The Honghe Hani Rice Terraces represent the coexistence between natural and cultural systems. Despite being listed as a World Heritage Site in 2013, certain natural and anthropogenic factors have changed land use/land cover, which has led to a reduction in the size of the paddy rice area. It is difficult to accurately assess these changes due to the lack of historical maps of paddy rice croplands with fine spatial resolution. Therefore, we integrated a random forest classifier and phenological information to improve mapping accuracy and stability. We then mapped the historical distribution of land use/land cover in the Honghe Hani Rice Terraces from 1989–1991 to 2019–2021 using the Google Earth Engine. Finally, we analyzed the driving forces of land use types in the Honghe Hani Rice Terraces. We found that: (1) forests, shrubs or grasslands, and other croplands could be discriminated from paddy rice during the flooding and transplanting period, and water bodies and buildings could also be discriminated from paddy rice during the growing and harvesting period. (2) Inputting phenological feature data improved mapping accuracy and stability compared with single phenological periods. (3) In the past thirty years, 10.651%, 8.810%, and 5.711% of paddy rice were respectively converted to forests, shrubs or grasslands, and other croplands in the Honghe Hani Rice Terraces. (4) Lower agricultural profits and drought led to problems in identifying the driving mechanisms behind paddy rice distribution changes. This study demonstrates that phenological information can improve the mapping accuracy of rice terraces. It also provides evidence for the change in the size of the rice terrace area and associated driving forces in Southwest China.

Human activities have both positive and negative impacts on forests, altering the extent and composition of various forest vegetation types, and increasing uncertainty in ecological management. A detailed understanding of the historical distribution of forest vegetation is crucial for local conservation efforts. In this study, we integrated phenological features with climatic and terrain variables to enhance the mapping accuracy of forest vegetation in Yunnan. We mapped the historical distributions of five forest vegetation type groups and nine specific forest vegetation types for 2001, 2010, and 2020. Our findings revealed that: (1) rubber plantations can be effectively distinguished from other forest vegetation using phenological features, coniferous forests and broad-leaved forests can be differentiated using visible spectral bands, and environmental variables (temperature, precipitation, and elevation) are effective in differentiating forest vegetation types under varying climate conditions; (2) the overall accuracy and kappa coefficient increased by 14.845% and 20.432%, respectively, when climatic variables were combined with phenological features, and by 13.613% and 18.902%, respectively, when elevation was combined with phenological features, compared to using phenological features alone; (3) forest cover in Yunnan increased by 2.069 × 10⁴ km² (10.369%) between 2001 and 2020. This study highlights the critical role of environmental variables in improving the mapping accuracy of forest vegetation in mountainous regions.

Mountain forests are critical for carbon sequestration, yet the effects of rapid warming on tree growth and climate sensitivity across elevational gradients remain poorly understood. Here, we focus on the largest mountain coniferous forests in southwest China and collected tree-ring cores from three key afforestation tree species, Picea likiangensis, Abies georgei, and Pinus yunnanensis, from 2600 m a.s.l. to 4100 m a.s.l. in Lijiang. Basal area increment (BAI) and Pearson correlation analyses were employed to evaluate long-term productivity trends and constrain the factors limiting tree growth, respectively. Across all elevational belts, tree growth is primarily limited by low dry-season moisture availability, with the strength of this limitation varying across the three species. Abies georgei shows the strongest positive correlation between tree-ring width and Standardized Precipitation Evapotranspiration Index, followed by Picea likiangensis, while Pinus yunnanensis exhibits the weakest correlation. We further found that tree productivity began to decline at the beginning of the twenty-first century, with larger declines in higher-elevation fir and spruce species, albeit not statistically significant. Future warming and warm-induced drought may exert more pronounced adverse effects on these three species. These results provide reference data for the potential sustainable management of mountain forests in southwest China.