Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data

Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting 20-year boreal growth values acquired with airborne laser scanning (ALS). The was examined Kalkkinen research site southern Finland as a continuation several earlier studies performed same area. data for were gathered three totally different systems, namely using Toposys-I Falcon June 2000 and Riegl VUX-1HA miniVUX-3UAV 2021 approximate point densities 11, 1360, 460 points/m2, respectively. ALS cloud preprocessed identify individual trees, from each which features extracted either direct or indirect measurement. In method, value predicted based differences features, whereas obtained by subtracting results two independent predictions years. tree attributes, such height, diameter at breast height (DBH), stem volume, calculated estimation. Field reference campaigns summer 2001 November validate values. showed that long-term series DBH, volume are possible record high-to-moderate coefficient determination (R2) 0.90, 0.48, 0.45 best-case scenarios. respective root-mean-squared errors (RMSE) 0.98 m, 0.02 0.17 m3, biases −0.06 0.00 m3. method produced better metrics terms RMSE-% bias, but best-fit lines. Additionally, mean diameter, intervals compared, they presumed be usable even modelling.