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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]

Quantifying how understory vegetation responds to individual neighboring trees is critical to understanding forest dynamics. To do so, we used a spatial neighborhood approach to quantify the competitive effect of individual trees on the density and height of dwarf bamboo (Sasa senanensis (Franch. et Savat.) Rehder) in a mixed conifer-broadleaf forest on the island of Hokkaido, northern Japan. Using hierarchical Bayesian models, we analyzed how the effect of neighboring trees varies with stem size, distance to the dwarf bamboo, and tree species. The effect of neighbors peaked when the tree reached a medium size (33.0-45.0 cm in diameter at breast height) and decreased for larger trees. The effect of neighbors decreased with increasing distance to the dwarf bamboo. The slope of the decrease was gentler for larger trees. Conifers exerted an average of 7.2 times the effect of broadleaved trees. Species with higher shade tolerance exerted larger effects. Species with late leaf flush and early defoliation tended to exert smaller effects. Our results provide evidence that neighborhood analysis is an effective approach for quantifying the effects of individual trees on understory vegetation and represents a critical step toward understanding how fine-scale interactions between understory vegetation and trees influence overall forest dynamics. Resume: Il est essentiel de quantifier la reaction de la vegetation du sous-etage a chacun des arbres avoisinants pour comprendre la dynamique forestiere. A cette fin, nous avons utilise une approche spatiale de voisinage pour quantifier l'effet de competition d'arbres individuels sur la densite et la hauteur du bambou nain (Sasa senanensis (Franch. & Savat.) Rehder) dans une foret mixte sur l'ile d'Hokkaido, dans le nord du Japon. A l'aide de modeles bayesiens hierarchiques, nous avons analyse la facon dont l'effet des arbres voisins varie selon le diametre de la tige, la distance du bambou nain et l'espece d'arbre. L'effet des voisins culmine lorsqu'un arbre atteint une taille moyenne (33 a 45 cm de diametre a hauteur de poitrine) et diminue lorsque les arbres sont plus gros. L'effet des voisins diminue avec l'augmentation de la distance du bambou nain. La pente de la diminution etait plus faible pour les arbres plus gros. L'effet produit par les coniferes etait en moyenne 7,2 fois plus grand que celui des feuillus. Les especes les plus tolerantes a l'ombre avaient plus d'effet. Les especes qui debourraient tardivement et perdaient leur feuilles tot avaient tendance a avoir moins d'effet. Nos resultats montrent que l'analyse de voisinage constitue une approche efficace pour quantifier les effets d'arbres individuels sur la vegetation du sous-etage et represente une etape essentielle pour comprendre comment les interactions a petite echelle entre la vegetation du sous-etage et les arbres influencent la dynamique forestiere globale. [Traduit par la Redaction], Introduction In many of the world's forest ecosystems, understory vegetation plays a crucial role in forest dynamics (Nakashizuka 1988; Abe et al. 2002; Royo and Carson 2006, 2008). The attributes [...]

Forest canopy structure is an important parameter in multipurpose forest management. An understanding of forest structure plays a particularly important role in the management of uneven-aged forests. The identification of vertical and horizontal variations in forest canopy structure using a ground-based survey is resource intensive, hence often demands for alternative data sources. In this study, one of the advanced remote sensing (RS) techniques, i.e. digital aerial photogrammetry was used to characterize forest canopy structure in a mixed conifer–broadleaf forest. We used aerial imagery acquired with a fixed-wing unmanned aerial vehicle (UAV) platform to produce RS metrics that could be used to classify and map forest structure types at landscape scale. Our results demonstrated that few structural and spectral metrics derived from UAV photogrammetric data, e.g. mean height, standard deviation of height, canopy cover, and percentage broadleaf vegetation cover, could characterize the forest structure across landscapes, particularly at the forest management compartment level, in a limited amount of time. We used cluster analysis for classification of forest structure types and identified five forest structure classes with varying levels of forest canopy structural complexity: (1) short, open-canopy, conifer-dominated structure; (2) short, dense-canopy, broadleaf-dominated structure; (3) tall, closed-canopy, broadleaf-dominated structure; (4) very tall, closed-canopy, conifer-dominated structure with a relatively high degree of variation in canopy height; and (5) very tall, closed-canopy, conifer-dominated structure with a relatively low degree of variation in canopy height. These classes showed relationships with forest management activities (e.g. selection harvesting) and natural disturbances (e.g. typhoon damage). Spatial distribution of forest canopy structural complexity that was revealed in this study is capable of providing important information for forest management planning and habitat modelling. Further, the simple, and flexible data-driven method used in this study to characterize forest structure has the potential to be applied with necessary changes over larger landscapes and different forest types for characterizing and mapping forest structural complexity. [ABSTRACT FROM AUTHOR]