The spatial pattern of color patches plays a crucial role in affecting the visual quality of peri-urban forests dominated by Cotinus coggygria var. cinerea Engl. in autumn. The impact mechanism has been studied to facilitate algorithm-based automatic visual quality estimation. The color patterns of 120 photographs were calculated after color quantization and automatic color substitution. The scenic beauty of the forest was estimated by 698 respondents. Multiple correlations between visual quality and color pattern metrics were explored with stepwise regression. Principal component analysis (PCA) was also employed to investigate the impact mechanism of color patterns on visual quality. Number of patches (NP), largest patch index (LPI), mean patch area (AREA_MN), patch size standard deviation (AREA_SD), and Shannon’s evenness index (SHEI) were the main factors affecting the visual quality of the Cotinus coggygria forest. AREA_MN correlated positively with visual quality, while NP, LPI, AREA_SD, and SHEI correlated negatively. Moreover, AREA_SD had the most significant impact on the visual quality of the landscape, while SHEI, LPI, and AREA_MN had the second-highest impact. The evenness and the size of color patches significantly affected the visual quality of the forest landscapes. Balancing the diversity and evenness of color patches plays a decisive role in creating a forest landscape with high visual quality.

Objective Urban ecosystem services play an irreplaceable role in maintaining life support and environmental dynamic balance. Urban green spaces, as an important component of urban ecosystems, are the only element that directly increases carbon sinks and indirectly reduces carbon emissions in cities. While beautifying the environment, they play an extremely important role in ecological benefits and carbon sequestration. Quantifying the carbon sequestration capacity of urban green spaces is of great significance for achieving carbon peaking and carbon neutrality goals and addressing climate change. Methods Based on Carnegie-Ames-Stanford approach (CASA) model, Landsat 8 TM remote sensing data and meteorological data from 2014, 2017, and 2020 are used to estimate the net primary productivity (NPP) and carbon sequestration capacity of parks in the central urban area of Beijing, and explore the spatial and temporal distribution pattern and change characteristics of carbon sequestration capacity of parks in the central urban area of Beijing. Pearson correlation analysis is used to study the driving relationship between the carbon sequestration capacity of park green space, vegetation index, climate environment and landscape pattern. Additionally, the biomass allometry equation method is used to measure the actual carbon sequestration of 33 typical plant communities in Beijing, in order to verify the accuracy of remote sensing model estimation. Results The results show that the area of park green space in the central urban area of Beijing increased year by year from 2014 to 2020, with a 10.89% increase in the area of park green space in 2020 compared to 2014. Varying significantly in spatial distribution, park green spaces in the central urban area of Beijing are mostly distributed in Haidian District and Chaoyang District. Due to regional limitations, the area of park green spaces in core areas such as Dongcheng District and Xicheng District is relatively small. The total carbon sequestration capacity of park green spaces in the central urban area of Beijing in 2014, 2017 and 2020 was 5.23×10⁴ tC, 7.09×10⁴ tC, and 7.45×10⁴ tC, respectively. Accordingly, the annual average carbon sequestration capacity per unit area was 2.46 tC/hm², 3.19 tC/hm², and 3.29 tC/hm², respectively, showing an increasing trend year by year. 82.15% of park green spaces have significantly improved their carbon sequestration capacity. There are significant differences in the spatial distribution of carbon sequestration capacity in park green spaces, showing a pattern of “high at the edge area and low in the center area overall”. Haidian District, Chaoyang District and Shijingshan District have higher carbon sequestration capacity. In 2014, park green spaces were mainly characterized by medium to high carbon sequestration capacity, while in 2017 and 2020, they shifted to medium to high carbon sequestration capacity and high carbon sequestration capacity, with large green patches playing an important role in achieving high carbon sequestration capacity. The carbon sequestration capacity of park green spaces is significantly positively correlated (p<0.01) with normalized difference vegetation index (NDVI), leaf area index (LAI), and rainfall, and significantly negatively correlated (p<0.05) with temperature, while having little correlation with wind speed. The carbon sequestration capacity of park green spaces is significantly positively correlated (p<0.01) with patch area (PA), largest patch index (LPI), percentage of like adjacency (PLADJ), patch cohesion index (COHESION), and aggregation index (AI), and significantly negatively correlated (p<0.01) with landscape shape index (LSI), mean perimeter-area ratio (MPAR), and mean patch fractal dimension (MPFD). To verify accuracy of the remote sensing model adopted, the measured and simulated values of carbon sequestration capacity were compared. Specifically, the relative error between the measured and simulated values is 5.64%, the mean square error is less than 0.1, and the fitting accuracy is 0.7314. The estimated result is good, basically meeting the accuracy requirements. Conclusion The carbon sequestration capacity of urban park green spaces in Beijing significantly improved from 2014 to 2020. The higher the carbon sequestration capacity of green spaces, the higher their vegetation coverage and LAI, and the better their cooling effect. The higher PA, the higher AI, and the simpler the patch shape of park green space patches, the stronger the carbon sequestration capacity to a certain extent. This research may provide ideas for scientific assessment and improvement of the carbon sequestration capacity of urban green space, formulation of climate change response measures, and lay a foundation for assessment of the carbon balance of urban green space ecosystem. The research results can directly support the whole process of planning, construction and management of urban green space, and provide a scientific basis for the optimization and regulation of the layout of urban park green space.

