Cities, the core of the global climate change and economic development, are high impact land cover land use change (LCLUC) hotspots. Comprehensive records of land cover land use dynamics in urban regions are essential for strategic climate change adaption and mitigation and sustainable urban development. This study aims to develop a Google Earth Engine (GEE) application for high-resolution (15-m) urban LCLUC mapping with a novel classification scheme using pan-sharpened Landsat images. With this approach, we quantified the annual LCLUC in Changchun, China, from 2000 to 2019, and detected the abrupt changes (turning points of LCLUC). Ancillary data on social-economic status were used to provide insights on potential drivers of LCLUC by examining their correlation with change rate. We also examined the impacts of LCLUC on environment, specifically air pollution. Using this approach, we can classify annual LCLUC in Changchun with high accuracy (all above 0.91). The change detection based on the high-resolution wall-to-wall maps show intensive urban expansion with the compromise of cropland from 2000 to 2019. We also found the growth of green space in urban regions as the result of green space development and management in recent years. The changing rate of different land types were the largest in the early years of the observation period. Turning points of land types were primarily observed in 2009 and 2010. Further analysis showed that economic and industry development and population migration collectively drove the urban expansion in Changchun. Increasing built-up areas could slow wind velocity and air exchange, and ultimately led to the accumulation of PM_2.5. Our implement of pan-sharpened Landsat images facilitates the wall-to-wall mapping of temporal land dynamics at high spatial resolution. The primary use of GEE for mapping urban land makes it replicable and transferable by other users. This approach is a first crucial step towards understanding the drivers of change and supporting better decision-making for sustainable urban development and climate change mitigation.

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO₂ Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect.

Urban green spaces (UGSs) provide important support for the health of urban residents and the realization of sustainable urban development. However, the spatiotemporal pattern of urban resident exposure to UGSs in cities is unclear, especially at the national scale in China. Based on the annual 30 m resolution Normalized Difference Vegetation Index (NDVI) data of the Landsat satellite, we quantitatively analyzed the change in UGS coverage from 2000 to 2020 for 320 cities in China and combined it with population data to understand the changing patterns of urban population exposure to different UGS coverage. The results indicated that the average UGS coverage decreased from 63% to 44% from 2000 to 2020 in China, which could be divided into two stages: a rapid decline phase (2000–2014) and a progressive decline phase (2015–2020). Geographically, UGS coverage declined faster in southwestern and eastern cities than in other regions, particularly in medium-sized cities. We also found that urban pixel-based areas in cities with the highest UGS coverage (80–100%) decreased rapidly, and the proportion of the urban population exposed to the highest UGS coverage also declined significantly from 2000 to 2020. Urban pixel-based areas with low UGS coverage (20–40%) continued to expand, and there was a rapid increase in the proportion of the urban population exposed to low UGS coverage, with an increase of 146 million people from 2000 to 2020. The expansion of impervious surfaces had the most significant effect on the change in UGS coverage during different periods (2000–2020, 2000–2014, and 2015–2020). Natural factors such as precipitation, surface maximum temperature, and soil moisture also affected UGS coverage change. These findings provide insights into the impact of urbanization on the natural environment of cities, availability of UGS for residents, and sustainable urban development under rapid urbanization.

During the 21st century, policies toward agriculture, forestry, and urbanization have emerged to ensure food security, ecological restoration, and human well-being by managing land in Northeast China. However, the integrated effects and relationships of various policies are still not well understood. This study observed the land use land cover changes in Central Jilin from 2000 to 2019 and, by considering policy involvement, aimed to understand the effects and trade-offs of policies. Results showed that the cropland, including dryland and rice paddy, and the forest, including coniferous forest and deciduous forest, are dominant land types in Central Jilin. During 2000–2019, the land changed diversely, of which the main changes were the expanded dryland (+0.43 million ha), the increased deciduous forest (+22 million ha), the decreased coniferous forest (−0.08 million ha), and the expanded urban settlement (+0.04 million ha). With these changes, despite the unit grain yield showing a rising trend, the yield contribution of Central Jilin to the national total decreased. The poor cultivating structure made for the cropland expansion and reduced the implementation space of environmental restoration projects such as the Grain to Green. Thus, in Central Jilin that transits from the agri-food production zone to the eco-regulation zone, environmental projects coexisted in a trade-off manner with agricultural policies that aim to liberate agricultural productivity. In the key urban agglomerations of Central Jilin, the increase in the proportion of green space improved the thermal environment and carbon balance. The gross domestic product of the large city and its local proportion also rose. These improvements benefited from the promotion of development policies and urbanization policies at key time points. In the future, it is necessary to coordinate agricultural policies and environmental projects and promote the progress of small- and medium-sized cities to ensure the equality of regional development. This study has implications for making decisions to revitalize Northeast China and researchers who inform decisions.

Urban vegetation plays a crucial role in regulating temperatures and heat waves in urban areas. However, the influence of vegetation coverage and its configuration on surface temperatures in different climate zones at a national scale is unclear. To address this, we utilized high-resolution data to detect spatial patterns for 31 provincial capital cities in China. We integrated day and night surface temperatures to determine the influence of vegetative coverage and configuration on urban temperatures across different climate zones and city sizes. Our study revealed that a subtropical monsoon climate and medium-sized cities had the highest vegetative coverage and shape complexity. The best connectivity and agglomeration of vegetation were found in a temperate monsoon climate and large cities. In contrast, small cities, especially those under a temperate continental climate, had low vegetation coverage, high fragmentation, and weak agglomeration and connectivity. In addition, vegetative coverage had a negative impact on daytime surface temperatures, especially in large cities in a subtropical monsoon climate. However, an increase in vegetation coverage could result in warming at night in small cities in temperate continental climates. Although urban vegetation configuration also contributed to moderating surface temperatures, especially at night, they did not surpass the influence of vegetation coverage. The effect on nighttime temperatures of the configuration of vegetation increased by 3–6% relative to that of daytime temperatures, especially in large cities in a temperate monsoon climate. The contribution vegetation coverage and configuration interaction to cooling efficiency decreased at night, especially in medium-sized cities in a temperate continental climate by 3–5%. In addition, this study identified several moderating effects of natural and social factors on the relationship between urban vegetation coverage and surface temperatures. High duration of sunshine, low humidity and high wind speed significantly enhanced the negative impact of vegetation coverage on surface temperatures. In addition, the moderating effect of vegetation coverage was more pronounced in low population density cities and high gross domestic product. This study enhances understanding of the ecological functions of urban vegetation and provides a valuable scientific basis and strategic recommendations for optimizing urban vegetation and improving urban environmental quality.