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ARS Home » Pacific West Area » Reno, Nevada » Great Basin Rangelands Research » Research » Publications at this Location » Publication #316642

Research Project: Invasive Species Assessment and Control to Enhance Sustainability of Great Basin Rangelands

Location: Great Basin Rangelands Research

Title: Assessing the performance of structure-from-motion photogrammetry and terrestrial lidar 1 at reconstructing soil surface microtopography of naturally vegetated plots

Author
item Weltz, Mark
item NOUWAKPO, SAYJRO - UNIVERSITY OF NEVADA
item MCGWIRE, KEN - DESERT RESEARCH INSTITUTE

Submitted to: Earth Surface Processes and Landforms
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/6/2015
Publication Date: 3/15/2016
Citation: Weltz, M.A., Nouwakpo, S.K., Mcgwire, K. 2016. Assessing the performance of structure-from-motion photogrammetry and terrestrial lidar 1 at reconstructing soil surface microtopography of naturally vegetated plots. Earth Surface Processes and Landforms. 41(3):308-322.

Interpretive Summary: Soil microtopography or soil roughness is a property of critical importance in many earth surface processes but is often difficult to measure. Advances in computer vision technologies have made image-based 3D depiction of the soil surface or Structure-from-Motion (SfM) available to many scientists as a low cost alternative to laser-based systems such as Terrestrial Laser Scanners (TLS). While the performance of SfM at acquiring soil surface soil roughness has been extensively compared to that of TLS on bare surfaces, little is known about the impact of vegetation on accuracy of predicting surface roughness. This paper evaluates the performance of SfM and TLS technologies at estimating soil roughness on 6m x 2m erosion plots with vegetation cover ranging from 0% to 77%. Results show that soil surface occlusion by vegetation was more pronounced with TLS compared to SfM, a consequence of the single viewpoint laser scanning strategy adopted in this study. On the bare soil surface, elevation values estimated with SfM were within 5 mm of those from TLS although long distance deformations were observed with the former technology. As vegetation cover increased, agreement between SfM and TLS slightly degraded but was significantly affected beyond 53% of ground cover. Detailed analysis on meter-square-scale surface patches showed that TLS and SfM surfaces were very similar even on highly vegetated plots but with fine scale details and the dynamic elevation range smoothed out with SfM. Errors in the TLS data were mainly caused by the distance measurement function of the instrument especially at the fringe of occlusion regions where the laser beam intersected foreground and background features simultaneously. From this study, we conclude that a realistic approach to digitizing soil surface soil roughness in field conditions can be implemented by combining strengths of the image-based method (simplicity and effectiveness at developing soil surface roughness under sparse vegetation) with the high accuracy of TLS-like technologies.

Technical Abstract: Soil microtopography or soil roughness is a property of critical importance in many earth surface processes but is often difficult to measure. Advances in computer vision technologies have made image-based 3D depiction of the soil surface or Structure-from-Motion (SfM) available to many scientists as a low cost alternative to laser-based systems such as Terrestrial Laser Scanners (TLS). While the performance of SfM at acquiring soil surface soil roughness has been extensively compared to that of TLS on bare surfaces, little is known about the impact of vegetation on accuracy of predicting surface roughness. This paper evaluates the performance of SfM and TLS technologies at estimating soil roughness on 6m x 2m erosion plots with vegetation cover ranging from 0% to 77%. Results show that soil surface occlusion by vegetation was more pronounced with TLS compared to SfM, a consequence of the single viewpoint laser scanning strategy adopted in this study. On the bare soil surface, elevation values estimated with SfM were within 5 mm of those from TLS although long distance deformations were observed with the former technology. As vegetation cover increased, agreement between SfM and TLS slightly degraded but was significantly affected beyond 53% of ground cover. Detailed analysis on meter-square-scale surface patches showed that TLS and SfM surfaces were very similar even on highly vegetated plots but with fine scale details and the dynamic elevation range smoothed out with SfM. Errors in the TLS data were mainly caused by the distance measurement function of the instrument especially at the fringe of occlusion regions where the laser beam intersected foreground and background features simultaneously. From this study, we conclude that a realistic approach to digitizing soil surface soil roughness in field conditions can be implemented by combining strengths of the image-based method (simplicity and effectiveness at developing soil surface roughness under sparse vegetation) with the high accuracy of TLS-like technologies.