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A geomorphology-based approach for digital elevation model fusion – case study in Danang city, Vietnam

机译:基于地貌的数字高程模型融合方法-越南岘港市的案例研究

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Global digital elevation models (DEM) are considered a source of vital spatial information and find wide use in several applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) and Shuttle Radar Topographic Mission (SRTM) DEM offer almost global coverage and provide elevation data for geospatial analysis. However, GDEM and SRTM still contain some height errors that affect the quality of elevation data significantly. This study aims to examine methods to improve the resolution as well as accuracy of available free DEMs by data fusion techniques and evaluating the results with a high-quality reference DEM. The DEM fusion method is based on the accuracy assessment of each global DEM and geomorphological characteristics of the study area. Land cover units were also considered to correct the elevation of GDEM and SRTM with respect to the bare-earth surface. The weighted averaging method was used to fuse the input DEMs based on a landform classification map. According to the landform types, the different weights were used for GDEM and SRTM. Finally, a denoising algorithm (Sun et al., 2007) was applied to filter the output-fused DEM. This fused DEM shows excellent correlation to the reference DEM, having a correlation coefficient R2 = 0.9986, and the accuracy was also improved from a root mean square error (RMSE) of 14.9 m in GDEM and 14.8 m in SRTM to 11.6 m in the fused DEM. The results of terrain-related parameters extracted from this fused DEM such as slope, curvature, terrain roughness index and normal vector of topographic surface are also very comparable to reference data.
机译:全球数字高程模型(DEM)被认为是重要空间信息的来源,并在多种应用中得到广泛应用。先进的星载热发射和反射辐射仪(ASTER)全球DEM(GDEM)和航天飞机雷达地形图任务(SRTM)DEM提供几乎全球范围的覆盖,并提供用于地理空间分析的高程数据。但是,GDEM和SRTM仍然包含一些高度误差,这些误差会严重影响高程数据的质量。这项研究旨在研究通过数据融合技术提高可用免费DEM的分辨率和准确性的方法,并使用高质量的参考DEM评估结果。 DEM融合方法基于每个全局DEM的准确性评估和研究区域的地貌特征。还考虑使用土地覆被单位来校正GDEM和SRTM相对于裸露地面的高度。加权平均方法用于基于地形分类图融合输入的DEM。根据地貌类型,GDEM和SRTM使用了不同的权重。最后,采用去噪算法(Sun等,2007)对输出融合的DEM进行滤波。此融合DEM与参考DEM表现出极好的相关性,相关系数R2 = 0.9986,并且精度也从GDEM中的均方根误差(RMSE)为14.9 m,SRTM中的均方根误差(RMSE)为14.8 m到融合后的11.6 m DEM。从此融合DEM中提取的与地形相关的参数(例如坡度,曲率,地形粗糙度指数和地形表面的法线矢量)的结果也与参考数据具有可比性。

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