Tropical rainforests in the central hilly section of Hainan Island are the source of the Nandu, Changhua, and Wanquan rivers, which are crucial for water conservation and ecological protection. The quantitative assessment of water yield in the three basins is beneficial for developing regional water resource protection plans, establishing ecological compensation mechanisms, and maintaining ecological balance. Based on land use data from five periods between 1980 and 2020, this paper adopts the InVEST model and geographic detectors to investigate the spatial-temporal variation characteristics and driving factors of water yield in three major basins of Hainan Island. The results demonstrate that forestland, which makes up more than 70% of the total area in the three basins of Hainan Island, is the predominant land use type. With a depth of 1269.18 mm, Wanquan Basin is the deepest of the three basins, followed by Nandu Basin and Changhua Basin. The total water yield of three basins shows a slightly decreasing trend from 17.991 billion m³ in 1980 to 17.864 billion m³ in 2020. The spatial distribution of water yield is high in the southeast region and low in the northwest region, with strong autocorrelation and significant aggregation. According to geographic detection, land use type is the dominant factor for the spatial differentiation of water yield in the three basins, with a contribution rate of 0.563, and soil type and annual precipitation are important impact factors. The interaction and synergy of soil types and land use types jointly affect the spatial differentiation of water yield in the basin. The results of this study can provide data support and scientific references for biodiversity conservation and ecosystem restoration in the three major basins of Hainan Island.

Strobilomyces, one of the most noticeable genera of Boletaceae (Boletales), is both ecologically and economically important. Although many studies have focused on Strobilomyces in China, the diversity still remains incompletely understood. In the present study, several collections of Strobilomyces from Hainan Island, tropical China were studied based on morphology and molecular phylogenetic analyses. Four species are described as new, viz. S. baozhengii, S. conicus, S. hainanensis, and S. pachycystidiatus. Detailed descriptions, color photos of fresh basidiomata, and line drawings of microstructures of the four species are presented.

Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.

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.

Keywords: Canopy Height Model, CHM, LiDAR, Tree Mean Height Canopy height model(CHM) and tree mean height are critical forestry parameters that many other parameters such as growth, carbon sequestration, standing timber volume, and biomass can be derived from. LiDAR is a new method used to rapidly estimate these parameters over large areas. The estimation of these parameters has been derived successfully from CHM. However, a number of challenges limit the accurate retrieval of tree height and crowns, especially in tropical forest area. In this study, an improved canopy estimation model is proposed based on dynamic moving window that applied on LiDAR point cloud data. DEM, DSM and CHM of large tropical forest area can be derived from LiDAR data effectively and efficiently.