Context-Dependent Associations Between Perceived and Measured Ecosystem Services in Urban Green Spaces in Shanghai: A Comparative Case Study

Changing urban green spaces in Shanghai: trends, drivers and policy implications

Urban green spaces are essential for regulating land surface temperature (LST), but current research frequently neglects their structural complexity and perceived accessibility by humans. To bridge this gap, our study utilizes two complimentary metrics: the satellite-derived Normalized Difference Vegetation Index (NDVI) and the street-level Green View Index (GVI), both employed to assess Guangzhou’s urban thermal environment. Distinct statistical and spatial distribution patterns of NDVI and GVI were identified among districts in Guangzhou, China. NDVI values varied between 0.12 and 0.64, whereas GVI values ranged from 0.18 to 0.47. The LST varied from 27.61 to 41.99 °C, with a global Moran’s I of 0.96 signifying robust spatial autocorrelation. To evaluate the impact of urban morphology on LST, we employed three regression models, with the multiscale geographically weighted regression (MGWR) demonstrating superior performance, with R2 = 0.727, AICc = 2185.43, and RSS = 328.11. Regression results revealed that building density (BD) and average building volume (BV) are positively connected with LST. In contrast, GVI and NDVI exhibit negative associations. This study integrates vertical (NDVI) and horizontal (GVI) greenery viewpoints with urban morphological characteristics, offering actionable insights for urban planners to enhance green infrastructure and more effectively offset the urban heat island effect.

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R2 = 0.27) but substantially stronger along arterial roads with continuous vegetation (R2 = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning.

Accurate retrieval of Land Surface Temperature (LST) is critical for assessing the cooling effects of urban green spaces (UGS) and blue spaces (UBS), which help mitigate the urban heat island effect and improve thermal comfort. This study introduces a novel methodology that integrates deep learning with domain expertise to predict LST, using real LST data, vegetation spectral indices, and spectral bands as inputs. A 1-Dimensional Convolutional Neural Network (1D-CNN) was developed, which outperformed conventional machine learning and alternative deep learning models, demonstrating high predictive accuracy and strong generalization ability. Spatial regression analyses were further employed to examine how UGS of varying sizes influence LST. Results revealed that larger UGS provide strong cooling benefits, while smaller patches contribute less. Remote sensing data from 1991 to 2022 confirmed the significant role of both green and blue spaces in mitigating urban heat, with notable cooling observed around wetlands, rivers, and urban parks. Importantly, combined green–blue configurations enhanced cooling more effectively than blue spaces alone, indicating synergistic benefits when vegetation is integrated with water bodies. Case analysis of afforestation in Daan Forest Park demonstrated how urban greening initiatives can substantially lower local temperatures. Similarly, UBS surrounded by adjacent vegetation exhibited greater temperature reductions compared to isolated water bodies. These findings underscore the need to preserve and expand large, continuous green areas while enhancing connections between green and blue infrastructures. The proposed framework provides robust evidence and practical guidance for urban planners and policymakers to design climate-resilient cities that maximize the co-benefits of UGS and UBS. Analyzing the spatiotemporally varying effects of UGS using time series Landsat data There is an increase in small UGS patches alongside a decline in large UGS areas A novel method was developed to accurately quantify blue spaces cooling temperatures Larger UGS and integrating UGS with Blue space offer significant cooling benefits The combined effect of blue-green interaction leads to an increase in cooling effect

This paper use the advanced experience of Japan in the area of disaster prevention green space planning and construction and, look at the status, gaps and challenges of China's construction and planning of Green Infrastructure, present the new thinking direction about the construction of disaster prevention green space combine with Green Infrastructure.Taking the construction of the disaster prevention green infrastructure network planning of Majiagou River in Harbin as an example and put forward related strategies.

Capturing urban green view with mobile crowd sensing

Urban green spaces play a crucial role in providing social services and enhancing residents’ mental health. It is essential for sustainable urban planning to explore the relationship between urban green spaces and human perceptions, particularly their visual comfort. However, most current research has analyzed green spaces using two-dimensional indicators (remote sensing), which often overlook human visual perceptions. This study combined two-dimensional and three-dimensional methods to evaluate urban green spaces. Additionally, the study employed machine learning to quantify residents’ visual comfort in green-space environments and explored the relationship between green spaces and human visual perceptions. The results indicated that Kitakyushu exhibited a moderate FCV and an extremely low Green View Index (GVI). Yahatanishi-ku was characterized as having the highest visual comfort. Tobata-ku demonstrated the lowest visual comfort. Natural, GVI, openness, enclosure, vegetation diversity, landscape diversity, and NDBI were positively correlated with visual comfort. FCV and ENVI were negatively correlated with visual comfort. Vegetation diversity had the most impact on improving visual comfort. By integrating remote sensing and street-view data, this study introduces a methodology to ensure a more holistic assessment of green spaces. Urban planners could use it to better identify areas with insufficient green space or areas that require improvement in terms of green-space quality. Meanwhile, it could be helpful in providing valuable input for formulating more effective green-space policies and improving overall urban environmental quality. The study provides a scientific foundation for urban planners to improve the planning and construction of healthy and sustainable cities.

