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Urbanization has significantly altered urban landscape patterns, leading to a continuous reduction in the proportion of green spaces. As critical carbon sinks in urban carbon cycles, urban green spaces play an indispensable role in mitigating climate change. This study aims to evaluate the carbon capture and storage potential of urban green spaces in Luohe, China, and identify the landscape factors influencing carbon sequestration. The research combines on-site data collection with high-resolution remote sensing, utilizing the i-Tree Eco model to estimate carbon sequestration rates across areas with varying levels of greenery. The study reveals that the carbon sequestration capacity of urban green spaces in Luohe City is 1.30 t·C·ha−1·yr−1. Among various vegetation indices, the Enhanced Vegetation Index (EVI) explains urban green space carbon sequestration most effectively through an exponential model (R2 = 0.65, AIC = 136.5). At the city-wide scale, areas with higher greening rates, better connectivity, and more complex edge morphology exhibit superior carbon sequestration efficiency. The explanatory power of key landscape indices on carbon sequestration is 78% across the study area, with variations of 71.5%, 62%, and 84.9% for low, medium, and high greening rate areas, respectively. Moreover, when greening rates reach a certain threshold, maintaining and optimizing the quality of existing green spaces becomes more critical than simply expanding the green area. These insights provide valuable guidance for urban planners and policymakers on enhancing the ecological functions of urban green spaces during urban development. [ABSTRACT FROM AUTHOR]

Greenscaping, a key sustainable practice, helps cities combat rising temperatures and climate change. Urban parks, a pivotal greenscaping element, mitigate the urban heat island (UHI) effect. In this study, we utilized high-resolution remote sensing imagery (GF-2 and Landsat 8, 9) and in situ measurements to analyze the seasonal thermal regulation of different park types in Zhengzhou, China. We calculated vegetation characteristic indices (VCIs) and landscape patterns (LMs) and employed boosted regression tree models to explore their relative contributions to land surface temperature (LST) across different seasons. Our findings revealed that urban parks lowered temperatures by 0.65 °C, 1.41 °C, and 2.84 °C in spring, summer, and autumn, respectively, but raised them by 1.92 °C in winter. Amusement parks, comprehensive parks, large parks, and water-themed parks had significantly lower LSTs. The VCI significantly influenced LST in autumn, with trees having a stronger cooling effect than shrubs. LMs showed a more prominent effect than VCIs on LST during spring, summer, and winter. Parks with longer perimeters, larger and more dispersed green patches, higher plant species richness, higher vegetation heights, and larger canopies were associated with more efficient thermal reduction in an urban setting. The novelty of this study lies in its detailed analysis of the seasonal thermal regulation effects of different types of urban parks, providing new insights for more effective urban greenspace planning and management. Our findings assist urban managers in mitigating the urban surface heat effect through more effective urban greenspace planning, vegetation community design, and maintenance, thereby enhancing cities' potential resilience to climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Urban ecosystem dysfunction, habitat fragmentation, and biodiversity loss caused by rapid urbanization have threatened sustainable urban development. Urban habitat quality is one of the important indicators for assessing the urban ecological environment. Therefore, it is of great practical significance to carry out a study on the driving mechanism of urban habitat quality and integrate the results into urban planning. In this study, taking Zhengzhou, China, as an example, the InVEST model was used to analyze the spatial differentiation characteristics of urban habitat quality and Geodetector software was adopted to explore the driving mechanism of habitat quality at different grid-scales. The results show the following: (1) LUCC, altitude, slope, surface roughness, relief amplitude, population, nighttime light, and NDVI are the dominant factors affecting the spatial differentiation of habitat quality. Among them, the impacts of slope, surface roughness, population, nighttime light, and NDVI on habitat quality are highly sensitive to varying grid-scales. At the grid-scale of 1000 to 1250 m, the impacts of the dominant factors on habitat quality is closer to the mean level of multiple scales. (2) The impact of each factor on the spatial distribution of habitat quality is different, and the difference between most factors has always been significant regardless of the variation of grid-scales. The superimposed impact of two factors on the spatial distribution of habitat quality is greater than the impact of the single factor. (3) Combined with the research results and the local conditions of Zhengzhou, we put forward some directions of habitat protection around adjusting urban land use structure, applying nature-based solutions and establishing a systematic thinking model for multi-level urban habitat sustainability. [ABSTRACT FROM AUTHOR]

Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou's central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers. [ABSTRACT FROM AUTHOR]

This paper reveals the role of urban green space (UGS) in regulating runoff and hence on urban hydrological balance. The modeling software i-Tree Hydro was used to quantify the effects of UGS on surface runoff regulation and canopy interception capacity in four simulated land-cover scenarios. The results showed that the existing UGS could mitigate 15,871,900 m3 volume of runoff (accounting for 9.85% of total runoff) and intercept approximately 9.69% of total rainfall by the vegetation canopy. UGS in midterm goal and final goal scenarios could retain about 10.74% and 10.89% of total rainfall that falls onto the canopy layer, respectively. The existing UGS in the Luohe urban area had a positive but limited contribution in runoff regulation, with similar responses in future scenarios with increased UGS coverage. UGS rainfall interception volume changed seasonally along with changing leaf area index (LAI) and precipitation, and the interception efficiency was distinctly different under various rain intensities and durations. The UGS had a relatively high interception performance under light and long duration rain events but performed poorly under heavy and short rain events due to limited surface storage capacities. Our study will assist urban planners and policy-makers regarding UGS size and functionality in future planning in Luohe, particularly regarding future runoff management and Sponge City projects. [ABSTRACT FROM AUTHOR]

Urban green spaces play a crucial role in maintaining urban ecosystem sustainability by providing numerous ecosystem services. How to quantify and evaluate the ecological benefits and services of urban green spaces remains a hot topic currently, while the evaluation is barely applied or implemented in urban design and planning. In this study, super-high-resolution aerial images were used to acquire the spatial distribution of urban green spaces; a modified pre-stratified random sampling method was applied to obtain the vegetation information of the four types of urban green spaces in Luohe, a common plain city in China; and i-Tree Eco model was further used to assess the vegetation structure and various ecosystem services including air quality improvement, rainfall interception, carbon storage, and sequestration provided by four types of urban green spaces. The modeling results reveal that there were about 1,006,251 trees in this area. In 2013, all the trees in these green spaces could store about 54,329 t of carbon, sequester about 4973 t of gross carbon, remove 92 t of air pollutants, and avoid 122,637 m3 of runoff. The study illustrates an innovative method to reveal different types of urban green spaces with distinct ecosystem service productivity capacity to better understand their various roles in regulating the urban environment. The results could be used to assist urban planners and policymakers to optimize urban green space structure and composition to maximize ecosystem services provision. [ABSTRACT FROM AUTHOR]