Forest biomass is an important indicator of forest ecosystem productivity, and it plays vital roles in the global carbon cycling, global climate change mitigating, and ecosystem researches. Multiscale, rapid, and accurate extraction of forest biomass information is always a research topic. In this study, comprehensive investigation of a larch (Larix olgensis) plantation was performed using remote sensing and field-based monitoring methods, in combination with LiDAR-based multisource data and machine learning methods. On this basis, a universal, multiscale (single tree, stand, management unit, and region), and unit-high-precision continuous monitoring method was proposed for forest biomass components. The results revealed the following. (1) Airborne LiDAR point cloud variables exhibited significant correlation with the aboveground components (except leaves) and the whole-plant biomass ($R_{adj}^2$ > 0.91), suitable for extraction or estimation of forest parameters such as biomass and stock volume. (2) In terms of biomass monitoring at forest stand and management unit scale, a random forest model performed well in fitting accuracy and generalization ability, whereas a multiple linear regression model produced clearer explanation regarding the biomass of each forest component. (3) Using seasonal phenological characteristics in the study area, larch distribution information was extracted effectively. The overall accuracy reached 90.0%, and the kappa coefficient reached 0.88. (4) A regional-scale forest biomass component estimation model was constructed using a long short-term memory model, which effectively reduced the probability of biomass underestimation while ensuring good estimation accuracy, with R² exceeding 0.6 for the biomass of the aboveground and whole-plant components. This research provides theoretical support for rapid and accurate acquisition of large-scale forest biomass information.

The spatial distribution of the forest canopy plays an important role in the transpiration and photosynthetic capacity of trees, ultimately affecting their growth and biomass production. Despite its importance, how canopy photosynthetic productivity enhancement depends on canopy spatial distribution remains unclear. To address this knowledge gap, we conducted a study on Larix kaempferi (Lamb.) Carrière (L. kaempferi) plantations in Gansu, China, investigating the relationship between canopy height, leaf area, seasonal variations in canopy spatial distribution, and photosynthetic parameters. The results showed that the net photosynthetic rate, stomatal conductance, and transpiration rate of L. kaempferi increase with greater canopy height, while photosynthetically active radiation shows the opposite trend. Canopy photosynthetic productivity peaked in April, May, and June when the height in the canopy was 40%, followed by 20%, and then 30% from the perspective of spatiotemporal canopy spatial distribution. Maximum leaf area (10.7 m²) and photosynthesis productivity (919.6 mg·C·h⁻¹) were observed when the height in the canopy ranged from 48%–59%. The changes increased sunlight exposure (75%–88%, 88%–100%) in different canopy areas. Additionally, there was a decrease in the amount of space covered by shade (25%–38%, 50%–63%, and 63%–75%), depending on the specific region within the canopy. By scientifically managing stand density, the canopy spatial distribution can be optimized for photosynthesis, resulting in maximum light interception rates, enhanced photosynthetic capacity, and reduced “non-functional canopy”. These findings offer effective and scientifically informed management strategies for the forestry industry. By optimizing the structure of the canopy, specifically in L. kaempferi, these strategies aim to maximize photosynthetic productivity.