的主要输入,影响渗透,侵蚀和蒸发散。 DEM和陆地覆盖在确定特定集水区的径流特征方面发挥着重要作用。最近,在地方一级,已经使用了许多数据来源来获得高分辨率研究的土地覆盖产品。已经为许多不同的应用进行了这些研究,包括估计生物质和植被映射。水文覆盖分类包括不仅有关植被物种的信息,而且还包括陆地表面以及水文学的重要性。因此,这种分类必须包含有关仰卧,坡,方面,表面粗糙度以及源自卫星附加值产品的植被物种的信息。产生水文陆地覆盖图的主要问题是缺乏准确的DEM和来自不同特征的光谱响应的混淆。在这项研究中,使用了在希腊克里特岛,希腊克里特岛地区获得的Terra / Aster图像。 Aster立体图像用于DEM生产,因为它在辐射变化方面具有强大的优势,而具有跨轨道立体声的多日立体数据采集,然后可以补偿弱立体几何形状。来自差分GPS测量的GCP(地面控制点)也用于绝对DEM生产。通过在预定义的训练场地结合紫色MultiSpectral Imager,Aster MultiSpectral Imagery,Aster MultiSpectral Imager,Aster MultiSpectral图像和光谱签名来开发水文覆盖分类方案。

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