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Urban green and blue space (GBS) significantly impacts the diffusion range of atmospheric particulate matter. By determining the diffusion distance of atmospheric particulate matter (PM2.5 and PM10) in Shanghai, combined with the GBS landscape pattern index, this study completed a stepwise multiple regression equation and correlation analysis to explore the relationship between the morphological structure and spatial configuration of GBS on the diffusion distance of atmospheric particles. The results show that the landscape shape index (LSI) and the number of patches (NP) of GBS have a significant positive correlation with the diffusion distance of atmospheric particles, while the coefficient of the percent of landscape (PLAND), as a key influencing factor, has a negative correlation. The mean Euclidean nearest neighbor distance (ENN_MN) and splitting index (SPLIT) in the spatial configuration metrics positively affect the diffusion distance. Studies have proved that complex and dispersed GBS will lead to the weakening of its purification ability, thereby increasing the pollution range of atmospheric particulate matter. The order of the influence of different GBS types on the diffusion distance of atmospheric particles is as follows: DT > BS > LP > ST > WA. Therefore, high-density GBS with simple shapes and concentrated distribution should be considered in the future construction of new cities. [ABSTRACT FROM AUTHOR]

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of "Jiangsu > Shanghai > Zhejiang > Fujian" in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region. [ABSTRACT FROM AUTHOR]

In China, rapid urbanization has profoundly transformed the spatial pattern of urban land use, including urban green spaces. The government plans to optimize green spaces to integrate them with urban development; this requires an understanding of the process of green space change. Quantifying landscape pattern and its change is essential for monitoring and assessing ecological consequences of urbanization. In this study, the moving window method and gradient analysis were used to analyze high-resolution satellite images and interpret Shanghai urban green space distribution data. A window of 5 km was found to be a rational size that can reflect urban green space patterns via changes in landscape metrics. Green space patterns can be quantified through gradient analysis and landscape metrics. Different types of green space exhibit obvious “spatial characteristics” along the landscape pattern gradient. Urban green space distribution patterns can be used to verify the urban space development mode. The ‘concentric circle’ development theory was applicable to the urban development of Shanghai. The research provides significant guidance for further study on the interrelations of urban green space pattern processes, for examination and assessment of urbanization processes and for urban planners and decision makers. [ABSTRACT FROM AUTHOR]

• Changes in patterns of fine-scale land use structures and intensity of intra-SUHI were analyzed. • The associations between changes in land use structures and intensity of intra-SUHI were analyzed. • Comparison of two regression models quantifies the importance of interactions of the predictors. • PLSR models perform slightly better than RFR models, but their combined usage will perform better. Understanding the associations between fine-scale land use structures and the surface urban heat island effect within the cities (synonymously defined as intra-SUHI) is vital for decision-making on sustainable land development, UHI mitigation, and climate adaptation. Taking downtown Shanghai as a case, this study quantified the associations between the changes in land use structures (indicated by eleven metrics of urban morphology, land use composition, and urban green infrastructure) and the intensity of intra-SUHI (intra-SUHII) at the land parcel level. Two model types, including Type-1 models that merely consider the main effect of the factors, and Type-2 models that consider both the main effect and potential interactions, were employed for analysis. The performance of each model type was parallelly assessed using the partial least square regression (PLSR) and random forest regression (RFR) methods. Our results revealed that Type-2 models exhibited higher interpretation power (PLSR:55.604–55.887% and RFR:53.841–55.739%) than Type-1 models (PLSR:47.531–47.591% and RFR: 47.111–50.300%) for explaining the variance in parcel-level intra-SUHIIs. This finding indicated the non-negligible importance of potential interactions of the factors in determining the models' interpretation power. Our study can be generalizable to many large cities with similar climatological conditions. [ABSTRACT FROM AUTHOR]

