Search Results (75)

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient global–local regression (EGLR) framework by integrating XGBoost-SHAP with global–local regression (GLR), enabling accelerated estimation of LST. In a case study of Wuhan, the EGLR reduces the computation time of GLR by 44.21%. The main contribution of computational efficiency improvement lies in the procedure of Moran eigenvector selecting executed by XGBoost-SHAP. Results of validation experiments also show significant time decrease of the EGLR for a larger sample size; in addition, transplanting the framework of the EGLR to two machine learning models not only reduces the executing time, but also increases model fitting. Furthermore, the inherent merits of XGBoost-SHAP and GLR also enables the EGLR to simultaneously capture nonlinear causal relationships and decompose spatial effects. Results identify population density as the most sensitive LST-increasing factor. Impervious surface percentage, building height, elevation, and distance to the nearest water body are positively correlated with LST, while water area, normalized difference vegetation index, and the number of bus stops have significant negative relationships with LST. In contrast, the impact of the number of points of interest, gross domestic product, and road length on LST is not significant overall. Full article

The rapid urbanization process has exacerbated the urban heat island (UHI) effect in megacities like Shanghai. Urban green infrastructure (UGI) plays a crucial role in mitigating the UHI effect; however, its cooling capacity is subject to various urban land development patterns. This study examined 39 typical locations in downtown Shanghai to measure how urban land development patterns affect the UGI’s cooling capacity. Using a data-driven framework, we identified 12 key influencing factors and explored 4 interactions for building three regression models: multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR). For each of these models, we considered two variations: a basic model neglecting interactions and an enhanced model including interactions. Results showed that all enhanced models outperformed their basic counterparts. On average, the enhanced models increased their predictive power by 14.59% for training data and 32.15% for testing data. Additionally, among the three enhanced models, the SVR-enhanced models show the best performance, followed by the PLSR-enhanced models. Their mean predictive power increased by 8.33−37.43% for training data and 31.77−43.558% for testing data vs. the MLR-enhanced models. Overall, our findings revealed that impervious surfaces contribute positively to urban warming, while UGI acts as a negative contributor. Moreover, we highlighted how urban land development metrics, particularly the UGI’s percentage and spatial arrangements in relation to adjacent buildings, significantly affect the thermal environment. The findings can offer valuable insights for urban planners and decision-makers involved in managing UGI and developing strategies for UHI mitigation and urban climate adaptation. Full article

Urban pluvial waterlogging has become a major challenge for densely populated cities due to increasingly extreme rainfall events and the rapid expansion of impervious surfaces. In response to the growing demand for localized waterlogging risk assessments, an automated evaluation framework is proposed that integrates high-resolution digital elevation models (DEMs), rainfall scenarios, and classified building data within a GIS-based modeling system. The methodology consists of four modules: (i) design of rainfall scenarios and runoff estimation, (ii) waterlogging depth simulation based on volume-matching algorithms, (iii) construction of depth–damage curves for residential and commercial buildings, and (iv) building-level economic loss estimation though differentiated depth–damage functions for residential/commercial assets—a core innovation enabling sector-specific risk precision. A case study was conducted in the Lixia District, Jinan City, China, involving 15,317 buildings under a 50-year return period rainfall event. The total economic losses were shown to reach approximately USD 327.88 million, with residential buildings accounting for 88.6% of the total. The model achieved a mean absolute percentage error within 5% for both residential and commercial cases. The proposed framework supports high-precision, building-level urban waterlogging damage assessment and demonstrates scalability for use in other high-density urban areas. Note: all monetary values were converted from Chinese Yuan (CNY) to U.S. Dollars (USD) using an average exchange rate of 1 USD = 7.28 CNY. Full article

