Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

Version 1 : Received: 28 July 2021 / Approved: 30 July 2021 / Online: 30 July 2021 (09:32:59 CEST)

How to cite: Stovall, A.E.L.; Atkins, J.W. Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters. Preprints 2021, 2021070690. https://doi.org/10.20944/preprints202107.0690.v1 Stovall, A.E.L.; Atkins, J.W. Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters. Preprints 2021, 2021070690. https://doi.org/10.20944/preprints202107.0690.v1

Abstract

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

Keywords

tls; tree; QSM; PAVD; foliage; sensor; lidar

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

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