A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR

Alijani, Zohreh; Meloche, Julien; McLaren, Alexander; Lindsay, John B.; Roy, Alexandre; Berg, Aaron

doi:10.1080/17538947.2022.2160842

Cited by 8 publications

(2 citation statements)

References 72 publications

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“…Handheld lidar has been quickly adopted for scientific applications as a novel methodology that bridges gaps in field and remote sensing methods. Three years after Apple's first release of the handheld lidar scanner, science and engineering‐based applications have emerged in the disciplines of forestry (Gollob et al, 2021; Tatsumi et al, 2023), civil engineering (Chase et al, 2022; Riquelme et al, 2021), and the geosciences (Alijani et al, 2022; King et al, 2022; Luetzenburg et al, 2021; Tavani et al, 2022; Table 1). Yet, to our knowledge, the use of handheld lidar to characterize large wood accumulations is still unexplored in the literature.…”

Section: Introductionmentioning

confidence: 99%

Handheld lidar as a tool for characterizing wood‐rich river corridors

Marshall,

Morrison,

Jones

et al. 2024

River Research & Apps

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Wood accumulations influence geomorphic, hydraulic, and ecologic functions within a river corridor, but characterizing these accumulations presents challenges across a range of field and remote sensing methodologies. We evaluate the ability of handheld lidar scanners, specifically lidar‐scanning capabilities of a fourth‐generation iPad Pro, to collect three‐dimensional wood accumulation data, which can be used to inform measurements of wood volume, porosity, complexity, and roughness. We discuss the potential and limitations of this novel methodology for river research and management. We found that handheld lidar presents a cost‐effective input for data‐processing workflows that field measurements of wood accumulation dimensions cannot as easily replicate including (1) a user‐friendly means of data collection and visualization; (2) accurate comparisons of wood volume over time; (3) integration into workflows to measure porosity parameters; and (4) potential use in informing hydraulic and morphodynamic models. Consideration of study area constraints and intended use of scans are prerequisites to using handheld lidar as an effective tool. We identified some specific limitations of using handheld lidar scanners in wood‐rich river corridors, including (1) scanners perform poorly when wood is under water or surrounded by dense vegetation; (2) scanners require physical access to areas of interest at distances less than 5 m; (3) scans need to be manually georeferenced; and (4) scans require manual measurements for any dimensional data, which still have associated user time and error. Handheld lidar as a scientific tool is rapidly developing and there is substantial room for expansion of applications, utilization, and advances in the use of this tool in river research and management.

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Section: Introductionmentioning

confidence: 99%

Handheld lidar as a tool for characterizing wood‐rich river corridors

Marshall,

Morrison,

Jones

et al. 2024

River Research & Apps

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“…and non-contact TLS, stereophotogrammetry, Xtion Pro method, etc.) have been evaluated previously [10,[15][16][17][18][19]. The results show that TLS not only ensure the highest accuracy and resolution in outdoor environments but also create representative three-dimensional images containing more information.…”

Section: Introductionmentioning

confidence: 99%

Soil Surface Roughness Characteristics Under Different Agricultural Tillage Practices—A Case Study in the Black Soil Region of Northeast China

Feng,

Zheng,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m × 25 m) and analyzes the characteristics of SSR under different tillage practices (ploughing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The ploughed and harrowed plot correspond to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times.2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded.3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the "true" value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m × 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.

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