Fangjie Mao

The accurate estimation of forest aboveground biomass (AGB) typically relies on extensive field surveys, which are highly time-consuming and cost-prohibitive. While unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) provides ultra-high point densities capable of reliable individual-tree analysis, its limited flight coverage restricts large-scale applications. Conversely, regional airborne laser scanning (ALS) offers broad spatial coverage, but its relatively low point cloud density makes individual-tree level analysis unreliable. To bridge this scale and data gap, this study develops a scale-consistent framework that integrates UAV-LiDAR, three-dimensional simulation, multisource remote sensing, and machine learning for Chinese fir (Cunninghamia lanceolata) plantation AGB estimation. High-density UAV-LiDAR data were first used to construct individual-tree AGB models, and the predicted tree-level biomass was aggregated to generate spatially representative “agent plots” for stand-scale modeling. A three-dimensional (3D) radiative transfer simulation framework was further employed to reproduce airborne LiDAR observations under different point densities, enabling the evaluation of structural information loss caused by LiDAR sparsity. Structural features derived from simulated LiDAR and spectral information from Sentinel-2 imagery were integrated using the Tabular Prior-data Fitted Network (TabPFN). Model reliability was assessed through 10-fold spatial block cross-validation and Monte Carlo simulations, which quantified spatial generalization and uncertainty propagation from individual-tree estimation to stand-level prediction. Feature interpretation using SHapley Additive exPlanations (SHAP) revealed that the LiDAR-derived vertical canopy structure provided the primary constraints for biomass estimation, whereas Sentinel-2 shortwave infrared features supplied complementary information related to canopy conditions. The optimal TabPFN model achieved a stand-level accuracy of R² = 0.88 and RMSE = 9.23 Mg·ha⁻¹ using LiDAR combined with Sentinel-2 data. Uncertainty analysis further demonstrated the robustness of the proposed framework under propagated errors, highlighting its potential for scalable and reliable forest biomass estimation in data-limited subtropical ecosystems.

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the southern part of Lishui City, serves as a typical representative of the mid-subtropical evergreen broad-leaved forest ecosystem. It is characterized by high biodiversity and serves as a concentration area for rare and endangered species. Therefore, assessing the spatiotemporal dynamics of forest AGC in the typical subtropical forests of Qianjiangyuan–Baishanzu National Park is of great scientific significance. Taking Qianjiangyuan–Baishanzu National Park as a case study, this research utilized three periods of Landsat satellite remote sensing data (2009, 2014, and 2019) alongside contemporaneous ground-based AGC survey samples. Feature variables were extracted and subsequently screened using the Boruta algorithm. There were three algorithms, including least squares (LS), support vector regression (SVR), and random forest (RF), constructed to estimate forest AGC. The optimal AGC estimation model was selected, and the spatiotemporal variation characteristics of forest AGC within the national park were systematically analyzed. Research has shown that (1) texture features are important parameters for constructing forest AGC estimation models, accounting for up to 71%, with the 7 × 7 window having the greatest impact. (2) All three models can achieve high accuracy in estimating the forest AGC and its spatial distribution in Qianjiangyuan Baishanzu National Park. Among them, the RF model has the highest overall accuracy in estimating forest AGC, with a training set R² of 0.938, RMSE of 5.550 Mg/ha, rRMSE of 12.517%, a test set R² of 0.954, RMSE of 4.653 Mg/ha, and rRMSE of 10.087%. (3) The distribution of forest AGC in Qianjiangyuan Baishanzu National Park shows significant spatial heterogeneity, with higher carbon storage in the central, southern, and southeastern regions, while the northern region has relatively lower carbon storage. From 2009 to 2019, the forest AGC in the Qianjiangyuan–Baishanzu National Park exhibited a steady annual increase, with AGC density rising from 37.64 Mg/ha to 66 Mg/ha and total AGC stock increasing from 2.16 Tg C to 4.36 Tg C. These findings provide precise data support and a scientific basis for decision-making regarding differentiated ecological carbon enhancement and functional zone management within the national park.

