The size–growth relationship (SGR) quantifies growth partitioning among different sized trees in a stand and helps to elucidate stand growth dynamics during stand development. SGR strongly correlates with stand density, stand age, and drought severity. This study focused on larch (Larix principis-rupprechtii (Mayr)) forests with different stand ages (17–19 years, 20–29 years, 30–39 years, and 40–46 years) and stand densities (300–1400 trees·ha⁻¹ and 1400–3300 trees·ha⁻¹) as the research subjects. Employing a linear mixed model, we aimed to quantify the effects of stand density, stand age, and drought index on SGR. The results revealed that the Gini coefficient had a significant variation between high-density and low-density larch stands (p < 0.01). Stand age, stand density, and drought index exerted varying degrees of influence on the growth dominance coefficient (GD) and SGR of larch forests. In stands aged less than 39 years, a pronounced growth advantage of large trees over small trees was evident (typically SGR > 1 and GD > 0), indicating a positive growth dominance stage where large trees dominated. Compared to high-density stands, low-density stands exhibited a notably greater positive growth dominance. After 40 years, the growth stage transitioned to a relatively symmetric stage (SGR approximately equal to 1), with a discernible shift towards a reverse growth dominance stage (GD < 0). Compared to stand density and stand age, the drought index had a more influential effect on SGR. As drought severity increased, SGR increased, amplifying the growth advantage of large trees over small ones. The findings underscored the significance of adjusting stand density and optimizing tree size structure to enhance larch resilience against the warming and drying effects.

Increased demand for forest-derived biomass has led to more intensive harvesting practices. However, the export of large nutrient quantities with the harvested biomass may lead to the depletion of soil nutrients. Therefore, improved knowledge concerning macronutrient allocation (N, P, K, Ca, and Mg) to different components in forests along age sequences is crucial for their sustainable management. In this study, we quantified nutrient allocation to different ecosystem components, including trees, understorey, forest floor, and different soil depths within a chronosequence (6-, 15-, 23-, and 35-year-old) of larch plantations in China. We then assessed the danger of significant nutrient losses from whole tree harvesting (WTH). Nutrient amounts in trees increased with stand age due to an increase in biomass. Stems accounted for 59%–72% of tree biomass and contained 40%–50% of total tree nutrients in the 15- to 35-year-old stands. The forest floor’s nutrient quantities increased from the 6- to 23-year-old stands and then decreased in the 35-year-old plantations. Conversely, most soil indicators initially declined from 15- to 23-year-old stands and then increased in the 35-year-old stand. The total nutrient stocks were greatest in the soil (0–40 cm), which accounted for about 93%–99% of total nutrients in the larch ecosystem. These data indicate that WTH causes nutrient losses about 2.0–2.5 times higher than stem-only harvesting, when thinning 15- or clear-cut harvesting 23- and 35-year-old stands. However, nutrient losses by WTH have little effect on the soil nutrient pools. Prolonging the crop cycle of larch plantations may be beneficial to improve nutrient return through litterfall and allow available soil nutrients to recover.

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.