Accelerating urbanization leads to the scarcity and fragmentation of greenspaces. Keeping biodiversity alive, i.e., enhancing greenspaces’ impacts on plant diversity in and around urban areas, is essential. This study evaluated greenspace patterns (GSPs) using landscape metrics, and calculated plant α- and β-diversity using field surveys. Bivariate correlation analysis was used to analyze the correlations among plant α- and β-diversity and landscape metrics from 2009 to 2022. Significant models were selected using stepwise regression analysis and verified by comparing fitted and field values. The results indicate that α-diversity was primarily influenced by the number of patches, wetland landscape shape index and patch richness density, imperviousness of surfaces, and forest and grassland at the 100–1000 m scale. The correlation between GSPs and α-diversity weakened with an increase in scale. Current patch richness density, Shannon’s diversity index, Shannon’s evenness index, and percentage of impervious surface and wetland significantly influenced β-diversity at the 100–300 m scale. By contrast, β-diversity was influenced by greenspace patterns at the 300–1000 m scale. There was an observed positive correlation between GSPGSPs and β-diversity that strengthened as the scale increased. These findings highlight the scale-dependent legacy effects of GSPs on plant diversity, primarily driven by the landscape pattern characteristics of urban greenspaces and the diversity of plant groups. Therefore, prioritizing the protection of large green patches and establishing designated protected areas or points for on-site conservation are crucial strategies for urban plant diversity conservation. Full article

An urban heat island (UHI) refers to urban areas that experience higher temperatures due to heat absorption and retention by impervious surfaces compared to the surrounding rural areas. Urban wetlands are crucial in mitigating the UHI effect and improving climate resilience via their cooling effect. This study examines Colombo, Sri Lanka, the RAMSAR-accredited wetland city in South Asia, to assess the cooling effect of urban wetlands based on 2023 dry season data for effective sustainable management. We used Landsat 8 and 9 data to create Land Use/Cover (LUC), Land Surface Temperature (LST), and surface-reflectance-based maps using the Google Earth Engine (GEE). The Enhanced Vegetation Index (EVI), Modified Normalized Difference Water Index (mNDWI), topographic wetness, elevation, slope, and impervious surface percentage were identified as the influencing variables. The results show that urban wetlands in Colombo face tremendous pressure due to rapid urban expansion. The cooling intensity positively correlates with wetland size. The threshold value of efficiency (TVoE) of urban wetlands in Colombo was 1.42 ha. Larger and more connected wetlands showed higher cooling effects. Vegetation- and water-based wetlands play an important role in <10 km urban areas, while more complex shape configuration wetlands provide better cooling effects in urban and peri-urban areas due to edge effects. Urban planners should prioritize protecting wetland areas and ensuring hydrological connectivity and interconnected wetland clusters to maximize the cooling effect and sustain ecosystem services in rapidly urbanizing coastal cities. Full article

The frequent occurrence of urban floods (UFs) poses significant threats to public safety and the national economy. Accurate estimation of urban flood susceptibility (UFS) and the identification of potential hotspots are critical for effective UF management. However, existing UFS studies often fall short due to a limited understanding of UFs’ nature, frequently relying on disaster factors analogous to those used for natural floods while neglecting key urban characteristics, limiting the accuracy of UFS estimates. To address these challenges, we propose a novel framework for UFS assessment. Unlike those studies that focus primarily on topographic and surface characteristics, our approach integrates urban-specific factors that capture the distinctive attributes of the urban environment, including Urban Heat Island Intensity, Urban Rain Island Intensity, Urban Resilience Index, and Impervious Surface Percentage. Guangzhou was selected as the study area, where machine learning methods were employed to calculate UFS, and Shapley Additive Explanation was utilized to quantify the contributions of employed factors. We evaluated the significance of urban factors from three perspectives: classifier performance, map accuracy, and factor importance. The results indicate that (1) urban factors hold significantly greater importance compared to other factors, and (2) the incorporation of urban factors markedly enhances both the performance of the trained classifier and the accuracy of the UFS map. These findings underscore the value of integrating urban factors into UFS assessments, thereby contributing to more precise UF management and supporting sustainable urban development. Full article

Biodiversity has important implications for the sustainable development of cities. Given the paucity of ground-based experiments, the responses of biodiversity to urbanization and its associated controls on a global scale remain largely unexplored. We present a novel conceptual framework for quantifying the direct and indirect impacts of urbanization on biodiversity in 1523 cities worldwide using the global 100 m grid biodiversity intactness index data (2017–2020) as a proxy for biodiversity. The results show a pervasive positive impact of urbanization on biodiversity in global cities, with a global mean direct and indirect impact of 24.85 ± 9.97% and 16.18 ± 10.92%, respectively. The indirect impact is relatively large in highly urbanized cities in the eastern United States, Western Europe, and the Middle East. The indirect impact is predominantly influenced by urbanization intensity, population density, and background climate. The correlation between urbanization intensity and indirect impact is most pronounced across all climate zones, while the other driving variables influencing the indirect effect exhibited considerable variations. Furthermore, our findings indicate that the biodiversity responses to urbanization are influenced by the biodiversity and development conditions of cities. Our findings have important implications for understanding the impact of urbanization on biodiversity and for future sustainable urban biodiversity. Full article