Forest spatial structure is of significant importance for studying forest biomass accumulation and management. However, above-ground biomass (AGB) estimation based on satellite remote sensing struggles to capture forest spatial structure information, which to some extent affects the accuracy of AGB estimation. To address this issue, this study focused on Chinese fir (Cunninghamia lanceolata) plantations in Zhejiang Province. Using UAV-LiDAR (unmanned aerial vehicle light detection and ranging) data and a seed-point-based individual tree segmentation algorithm, information on individual fir trees was obtained. Building on this foundation, structural parameters such as neighborhood comparison (U), crowding degree (C), uniform angle index (W), competition index (CI), and canopy openness (K) were calculated, and their distribution characteristics analyzed. Finally, these parameters were integrated with UAV-LiDAR point cloud features to build machine learning models, and a geographical detector was used to quantify their contribution to AGB estimation. The research findings indicate the following: (1) The studied stands exhibited a random spatial pattern, moderate competition, and sufficient growing space. (2) A significant correlation existed between the U and AGB (r > 0.6), followed by CI. The optimal stand structure for AGB accumulation was C = 0.25, U < 0.5, CI in (0, 0.8], and K > 0.3. (3) The four machine learning models constructed by coupling spatial structure with point cloud features all improved the accuracy of AGB estimation for the fir forest to some extent. Among them, the XGBoost model performed best, achieving a model accuracy (R²) of 0.92 and a relatively low error (RMSE = 14.02 kg). (4) Geographical detector analysis indicated that U and CI contributed most to AGB estimation, with q-values of 0.44 and 0.37, respectively.

The carbon cycle of forest ecosystems is a component of the global terrestrial ecosystem carbon cycle, and the productivity of forest ecosystems is significantly influenced by vegetation phenology. In this investigation, we simulated the spatiotemporal trends of the carbon cycle in forest ecosystems in Hangzhou between 2001 and 2020 by means of the phenology-driven InTEC model and analyzed the mechanisms of carbon cycle changes in response to phenological changes. The results of this study suggested that the gross primary productivity (GPP), the net primary production (NPP), and the net ecosystem productivity (NEP) have obvious heterogeneity in spatiotemporal distribution, and the tendency of the start of the growing season (SOS) advancement, the end of the growing season (EOS) postponement, and the length of the growing season (LOS) lengthening is significant for a GPP increase with positive effects. Both phenology and climate have direct impacts on carbon cycle changes, while climate change indirectly affects carbon cycle changes through phenology changes.

Diameter at Breast Height (DBH) is a fundamental measurement indicator in forest resource surveys. This study explores the use of Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) for individual tree DBH inversion in Chinese fir (Cunninghamia lanceolata) plantations with complex terrain and rich understory vegetation, and compares the results with those from Backpack-LiDAR. Firstly, individual tree segmentation was performed on the UAV-LiDAR point cloud data from the study area, and individual tree point cloud feature variables were extracted by applying different height thresholds. Then, three types of models—statistical model multiple linear regression (MLR), traditional machine learning models including K-nearest neighbor regression (KNN) and support vector regression (SVR), and ensemble learning models including random forest (RF), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)—were employed for DBH inversion. The results show that: (1) Using data above a 5-meter height threshold effectively reduces interference from understory vegetation; (2) Key feature variables, such as canopy volume (V), tree height (Hmax), the interquartile range of cumulative height percentiles (AIHiq), and canopy area (S), significantly affect DBH inversion, with V contributing 25% to the feature importance; (3) Ensemble learning models, particularly CatBoost, outperform the other models, achieving an R² of 0.81—14.1% higher than MLR; (4) DBH inversion closely matches field observed data, and UAV-LiDAR performs better than Backpack-LiDAR in complex forest environments. This study provides an efficient and cost-effective approach to forest resource monitoring.

As the basic unit of nature, basins concentrate most of the vegetation cover of terrestrial ecosystems and play an important role in forest carbon fixation and regulation of local climates. However, there are obvious differences between different basins in terms of topography, climate, population, economy, and other factors, so it is important to conduct a comparative study on the spatiotemporal patterns of factors affecting forest carbon storage in different basins. The province of Zhejiang is rich in vegetation resources, and there are obvious differences in the natural and economic factors within the province; GDP is higher in the eastern and northern regions, and natural resources are more abundant in the western and southern regions. Therefore, we used the BIOME-BGC model and the Optimal Parameters-based Geographical Detector (OPGD) model to simulate and analyze the spatiotemporal evolution and driving mechanism of forest aboveground carbon (AGC) storage in eight basins of Zhejiang Province over the past 30 years (1984–2014). The results showed that (1) the overall simulation accuracy of AGC in different basins based on the BIOME-BGC model is high, with the overall simulation accuracy ranging from 0.67 to 0.77. (2) The forest AGC of the eight basins showed an increasing trend over the past 30 years, with a growth rate ranging from 0.07 Tg C/10 yr to 3.45 Tg C/10 yr. (3) Climatic conditions (temperature and precipitation) play a dominant role in the variation in AGC, with an explanatory power above 16% in the southern and northern basins, and the explanatory power of human activities on the AGC is secondary, with more than 9% in the central basins. (4) The interaction between natural factors and socio-economic factors (especially the population density factor) has a more obvious effect on the changes in AGC in each basin, and the explanatory power of the interaction is much larger than that of the single factor. (5) The results of the risk detection showed that human activities were negatively correlated with AGC in all basins.