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Evaluation of DEM interpolation techniques for characterizing terrain roughness
Catena ( IF 5.4 ) Pub Date : 2020-12-13 , DOI: 10.1016/j.catena.2020.105072
Maan Habib

The emergence of modern geospatial techniques has brought a considerable change in data capturing, processing, and visualization tools. Digital elevation models (DEMs) are a 3D mathematical representation of the earth’s surface. Due to enormous advancement in geographic information system (GIS) tools and technologies, DEMs have a wide range of applications in natural resources management, environmental science, and engineering. In general, primary topographic variables extracted from DEMs are frequently less useful than secondary ones, e.g., surface roughness. Therefore, the precise values of secondary terrain features need to be derived from base DEMs. Generally, topographic roughness is an essential geomorphological variable that is a vital issue in geoscience. This research presents a comparison of deterministic interpolation methods, inverse distance weighted (IDW) and a natural neighbor (NN), with geostatistical technique, ordinary kriging (OK), to investigate the influence of generated DEM on quantifying terrain roughness relied on synthetic data and applying the zonal statistics tool. At the same time, a quantitative and qualitative approach is adopted to assessing the results through statistical methods and visual representations. The findings indicated that roughness values' behavior is broadly related to the quality of the built DEM. The NN approach yielded the greatest DEM accuracy, standard deviation, from a quantitative perspective (±0.930 m) than IDW (±3.748 m) and OK (SD = ±5.544 m) methods.



中文翻译:

评价用于表征地形粗糙度的DEM插值技术

现代地理空间技术的出现带来了数据捕获,处理和可视化工具的重大变化。数字高程模型(DEM)是地球表面的3D数学表示。由于地理信息系统(GIS)工具和技术的巨大进步,DEM在自然资源管理,环境科学和工程学中具有广泛的应用。通常,从DEM中提取的主要地形变量通常不如次要变量有用,例如表面粗糙度。因此,需要从基本DEM中得出次要地形特征的精确值。通常,地形粗糙度是必不可少的地貌变量,这是地球科学中的重要问题。本研究对确定性插值方法进行了比较,反距离加权(IDW)和自然邻域(NN),采用地统计学技术,普通克里金法(OK),以合成数据为基础并应用区域统计工具来研究生成的DEM对量化地形粗糙度的影响。同时,采用定量和定性方法通过统计方法和视觉表示评估结果。研究结果表明,粗糙度值的行为与所构建DEM的质量广泛相关。从定量角度(±0.930 m)来看,与IDW(±3.748 m)和OK(SD =±5.544 m)方法相比,NN方法产生的DEM精度最高,标准差最大。依靠合成数据并应用区域统计工具来调查生成的DEM对量化地形粗糙度的影响。同时,采用定量和定性方法通过统计方法和视觉表示评估结果。研究结果表明,粗糙度值的行为与所构建DEM的质量广泛相关。从定量角度(±0.930 m)来看,与IDW(±3.748 m)和OK(SD =±5.544 m)方法相比,NN方法产生的DEM精度最高,标准差最大。依靠合成数据并应用区域统计工具来调查生成的DEM对量化地形粗糙度的影响。同时,采用定量和定性方法通过统计方法和视觉表示评估结果。研究结果表明,粗糙度值的行为与所构建DEM的质量广泛相关。从定量角度(±0.930 m)来看,与IDW(±3.748 m)和OK(SD =±5.544 m)方法相比,NN方法产生的DEM精度最高,标准差最大。行为在很大程度上与内置DEM的质量有关。从定量角度(±0.930 m)来看,与IDW(±3.748 m)和OK(SD =±5.544 m)方法相比,NN方法产生的DEM精度最高,标准差最大。行为在很大程度上与内置DEM的质量有关。从定量角度(±0.930 m)来看,与IDW(±3.748 m)和OK(SD =±5.544 m)方法相比,NN方法产生的DEM精度最高,标准差最大。

更新日期:2020-12-14
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