A well-constructed tsunami evacuation facility can be crucial in a disaster. Understanding a tsunami’s force and the flow structure variation across various building configurations are essential to engineering designs. Hence, this study assessed the steady-state flow structure at building models (BM) incorporating outer frame openings, including piloti-type designs with a different width-to-spacing ratio of piloti-type columns following an embankment model (EM) with a vegetation model (VM). The experiments also demonstrated the outer frame opening percentage’s impact and orientation toward the overtopping tsunami flow at the BM. The results show that the arrangement of an opening on the outer frame and the piloti-type columns are critical in reducing the tsunami force concerning the experimental setup. Moreover, allowing a free surface flow beneath the BM implies that the correct piloti-pillar arrangement is crucial for resilient structure design. In addition, the three-dimensional numerical simulation was utilized to explain the turbulence intensity of the overtopping flow around the critical BM type. The derived resistance coefficient ($C_{\mathrm{R}}$) defined the drag and the hydrostatic characteristics at the BM due to the overtopping tsunami flow. Furthermore, for the impervious BM, the value $C_{\mathrm{R}}$ was consistent with the previous studies, while the $C_{\mathrm{R}}$ value for the BMs with an outer frame opening was directly coincident with the percentage of porosity. Full article

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development. Full article

Over the past two decades, urbanization in China has been advancing rapidly. The intricate effects of urbanization on vegetation growth in the urban core have been studied and reported. However, the percentage of impervious surfaces in the urban core, as defined in previous studies, was relatively low, and included some pixels containing farmland and water bodies. Consequently, their results may be affected by urbanization processes, such as the transformation of land types. Hence, this paper extracted 100% impervious surfaces from 2000 to 2022 as urban core areas in China using a 30 m resolution China land cover dataset (CLCD), which completely excluded the effect of urbanization itself on the experimental results, obtaining the trend of vegetation change in the real urban core area. Employing the remote sensing imagery of the Enhanced Vegetation Index (EVI) from 2000 to 2022, we analyzed the growth of vegetation in 1559 urban cores and the surrounding rural areas in China. The study’s findings revealed that the majority of the core areas (85.3%) studied in this paper exhibited a significant (p < 0.05) increase in vegetation, indicating that the various urban greening policies in China have been effective. However, only about 23.7% (369) of the urban core areas showed a faster increase in vegetation than the rural areas. This suggests that for most urban cores (1190), vegetation increase is not as pronounced as it is in surrounding rural areas. Additionally, the EVI rate of change in the urban cores obtained using CLCD versus MODIS land cover data significantly differed. The latter obtained a less pronounced trend of vegetation growth compared to the former, attributable to the disparity in their spatial resolution and the methodology used to define urban areas. The study underscores the importance of vegetation growth and its distribution in various urban core areas to comprehend the dynamics of urban cores’ vegetation growth and to offer insights for the subsequent formulation of greening policies. Moreover, data with different resolutions will significantly impact the results, thus highlighting the necessity of employing high spatial resolution data for more comprehensive research. Full article

