The distribution of forest vegetation and forest carbon sequestration potential are significantly influenced by climate change. In this study, a map of the current distribution of vegetation in Yunnan Province was compiled based on data from remote sensing imagery from the Advanced Land Observing Satellite (ALOS) from 2008 to 2011. A classification and regression tree (CART) model was used to predict the potential distribution of the main forest vegetation types in Yunnan Province and estimate the changes in carbon storage and carbon sequestration potential (CSP) in response to increasing temperature. The results show that the current total forest area in Yunnan Province is 1.86 × 10⁷ ha and that forest covers 48.63% of the area. As the temperature increases, the area of forest distribution first increases and then decreases, and it decreases by 11% when the temperature increases from 1.5 to 2 °C. The mean carbon density of the seven types of forest vegetation in Yunnan Province is 84.69 Mg/ha. The total carbon storage of the current forest vegetation in Yunnan Province is 871.14 TgC, and the CSP is 1100.61 TgC. The largest CSP (1114.82 TgC) occurs when the temperature increases by 0.5 °C. Incremental warming of 2 °C will sharply decrease the forest CSP, especially in those regions with mature coniferous forest vegetation. Semi-humid evergreen broad-leaved forests were highly sensitive to temperature changes, and the CSP of these forests will decrease with increasing temperature. Warm-hot coniferous forests have the greatest CSP in all simulation scenarios except the scenario of a 2 °C temperature increase. These results indicate that temperature increases can influence the CSP in Yunnan Province, and the largest impact emerged in the 2 °C increase scenario.

Ongoing climate changes reportedly affect the potential distribution and carbon sequestration potential (CSP) of forest vegetation. The combined effects of increasing temperature and decreasing precipitation on these features of forest vegetation are poorly understood. In this study, classification and regression tree (CART) models were used to predict the potential distribution and estimate the CSP of forest vegetation in Yunnan Province, Southwest China, under different simulation scenarios. The minimum temperature of the coldest month (TMW) was the main factor limiting the suitable habitat of all forest vegetation types except for warm–temperate coniferous (WTC) forests. When the temperature increased by 1 °C and the precipitation decreased by 20%, the potential distribution area of the 7 forest vegetation types decreased by 12.41% overall. The potential distribution of WTC forests was the least sensitive to temperature increases and precipitation decreases. The CSP of vegetation was higher (1187.69 TgC) under the constant temperature and 10% precipitation decrease scenario than the CSP of vegetation under the 2 °C temperature increase and constant precipitation scenario (647.24 TgC). Specifically, the highest CSP (1337.88 TgC) was observed under the 1 °C temperature increase and 10% precipitation decrease scenario, and the lowest (617.91 TgC) occurred under the constant temperature and 20% precipitation decrease scenario. In summary, the forest vegetation in Yunnan Province has a high CSP under climate change, and the combined effect of increased temperature and decreased precipitation can increase the CSP of forest vegetation in Yunnan Province. This finding is important for improving scientific decision making and policy planning.

Introduction: The deployment of digital cameras on flux towers offers a practical approach to exploring how seasonal canopy photosynthesis is related to canopy color indices (CCIs). However, the pronounced spatial and temporal heterogeneity of savanna ecosystems remains a major challenge. Methods: We compared EC-derived gross primary production (GPP) with six LUE model variants, including two direct-FPAR models, three CCI-proxy models, and one MODIS NDVI-proxy model. This framework allowed us to distinguish direct FPAR inputs from vegetation-index proxies for the FPAR term in LUE-based GPP estimation. Results: During the extreme high-temperature drought of 2019, the peak values of S _green , Hue, and GEI decreased by approximately 50%. Annual GPP declined from 850 to 567 g C m ⁻² , leaf area index (LAI) decreased from 0.989 to 0.670 m ² m ⁻² , and LUE decreased from 0.40 to 0.25 g C MJ ⁻¹ . The seasonal pattern of GPP remained broadly consistent, although the length of the growing season (LOS) varied substantially, ranging from 213 to 111 d in 2016 and 2019, respectively. During the growing season (GS), the CCIs were closely synchronized with daily GPP and LAI; during the non-growing season (NGS), a lag of approximately 20 days was observed. Among the six LUE variants, the MODIS NDVI-proxy model performed best ( R ² = 0.77, slope = 1.19), whereas the MODIS FPAR model showed moderate performance and underestimated GPP ( R ² = 0.61, slope = 0.66). The tower-derived FPAR model overestimated GPP, and the CCI-proxy models performed moderately, especially with lower accuracy during the non-growing season. Discussion: Overall, digital camera-derived CCIs can be used as empirical FPAR-like proxies for estimating seasonal GPP dynamics in savanna ecosystems, but they should not be interpreted as physical FPAR measurements. The comparison with tower-derived FPAR and MODIS FPAR highlights the importance of explicitly distinguishing direct FPAR inputs from vegetation-index proxies in LUE-based GPP modeling.