Effects of Green Space Patterns on Urban Thermal Environment at Multiple Spatial–Temporal Scales

An Optimized Instance Segmentation of Underlying Surface in Low-Altitude TIR Sensing Images for Enhancing the Calculation of LSTs

The calculation of land surface temperatures (LSTs) via low-altitude thermal infrared remote (TIR) sensing images at a block scale is gaining attention. However, the accurate calculation of LSTs requires a precise determination of the range of various underlying surfaces in the TIR images, and existing approaches face challenges in effectively segmenting the underlying surfaces in the TIR images. To address this challenge, this study proposes a deep learning (DL) methodology to complete the instance segmentation and quantification of underlying surfaces through the low-altitude TIR image dataset. Mask region-based convolutional neural networks were utilized for pixel-level classification and segmentation with an image dataset of 1350 annotated TIR images of an urban rail transit hub with a complex distribution of underlying surfaces. Subsequently, the hyper-parameters and architecture were optimized for the precise classification of the underlying surfaces. The algorithms were validated using 150 new TIR images, and four evaluation indictors demonstrated that the optimized algorithm outperformed the other algorithms. High-quality segmented masks of the underlying surfaces were generated, and the area of each instance was obtained by counting the true-positive pixels with values of 1. This research promotes the accurate calculation of LSTs based on the low-altitude TIR sensing images.

Urban green and blue infrastructure: unveiling the spatiotemporal impact on carbon emissions in China's Yangtze River Delta

Blue-green infrastructure (BGI) plays a crucial role in regulating urban carbon cycles. Nonetheless, the spatiotemporal effect of BGI on carbon emissions has not received extensive attention. This study used the Yangtze River Delta (YRD) region as the study area and quantified the landscape patterns of BGI. Using a spatiotemporal geographically weighted regression model, we analyzed the impact of evolving spatiotemporal characteristics of BGI on carbon emissions. Additionally, we constructed a spatiotemporal weight matrix using the Moran index ratio to examine the spillover effects of BGI among different regions. Our results show that the aggregation effect of carbon emissions in the YRD region is gradually increasing while BGI has a dynamic impact on carbon emissions. In terms of spatial and temporal spillovers, under the influence of economic connections between regions, patch fragmentation and distance exert a persistent positive influence on carbon emissions, while shape complexity has a negative impact, with area and layout characteristics showing no significant effects. However, area and patch distance have a persistent positive influence on carbon emissions in adjacent areas, while shape complexity exhibits a negative impact. Therefore, optimizing urban BGI through a regional synergistic governance system is important to promote low-carbon urban development.

Blackland Conservation and Utilization, Carbon Storage and Ecological Risk in Green Space: A Case Study from Heilongjiang Province in China

Clarifying the relationship between carbon storage and ecological risks is critical to ensuring regional sustainable development. Land use changes caused by land use policy invariably result in substantial changes in carbon storage and ecological risks. The link between carbon storage and ecological risks in green space is still unknown, even though green space is an essential ecological function carrier. According to the Blackland Conservation Utilization (BCU) policy document and natural exploitation (NP) status, this study compared and projected the carbon storage and landscape ecological risk characteristics of green space in Heilongjiang Province (HLJP) for 2030. It also quantitatively assessed the interactions and synergistic changes of the two variables in terms of coupled coordination relationships, quantitative correlations, and spatial correlations. The results demonstrated the following: (1) the green space evolution of HJLP under the BCU scenario is significantly more drastic than under the NP scenario; (2) In 2020-2030, the NP scenario's evolution of green space results in the ecosystem losing 323.51 × 106 t of carbon storage, compared to the BCU scenario's loss of just 216.07 × 106 t. The BCU policy will increase the agglomeration of high-risk ranges in the northeast and southwest will but decrease the overall landscape ecological risk level of green space; (3) BCU policy will prevent the system's orderly development and benign coupling, but it will increase the interdependence between carbon storage and landscape ecological risks in green space; (4) Green space exchange and loss will result in the simultaneous rise or decrease in both variables. The magnitude of carbon storage increase owing to green space expansion tends to increase simultaneously with the magnitude of landscape ecological risk reduction. To a certain extent, the HLJP black land conservation and utilization policy can improve carbon storage and ensure ecological security, and the matching of dominant regions with the status of the landscape evolutionary process can support future carbon-neutral actions.