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.

As a very important part of the urban ecosystem, the urban green space system plays an active role in maintaining the urban ecosystem stability, providing ecosystem services, and improving the quality of the urban environment. In order to deal with the problems brought about by the deterioration of the urban ecological environment, it is necessary to study and analyze the spatial distribution pattern, evolutionary characteristics and ecosystem services of urban green space to maximize its ecological benefits and comprehensive functions. In this study, we took Beijing urban area as an example, and based on the spatial distribution data of urban green space and remote sensing data, we first calculated the urban green space type transition matrix, landscape pattern index and ecosystem services. Then, we analyzed the changes in urban green space landscape patterns, ecosystem services and their spatial distributions from 2000 to 2020, and studied the interactive relationships between landscape changes and changes in ecosystem services. The results showed three key findings. (1) Beijing's urban green space construction has achieved remarkable results from 2000 to 2020. The area of green space has increased by 77.41%, mainly from cultivated land and construction land. (2) From 2000 to 2020, the amounts of dust retention, SO2 absorption, NO2 absorption, cooling and humidification, carbon fixation and oxygen release, and rainwater runoff reduction in Beijing's urban green space have shown continuous increases in general. (3) There is a close relationship between urban green space landscape changes and green space ecosystem services, and total area (TA) has the highest correlation with ecosystem services. Except for rainwater runoff reduction, the correlation coefficients between TA and ecosystem services are all higher than 0.85. This research can provide theoretical guidance for optimizing Beijing's green space and determining how to maximize the effect of green space for improving the ecological environment, and ultimately provide a scientific basis for the construction of Beijing's ecological environment.

Summary The urban dimension of ecosystem services (ES) is underexposed, while the importance of ES for human well‐being is nowhere as evident as in cities. Urban challenges such as air pollution, noise and heat can be moderated by urban green space (UGS), simultaneously providing multiple other services. However, available methods to quantify ES cannot typically deal with the high spatial and thematic resolution land cover data that are needed to better understand ES supply in the urban context. This study derives methods to quantify and map a bundle of six ES as supplied by UGS, using land cover data with high spatial and thematic resolution, and applies these to the city of Rotterdam, the Netherlands. Land cover data comprise eight classes of UGS. Methods are derived from an evidence base on the importance of UGS types for the supply of each of the six ES that was built using literature review. The evidence base reveals that UGS types differ in their contribution to various ES, although the strength of the evidence varies. However, existing indicators for urban ES often do not discriminate between UGS types. To derive UGS‐specific indicators, we combined methods and evidence from different research contexts (ES, non‐ES, urban, non‐urban). Rotterdam shows high spatial variation in the amount of UGS present, and accounting for this in ES supply reveals that ES bundles depend on UGS composition and configuration. While the contribution of UGS types to ES supply differed markedly with UGS type and ES considered, we demonstrate that synergies rather than trade‐offs exist among the ES analysed. Synthesis and applications. Our findings underline the importance of a careful design of urban green space (UGS) in city planning for ecosystem services (ES) provision. Based on the latest insights on how different UGS provide ES, the methods presented in this study enable a more detailed quantification and mapping of the supply of ES in cities, allowing assessments of current supply of key urban ES and alternative urban designs. Such knowledge is indispensable in the quest for designing healthier and climate‐resilient cities.

Bangkok Metropolitan Administration (BMA) is the capital city of Thailand. In recent years, city surface has been changed greatly by rapid urbanization. Many green spaces in the urban area are destroyed and replaced with the commercial area. It can have effect on the climate and weather that could lead to the Urban Heat Island (UHI). This research analyzes the correlation between Urban Green Spaces (UGS) and Land Surface Temperature (LST) using quantitative remote sensing technology in BMA. More than five years of Landsat-5 TM and Landsat-8 images are used to study urban temperature. The LST, UGS and their correlation of whole area of BMA are analyzed. The LST is retrieved from mono-window algorithm, which used only one thermal band. Several indices of UGS, including the Normalized Difference Vegetation Index (NDVI), and measurement of vegetation cover percentage are also used to study on UGS. The result showed that from 2008 to 2018 LST is increased while UGS is decreased. In addition, Pearson product-moment correlation coefficient is used to analyze the linear correlation between UGS and LST. The correlation between LST and NDVI indicates the negative correlation. The average correlation coefficient is -0.44. That can imply that the higher vegetated area, the LST was lower. To get more detail relationship between LST and UGS, three typical regional areas are selected to study. LST contour is used to analyze LST and HERCULES system is used to classify land cover. The result showed that firstly, the density of tree affect LST. Secondly, the location of the public park is important. Thirdly, water bodies help to decrease LST. The results of this study have key implications for BMA sustainable urban planning and development; to mitigate UHI effects it is important to not only increase canopy cover or the size of UGS, but also to optimize their spatial configuration.

