With the acceleration of urbanization, the urban heat island effect has garnered increasing attention. However, few studies have explored the differential impacts of urban green spaces on the UHI across various urban functional zones (UFZs). This study takes Xiamen Island as the research object and selects nine representative landscape pattern indices to characterize the spatial patterns of UGS in each urban functional zone. Through Pearson correlation analysis, four landscape indices—largest patch index (LPI), mean patch area (AREA_MN), area-weighted average shape index (SHAPE_AM), and aggregation index (AI)—were chosen to reveal the varying influences of UGS spatial patterns on the UHI in different urban functional zones. These four landscape indices reflect aspects such as area, shape complexity, density size, and variation, as well as the aggregation of UGS. To address the spatial autocorrelation of variables, a spatial regression model was established. Given that the parameters of the spatial lag model outperformed those of the spatial error model, the spatial lag model was selected. Key findings reveal that the cooling efficiency of UGS varies across UFZs. In urban residential zones (URZs), UGS with complex shapes significantly enhances cooling, as indicated by a negative correlation between SHAPE_AM and LST (β = −0.446, p < 0.05). In urban village zones (UVZs), larger green patches have a stronger cooling effect, with AREA_MN showing a significant negative correlation with LST (β = −1.772, p < 0.05). The results indicate that UGS in different urban functional zones plays distinct roles in mitigating the UHI, with its cooling effects being associated with the spatial patterns of UGS. Therefore, it is recommended to adopt differentiated planning strategies for UGS in various urban functional zones to contribute to a more sustainable and thermally comfortable urban environment.

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research.

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization.