Under rapid urbanization, the urban heat island (UHI) effect is increasing, which poses a serious threat to human settlements. Changes in neighborhood land surface temperature (LST) reflect the UHI effect at a finer scale, with implications for the thermal comfort of residents. Landsat images were used to analyze the distribution of the urban neighborhood heat/cool island (UNHI/UNCI) within the fourth ring area of Shenyang City. Three-dimensional buildings and the urban functional zones (UFZs) were combined to explore the relationships with the UNHI and UNCI. Using boosted regression trees to analyze the relative importance of UFZs in the UNHI and UNCI, the results showed a significant lowering effect on the neighborhood LST with increased building height, which may be due to the fact of more architectural shadows generated by higher buildings. As the size of the green space patches increased, the cooling amplitude and the influence distance had an increasing trend. Industrial and public service zones had the most significant effect on the UNHI, with influences of 30.46% and 19.35%, respectively. In comparison, green space zones and water contributed the most to the UNCI effect, with influences of 18.75% and 11.95%, respectively. These results will provide urban decision-makers with crucial information on mitigating UHI problems through urban planning.

The carbon sequestration of forest ecosystems plays a pivotal role in constraining global warming and mitigating climate change. The landscape pattern of forests is being altered due to the combined effects of climate change and human interference. Furthermore, the relationship between forest pattern changes and carbon storage distribution in a long time series remains unclear. Therefore, it is necessary to examine the relationship between forest patterns and carbon density, investigating the variations and similarities in the changes in carbon density across different modes of pattern change over time, and suggestions for forest planning were provided from a perspective focused on pattern change to enhance carbon storage. The Google Earth Engine (GEE) platform’s random forest model was used to map the spatial distribution of forests in Hunan Province for 1996 and 2020, followed by analyzing the correlation between the changes in forest patterns using the morphological spatial pattern analysis (MSPA) and carbon density simulated by the model. Results show that the net growth rate ((area in 2020-area in 1996)/area in 2020) of the forest in Hunan increased 26.76% between 1996 and 2020. The importance scores for the decade average temperature, short-wave length infrared band 1 (SWIR-1), and slope were the highest metrics in the model of carbon density, and were 0.127, 0.107 and 0.089, respectively. The vegetation carbon storage in Hunan Province increased by 31.02 Tg, from 545.91 Tg to 576.93 Tg in 25 years. This study demonstrates that vegetation carbon storage is influenced by the pattern type in both newly established and pre-existing forests (p < 0.05). The findings of this study offer empirical evidence to support forest management strategies targeted at enhancing carbon sequestration.