This study delved into the spatiotemporal evolution and impact mechanisms of areca palm (Areca catechu L.) plantations in China. Using Landsat and Google Earth imagery combined with machine learning, the geographical distribution of areca palm was mapped at a 30 m resolution from 1987 to 2022, achieving an overall classification accuracy of 0.67 in 2022. The plantation area rapidly expanded from 8064 hectares in 1987 to 193,328 hectares in 2022. Spatially, there was a pronounced trend of overall agglomeration in areca palm plantations, primarily displaying two distribution patterns: high-value aggregation and low-value aggregation. Moreover, the plantation area exhibited a significant positive correlation with both GDP (r = 0.98, p < 0.001) and total population (r = 0.92, p < 0.01), while negatively correlating with rural population (r = −0.76, p < 0.05). No significant correlation was observed with environmental factors. This study elucidated the patterns and trends concerning economic development across regions and the impact of monoculture on Hainan Island’s ecological environment. Comprehensive, large-scale, long-term mapping of areca palms will enhance our understanding of global agriculture’s sustainability challenges and inform policy development.

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R² = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations.

Accurate national-scale mangrove mapping remains challenging because mangroves occur in narrow intertidal belts affected by tidal variation, residual clouds, water background effects, and spectral confusion with adjacent vegetation. This study evaluated five Sentinel-2 annual compositing rules for 10 m mangrove mapping in China: MAX-KNDVI, MAX-EVI, a negative-NDWI-based composite, MAX-MFI, and Median compositing. A single shared ResNet-34 U-Net model was trained using pooled patches from the five rule-specific composites and was then applied separately to each composite. Labelled sample locations, validation data, probability threshold, and post-processing settings were kept consistent to focus the comparison on the compositing rule. Using 695 labelled patch locations and 10,354 spatially independent validation points from 29 coastal regions, the Median-based output achieved the most balanced performance among the tested rules, with an overall accuracy of 90.1% and a Kappa coefficient of 0.801. Compared with GMW v3.0, HGMF_2020, and LREIS_v2_2020, the Median-based output showed higher agreement with the independent validation samples and fewer omissions in selected fragmented coastal zones. Applying the Median configuration to annual Sentinel-2 composites from 2019 to 2023 indicated an increase in mapped mangrove area in China from 22,731.76 ha to 24,631.62 ha. These results show that annual compositing-rule selection is an important source of performance variation in Sentinel-2 deep learning-based mangrove mapping and should be considered explicitly in national-scale coastal wetland monitoring.