The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R² = 0.91, RMSE = 35.13 m³ ha⁻¹, n = 321) and in the test phase (i.e., R² = 0.58, RMSE = 65.03 m³ ha⁻¹, and n = 138). This model was followed by the SVR model (R² = 0.54, RMSE = 65.60 m³ ha⁻¹, n = 321 in training; R² = 0.54, RMSE = 66.00 m³ ha⁻¹, n = 138 in testing), which was slightly better than the MLR model (R² = 0.38, RMSE = 75.74 m³ ha⁻¹, and n = 321 in training; R² = 0.49, RMSE = 70.22 m³ ha⁻¹, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10⁸ m³) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10⁸ m³). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.

Fine roots play a prominent role in forest carbon flux, nutrient and water acquisition; however, information on fine roots remains scarce due to the limitation of measuring methods. In this study, a nested regression method was used to estimate the biomass and surface area of fine roots of individual Larix gmelinii trees that dominate northernmost China. Aboveground traits including leaf biomass, leaf area, stem volume and aboveground biomass were also investigated. In particular, the relationships between leaves and fine roots, in terms of biomass and area, were examined. The results revealed that allometric models of fine roots, total roots, and leaves consistently fit well with Adj. R² = 0.92–0.97. The root-shoot ratio at the individual tree level was approximately 0.28. There were robust positive linear correlations between absorption (fine root biomass, fine root surface area) and production (leaf biomass, leaf area) (Adj. R² = 0.95, p < 0.001). In conclusion, the close coupling between fine roots and leaves presented in this study provides support for the theory of functional equilibrium.

Carbon (C), nitrogen (N), phosphorus (P) and potassium (K) are the main nutrient elements widely found in soil, litter and leaves, and their stoichiometric ratios are important indicators of ecosystem functions. However, there is little research on the effects that nutrient cycle and vegetation succession have on leaf, litter and soil nutrients and stoichiometric ratios, especially in the fragile karst areas. To reveal the nutrient cycling characteristics and ecosystem stability mechanism during vegetation succession, leaf, litter and soil samples were collected from the herbaceous community (HC), shrub community (SC), secondary forest community (SF) and primary forest community (PF) in a typical karst area at growth and senescence phases. The results showed that the nutrient contents and stoichiometric ratios of leaf, litter and soil in the main layers of each community were significantly different at the growth and senescence phase. The utilization efficiency of N in the leaves of the main layers of each succession community first decreased and then increased at different growth stages, and the utilization efficiency of P increased but the reabsorption rates of N and P showed a continuous decreasing trend. In addition, there was a significant allometric relationship between N and P contents in plant leaves during the growth phase. More importantly, the internal stability of N content in plant leaves was higher than the P content, suggesting that vegetation succession significantly affected leaf, litter and soil nutrient contents and their stoichiometric ratios in our study region. The strength of the relationship between them reflects the inheritance and co–variation of nutrient content to a certain extent, and the differences in the strategies that different species in the community use to adapt to the fragile karst environment. This study concludes that plants in the karst region mainly improve their P utilization efficiency to adapt to low phosphorus stress in soil and ensure the normal physiological and biochemical responses in the process of vegetation succession.

Southwest China is distributed with the largest area of limestone and dolomite karst landscapes in the world, and its ecological environment is very fragile; the problem of rock desertification is particularly prominent. Karst lithological differences make habitats and soil heterogeneity more complex, and vegetation has developed certain morphological and physiological structural characteristics to adapt to these special environments, which will most likely lead to differentiation in vegetation functional characteristics, such as seed size, wood density, life type and flowering time, thus affecting the number and growth of seedlings and young trees and the future succession potential of forest stands in karst-vegetation restoration. Therefore, it is important to understand the differences in species composition and the functional characteristics of dolomite and limestone karst forests. Over a 2-year period, we investigated a total of 3170 individuals from 123 species and analyzed the relationship between the species compositions and the functional characteristics of two karst-forest areas with different lithology (i.e., limestone and dolomite karst). It was found that the average number of species with medium-diameter seeds and medium wood density in dolomite plots was obviously greater than the average number of species in limestone plots, but this reached no significant difference. However, with the gradual restoration of each forest, the height and carbon sequestration capacity of the trees began to diverge, with the relative abundance of species with tall trees and high wood density being significantly higher in limestone plots than in dolomite plots. The species richness of evergreen trees was significantly higher in the limestone plots than in the dolomite plots. There was no significant difference in the relative species richness of flowering plants in the two karst-forest areas during spring–summer and winter–spring seasons; however, the species richness of flowering plants in the limestone karst forests was significantly higher than that of flowering plants in the dolomite karst forests during summer and autumn seasons. These significant traits were very closely related to the species richness of the understory plants. Further insight into the temporal aspects of karst-forest succession is needed to grasp how functional traits affect the restorations of different lithological karst forests to their primary states.

The cycling process of carbon (C), nitrogen (N), phosphorus (P), and potassium (K) elements plays a crucial role in ecosystem functions of karst forests. The leaf stoichiometry characteristics in four successional stages are studied in a karst area, southwest China. The N and P contents in each successional stage are generally higher in the growth phase (May–June 2016) than in the senescence phase (October–November 2015) but there are lower N: P ratios in the growth phase than in the senescence phase, showing a clear seasonal controls and coinciding with the Growth Rate Hypothesis. In the growth phase, the phytocommunity is mainly limited by the N content. In the senescence phase, all communities from the early successional stage to the mid successional stage are prone to N limitation but they are significantly affected by a combination of N, P, and K from the late successional stage to the climax successional stage and the P limitation are stronger in the climax successional stage. The specific leaf area is the most important impact factor to leaf stoichiometric characteristics at each successional stage. These results indicate that nitrogen addition in growth stage may promote vegetation restoration and karst forest ecosystem stability.