Residential green spaces are one of the most frequently used urban green space types. Aiming at filling a gap in the existing greening indicators with considerations on the spatial differences of residential green spaces, as well as to inform the improvement of urban green space service, three greening indicators, i.e. residential unit’s green coverage rate, green view index, and park ratio within a 500 m service radius, are proposed in this paper. This study selects 14,196 residential units in built area of Shenzhen City in 2017 to measure the greening rate and the geographic spatial factors of the units upon multi-sourced geographic databases such as land cover maps and street view images. The research reveals that: 1) the three indicators can all independently measure the greening rate within or around residential units; 2) the studied residential units are low in residential unit’s green coverage rate and park ratio within a 500 m service radius, but high in green view index; 3) there are significant disparities of the greening rate and the surrounding parks in 500 m service radius among the studied units, and among different housing property rights, showing a disequilibrium in green space service; and 4) the greening rate of residential units is mainly impacted by factors such as development intensity, types of housing property right, altitude, and location. In conclusion, it is suggested that urban green space layout should prioritize improving the spatial distribution and layout of residential green spaces, especially for the socially vulnerable population. Finally, the study points out that the park ratio within a 500 m service radius can be adopted as a supplement to existing greening indicators for residential areas.

Urban green space (UGS) plays a vital role in mitigating the urban heat island effect. Existing studies on its cooling impacts in major Chinese cities are limited by low- to medium-resolution data, small geographic coverage, and the inconsistent analytical framework, which hinder systematic, comparable, and large-scale assessment of UGS cooling effects. This study addresses these gaps by developing a sub-meter UGS dataset covering densely populated Chinese cities and employs econometric methods to assess its cooling effect while accounting for spatial and environmental heterogeneity. Results reveal an overall land surface temperature (LST) reduction of 2.972 °C associated with UGS presence, and a one-standard-deviation increase in UGS corresponds to an approximate 0.123 °C LST decrease. Beyond the overall effect, we observe substantial spatial variation: UGS cooling is stronger in public and residential areas, open and high-rise environments, higher elevations, and drier climates. However, extreme heat diminishes UGS effectiveness, highlighting the need to strengthen green infrastructure in hotter regions. Notably, super-large cities show weaker cooling despite considerable UGS coverage, indicating inefficiencies in UGS configuration. These findings underscore the dependence of UGS effectiveness on local climatic and geographical conditions and highlight the significance of high-resolution UGS data in serving as a foundation for evidence-based urban climate adaptation strategies. The dataset is publicly released to support future research in this area. Overall, this study advances the understanding of UGS as a nature-based solution and provides insights into enhancing urban resilience to climate change. • A national sub-meter UGS dataset for 49 China’s major cities was developed. • UGS cooling effects were assessed using econometric methods. • UGS lowers LST by 2.972 °C on average in China’s major cities. • Cooling effects exhibit obvious spatial heterogeneity. • Tailored green planning and high-resolution UGS data are essential.

To understand the development progress and relevant Frontier research of urban green space carbon sequestration, based on the core databases such as Web of Science, CiteSpace software, and bibliometric analysis methods were used to analyze the research status. The results showed that the number of papers on urban green space carbon sequestration research was on the rise. China and the United States had closer cooperation from the perspective of international cooperation institutions. Peking University, the Chinese Academy of Sciences, and the US Forest Service made the greatest contributions to this research field. Analyzing the keywords and the literature co-citation map, it was inferred that the hot keywords of future urban green space carbon sequestration research include carbon sequestration, ecosystem services, and climate change. It was found that carbon sequestration of urban green space ecosystems research primarily focuses on the correlation between urban green space and ecosystem services, and UGS carbon sequestration accounting and urban green space management. Finally, two perspectives were proposed: 1) Urban green spaces diversified ecological benefits are achieved through the function of carbon sequestration, and 2) Urban green space carbon sequestration accounting and urban green space management promote the development of urban green space. An overview of the international progress and basic state of urban green spaces and carbon sequestration theme research is presented in this paper, Additionally, it provides valuable references for future research and helps gain a comprehensive understanding of this field of research.

Rapid urbanization along with multiplicity of economic activities is degrading the quality of urban environment globally. The slowly rising temperature of urban environment also brings about a complex microclimatic condition with far reaching implications to urban dwellers. The role of urban greenery towards regulation and management of urban microclimate is considered highly significant. Therefore, this chapter makes an attempt to assess the effect of urban growth and declining urban green space on land surface temperature (LST) and associated microclimatic conditions in Siliguri city of West Bengal. The formation of urban heat island conditions in the city is studied using the Landsat data for (1990–2020). The spatial distribution of LST across the city area is analysed to understand its local effects on urban heat island (UHI) development. The nature and extent of relationship among LST, the built-up index (BI) and the normalized difference vegetation index (NDVI) is examined to explore the influence of the growth of built-up land and urban green space loss on urban micro-climate. The results reveal that the densely built-up core areas display higher temperature than the other part of the city and the areas covered by vegetation and water bodies’ exhibit lower temperature. Thus, the study shows that urban green space can help mitigate the condition of rising surface temperature and associated urban micro-climate, which would be important for sustainable urban development as well as for maintenance of healthy urban life.