Terrain surface roughness, often described abstractly, poses challenges in quantitative characterisation with various descriptors found in the literature. This study compares five commonly used roughness descriptors, exploring correlations among their quantified terrain surface roughness maps across three terrains with distinct spatial variations. Additionally, the study investigates the impacts of spatial scales and interpolation methods on these correlations. Dense point cloud data obtained through Light Detection and Ranging technique are used in this study. The findings highlight both global pattern similarities and local pattern distinctions in the derived roughness maps, emphasizing the significance of incorporating multiple descriptors in studies where local roughness values play a crucial role in subsequent analyses. The spatial scales were found to have a smaller impact on rougher terrain, while interpolation methods had minimal influence on roughness maps derived from different descriptors.

翻译：地形表面粗糙度常以抽象形式描述，在文献中存在着多种量化表征方法，这对定量化表征构成了挑战。本研究比较了五种常用粗糙度描述符，探讨了它们在具有不同空间变异性的三种地形上量化的粗糙度地图之间的相关性。此外，还研究了空间尺度和插值方法对这些相关性的影响。本研究采用通过激光雷达技术获取的密集点云数据。研究结果揭示了所得粗糙度地图在全局模式上的相似性与局部模式上的差异性，强调了在后续分析中局部粗糙度值起关键作用的研究中，应综合使用多种描述符的重要性。研究发现，空间尺度对较粗糙地形的影响较小，而插值方法对不同描述符生成的粗糙度地图影响甚微。