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Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Forests, Volume 13, issue 12
Abstract
Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern Finland as a continuation of several earlier growth studies performed in the same area. The data for the study were gathered with three totally different airborne laser scanning systems, namely using Toposys-I Falcon in June 2000 and Riegl VUX-1HA and miniVUX-3UAV in June 2021 with approximate point densities of 11, 1360, and 460 points/m2, respectively. The ALS point cloud was preprocessed to identify individual trees, from each of which different features were extracted either for direct or indirect growth measurement. In the direct method, the growth value is predicted based on differences of features, whereas in the indirect method, the growth value is obtained by subtracting the results of two independent predictions of different years. The growth in individual tree attributes, such as growth in height, diameter at breast height (DBH), and stem volume, were calculated for direct estimation. Field reference campaigns were performed in the summer of 2001 and in November 2021 to validate the obtained growth values. The study showed that long-term series growth of height, DBH, and stem volume are possible to record with a high-to-moderate coefficient of determination ((Formula presented.)) of 0.90, 0.48, and 0.45 in the best-case scenarios. The respective root-mean-squared errors (RMSE) values were 0.98 m, 0.02 m, and 0.17 m3, and the biases were −0.06 m, 0.00 m, and 0.17 m3. The direct method produced better metrics in terms of RMSE-% and bias, but the indirect method produced better best-fit lines. Additionally, the mean growth values for height, diameter, and stem volume intervals were compared, and they are presumed to be usable even for forest modelling.
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Funding Information: We gratefully acknowledge the Academy of Finland projects “Forest-Human-Machine Interplay” (Academy decision 337656), “Estimating Forest Resources and Quality-Related Attributes Using Automated Methods and Technologies” (334829, 334830), “Capturing structural and functional diversity of trees and tree communities for supporting sustainable use of forests” (348644), “Feasibility of Inside-Canopy UAV Laser Scanning for Automated Tree Quality Surveying” (334002), and the Ministry of Agriculture and Forestry project “Future forest information system at individual tree level” (VN/3482/2021). The laser scanning data from year 2000 was obtained from J.H. Academy Senior Fellow and EU IV FrameWorkprogramme project HIGH-SCAN, coordinated by J.H. during 1998–2001. Publisher Copyright: © 2022 by the authors.
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Soininen, V, Kukko, A, Yu, X, Kaartinen, H, Luoma, V, Saikkonen, O, Holopainen, M, Matikainen, L, Lehtomäki, M & Hyyppä, J 2022, 'Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data', Forests, vol. 13, no. 12, 2040. https://doi.org/10.3390/f13122040