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Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

机译:苏格兰米德兰河谷地形表面粗糙度的多尺度分析

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Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter's behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope $(hbox{SD}_{rm slope})$ also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as $hbox{SD}_{rm slope}$, and it is sensitive to noise and spurious data. In general, $hbox{SD}_{rm slope}$ offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit.
机译:表面粗糙度是重要的地貌变量,已在地球和行星科学中用于推断材料属性,当前/过去的过程以及自形成以来经过的时间。没有单一定义;但是,在地貌学的背景下,我们使用表面粗糙度来表示给定尺度上的地形表面变化性,其中分析的规模取决于感兴趣的地貌或地貌特征的大小。选择了六种用于计算表面粗糙度的技术,以评估在不同空间比例和数据集分辨率下的参数行为。面积比独立于比例尺运行,可提供跨空间分辨率的一致结果。向量分散产生的结果是,在较粗的分辨率和较大的窗口尺寸下,地形的粗糙度和均质性增加。残余地形的标准偏差突出了局部特征,未发现区域起伏。高程的标准偏差可正确识别坡度折断,并擅长检测区域性起伏。坡度$(hbox {SD} _ {rm坡度})$的标准偏差还可以正确识别平滑的坡度区域和坡度折断,从而为地貌分析提供最佳结果。轮廓曲率的标准偏差确定了斜率的折断,尽管不如$ hbox {SD} _ {rm斜率} $那样强,并且对噪声和杂散数据敏感。通常,$ hbox {SD} _ {rm斜率} $在各种规模上都具有良好的性能,而计算的简单性也许是它的最大优点。

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