Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.

Land subsidence poses a persistent challenge to Tianjin, a major coastal city in China, with implications for urban infrastructure and sustainable development. This study examines the spatiotemporal evolution of ground subsidence in Tianjin from 2003 to 2024 using multi-source SAR observations from Envisat ASAR (C-band), ALOS PALSAR (L-band), and Sentinel-1 (C-band). Surface deformation was derived using SBAS-InSAR with atmospheric phase correction. Due to limitations in data availability, SAR observations are temporally discontinuous; therefore, the long-term subsidence evolution was reconstructed by integrating multi-sensor deformation rates through a model-based time-series fitting approach. The results show pronounced subsidence during 2003–2010 in inland districts such as Wuqing, Beichen, Jinnan, and Jinghai, with maximum rates exceeding 50 mm/yr. After 2017, regional subsidence rates generally declined, while localized deformation became increasingly concentrated in coastal reclamation areas of the Binhai New Area, particularly around Dongjiang Port and Fuzhuang. Spatial and temporal patterns of subsidence exhibit clear correspondence with changes in groundwater use intensity and phases of urban construction and land reclamation. These observations suggest a transition in dominant subsidence controls over time. The results provide a long-term observational perspective on subsidence evolution in Tianjin and offer a geospatial basis for land-use planning and infrastructure risk assessment in coastal cities.

Enhancing Energy-Embedded Green Utilization Efficiency of Urban Land (E-GUEUL) is crucial for reconciling economic growth with carbon neutrality targets, with the Integration of the Digital–Real Economy (IDRE) emerging as a key driver. This study measures city-level E-GUEUL using the super-efficiency SBM–Malmquist index model. To rigorously identify the causal effect of IDRE on E-GUEUL and address potential model misspecification and high-dimensional confounding factors, a Double Machine Learning (DML) framework is employed. Findings reveal a robust and significant positive effect of IDRE on E-GUEUL, a conclusion that holds across a series of robustness checks and endogeneity controls. Heterogeneity analysis indicates that the efficiency enhancement is more pronounced in non-resource-based, digitally developed, and eastern or central cities. Mechanism analysis reveals that optimizing Energy Consumption Intensity acts as a short-term driver, while Green Technology Innovation and Environmental Regulation serve as long-term sustainers. Furthermore, moderating effects reveal that Marketization exerts a positive moderating influence. This study provides empirical evidence and policy insights for leveraging IDRE to advance green growth through tailored approaches.

The uneven distribution of populations within urban and rural built-up areas restricts balanced regional development. This study employs statistical data from 296 cities in China covering the years 2010, 2015, and 2020 to analyze the coupling coordination relationship of population density in urban and rural built-up land and its influencing factors. The findings reveal the following: (1) Throughout the study period, population density in urban built-up areas (PUL) experienced a slow increase, whereas population density in rural built-up areas (PRL) declined rapidly. (2) Spatially, high levels of coupling coordination in population density between urban and rural built-up areas are primarily concentrated in the southwestern region, particularly in Sichuan, demonstrating a trend of gradual diffusion from the core to the periphery. Overall, a southwest-high to northeast-low pattern emerged. (3) The regression results indicate that economic development, agricultural structure, public services, and urbanization significantly affect the coupling coordination of population densities in urban and rural built-up areas. Among natural conditions, both elevation and temperature show significantly positive effects. This research provides theoretical foundations and policy recommendations for promoting urban-rural integrated development and achieving regional sustainable development.