The most visible aspect of urbanization is that more and more natural landscape is replaced by anthropogenic land cover/land use, which is the driving force of many ecological and environmental consequences such as urban heat islands. However, the difference between land use and land cover and their implications in ecology is often overlooked. The impacts of urban land cover composition and configuration on land surface temperature (LST) have been extensively investigated, but few studies have explored the relation between LST and land use category. This research takes the inner city of Shanghai as a case and comprehensively investigates the discrepant impacts of land use and land cover on LST. Land use and land cover data are derived respectively from aerial photography and high-resolution satellite imagery (ALOS), and the LST is estimated from Landsat TM images. There are five dominant land use types (new residential, old residential, villas, industrial, and institutional land use) and two major land cover types (vegetated and impervious land cover) in the study area. For most land use types, the land cover composition and configuration are varied. By contrast, no statistical difference is observed among old residential, industrial and institutional land uses for LST. The mean LST of new residential and industrial land use is significantly different, although their land cover compositions and configurations are quite similar. These results indicate that the key factors affecting urban LST are not only land cover patterns, but also other anthropogenic forces. Therefore, the explanation of urban LST by land cover alone is inadequate. Especially at fine spatial scales, information on land use is more meaningful than that of land cover to indicate the impacts of urbanization on ecosystems. [ABSTRACT FROM AUTHOR]

Quantifying the spatiotemporal pattern of urbanization is necessary to understand urban morphology and its impacts on biodiversity and ecological processes, and thus can provide essential information for improving landscape and urban planning. Recent studies have suggested that, as cities evolve, certain general patterns emerge along the urban-rural gradient although individual cities always differ in details. To help better understand these generalities and idiosyncrasies in urbanization patterns, we analyzed the spatiotemporal dynamics of the Shanghai metropolitan area from 1989 to 2005, based on landscape metrics and remote sensing data. Specifically, the main objectives of our study were to quantitatively characterize the spatiotemporal patterns of urbanization in Shanghai in recent decades, identify possible spatial signatures of different land use types, and test the diffusion coalescence hypotheses of urban growth. We found that, similar to numerous cities around the world reported in previous studies, urbanization increased the diversity, fragmentation, and configurational complexity of the urban landscape of Shanghai. In the same time, however, the urban-rural patterns of several land use types in Shanghai seem unique-quite different from previously reported patterns. For most land use types, each showed a distinctive spatial pattern along a rural-urban transect, as indicated by landscape metrics. Furthermore, the urban expansion of Shanghai exhibited an outward wave-like pattern. Our results suggest that the urbanization of Shanghai followed a complex diffusion-coalescence pattern along the rural-urban transect and in time. [ABSTRACT FROM AUTHOR]

Urbanization is one of the most important driving forces for land use and land cover change. Quantifying urban landscape pattern and its change is fundamental for monitoring and assessing ecological and socioeconomic consequences of urbanization. As the largest city in the country, Shanghai is now the fastest growing city in China. Using land use data set of 2002 and combining gradient analysis with landscape metrics, we analyzed landscape pattern of Shanghai with increasing grain size to study the impacts of road corridors on urban landscape pattern. Landscape metrics were computed along a 51×9 km² transect cutting across Shanghai with a moving window. The results showed that the urban landscape pattern of Shanghai was greatly changed when road corridors were merged with urban patches and the variation of patch density would alter when grain size changed. As a linear land use type, road corridors exhibited a different spatial signature comparing with other land use types and distinctive behavior with increasing grain size. Merging road and urban patches resulted in a sharp reduction in patch density, mainly caused by segmentation of roads corridors. The results suggested that grain size around 7.5 m might be optimal for urban landscape analysis. Landscape patch density is significantly correlated with road percent coverage and the most important effect of road corridors in urban landscape is increased habitat fragmentation. [ABSTRACT FROM AUTHOR]

Rapid urbanization influences the landscape pattern of impervious surfaces, and potentially affects surface water quality. Using ArcGIS and Fragstats, this study analyzed the temporal change of the landscape pattern of impervious surfaces in Shanghai over the past 45 years, and its driving forces and impact on water quality were also analyzed. The results show that both low and high impervious surfaces showed different degrees of expansion, and as a result, the pervious surfaces and water area reduced by 40.1% and 13.8%, respectively. It proves that the fragmentation and diversity of impervious surfaces in Shanghai notably increased in the past decades, and especially the low and high impervious surfaces show substantial changes. The primary driving forces of the landscape pattern change are population density, unit area Gross Domestic Product (GDP), and the percentage of primary industry. The result of Redundancy analysis (RDA) is that the explanatory ability of landscape pattern to water quality variations decreased from 68.7% to 46.4% in the period 2000–2010. It should be stressed that the contribution of the configuration of impervious surfaces to water quality variation is less than that of the percentage of impervious surfaces. [ABSTRACT FROM AUTHOR]