Urban greening is becoming an important strategy in improving urban ecosystem services and sustainability. Identifying the response of vegetation to urbanization and urban landscape patterns can help in planning for urban greening. Urbanization may lead to both direct and indirect effects on vegetation, and the indirect effects of urbanization on vegetation growth (UIE-VG) have been paid much attention recently in large scale. In this study, we investigated the spatiotemporal evolution of UIE-VG and the effects of landscape patterns on UIE-VG using the boosted regression tree model and remotely sensed data. An increase in average UIE-VG from 4 to 56% was found during urbanization of Kunming, the case study area in southwest China. However, UIE-VG exhibited high variations due to landscape pattern changes at the local scale. Overall, area-related and aggregation-related landscape metrics had greater effects on UIE-VG than the other metrics. The increase and aggregation of built-up land enhanced UIE-VG by 3.1–81.3% while the increase and aggregation of unused land and waterbodies reduced UIE-VG by 0.7–20.6%. Moreover, we found that the large and aggregated vegetation areas may mitigate the negative UIE-VG in low urbanization areas. Our findings have important implications for integrating urban landscape planning into sustainable urban greening strategies.

Local climate zones (LCZs) provide a comprehensive framework to examine surface urban heat islands (SUHIs), but information is lacking on their thermal contributions and spatial effects in different macroclimate cities. A standard framework for distinguishing between the cooling effect and heating effect and spatial effect analysis based on the LCZ scheme was conducted in five distinct macroclimate cities, i.e., Yuanjiang (arid climate), Jinghong (tropical climate), Kunming (subtropical climate), Zhaotong (temperate climate), and Shangri-La (alpine climate). The results indicated that (1) built-up zones presented heating effects in Jinghong and Shangri-La, but opposite results were observed in Yuanjiang and Zhaotong. (2) The thermal contributions of natural zones with dense trees (LCZAs) and waterbodies (LCZGs) showed cooling effects in the five cities regardless of season. (3) The spatial effect of heating LCZs on land surface temperature (LST) was more significant than that of cooling LCZs in Jinghong and Shangri-La, but the opposite results occurred in Yuanjiang and Kunming. Moreover, the spatial effect was lower in Zhaotong than in other cities. (4) Lower LST differences between natural zones and built-up zones in winter than in summer decreased the spatial effects. In summary, the thermal contributions of LCZs and their spatial heating/cooling effects were different among five distinct climate backgrounds, which implies that targeted measures must be used in different macroclimates.

Ecotones, i.e., transition zones between habitats, are important landscape features, yet they are often ignored in landscape monitoring. This study addresses the challenge of delineating ecotones at multiple scales by integrating multisource remote sensing data, including ultra-high-resolution RGB images, LiDAR data from UAVs, and satellite data. We first developed a fine-resolution landcover map of three plots in Yunnan, China, with accurate delineation of ecotones using orthoimages and canopy height data derived from UAV-LiDAR. These maps were subsequently used as the training set for four machine learning models, from which the most effective model was selected as an upscaling model. The satellite data, encompassing Synthetic Aperture Radar (SAR; Sentinel-1), multispectral imagery (Sentinel-2), and topographic data, functioned as explanatory variables. The Random Forest model performed the best among the four models (kappa coefficient = 0.78), with the red band, shortwave infrared band, and vegetation red edge band as the most significant spectral variables. Using this RF model, we compared landscape patterns between 2017 and 2023 to test the model’s ability to quantify ecotone dynamics. We found an increase in ecotone over this period that can be attributed to an expansion of 0.287 km² (1.1%). In sum, this study demonstrates the effectiveness of combining UAV and satellite data for precise, large-scale ecotone detection. This can enhance our understanding of the dynamic relationship between ecological processes and landscape pattern evolution.

Understanding the factors and mechanisms that influence the impact of urbanization on vegetation growth is crucial for climate change mitigation and urban greening initiatives. However, the long-term evolution mechanisms of these impacts, particularly in the context of changing climate and water scarcity, are not yet fully understood. In this study, we evaluated the long-term indirect impacts of urbanization on vegetation growth across 2,385 county-level cities in China. We found that the trend of vegetation enhancement induced indirectly by urbanization is less pronounced in arid zones compared to humid zones. In addition to the vegetation removal caused by urbanization, the expansion of urban spaces can compensate for vegetation loss by fostering growth, surpassing the effects of economic and population growth. However, in arid zones, the positive impacts and regulation amplitudes of urban spatial expansion on vegetation growth are constrained due to high urban water scarcity. These insights may contribute to a more accurate assessment of carbon gains or losses in urbanized vegetation.