ABSTRACT This study analysed the spatial distribution of urban tree cover and its relationship with socioeconomic variables and environmental quality in Ipatinga, Minas Gerais, Brazil. Tree cover was mapped via remote sensing and geoprocessing to calculate the tree cover index (ICA), which represents the tree canopy area per inhabitant, and the tree cover percentage (PCA), which is the proportion of urban land occupied by tree canopies. Socioeconomic data, including the population density and average household income, were obtained from the IBGE Census. The urban area of Ipatinga exhibited 32.5% tree cover and an average ICA of 102 m2 per inhabitant, with a heterogeneous spatial distribution. Tree cover was negatively correlated with population density (ICA: r = –0.5782; PCA: r = –0.7926; p < 0.001). For income, the correlations were positive but weak and were only statistically significant for PCA (r = 0.4365; p < 0.05). A qualitative checklist complemented the quantitative analysis by evaluating the functional, ecological, and landscape aspects of afforestation, such as vegetation distribution, connectivity, conservation status, and benefits related to shading, thermal comfort, and visual quality. Overall, these findings demonstrate that afforestation significantly contributes to urban environmental quality, highlighting the social inequalities in access to green infrastructure across the city. The integrated approach, combining spatial indices and qualitative evaluation, provides an innovative framework for diagnosing urban vegetation patterns and demonstrates the role of afforestation in promoting environmental quality and equity in industrialised urban contexts.

40-year (1984-2024) mapping of urban land use dynamics in China.

Modelling land use influence on polymer-specific microplastics abundance and transportation from terrestrial to aquatic environments.

This study investigates the relationship between urban subway passenger flow and land use intensity, proposing an innovative hybrid model that combines graph convolutional networks and multiscale geographically weighted regression (GMGWR). This model addresses the limitations of traditional methods in handling nonlinearity and spatial heterogeneity. Using metro data from Chengdu, Sichuan, China, this study analyzes the effects of various land use types on metro passenger flow during different time periods, revealing the spatial and temporal dynamics of land use on the urban rail transit system. The results indicate that land use characteristics are key determinants of urban rail transit passenger flow and that the effects of land use intensity on metro passenger flow exhibit dynamic characteristics that change with time and space. The innovation of this study lies in integrating machine learning and spatial econometrics methods. The proposed GMGWR model provides a more accurate representation of the complex nonlinear relationship between land use and metro passenger flow, offering urban transportation planners valuable strategies to enhance public transportation systems. By strategically planning land use around metro stations and promoting transit-oriented development policies, it is possible to create livable, pedestrian-friendly communities that foster green, sustainable urban growth.

Abstract Urban forests and other green infrastructures have been viewed as part of the “Nature‐based Solutions” (NbS) to mitigate emerging urban environmental change. This study focuses on the role of evapotranspiration (ET) in regulating water balances of small watersheds in the eastern United States. We compared streamflow and ET patterns at daily, monthly and annual scales and linked these hydrological variables to the physical properties of 11 paired watersheds dominated by forests (FW) or urban (UW) land covers. The annual precipitation ranged from 1028 mm to 1683 mm and potential ET (PET) from 815 mm to 1450 mm. The mean annual flow/precipitation (Q/P) ratios were 0.26 ± 0.13 and 0.41 ± 0.1 for FW and UW, respectively. Overall, UW had lower annual ET (772 mm in UW vs. 947 mm in FW), but higher mean annual and (∼58% higher), monthly water yield (17%–186% higher), and peakflow rates (up to 100 times higher) than FW. The streamflow differences between FW and UW were most pronounced during the growing season and early winter (June‐November). The mean Q/P ratios for 30 large hurricane events (2016–2021) were 0.12 ± 0.11 and 0.38 ± 0.23 for FW and UW, respectively. The flow rates in the dormant season (around December‐May) in UW were similar or lower than FW. We developed conceptual models to explain the seasonal and storm event streamflow differences using background climate (PET), ET, and land surface characteristics. Urban NbS designs should factor in strategies that maximize ET while minimizing impervious surfaces enhancing watershed “sponge” and “pump” functions.

Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning.

