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.

As major carbon (C) pools in cities, urban green spaces play a crucial role in reducing atmospheric carbon. To determine the importance of litterfall C storage in urban green spaces, we selected the leaf area index (LAI) as a proxy indicator for litterfall C density (LCD), and established a log-linear regression model between LCD and LAI to predict the annual litterfall C pool in large-scale urban green spaces using Sentinel-2 satellite remote sensing data. Forty-five sample units were randomly selected in typical urban green spaces in Beijing, China. A high-temperature combustion method was used to measure the LCD of the sampling units, and stepwise linear regression was used to filter the proxy indicator for LCD. The annual litterfall C pool in regions within the Fifth Ring Road was also estimated with inversion using remote sensing data. From 2015 to 2021, the estimated annual litterfall C pool was in the range of 4.5–5.8 × 10¹⁰ g, i.e., approximately 18.9% of the total C storage recorded for the urban green space, which was far greater than that observed in forest ecosystems. We concluded that the litterfall C pool in urban green spaces is seriously underestimated, and that urban tree litterfall has the potential to reduce greenhouse gas emissions if used as a carbon-neutral resource.

Background: The visual forms of individual trees in peri-urban forests are driven by a complex array of simultaneous cause-and-effect relationships. Materials and Methods: Structural Equation Modeling (SEM), as a specialized analytical technique, was used to model and understand the complex interactions. It was applied to find out responses of visual forms to neighboring competition in a peri-urban forest dominated by Cotinus coggygria var. cinerea Engl. in Beijing, China. Research Highlights: Light interception and space extrusion have substantial effects on visual forms, expressed as crown forms and foliage forms. The structural model in SEM tested hypothetical correlations among latent variables, namely neighboring competition, crown forms, and foliage forms. Results: The fitted model suggested a direct negative effect of neighboring competition on crown forms and an insignificant negative direct effect on foliage forms. Moreover, an indirect positive effect on foliage forms mediated by crown forms was revealed. Conclusions: The fitted SEM and associated findings should facilitate peri-urban forest landscape management by providing insight into causal mechanisms of visual forms of individual trees and thereby assisting in the visual quality promotion.