Xiaoyan Pan

Soil microorganisms play essential roles in maintaining the function and stability of forest ecosystems. In this study, conducted in the Jianfengling Nature Reserve in Hainan, we performed Illumina high-throughput sequencing, in combination with principal coordinate analysis (PCoA), redundancy analysis, and other statistical methods, to compare the composition, community structure, and diversity of the soil microbiota in four types of forest vegetation, namely, monsoon forest, lowland rainforest, montane rainforest, and artificial forest (AF), and to identify the environmental variables associated with differences among the forest types. Different vegetation types differed significantly with respect to pH, total phosphorus content (TP), elevation, and concentrations of manganese (Mn) and iron (Fe). In contrast, no significant differences were observed in soil water content, bulk density, total nitrogen (TN), and total organic carbon (TOC) content. Compared to AF, natural forests were characterized by higher concentrations of TN, TOC, TP, Mn, and Fe, although the differences in TN and TOC were not statistically significant. For all four of the assessed vegetation types, Pseudomonadota, Actinomycetota, Acidobacteriota, and Verrucomicrobiota were identified as the dominant bacterial phyla, whereas Ascomycota, Basidiomycota, and Mucoromycota were predominant among the fungal phyla, indicating that these phyla are capable of surviving in different soil ecosystems. Although no significant differences in bacterial diversity were detected among vegetation types, PCoA revealed pronounced differentiation in bacterial community composition (PERMANOVA, p = 0.0001). Notably, within-group multivariate dispersion also differed significantly (PERMDISP, p = 0.0129), indicating that the observed compositional differences may be associated with the combined effects of centroid location effect and within-group multivariate dispersion. In contrast, significant differences were observed in fungal diversity, whereas differences in fungal community composition were not significant in the PCoA (PERMANOVA, p = 0.1777). These results suggest that vegetation type primarily influenced fungal diversity rather than fungal community structure. Mn concentration exhibited the highest marginal explanatory power for the variation in the bacterial community, with a significant effect; it was positively correlated with the ACE, Chao1, and Simpson indices, and negatively correlated with the Shannon index. TOC exhibited the highest marginal explanatory power within the fungal community, showing a negative correlation with the ACE, Chao1, and Shannon indices and a positive correlation with the Simpson index. Collectively, these findings provide insights into microbial differentiation within forest soils associated with different vegetation types and highlight distinct patterns in bacterial and fungal diversity, which will potentially serve as a basis for the development of conservation strategies.

The construction of the Hainan Free Trade Port (FTP) is guided by the core philosophy of “ecological priority and green development.” To meet the practical requirements of building a “Green and Beautiful FTP,” this study constructed a kernel Normalized Difference Vegetation Index (kNDVI) dataset using Landsat series satellite imagery. By integrating methods including the Mann–Kendall (MK) trend test, Hurst exponent, and coefficient of variation (CV), an in-depth analysis was conducted on the spatiotemporal evolution characteristics and trends of growing-season vegetation in Hainan Island from 1994 to 2023. Additionally, the Extreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) interpretation method were employed to quantitatively unravel the driving mechanisms of climatic factors and human activities on kNDVI variations. The results indicate that: (1) Over the past three decades, the overall kNDVI of Hainan Island has exhibited a significant upward trend, characterized spatially by an evolution pattern of “stable recovery in the central region and localized degradation along the coast.” (2) The vegetation evolution demonstrates strong persistence and is highly consistent with community stability. The high stability in the central mountainous areas stems from a superior natural background and strict ecological protection; the transition zone is jointly influenced by vegetation types and anthropogenic management; meanwhile the coastal areas exhibit significant degradation characteristics driven by high-intensity human disturbances. (3) The analysis of driving mechanisms reveals a complex control logic of “topographical foundation—human reshaping—extreme climate triggering.” Static topographical factors, such as elevation and slope, occupy an absolute dominant position; human activities, represented by rubber plantation expansion and urbanization, exert a bidirectional reshaping effect characterized by “inland greening and coastal suppression”; furthermore, extreme drought and high temperatures in the later stages of the study demonstrated a significant pulse-like impact, exacerbating the risks of short-term climatic stress. This study clarifies the core patterns of vegetation evolution in Hainan Island, validates the effectiveness of ecological policies, and provides a quantitative scientific basis for ecological conservation and sustainable development in tropical island regions.

