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Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests

Terryn, L; Calders, K; Bartholomeus, H; Bartolo, RE; Brede, B; D'hont, B; Disney, M; ... Verbeeck, H; + view all (2022) Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests. Remote Sensing of Environment , 271 , Article 112912. 10.1016/j.rse.2022.112912.

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Abstract

Accurately quantifying tree and forest structure is important for monitoring and understanding terrestrial ecosystem functioning in a changing climate. The emergence of laser scanning, such as Terrestrial Laser Scanning (TLS) and Unoccupied Aerial Vehicle Laser Scanning (UAV-LS), has advanced accurate and detailed forest structural measurements. TLS generally provides very accurate measurements on the plot-scale (a few ha), whereas UAV-LS provides comparable measurements on the landscape-scale (>10 ha). Despite the pivotal role dense tropical forests play in our climate, the strengths and limitations of TLS and UAV-LS to accurately measure structural metrics in these forests remain largely unexplored. Here, we propose to combine TLS and UAV-LS data from dense tropical forest plots to analyse how this fusion can further advance 3D structural mapping of structurally complex forests. We compared stand (vertical point distribution profiles) and tree level metrics from TLS, UAV-LS as well as their fused point cloud. The tree level metrics included the diameter at breast height (DBH), tree height (H), crown projection area (CPA), and crown volume (CV). Furthermore, we evaluated the impact of point density and number of returns for UAV-LS data acquisition. DBH measurements from TLS and UAV-LS were compared to census data. The TLS and UAV-LS based H, CPA and CV measurements were compared to those obtained from the fused point cloud. Our results for two tropical rainforest plots in Australia demonstrate that TLS can measure H, CPA and CV with an accuracy (RMSE) of 0.30 m (Haverage =27.32 m), 3.06 m2 (CPAaverage =66.74 m2), and 29.63 m3 (CVaverage =318.81 m3) respectively. UAV-LS measures H, CPA and CV with an accuracy (RMSE) of <0.40 m, <5.50 m2, and <30.33 m3 respectively. However, in dense tropical forests single flight UAV-LS is unable to sample the tree stems sufficiently for DBH measurement due to a limited penetration of the canopy. TLS can determine DBH with an accuracy (RMSE) of 5.04 cm, (DBHaverage =45.08 cm), whereas UAV-LS can not. We show that in dense tropical forests stand-alone TLS is able to measure macroscopic structural tree metrics on plot-scale. We also show that UAV-LS can be used to quickly measure H, CPA, and CV of canopy trees on the landscape-scale with comparable accuracy to TLS. Hence, the fusion of TLS and UAV-LS, which can be time consuming and expensive, is not required for these purposes. However, TLS and UAV-LS fusion opens up new avenues to improve stand-alone UAV-LS structural measurements at the landscape-scale by applying TLS as a local calibration tool.

Type: Article
Title: Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests
DOI: 10.1016/j.rse.2022.112912
Publisher version: https://doi.org/10.1016/j.rse.2022.112912
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Life Sciences & Biomedicine, Technology, Environmental Sciences, Remote Sensing, Imaging Science & Photographic Technology, Environmental Sciences & Ecology, Terrestrial laser scanning, Forest structure, Data fusion, Unoccupied aerial vehicle, Tropical forests, ABOVEGROUND BIOMASS, TREE, ARCHITECTURE, RANGE, LIDAR
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10146690
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