Ecological zoning is important for understanding spatial heterogeneity and supporting landscape-level management. However, existing approaches rarely integrate ecosystem service supply with ecological risk, and their underlying nonlinear relationships remain insufficiently explored. This study aims to develop an integrated framework linking ecosystem service value (ESV) and landscape ecological risk (LER) based on a two-dimensional quadrant model. This framework integrates ESV and LER from complementary benefit–risk perspectives, advancing ecological zoning beyond single-indicator approaches. Using the Miyun Reservoir Basin as a case study, multi-source data from 2000 to 2020 were used to quantify ESV and LER and to examine their spatiotemporal dynamics. The ESV-LER framework was applied to identify ecological functional zones. In addition, the XGBoost-SHAP model combined with the Geographical Detector was used to explore the nonlinear effects and interactions of natural and anthropogenic drivers. ESV showed a “decline-recovery” trend, whereas LER exhibited an opposite “decrease-increase” pattern. Areas with both high ESV and high LER were mainly distributed around the reservoir and river networks, suggesting a spatial mismatch between ecological value and risk. Ecological improvement and conservation zones accounted for approximately 79% of the basin, while ecological risk prevention zones expanded over time, indicating increasing human disturbance. NDVI was identified as a dominant factor with dual effects, enhancing ESV while reducing LER, whereas population density and NPP exhibited nonlinear threshold effects that increased ecological risk. Overall, this study advances ecological zoning by integrating functional value and risk perspectives while explicitly revealing their nonlinear drivers. The proposed framework provides a transferable and interpretable approach for watershed-scale ecological management and supports more targeted and differentiated governance strategies.

Plant functional traits are central to regulating ecosystem multifunctionality (EMF), yet how coordinated above- and below-ground resource acquisition strategies mediate the effects of forest structural diversity on EMF remain insufficiently understood, particularly in typical south subtropical forests. Here, we applied a trait-based framework to disentangle the pathways linking forest structural diversity to EMF through plant resource acquisition strategies. Typical south subtropical forests were sampled for community-level leaf and root traits, including leaf total nitrogen and total phosphorus content, specific leaf area, leaf dry matter content, root diameter, specific root length, root tissue density, root total nitrogen and root total phosphorus content. EMF was quantified using 13 indicators associated with carbon storage, litter decomposition, primary productivity, and nutrient cycling, evaluated using both averaging and multi-threshold approaches. Principal component analysis was used to summarize trait variation along major functional axes representing the leaf and root economics spectra, and structural equation modeling was employed to quantify direct and trait-mediated pathways linking forest structural diversity to EMF. We found pronounced variation in EMF among forest types, with multifunctionality increasing along the classical fast-slow plant economics spectrum. Communities dominated by fast-growing species exhibited consistently higher EMF than those dominated by slow-growing species, with below-ground traits showing stronger associations with EMF than above-ground traits. In contrast, EMF was unrelated to the root collaboration gradient, suggesting that alternative below-ground foraging strategies contributed little to multifunctionality. Moreover, the positive effects of structural diversity on EMF were indirectly mediated through both leaf and root conservation gradients. Notably, the relative importance of these trait-mediated pathways was threshold-dependent. Root conservation gradients dominated EMF at low multifunctionality thresholds, whereas leaf conservation gradients became increasingly important at higher thresholds. Our findings show that forest structural diversity enhances ecosystem multifunctionality through coordinated leaf and root strategies. By revealing trait-mediated links between biodiversity and EMF, this study clarifies how community composition and species turnover shape multifunctionality in typical south subtropical forests.