Hainan Tropical Rainforest National Park is a prime example of a “continental island” tropical rainforest and holds significant value for biodiversity conservation. However, human activities have led to frequent changes in land use and increased habitat fragmentation within the park; a precise analysis of the underlying mechanisms is necessary for ecological restoration. Consequently, drawing upon land-use data from 2000 to 2020, this study coupled multi-dimensional fragmentation metrics (CFI, AFI, and SFI) with the XGBoost-SHAP framework to systematically unravel the spatiotemporal dynamics and underlying driving mechanisms of landscape fragmentation in Hainan Tropical Rainforest National Park. Our findings revealed that the spatial configuration of fragmentation predominantly propagated along river networks and transport corridors, accompanied by a fluctuating ‘decline–rise–decline’ temporal trajectory. Notably, the XGBoost-SHAP attribution highlighted a distinct temporal shift in the dominant drivers: fragmentation was primarily mitigated (negatively driven) by NDVI between 2000 and 2010 but was subsequently exacerbated (positively driven) by GDP growth from 2010 to 2020. The findings of this study provide data support and a scientific basis for ecosystem restoration and land use planning in Hainan Tropical Rainforest National Park.

Mangrove ecosystems are recognized for their exceptional carbon sequestration potential and crucial contribution to coastal ecological balance. However, the sharp decline in mangrove area necessitates efficient monitoring via remote sensing. While Deep Neural Networks (DNNs) have excelled in segmentation tasks, their robustness remains inadequate. This limitation stems from the lack of theoretical guarantees regarding the continuity of layer-by-layer discrete transformations, rendering models susceptible not only to man-made adversarial attacks but also to natural degradations. To address these vulnerabilities, this paper leverages Neural Ordinary Differential Equations (NODEs) to enhance the robustness of mangrove segmentation. We designed and integrated various NODE architectures, including a novel NODE-SE-Block inspired by adaptive feature recalibration, to achieve more stable feature representations. Crucially, our findings reveal that by employing an adversarial training framework based on known attacks, the NODE-integrated network demonstrates superior cross-domain robustness. It not only defends against malicious exploits but also exhibits significantly enhanced resilience toward natural degradations, such as Gaussian noise and sensor-induced artifacts. Experimental results on mangrove datasets verify that the proposed methodology provides a reliable and interference-resistant foundation for ecological management in mission-critical scenarios.

As the net gain of carbon by plants after accounting for respiration, vegetation net primary productivity (NPP) plays a central role in the terrestrial carbon cycle. However, a systematic and quantitative analysis of the spatiotemporal evolution and driving mechanisms of vegetation NPP on Hainan Island, a tropical region, is still lacking. Focusing on Hainan Island, this study employs an integrated approach—including the coefficient of variation, Mann–Kendall test, Hurst exponent, geographical detector, and PLS-SEM—to investigate the spatiotemporal dynamics of vegetation NPP and its underlying drivers from 2001 to 2022. The main conclusions as follows: (1) Vegetation NPP on Hainan Island showed a fluctuating upward trend from 2001 to 2022, with a mean annual increase of 3.6 g C·m⁻²·yr⁻¹, and displayed a spatial pattern of decrease from the central-southern mountainous areas toward the coastal regions. (2) NPP changes were generally stable; historically, areas showing an increasing trend exceeded those with a decreasing trend by 30.55%. In the future, the predominant projected trends are “persistent decrease” and “increase to decrease,” which together account for over 80% of the total area. (3) Topography and climate were the dominant drivers of NPP spatial heterogeneity. Elevation had the strongest explanatory power, followed by evapotranspiration and temperature. A significant, nonlinear enhancement effect was observed in the interaction between any two factors. (4) Topographic, climatic, anthropogenic, and vegetation factors all exerted direct positive effects on vegetation NPP. Anthropogenic activities also indirectly promoted NPP by influencing pathways such as vegetation growth. The conclusions of this research provide support for the implementation and evaluation of land-use planning, afforestation projects, and ecological protection and restoration measures on Hainan Island.

Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R² of 0.51, followed by SVM (R² = 0.44) and DNN (R² = 0.32); the accuracy of XGBoost (R² = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km², 0.13 km², and 1.35 km², respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community.

This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R² = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R² = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R² = 0.90–0.94, RMSE = 0.10–0.18 m³, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments.

