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.

Accurate estimation of crop evapotranspiration (ET_c) is important to determine crop water requirements in greenhouse agriculture and to improve the irrigation water use efficiency. Here, a 3-year (2017–2019) experiment with spring greenhouse-grown eggplant (Solanum melongena L.) was conducted to investigate variation in the crop coefficient (K_c) measured with a weighing lysimeter, adjust K_c based on the local climate (K_c,Adj), and estimate daily ET_c using a crop coefficient model. The 3-years mean local K_c (K_c,Loc) were 0.23 ± 0.03, 0.62 ± 0.06, 1.05 ± 0.03 and 0.87 ± 0.03 at the initial, development, mid-season, and end-season stages, respectively. Significant linear correlation was observed between K_c,Adj and K_c,Loc in the 3 years (R² = 0.873, 0.901, and 0.897 in 2017–2019, respectively). Compared with the FAO-56 recommended K_c value (K_c,FAO), the mean K_c,Adj and K_c,Loc in the 3 years were by 66.3% and 61.8% lower, respectively. The single crop coefficient model accurately estimated daily ET_c for greenhouse-grown eggplant. The coefficient of determination (R²), mean absolute error (MAE), root-mean-squared error (RMSE), and index of agreement between measured ET_c and that estimated by the single crop coefficient model were 0.94, 0.35 mm‧d⁻¹, 0.26 mm‧d⁻¹, and 0.98, respectively, for the means in 2018 and 2019. Therefore, the crop coefficient method reliably estimated evapotranspiration with adjustment for the actual environment and can serve as a useful tool to improve water use efficiency.

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.