Urban parks serve as essential carbon sinks in cities, mitigating climate change by sequestering atmospheric CO₂. Maximizing the carbon sequestration potential within constrained urban spaces is a critical step toward carbon neutrality. However, few studies have systematically examined how the internal spatial composition and shape of green spaces affect their vegetation carbon sequestration capacity. This study analyzes the relationship between landscape indices and vegetation carbon sequestration density (VCSD) using field surveys and high-resolution remote sensing data from 123 urban parks in Zhengzhou, China. The results indicate that Zhengzhou’s parks sequester 14.03 Gg C yr⁻¹, with a VCSD of 0.53 kg C m⁻² yr⁻¹. Significant differences in VCSD were observed among park types, with theme parks having the highest average VCSD (0.69 kg C m⁻² yr⁻¹) and community parks the lowest (0.43 kg C m⁻² yr⁻¹). The key drivers primarily consist of landscape indices that characterize green space distribution and configuration, including the proportion of green space (Pg), largest green patch index (LPI), number of green patches (NP), green patch dispersion index (SPL), and landscape shape index (LSI), with specific thresholds identified for each. Based on these findings, category-specific spatial composition strategies are proposed to precisely enhance the carbon sequestration of park vegetation. This study provides actionable guidance for urban park designers to maximize the carbon sequestration potential of green spaces, thereby mitigating climate change and promoting human health and well-being through green space design.

Understanding the relationship between different essential urban land use categories and the urban thermal environment is essential for urban planning, resource allocation and decision support. However, most of the spatiotemporal correlations between land use types and LST are focused on industrial land use and urban green space, and there are fewer discussions on the totality of urban land use types. Here, using multi-source remote sensing images, correlation analysis and the stepwise regression method, we elaborate the relationship between landscape structure and land surface temperature (LST) across the different seasons of 850 planning management units in Jiaozuo, China. Our results show that the degree of explanation of surface temperature by landscape structure increased with a fine division of land use. The imprint of urban–rural gradients on LST was largely masked by the land use categories at the regional scales. Moreover, the tridimensional structure of buildings significantly affected the LST of residential regions, and the large number of low-rise buildings in urban planning practice contributes to high LSTs. This study provides a comprehensive analysis of the effects of each land use type and landscape structure on surface temperature in urban space and also provides strategies and methods for urban planning in rapidly developing regions of the country.

The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention because its significant effects on local weather and climate, and serious consequences for people. However, systematic study of the relationship between urbanization and UTE has been undertaken only to a limited extent. Using quantitative thermal remote sensing and multi-buffer ring method and multiple spatial scales method, here we analyze Landsat TM/ETM+ images of Zhengzhou in Central China acquired on four different dates in 2017 to investigate the spatiotemporal variations, trends, and driving force in the land surface temperature (LST). Our results showed that LST generally increased with urbanization intensity. This trend was extremely obvious in spring and summer, weak in winter, and slightly downward in autumn. Moreover, PLAND (e.g., percentage of impervious surface in a landscape) has the most significant effect on urban LST, and generally increases as the spatial scale becomes larger. In conclusion, the study recommends that urban planning in Zhengzhou should prioritize PLAND, especially at large spatial scales. These results provide a scientific reference for urban planners who are committed to the sustainable development of Zhengzhou City.

Urban parks can mitigate the urban heat island (UHI) and effectively improve the urban microclimate. In addition, quantifying the park land surface temperature (LST) and its relationship with park characteristics is crucial for guiding park design in practical urban planning. The study’s primary purpose is to investigate the relationship between LST and landscape features in different park categories based on high-resolution data. In this study, we identified the land cover types of 123 parks in Luoyang using WorldView-2 data and selected 26 landscape pattern indicators to quantify the park landscape characteristics. The result shows that the parks can alleviate UHI in most seasons, but some can increase it in winter. While the percentage of bare land, PD, and PAFRAC have a positive impact on LST, AREA_MN has a significant negative impact. However, to deal with the current urban warming trend, a compact, clustered landscape configuration is required. This study provides an understanding of the major factors affecting the mitigation of thermal effects in urban parks (UP) and establishes a practical and feasible urban park renewal method under the idea of climate adaptive design, which provides valuable inspiration for urban park planning and design.

Urban green spaces (UGSs) play a critical role in regulating global carbon cycling and mitigating the increase in atmospheric CO₂ concentrations. Research increasingly demonstrates that UGSs not only sequester carbon through photosynthesis but also effectively save carbon emissions by mitigating the urban heat island (UHI) effect. However, understanding the carbon-saving capacity (CSC) and the role of landscape patterns of UGSs in warming cities remains limited. Therefore, we have evaluated the carbon-saving capacity of UGSs in the main urban area of Shangqiu City by utilizing high-resolution remote sensing data and machine learning techniques. The study has focused on green patches larger than 10,000 m² and has analyzed the influence of landscape patterns of UGSs on carbon saving intensity (CSI) and carbon saving efficiency (CSE). The results reveal that the total CSI and the average CSE of UGSs are 7716 t CO₂ and 2.9 t CO₂ ha⁻¹ in Shangqiu, respectively. Landscape patterns of UGSs can explain 82% and 64% of the variability in CSI and CSE variance, respectively. Specifically, green space area is the critical determinant of CSI and CSE, followed by the perimeter–area ratio, shape index, and fractal dimension of UGSs. Therefore, this study advocates for the strategic integration of UGSs into city planning, emphasizing their spatial distribution and configuration to maximize their cooling and carbon-saving capacity.

This research uses the ”Green Space Intensity” (GSI) index to describe the changes of green space within Újbuda, Budapest’s 11th district, from 1992 to 2023. The GSI index is based on the Normalized Difference Vegetation Index (NDVI) generated from satellites, but the values have been revised and adjusted to high-resolution orthoimagery and validated by VHR satellite images. The index has been developed over the last two decades, is well known and has been used in the last ten years in research to analyse green spaces within the towns and cities of Hungary. Analysis of green space changes provides reasonable decision-support in urban planning and city development. It helps to define the drivers of change and the effects of changes on the urban climate, paved and built-up areas, the urban habitat of wildlife species and the role of endemic, invasive and ornamental plant species in the city’s green infrastructure. From an urban ecology perspective, this research supports better and more sustainable planning and design solutions. In the 11th district of Budapest, called Újbuda or “New Buda”, using the available Landsat and Sentinel satellite images, we examined changes over three decades. During the first part of the three decades, the increase in green space intensity was more due to the abandonment of former uses and due to the increase in “block of flat” socialist type residential areas built before the 1990s. Meanwhile, several greenfield investment projects resulted in a decrease. For the period after 2015 the decrease in GSI was due to development on abandoned or areas of low land-use intensity, while increases were predominantly associated with the greening and landscaping phase of previous constructions.