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.

使用激光探测遥测 (LiDAR) 技术获得的高密度点云数据比较了五种常用的地表粗糙度描述方法，在三种具有不同空间变化的地形上，探索了它们之间的相互关系。研究发现了派生粗糙度地图的全局模式相似性和局部模式差异，强调了在后续分析中局部粗糙度值起关键作用的研究中，结合多个描述符的重要性。结果还表明，对于较粗糙的地形，空间尺度对其影响较小，而不同描述符派生的粗糙度地图对插值方法影响有限。