Urban green spaces have been shown to decrease land surface temperature (LST) significantly. However, few studies have explored the relationships between urban green spaces and LST across different seasons at different spatial scales. In this study, using Changchun, China as a case study, landscape ecology and comparative approaches were employed quantitatively to investigate the effects of the composition and configuration of urban green spaces on the urban thermal environments. LST maps were retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) data acquired on four dates that represented four different seasons, and detailed information of urban green spaces was extracted from high resolution imagery GF-1. Normalized differential vegetation index (NDVI) and six landscape metrics at patch, class, and landscape level were used to characterize the spatial patterns of urban green spaces. The results showed that urban green spaces did have significant cooling effects in all seasons, except for winter, but the effects varied considerably across the different seasons and green types, and seemed to depend on the NDVI and size of urban green spaces. Compared to shape metrics, the negative relationships between the LST and the area and the NDVI of urban green spaces were more significant. Both the composition and configuration of urban green spaces can affect the distribution of LST. Based on findings with one city, given a fixed area of urban green spaces, the number of green patches can positively or negatively affect the LST, depending on if the number is larger than a threshold or not, and the threshold varies according to the given area. These findings provide new perspectives, and further research is also suggested, to generate a better understanding of how urban green spaces affect the urban thermal environment.

Urban parks have been shown to form park cool islands (PCIs), which can effectively alleviate the negative influences of urban heat islands (UHI). However, few studies have examined the detailed characteristics of PCIs, the effect of urban park features on their individual temperatures, and monthly variation in PCIs. Land surface temperature (LST) retrieved from Landsat 8 TIR images between May and October were used to represent the thermal environment. Urban park characteristics were extracted from high-resolution GF-2 images. Using these datasets, the relationships between urban park characteristics and PCIs were explored in this study using Changchun, which has a snow climate, as a case study. The results showed the following: (1) the urban parks exhibited a cooling island effect, and the PCIs showed significant monthly variations with the highest intensities in the hot months; (2) the effects of composition (e.g., park size and the percentage of water area) on LSTs and PCIs showed significant monthly variability and were stronger than the configuration effects. Furthermore, an unexpected, negative correlation between PCIs and the area of park grass was also found; and (3) larger parks tended to have stronger PCI intensities and extents of influence. For parks larger than 30 ha, the cooling effects extended approximately 480 m from the park edge between June and August. For all of parks during the study duration, the rate of temperature increase was highest within 60 m from the park edge. The PCI we employ specifically in this study is characterized by LST.

The spatio-temporal patterns of land use/land cover changes (LUCC) can significantly affect the distribution and intensity of the urban heat island (UHI) effect. However, few studies have mapped a clear picture of the influence of LUCC on UHI. In this study, both qualitative and quantitative models are employed to explore the effect of LUCC on UHI. UHI and LUCC maps were retrieved from Landsat data acquired from 1984, 1992, 2000, 2007, and 2014 to show their spatiotemporal patterns. The results showed that: (1) both the patterns of LUCC and UHI have had dramatic changes in the past 30 years. The urban area of Changchun increased more than four times, from 143.15 km2 in 1984 to 577.45 km2 in 2014, and the proportion of UHI regions has increased from 15.27% in 1984 to 29.62% in 2014; (2) the spatiotemporal changes in thermal environment were consistent with the process of urbanization. The average LST of the study area has been continuously increasing as many other land use types have been transformed to urban regions. The mean temperatures were higher in urban regions than rural areas over all of the periods, but the UHI intensity varied based on different measurements; and (3) the thermal environment inside the city varied widely even within a small area. The LST possesses a very strong positive relationship with impervious surface area (ISA), and the relationship has become stronger in recent years. The UHI we employ, specifically in this study, is SUHI (surface urban heat island).

Unprecedented urbanization has occurred globally, which has converted substantial natural landscapes into impervious surfaces and further impacted ecosystem services and functioning. In this study, we quantified the spatiotemporal patterns of urbanization and investigated the impacts of urbanization on the ecosystem service value (ESV) in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 1980 to 2018. The results show that the GBA has experienced extensive urbanization, with the urban area increasing from 2607.4 to 8243.5 km² from 1980 to 2018. Zhongshan, Zhuhai, Dongguan, Shenzhen, and Foshan exhibited the top five highest urban expansion rates. Throughout the study period, edge expansion was the most dominant growth mode, with a decreasing trend, while infilling increased in the GBA. The total ESV loss induced by urban expansion in the GBA reached 40.5 billion yuan over the past four decades. The ESV loss due to the water body decrease caused by urbanization was the largest. Our study suggests that decision-makers should control new urban areas and protect water bodies, wetlands, and forests with high ESVs to promote the sustainable development of urban agglomerations.

Decreased human activities from COVID-19 containment measures caused a brighter, earlier, and greener 2020 spring in China.

Sanjiang Plain has undergone dramatic land degradation since the 1950s, which has caused negative effects on ecosystems services and sustainability. In this study, we used trajectory analysis as well as the Lorenz curve, Gini coefficient and relative land use suitability index (R) to analyze spatiotemporal changes of land degradation from 1954 to 2013 and to make a preliminary estimation of the role of human activities in observed environmental changes using a five-stage LULC data. This study also explored the effect of land degradation on the values and structure of ecosystem services. Our results indicated that more than 70% of marsh area originally present in the study area has been lost, whereas less than 30% was preserved. Dry farmland and paddy increased rapidly at the expense of marsh, forest and grassland. Land use structure became more unsuitable during the past 60 years. Compared with natural factors, human activities played a dominant role (89.67%) in these changes. This dramatic land degradation caused the significant loss of ecosystem services values and the changes in the structure of ecosystem services. These results confirmed the effectiveness of combining temporal trajectory analysis, the Lorenz curve/Gini coefficient and the R index in analyzing spatiotemporal changes in progressive land degradation. Also, these findings highlight the necessity of separating dry farmland from paddy when studying land degradation changes and the effects on ecosystem services in regions where dry farmland has often been converted to paddy.