Study on Spatial-Temporal Change of Urban Green Space in Yangtze River Economic Belt and Its Driving Mechanism

Urban green space plays an important role in beautifying the environment, improving the quality of life of residents, and promoting sustainable urban development. Rapid urbanization has led to great changes in the spatial structure and layout of urban green space. It is urgent to put forward the sustainable development strategy of green space through the research on the change of urban green space. Based on the geographical spatial differences of urban green space and integrating the factors of economy, society, industry, land use, and the environment, we constructed a research framework of "space-supply-demand" integration of urban green space by GI and geodetector methods, and we conducted an empirical study on the spatial-temporal changes of urban green space and its driving mechanism in prefecture-level cities along the Yangtze River Economic Belt in China. First, the urban green space along the Yangtze River Economic Belt is concentrated in spatial distribution, while uneven development appears in urban greening among the zones. Second, the influence of different factors on urban green space change varies greatly and can be divided into three types: key factors, important factors, and auxiliary factors. The driving mechanism of the spatial distribution of urban green space supply and demand is quite different, but urban population and commercial service facilities land are their key influence factors, having a comprehensive influence on the spatial-temporal changes of urban green space. Third, the factors are classified into three categories of high, medium, and low levels according to the mean of interacting forces; in particular, the factors of per capita GDP, utility land, industrial smoke (dust) emissions, and other factors have a very strong interactive effect with other factors. Fourth, according to the spatial distribution characteristics of urban green space and its driving mechanism, this paper puts forward planning and policy suggestions, providing reference for other areas to deal with the green space change.

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied.

Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.

Effect of Land Surface Temperature on Urban Heat Island in Varanasi City, India

Crucial changes in urban climate can be witnessed due to rapid urbanisation of cities across the world. It is important to find a balance between urban expansion and thermal environment quality to guarantee sustainable urban development. Thus, it is a major research priority to study the urban heat island (UHI) in various fields, i.e., climate change urban ecology, urban climatology, urban planning, mitigation and management, urban geography, etc. The present study highlighted the interrelationship between land surface temperature (LST) and the abundance of impervious cover and green cover in the Varanasi city of Uttar Pradesh, India. For this purpose, we used various GIS and remote-sensing techniques. Landsat 8 images, land-use–land-cover pattern including urban/rural gradients, and grid- and metric-based multi-resolution techniques were used for the analysis. From the study, it was noticed that LST, density of impervious cover, and density of green cover were correlated significantly, and an urban gradient existed over the entire city, depicting a typical UHI profile. It was also concluded that the orientation, randomness, and aggregation of impervious cover and green cover have a strong correlation with LST. From this study, it is recommended that, when planning urban extension, spatial variation of impervious cover and green cover are designed properly to ensure the comfort of all living beings as per the ecological point of view.

Impacts of Composition and Canopy Characteristics of Plant Communities on Microclimate and Airborne Particles in Beijing, China

As the basic component of urban green-spaces, plant communities regulate both the microclimate and air particle levels. Understanding the regulatory mechanism of plant communities represents the theoretical basis for using green spaces to improve the urban climate and mitigate air particle pollution. Based on field investigations, differences in the daily air temperatures (AT), relative humidity (RH), and PM10 and PM2.5 concentrations in eight compositional types of plant communities were quantitatively analyzed. In addition, the correlations between these variables and various canopy parameters were further established in order to detect critical thresholds. The results showed that, among the eight compositional types, significant differences existed in daily AT, RH, PM10 and PM2.5 levels. The mixed tree, shrub and grass (M-TSG) community had the strongest cooling and PM10 reduction effects; the broad-leafed tree, shrub and grass (B-TSG) community had the best humidifying effect; while the mixed tree and grass (M-TG) community most effectively reduced PM2.5 concentrations. The daily AT and PM10 concentrations were significantly negatively correlated with canopy density (CD) and leaf area index (LAI), but positively correlated with canopy porosity (CP) and sky view factor (SVF), while these correlations were opposite for daily RH. The response of daily PM2.5 concentrations to canopy characteristics was complex, featuring multiple non-linear relations. Critical thresholds were found in some cases. Overall, M-TSG or M-TG communities with about 75% CD, 55% CP, 2.5 LAI and 0.18 SVF perform most noticeable both microclimate and air particle regulation services.