The degradation of urban ecology, particularly in metropolitan areas distinguished by dense populations and impervious surfaces, presents a worldwide challenge linked to swift urban expansion. Despite extensive documentation of urbanization’s impact on broad regions or specific urban ecosystems over defined time periods, there remains a scarcity of studies investigating the spatiotemporal dynamics of landscape pattern (LP) changes in specific ecosystems at small-to-medium scales within inland megacities as a response to urbanization. Therefore, this work focused on the Bailuwan Wetland Park (BWP) in Chengdu, an inland megacity in southwestern China. Employing satellite imagery data from selected years spanning the previous decade (2010–2021, encompassing 2010, 2012, 2015, 2018, and 2021), this investigation delved into the influences of urbanization on the LP over various time-frames and across different land use/land cover (LULC) types. Our study revealed that urbanization has a significant impact on the patch-/landscape-level characteristics, including the class area (CA), number of patches (NP), patch density (PD), percentage of landscape (PLAND), aggregation index (AI), contagion index (CONTAG), largest patch index (LPI), landscape shape index (LSI), fractal dimension index (FRAC_MN), Shannon’s diversity (SHDI), and evenness index (SHEI). Over the period from 2010 to 2021, NP and PD experienced notable increases, while landscape shape (LSI/FRAC_MN) exhibited greater complexity and fragmentation (PLAND) intensified. Further, landscape heterogeneity (AI/CONTAG) and diversity (SHDI/SHEI) decreased. Particularly significant was the conversion of 52 ha of agricultural land to vegetation, resulting in heightened complexity and fragmentation in vegetation patterns. Additionally, the CA of lakes and rivers decreased following the establishment of the park, while the CA and NP of bare land presented significant increases. These findings suggest that rapid urbanization significantly influences the spatial–temporal dynamics of wetland landscape patterns. Consequently, it is imperative for society to prioritize the restoration and protection of urban constructed wetlands. Full article

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 10⁵ hm², with the predominant decline occurring in the 0–1 hm² and 1–3 hm² size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors. Full article

Infiltration models and impervious surface models have gained significant attention in recent years as crucial tools in urban and environmental planning, to assess the extent of land-surface changes and their impacts on hydrological processes. These models are important for understanding the hydrological dynamics and ecological impacts of urbanization and for the improvement of sustainable land-use planning and stormwater-management strategies. Due to the fact that many authors partially or entirely overlook the significance of the infiltration process in geographic information system (GIS) analyses, there is currently no universally accepted method for creating an infiltration model that is suitable for GIS multicriteria decision analysis (GIS-MCDA). This research paper presents an innovative approach to modeling the infiltration-efficiency index (IEI) for GIS analysis, with a focus on achieving high-quality results. The proposed methodology integrates very-high-resolution (VHR) remote-sensing data, GIS-MCDA, and statistical methods. The methodology was tested and demonstrated on a small sub-catchment in Metković, Croatia. The study developed a VHR IEI model from six specific criteria that produced values between 0 and 0.71. The model revealed that 14.89% of the research area is covered by impervious surfaces. This percentage is relatively favorable when compared to urban areas globally. The majority of the research area (62.79%) has good infiltration efficiency. These areas are predominantly characterized by agricultural land use, encompassing orchards, tangerines, olive groves, vineyards, and a diverse range of low-lying and high vegetation on flat terrain. The IEI model can provide input spatial data for high-resolution GIS analysis of hydrological processes. This model will aid decision-makers in stormwater-management, flood-risk assessment, land-use planning, and the design of green infrastructure. By utilizing the information derived from this study, policymakers can make informed decisions to mitigate flooding risks and promote sustainable urban development. Full article

The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance. Full article

The establishment of nature reserves and ecological function areas is crucial for preserving the natural environment and the invaluable services provided by ecosystems. In our study, we conducted a comprehensive analysis using the 2011–2020 Chinese land cover dataset to examine the impact of ecological function areas on regional land use and cover change. This analysis allowed us to quantify and visualize the intensity, aggregation effects, and transformation paths of land cover change while considering China’s ecological function areas. Our findings highlight notable disparities in land cover types between the ecological function area and its surroundings. Within the ecological function area, forest and grassland dominate, constituting 67% of the total land cover. In contrast, outside the ecological function area, there is a greater presence of wasteland, in addition to forest and grassland. Moreover, the abundance of impervious surfaces, which are closely linked to human activities, is significantly higher outside the ecological function area, almost double the amount found inside. By examining specific land cover types, we observed that forests exhibit the least change within the ecological function area, whereas croplands experience the least change outside. Throughout the study period, approximately 8.1% of land cover pixels underwent changes, with some areas displaying a frequency of change reaching up to 2. Interestingly, the number of high-frequency land use and cover change pixels inside the ecological function area is only half of the outside. Notably, a higher percentage of impervious surfaces within the ecological function area (0.13%) were converted into cropland compared to the outside (0.07%). Understanding the dynamics of land cover change within China’s ecological function areas provides valuable insights for effective land resource management and planning. It enables us to make informed decisions to ensure the sustainable development and conservation of these areas. Full article