Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems.

Soil organic carbon (SOC) strongly contributes to the operation of the global carbon cycling, and topographical factors largely influence its spatial distribution. However, SOC distribution and its leading topographical impact factors in subtropical forest ecosystems (e.g., the Zhejiang Province in China) have received relatively limited attention from researchers. In this study, 255 forest soil samples were collected from the Zhejiang Province to quantify the spatial variation in SOC and impact factors in subtropical forests. The SOC contents over soil profiles were 35.95 ± 22.58 g/kg, 20.98 ± 15.26 g/kg, and 13.77 ± 11.28 g/kg at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively. The coefficient variations at different depths were 62.81% (0–10 cm), 72.74% (10–30 cm), and 81.92% (30–60 cm), respectively. SOC content shows a moderate intensity variation in the Zhejiang Province. The nugget coefficients of the SOC content for the three depths were 0.809 (0–10 cm), 0.846 (10–30 cm), and 0.977 (30–60 cm), respectively. Structural factors mainly influence SOC content. SOC content is positively correlated with elevation and slope, and negatively correlated with slope position (p < 0.05). However, the SOC content was negatively correlated with slope in mixed coniferous and broad-leaved forest. The distribution of the SOC content was relatively balanced between different slope positions. However, the differences became obvious when forest types were distinguished. Topographical factors affected the SOC content differently: elevation > slope > slope position. Slope becomes the main influencing factor in 30–60 cm soil. Forest type significantly influenced the SOC content but with a low statistical explanation compared to topographical factors. Topography has different effects on SOC of different forest types in subtropical forests. This reminds us that in future research, we should consider the combination of topography and forest types.

In recent years, the automatic recognition of tree species based on images taken by digital cameras has been widely applied. However, many problems still exist, such as insufficient tree species image acquisition, uneven distribution of image categories, and low recognition accuracy. Tree leaves can be used to differentiate and classify tree species due to their cognitive signatures in color, vein texture, shape contour, and edge serration. Moreover, the way the leaves are arranged on the twigs has strong characteristics. In this study, we first built an image dataset of 21 tree species based on the features of the twigs and leaves. The tree species feature dataset was divided into the training set and test set, with a ratio of 8:2. Feature extraction was performed after training the convolutional neural network (CNN) using the k-fold cross-validation (K-Fold–CV) method, and tree species classification was performed with classifiers. To improve the accuracy of tree species identification, we combined three improved CNN models with three classifiers. Evaluation indicators show that the overall accuracy of the designed composite model was 1.76% to 9.57% higher than other CNN models. Furthermore, in the MixNet XL CNN model, combined with the K-nearest neighbors (KNN) classifier, the highest overall accuracy rate was obtained at 99.86%. In the experiment, the Grad-CAM heatmap was used to analyze the distribution of feature regions that play a key role in classification decisions. Observation of the Grad-CAM heatmap illustrated that the main observation area of SE-ResNet50 was the most accurately positioned, and was mainly concentrated in the interior of small twigs and leaflets. Our research showed that modifying the training method and classification module of the CNN model and combining it with traditional classifiers to form a composite model can effectively improve the accuracy of tree species recognition.

Managing forest understory has a significant impact on soil greenhouse gas (GHG) emissions and the ecosystem’s capacity for carbon sequestration. However, its specific impacts and mechanisms within hickory (Carya cathayensis Sarg) forests remain unclear. The objective of this study was to examine the effects of different understory vegetation treatments on hickory stands with similar growth history, site conditions, and slopes: Cinnamomum chekiangense (Cinnamomum chekiangense Nakai) and strip-sown ryegrass (Lolium perenne L.) (CR1), Cinnamomum chekiangense and scattered ryegrass (CR2), Torreya grandis (Torreya grandis’ ‘Merrillii’ Hu) and strip-sown wild rapeseed (Brassica napus L.) (TW1), Torreya grandis and scattered wild rapeseed (TW2), and removal of understory vegetation (CK). Twenty experimental plots were established at the Lin’an Forestry Carbon Sink Pioneer Base, and after 12 months of monitoring, the responses of GHG emissions, vegetation, and soil organic carbon (SOC) sequestration were analyzed, revealing the differences in ecosystem carbon sequestration capacity. Compared to CK, CR1, CR2, TW1, and TW2 increased the global warming potential (GWP) by approximately 26%, 55%, 26%, and 16%, respectively. The SOC increased by approximately 76%, 102%, 51%, and 32%, respectively, while the vegetation carbon sink increased by approximately 30%, 27%, 53%, and 62%, respectively. In summary, ecosystem carbon sequestration increased by approximately 109%, 98%, 95%, and 92%, respectively. The findings indicate that managing understory vegetation in pecan forests significantly enhances ecosystem carbon sequestration but also increases soil GHG emissions. To enhance future research priorities, it is essential to focus on increasing carbon sequestration in hickory forests and managing soil GHG emissions through effective and rational understory vegetation management.

The safe operation of power transmission lines is critical for ensuring the stability of the power supply, especially given the increasing frequency of extreme weather events and the risks posed by tree growth. This study proposes a novel method for detecting and predicting the tree barrier risks on transmission lines using Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) technology. The method employs point cloud classification to effectively separate ground, conductor, tower, and vegetation points, followed by 3D reconstruction of the power lines using the catenary equation. Tree growth models are integrated with measured data to predict future tree barrier risks. The experimental results demonstrate that the point-cloud-based method detects 31 tree barriers, with an RMSE of 0.08 m, while the 3D-reconstruction-based method detects 32 tree barriers, with an RMSE of 0.04 m, indicating its higher accuracy. The Cloth Simulation Filter (CSF) ground point classification method achieved the lowest roughness (1.5%), mean error (0.147 m), and RMSE (0.174 m), proving its effectiveness for flat terrain. Additionally, the assisted seed point individual tree segmentation method extracted tree height with high accuracy (R² = 0.84, RMSE = 1.01 m). This study predicts an average tree growth rate of 0.248 m/year over the next five years, identifying a new tree barrier at the coordinates 30°15′16.64″ N, 119°43′16.01″ E. This method enhances the efficiency and accuracy of transmission line inspections, supporting both power line safety and sustainable forest management. Its findings provide a robust technical approach to improving power line operations and forest resource utilization.

Aboveground wood carbon (AWC) stocks in forest ecosystems are mediated by biotic and abiotic variables. Understanding the internal regulatory mechanisms of forests is important for future forest management and global climate change mitigation. However, how these factors affect AWC in subtropical mixed forests remains poorly understood. Using a database from the National Forest Inventory (NFI) from China, we observed the effects of climate variables (temperature and precipitation), stand structure indices (stand density and DBH coefficient of variation and diversity), stand diversity indices (taxonomic diversity, functional diversity, and phylogenetic diversity), and stand functional indices on coniferous mixed forests (CMF), coniferous–broadleaf mixed forests (CBMF), and broadleaf mixed forests (BMF). Meanwhile, we examined the AWC based on a linear mixed model and a structural equation model for each mixed forest. We found that both stand structure and stand diversity can affect the AWC through their indirect effects on the stand function, aligning with the niche complementarity effect. Stand age is an important factor affecting AWC because it interacts with stand structure and stand diversity. Our study highlights that AWC is dependent on the regulation of stand age and structure, which can be crucial for boosting high carbon stocks in subtropical forests.