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In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest
- Wang, Yunsheng, Pyörälä, Jiri, Liang, Xinlian, Lehtomäki, Matti, Kukko, Antero, Yu, Xiaowei, Kaartinen, Harri, Hyyppä, Juha
- Remote sensing of environment 2019 v.232 pp. 111309
- aboveground biomass, algorithms, allometry, automation, boreal forests, computer software, data collection, forest stands, information processing, remote sensing, stems, tree height, trees, unmanned aerial vehicles
- Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of data processing. Therefore, insufficient information has been available for hardware and software developers to prioritize future developments. In the present study, we evaluated the amount of trees digitized in terrestrial and aerial point clouds by means of manual tree detection. On a plot-level (a fixed size of 32 m × 32 m), approximately 97%, 93%, and 75% of individual trees could be recorded in easy (ca. 700 stems/ha), medium (ca. 900 stems/ha), and difficult stands (ca. 2, 200 stems/ha), respectively, using five-scan terrestrial laser scanning (TLS). With aerial point cloud from unmanned aerial vehicle (UAV)-borne laser scanning (ULS) (ca. 450 points/m2), promising digitization can be expected for 87%, 69%, and 55% of individual trees in easy, medium and difficult stands, respectively. Plot-level AGB concentrates on big trees. The dominant and codominant trees combined accounted for 90.7%, 86.0%, and 69.7% of the plot-level AGB, and their population combined accounted for 73.2%, 55.3%, and 31.0%, respectively, in easy, medium, and difficult stands. Therefore, missing of dominant and codominant trees in data has a greater influence on plot-level AGB estimates than missing intermediate and suppressed trees. At a tree-level, when AGB was predicted from tree height and DBH, the relative root mean square error (RMSE%) of AGB estimates in easy, medium, and difficult plots were 10.1%, 14.6%, and 20.4%, respectively, based on manually measured tree parameters from TLS point clouds. The manual measurements from ULS point clouds provided RMSE%s of 30.4%, 51.1%, and 76.9% in easy, medium, and difficult plots, respectively. These results indicate the magnitude of errors introduced by the data, and for ULS, by the tree height–DBH allometry used. When using automated in-house algorithms of the Finnish Geospatial Research Institute (FGI), the RMSE%s of tree-level AGB estimates using TLS were 11.5%, 17.8%, and 43.9% in easy, medium, and difficult plots, respectively. When using ULS, the corresponding errors were 31.7%, 57.7%, and 73.3%, respectively. Such results suggest that the automated algorithms perform similarly to manual processing in the tree-level ABG estimation from terrestrial and aerial point clouds, specifically in easy and medium forest stands. The incomplete tree digitization in the data was the main factor affecting the accuracy of tree-centric AGB estimates. Fusion of the terrestrial and aerial observation perspectives provided promising results. When manual TLS-based DBH and manual ULS-based tree height were used, the RMSE% was reduced to 8.6%–12.7%, compared to 10.1%–20.4% when only TLS-manual tree parameters were used and 30.3%–76.9% when only ULS-manual tree parameters were used.