Detecting tree cover is crucial for sustainable land management and climate mitigation. Here we develop an automatic detection algorithm using high-resolution satellite data (<5 m) to map pan-tropical tree cover (2015–2022), enabling identification and change analysis for previously undetected tree cover (PUTC). Our findings reveal that neglecting PUTC represents 17.31 ± 1.78% of the total pan-tropical tree cover. Tree cover net decreased by 61.05 ± 2.36 Mha in both forested areas (63.93%) and non-forested areas (36.07%) between 2015 and 2022. Intense changes in tree cover are primarily observed in regions with PUTC, where the World Cover dataset with a resolution of 10 m often fails to accurately detect tree cover. We also conduct a sensitivity analysis to quantify the contributions f climate factors and anthropogenic impacts (including human activities and land use cover change) to tree cover dynamics. Our findings indicate that 43.98% of tree cover gain is linked to increased precipitation, while 56.03% of tree cover loss is associated with anthropogenic impacts. These findings highlight the need to include undetected tree cover in strategies combating degradation, climate change, and promoting sustainability. Fine-scale mapping can improve biogeochemical cycles modeling and vegetation-climate interactions, improving global change understanding.

Forests play a pivotal role in global carbon cycling and biodiversity conservation, yet they face increasing disturbances from both anthropogenic and natural drivers. This study presents the first high-resolution (30 m) global forest disturbance dataset (GFD) for 2000–2020, classifying 11 disturbance types by integrating Landsat-based Continuous Change Detection and Classification (CCDC) time-series analysis with spatial metrics and machine learning. A total of 57 000 expert-validated sample points were used to train and validate a decision tree model, achieving an overall accuracy of 99 %. The results reveal that forestry replanting (44 %), shifting cultivation (24 %), and forest fires (11 %) dominate global forest loss. There are regional differences in global forest disturbance, such as farmland expansion in South America and Africa, forest fires in northern regions, and shifting cultivation in tropical regions. Disturbed forests span 1247.06 Mha, accounting for 31 % of the global forest area. Notably, 3 % of global forests were newly established, primarily in China, India, and Brazil. The spatial consistency analysis (R²= 0.93) highlights a strong overall agreement between the GFD product and other datasets, while the GFD product offers superior spatial resolution. The GFD dataset advances our understanding of forest dynamics and underscores the need for targeted conservation strategies in an era of escalating environmental change. The 30 m resolution GFD generated by this study is openly available at https://doi.org/10.6084/m9.figshare.28465178 (Liu et al., 2025a).

Rapid urbanization in China has exacerbated the dual challenges of urban heat islands (UHIs) and air pollution, threatening urban sustainability. We conducted a national-scale analysis of the spatiotemporal dynamics and synergy between the surface UHI intensity, distinguished as daytime (DUHI) and nighttime (NUHI), and major air pollutants (PM_2.5, PM₁₀, NO₂) in 370 Chinese cities (2000–2019). Using multi-source remote sensing, ground-based monitoring, and urban data, we applied coupling coordination and correlation analyses to quantify these interactions. Key findings reveal distinct patterns: (1) The annual mean land surface temperature (LST) rose, with the nighttime LST (NLST) increasing faster than the daytime LST (DLST). Conversely, the UHI intensity showed an overall decline, with the DUHI decreasing more than the NUHI. (2) Air pollutants displayed strong seasonality; while PM10 concentrations decreased slightly over the long term, NO₂ levels rose significantly. (3) Monthly, pollutants correlated negatively with LST (R² > 0.92 for PM_2.5), suppressing the DUHI but intensifying the NUHI. Long-term, the correlation trend revealed a strengthening synergy, particularly between particulate matter and NUHI (trend R² = 0.50). (4) Spatially, over 90% of cities exhibited high UHI–particle coordination. Key associated factors include anthropogenic activities, urban morphology, and natural mitigation factors. We conclude that disrupting the heat–pollution synergy requires integrated strategies, namely reducing emissions at the source, optimizing the urban form, and enhancing ecological regulation. This is essential for advancing low-carbon, climate-resilient urban development.