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Terrain surface roughness, often described abstractly, poses challenges inquantitative characterisation with various descriptors found in the literature.This study compares five commonly used roughness descriptors, exploringcorrelations among their quantified terrain surface roughness maps across threeterrains with distinct spatial variations. Additionally, the study investigatesthe impacts of spatial scales and interpolation methods on these correlations.Dense point cloud data obtained through Light Detection and Ranging techniqueare used in this study. The findings highlight both global pattern similaritiesand local pattern distinctions in the derived roughness maps, emphasizing thesignificance of incorporating multiple descriptors in studies where localroughness values play a crucial role in subsequent analyses. The spatial scaleswere found to have a smaller impact on rougher terrain, while interpolationmethods had minimal influence on roughness maps derived from differentdescriptors.

Comparing roughness descriptors for distinct terrain surfaces in point cloud data