This study developed an integrated approach for estimating tropical forest aboveground biomass (AGB) by combining UAV–LiDAR structural metrics and Sentinel-2B spectral data, optimized through successive projections algorithm (SPA) feature selection and random forest (RF) regression. Field surveys across three tropical forest sites in Hainan Province (49 plots) provided ground-truth AGB measurements, while UAV–LiDAR (1 m resolution) and Sentinel-2B (10 m) data were processed to extract 98 and 69 features, respectively. The results showed that LiDAR-derived elevation metrics (e.g., percentiles and kurtosis) correlated strongly with the AGB measurements (r = 0.652–0.751), outperforming Sentinel-2B vegetation indices (max r = 0.520). SPA–RF models with selected features significantly improved accuracy compared to full-feature RF, achieving R² = 0.670 (LiDAR), 0.522 (Sentinel-2B), and 0.749 (coupled data), with the fusion model reducing errors by 46–54% in high-biomass areas. Despite Sentinel-2B’s spectral saturation limitations, its integration with LiDAR enhanced spatial heterogeneity representation, particularly in complex canopies. The 200-iteration randomized validation ensured a robust performance, with mean absolute relative errors of ≤0.071 for fused data. This study demonstrates that strategic multi-sensor fusion, coupled with SPA-optimized feature selection, significantly improves tropical AGB estimation accuracy, offering a scalable framework for carbon stock assessments in support of Reducing Emissions from Deforestation and Forest Degradation (REDD+) and climate mitigation initiatives.

Forest biomass, as a carrier of carbon, is an important indicator for judging forest productivity, stability and sustainable development capacity. Using the survey data of sample plots in eight forest areas in central Hainan, the biomass distribution of tropical mountain rainforests in National Park of Hainan Tropical Rainforest in different community sizes, diameter classes, altitudes and spaces was measured to explore the relationship between forest biomass and environmental factors. The results show that (1) the total area of tropical montane rainforests in National Park of Hainan Tropical Rainforest was about 983.70 km², distributed within an altitude range of 700–1300 m; the total aboveground biomass was about 25.208 million tons, which decreased first and then increased with increasing altitude, with an average aboveground biomass per unit area of 236.00 t·hm⁻²; (2) the primary forest accounted for 83.23% of the total aboveground biomass of the tropical mountain rainforest with only 29.84% of the total area, and the aboveground biomass per unit area was generally higher than that of the secondary forest; and (3) medium- and large-diameter trees were the main carriers of aboveground biomass in tropical mountain rain forests. More than 83.73% of the aboveground biomass was concentrated in large-diameter trees. The results of this study provide a reference for others aiming to perform measurement and evaluation of the carbon sink and the capacity for carbon neutrality in tropical rainforest ecosystems or to maintain regional biodiversity.

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve soil management practices to replace the time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability of soil properties over linear models, their practical and automated application for predicting soil properties using remote sensing data requires further assessment. Therefore, this study aims to integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images and Light Detection and Ranging (LiDAR) points to predict the soil properties indirectly in two tropical rainforest mountains (Diaoluo and Limu) in Hainan Province, China. A total of 175 features, including texture features, vegetation indices, and forest parameters, were extracted from two study sites. Six ML models, Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), were constructed to predict soil properties, including soil acidity (pH), total nitrogen (TN), soil organic carbon (SOC), and total phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced to obtain optimal model hyperparameters. The results showed that compared with the default parameter tuning method, BOA always improved models’ performances in predicting soil properties, achieving average R² improvements of 202.93%, 121.48%, 8.90%, and 38.41% for soil pH, SOC, TN, and TP, respectively. In general, BOA effectively determined the complex interactions between hyperparameters and prediction features, leading to an improved model performance of ML methods compared to default parameter tuning models. The GBDT model generally outperformed other ML methods in predicting the soil pH and TN, while the XGBoost model achieved the highest prediction accuracy for SOC and TP. The fusion of hyperspectral images and LiDAR data resulted in better prediction of soil properties compared to using each single data source. The models utilizing the integration of features derived from hyperspectral images and LiDAR data outperformed those relying on one single data source. In summary, this study highlights the promising combination of UAV-based hyperspectral images with LiDAR data points to advance digital soil property mapping in forested areas, achieving large-scale soil management and monitoring.