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

被引:0
|
作者
Chrysoulakis, N [1 ]
Keramitsoglou, I [1 ]
Cartalis, C [1 ]
机构
[1] Fdn Res & Technol Hellas, Inst Appl & Computat Math, Reg Anal Div, GR-71110 Iraklion, Greece
关键词
digital elevation model; GPS; multispectral satellite imagery; hydrologic land cover classification;
D O I
10.1117/12.515575
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:532 / 541
页数:10
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