TY - GEN
T1 - Comparisons of five indices for estimating local terrain surface roughness using LiDAR point clouds
AU - Fan, Lei
N1 - Funding Information:
This research was funded by XJTLU Key Program Special Fund (grant number KSF-E-40) and XJTLU Research Enhancement Funding (grant no. REF-21-01-003).
Funding Information:
ACKNOWLEDGMENT LiDAR data access is based on [LiDAR, ground] services provided by the OpenTopography Facility with support from the National Science Foundation under NSF Award Numbers 1226353 & 1225810. Lidar data acquisition completed by the National Center for Airborne Laser Mapping (NCALM - http://www.ncalm. org). NCALM funding provided by NSF’s Division of Earth Sciences, Instrumentation and Facilities Program. EAR-1043051. https://doi.org/10.5069/G9PR7SX0
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Terrain surface roughness is an abstract concept, and its quantitative description is often vague. As such, there are various roughness indices used in the literature, the selection of which is often challenging in applications. This study compared the terrain surface roughness maps quantified by five commonly used roughness indices, and explored their correlations for four terrain surfaces of distinct surface complexities. These surfaces were represented by digital elevation models (DEMs) constructed using airborne LiDAR (Light Detection and Ranging) data. The results of this study reveal the similarity in the global patterns of the local surface roughness maps derived, and the distinctions in their local patterns. The latter suggests the importance of considering multiple indices in the studies where local roughness values are the critical inputs to subsequent analyses.
AB - Terrain surface roughness is an abstract concept, and its quantitative description is often vague. As such, there are various roughness indices used in the literature, the selection of which is often challenging in applications. This study compared the terrain surface roughness maps quantified by five commonly used roughness indices, and explored their correlations for four terrain surfaces of distinct surface complexities. These surfaces were represented by digital elevation models (DEMs) constructed using airborne LiDAR (Light Detection and Ranging) data. The results of this study reveal the similarity in the global patterns of the local surface roughness maps derived, and the distinctions in their local patterns. The latter suggests the importance of considering multiple indices in the studies where local roughness values are the critical inputs to subsequent analyses.
KW - digital elevation model
KW - LiDAR
KW - point cloud
KW - Remote Sensing
KW - roughness
KW - terrain surface
UR - http://www.scopus.com/inward/record.url?scp=85143897897&partnerID=8YFLogxK
U2 - 10.1109/Geoinformatics57846.2022.9963877
DO - 10.1109/Geoinformatics57846.2022.9963877
M3 - Conference Proceeding
AN - SCOPUS:85143897897
T3 - International Conference on Geoinformatics
BT - Proceedings - 2022 29th International Conference on Geoinformatics, Geoinformatics 2022
A2 - Hu, Shixiong
A2 - Ye, Xinyue
A2 - Lin, Hui
A2 - Gao, Song
A2 - Zheng, Xinqi
A2 - Zhang, Chunxiao
PB - IEEE Computer Society
T2 - 29th International Conference on Geoinformatics, Geoinformatics 2022
Y2 - 15 August 2022 through 18 August 2022
ER -