The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R² of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R² = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.

Introduction: Selective thinning is a widely applied silvicultural practice aimed at reducing competition and accelerating the growth of target trees. However, excessive tree removal can lead to a significant short-term decline in stand-level productivity and delay structural recovery. Therefore, understanding the trade-offs between individual tree release and stand basal area recovery is crucial for sustainable forest management. Methods: Using 11 years of repeated inventory data from a mixed broadleaf–Korean pine forest in northeastern China, we examined tree growth and stand recovery under four thinning intensities (0%, 20%, 40%, and 60%). Principal component analysis (PCA) and hierarchical clustering were used to classify 19 tree species into three functional groups. Results: The results showed that thinning effects were time dependent. A growth lag phase was observed in the first post-thinning period (2011–2013), followed by a stronger growth response in later study periods, when the high-intensity treatment (60%) showed nearly double the individual growth rate of the control. However, species responses were asymmetric; shade-tolerant climax species (Cluster 3) exhibited the strongest competitive release, whereas pioneer species (Cluster 1) showed minimal sensitivity possibly because of species strategy and tree size. At the stand level, low-intensity thinning (20%) showed a compensatory growth trend, and the fitted linear trend suggested that stand basal area may approach or exceed the control level within about 16 years. In contrast, the high-intensity treatment showed a much slower projected recovery trajectory. The linear mixed-effects model showed that tree growth was mainly related to tree size and spatial structure. Variance decomposition indicated that individual-tree variables contributed the largest share of the explained variance (59.5%). Discussion: Under the conditions of this study, the 20% treatment plot showed the most balanced outcome between stand basal area recovery and individual tree growth during the observed period. These results support the use of structure-based silviculture in mixed broadleaf–Korean pine forests, especially by improving growing space for desirable Cluster 3 individuals while maintaining stand heterogeneity.

Understory plants are important components of forest ecosystem productivity and diversity. Compared to biomass models of overstory canopy trees, few are available for understory saplings and shrubs and therefore their roles in estimation of forest carbon pools are often ignored. In this study, we harvested 24 understory species including 4 saplings, 9 tree-like shrubs and 11 typical shrubs in coniferous and broadleaved mixed forest in northeastern China and developed the best fit allometric equations of above- and below-ground and total biomass by species-specific or multispecies using morphological measurements of basal diameter, height and crown area as independent variables. The result showed that single basal diameter, height or crown area had good explanatory power for both species-specific and multispecies (p<0.001). The best-fit models included only basal diameter in sapling and tree-like shrubs, and combinations of crown area, height, and basal diameter in typical shrubs. The logarithmic model was most desired among the 4 model forms of linear, quadratic, multiple linear and logarithmic, for species-specific and multispecies. The models we developed should help the estimation of forest ecosystem carbon stocks, especially for belowground component, and provide tools for quantification of individual species biomass of understory plants.

Background: Understory plants are important components of forest ecosystem productivity and diversity. Compared to biomass models of overstory canopy trees, few are available for understory saplings and shrubs and therefore their contributions to forest carbon pools are often ignored.Methods: We harvested 24 understory species including 4 saplings, 9 tree-like shrubs and 11 typical shrubs in coniferous and broadleaved mixed forest in north-eastern China. We developed the species-specific or multispecies best fit allometric equations of total, above- and below-ground biomass using morphological measurements of basal diameter, height and crown area as independent variables.Results: Single basal diameter, height or crown area had good explanatory power for both species-specific and multispecies equations (p<0.001). The best-fit models included only basal diameter in sapling and tree-like shrubs, and combinations of crown area, height, and basal diameter in typical shrubs. The logarithmic model was most desired among the 4 model forms of linear, quadratic, multiple linear and logarithmic, for species-specific and multispecies equations.Conclusion: The models we developed should help the estimation of forest ecosystem carbon stocks, especially for the belowground component, and provide tools for quantification of individual species biomass of understory plants.