Search

Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management. [ABSTRACT FROM AUTHOR]

Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8–0.87) and TH (R2: 0.76–0.77; RMSE: 1.45–1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering. [ABSTRACT FROM AUTHOR]

Context: Forest management practices that conserve biodiversity and maximize carbon sequestration under climate change are needed. Although post-windthrow salvage logging and scarification can reduce carbon dioxide (CO2) emissions within ecosystems by removing downed logs, they can greatly affect species composition. Additionally, salvage logging may increase CO2 emissions based on a cradle-to-grave analysis of salvaged wood. Objectives: We aimed to assess the effects of changes in climate, windthrow regimes and post-windthrow management on aboveground biomass, species composition, and carbon balance in the forest sector by combining forest landscape simulations and life cycle assessment (LCA). Methods: The study landscape is a 12,169 ha hemiboreal forest located in northern Japan. We simulated 115 years (2015–2130) of forest dynamics in 36 scenarios based on features of the climate, windthrow regime, and management using the LANDIS-II forest landscape model. CO2 emissions related to management and salvaged wood were estimated by LCA. Results: Increases in the windthrow area, which was more vulnerable to climate warming, caused a shift to temperate broadleaved forests and a decrease in aboveground biomass. These were accelerated by the removal of advanced seedlings and dead wood, which greatly reduced the recruitment of Picea species. The 115-year cumulative net CO2 absorption of the forest sector, including carbon balance within ecosystems and CO2 emissions estimated by LCA, greatly decreased due to salvage logging (maximum 81%) and scarification (maximum 114%). Conclusions: Leaving downed logs and advanced seedlings is recommended to conserve boreal conifers and carbon sinks and maximize net CO2 absorption under climate change. [ABSTRACT FROM AUTHOR]