Global climate change has raised concerns about the relationship between ecosystems and forests, which is a core component of the carbon cycle and a critical factor in understanding and mitigating the effects of climate change. Forest models and sufficient information for predictions are important for ensuring efficient afforestation activities and sustainable forest development. Based on the theory of difference equations and the general rules of tree growth, this study established a difference equation for the relationship between the ratio of tree diameter at breast height (DBH) to the tree height and age of age of China’s main arbor species. A comparison with equations that represent the traditional tree growth models, i.e., Logistic and Richards equations, showed that the difference equations exhibited higher precision for both fitting and verification data. Moreover, the biomass carbon stocks (BCS) of Chinese forests from 2013 to 2050 were predicted by combining the 8th Chinese Ministry of Forestry and partial continuous forest inventory (CFI) data sets. The results showed that the BCS of Chinese forests would increase from 7342 to 11,030 terra grams of carbon (Tg C) in 2013–2050, with an annual biomass C (carbon) sink of 99.68 Tg C year⁻¹, and they indicated that the Chinese land-surface forest vegetation has an important carbon sequestration capability.

The Relief Degree of Land Surface (RDLS) is an important index to evaluate regional environment. It has a significant effect on the local climate, geologic hazards, the path and speed of fire spreading, the migrations of wild animals, and the runoff path and speed of precipitation. The forest-steppe ecotone in northern China is one of ecological fragile zones. In-depth study of the RDLS of the forest-steppe ecotone in northern China will help to implement ecological projects scientifically and promote the construction of the national ecological security barrier. The Shuttle Radar Topography Mission (SRTM-GL1 30 m) data were used to determine the optimal analysis window for RDLS based on the mean change-point method, and the elevation difference was extracted based on the window analysis method. The RDLS model was used to extract RDLS of the forest-steppe ecotone and analyzed with the help of a spatial auto-correlation model. The correlation between mean elevation, relative elevation difference, and RDLS was also analyzed. The results show that the optimal analysis window size for RDLS was 29 × 29, corresponding to an area of 0.76 km². The RDLS under the optimal analysis window extracted from SRTM-GL1 (30 m) ranged from 0.084 to 3.516. The RDLS had significant spatial clustering, with high RDLS mainly distributed in the mountainous areas and low RDLS mainly distributed in mountain-to-plain transition zone; the RDLS between different administrative units and different watersheds had obvious variability. Overall, the RDLS was characterized as decreasing, increasing, and then decreasing from the south to north, while it was high in the west and low in the east. And the RDLS was linearly positively correlated with mean elevation and relative elevation difference. In the future, the implementation of major ecological projects in the forest-steppe ecotone in northern China, such as soil and water conservation, afforestation tree species selection, ecological corridor design, ecological management, geological disaster prevention, and forest fire prevention, should fully consider the local topographic conditions. These research results can provide topographic references for the implementation of ecological planning and engineering in this area and similar areas. It contributes to sustainable development and maximization of ecological benefits and promotes the establishment of a national ecological security barrier.

Forests play a crucial role in the global carbon cycle, climate regulation, and biodiversity conservation, making them essential for understanding ecosystem responses to environmental change. However, the spatiotemporal dynamics of forest vegetation and their responses to climate change have yet to be fully explored. This study assessed the spatiotemporal dynamics and adaptation of forest vegetation from Northern China by extracting changes in forest vegetation and phenological characteristics from 2001 to 2023 with the time-series MODIS Normalized Difference Vegetation Index (NDVI) data and analyzing the impact of climate variables on these changes. The linear regression analysis method and the four-parameter double logistic model were employed to assess forest vegetation changes and identify forest vegetation phenological phases, respectively. Partial correlation analysis was used to assess the relationship between forest vegetation and climate variables. The results of this study indicate that over the past two decades, the annual mean NDVI of forest vegetation has exhibited a slow increasing trend of approximately 0.002 yr⁻¹, with a spatial distribution pattern that gradually decreases from south to north, showing a significant correlation with latitude. The magnitude of annual mean NDVI changes varies considerably among different forest vegetation types. However, except for evergreen broadleaf forests, the NDVI of all other forest types has shown a significant increasing trend. Additionally, central North China and southeastern Tibet exhibit higher NDVI values in both spring (>0.55) and autumn (>0.65) than other areas, while the NDVI values in Northeast China and North China are higher in summer (>0.8) compared to other areas. The study reveals substantial spatial heterogeneity in the average phenological phases and NDVI values of forest vegetation across different regions, influenced by latitude, altitude, and regional climatic conditions. The spatial distribution patterns of NDVI during the green-up and senescence phases remain relatively consistent, yet significant regional differences exist within the same phenological phase. Partial correlation analysis indicates that forest vegetation in different regions responds distinctly to meteorological factors. These findings contribute to a deeper understanding of the spatiotemporal dynamics of vegetation change and its complex interactions with climate change, offering valuable insights for forest ecosystem management and climate adaptation of forest vegetation.