Influence of groundwater discharge on stream chloride concentrations in mixed urban land use sub-watersheds receiving road salt applications

Long-term spatiotemporal dynamics of urban land and surface water bodies in Shanghai over the past 90 years using old maps and dense Landsat time series

Deciphering urban land use: A typological analysis of built form across 105 urban areas in the U.S. using representation learning

The Mun River Basin, the largest Mekong tributary in Northeast Thailand, has experienced extensive agricultural expansion and forest decline, raising concerns over increasing soil erosion and sediment transfer. This study provides an integrated assessment of soil loss, sediment yield (SY), and sediment delivery ratio (SDR) across 19 sub-watersheds using the Universal Soil Loss Equation (USLE), field-based SY data, and multivariate statistical analyses in 2024. Basinwide soil loss was estimated at ~35 million t y−1 (mean 4.96 t ha−1 y−1), with more than 80% of the basin classified in the no erosion to very low erosion classes. Despite substantial hillslope erosion, only 402,405 t y−1 of sediment reaches the river network, corresponding to a low SDR of 1.15%, which falls within the range reported for large tropical watersheds with significant reservoir infrastructure. Soil loss is most strongly influenced by slope and forested terrain, while SY responds primarily to rainfall and tree plantations; urban land, croplands, and reservoirs act as sediment sinks. Principal Component Analysis (PCA) resolved multicollinearity and produced six components explaining over 90% of predictor variance. A PCA-based regression model predicted SY per unit area with high accuracy (r = 0.81). The results highlight the dominant roles of hydroclimate and land-use structure in shaping sediment connectivity, supporting targeted soil and watershed-management strategies.

Abstract The number of OpenStreetMap (OSM) contributors is closely related to both the quality of OSM data and various socioeconomic factors. This study applies the Geographically and Temporally Weighted Regression (GTWR) model to explore the spatiotemporal impacts of urban socioeconomic variables on OSM contributor numbers. Using empirical data from mainland China, covering OSM contributor statistics and national socioeconomic indicators from 2013 to 2021, we demonstrate the effectiveness of the GTWR model. In comparison with traditional methods like Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR), the GTWR model shows significantly improved performance. Key influencing factors include Urban Construction Land Area, Gross Regional Domestic Product (GRDP), the Number of Employees, Employees in Education, and the Number of Internet Users. Furthermore, we select three representative cities with different urban characteristics to examine the temporal variation in these factors. This understanding will fundamentally contribute to ensuring the long-term development of OSM.

Purpose The purpose of this paper is to address the critical lack of traditional data in rapidly urbanizing, data-scarce cities by proposing a novel spatial big data mining framework that leverages building density as a reliable proxy for urban land market patterns. Design/methodology/approach This study used building density to infer urban land market patterns in Kigali, Rwanda. The core analysis confirmed significant spatial clustering (Moran’s I = 0.9780) and multi-metric validation of five clustering algorithms selected the k-means model (k = 5) for robust urban segmentation. Findings The clustering delineated five distinct housing density zones, confirming a clear spatial gradient consistent with the classical bid-rent theory and monocentric city model. The high-density core (density: 0.34) comprises 9.93% of the land area, while extensive low-density zones dominate the periphery, empirically validating the applicability of traditional urban economic models in this data-scarce African context. Practical implications This study provides urban planners and policymakers with an evidence-based map of land market pressure. This granular segmentation enables targeted land-use planning, optimized infrastructure investment and the development of equitable policies for managing urban growth and densification in the future. Originality/value This study used building footprints density to infer land market patterns in Kigali, offering replicable methodology for data-driven spatial analysis in the Global South.

Este trabalho analisa a expansão urbana do município de Assis –SP entre os anos de 1960 e 2025, utilizando fotografias aéreas históricas de 1960 e 1970 e imagens satelitais de 2000 e 2025 georreferenciadas em ambiente CAD. A partir dessas bases, foram calculadas as áreas urbanizadas, as densidades populacionais (hab/m²) e as taxas de crescimento urbano, baseadas em dados censitários do IBGE. Posteriormente, técnicas de geoprocessamento e modelos de Inteligência Artificial foram aplicadas para projetar a possível configuração do perímetro urbano em 2075. Os resultados indicam uma tendência de expansão predominantemente em direção ao sudoeste do município, refletindo padrões de ocupação. Observou-se, ainda, que o uso de IA apresenta potencial para subsidiar o planejamento urbano de forma acessível e automatizada, entretanto, sua precisão depende da integração com um conjunto mais robusto de dados geográficos, ambientais e socioeconômicos, de modo a representar de forma mais fidedigna a complexidade do processo de urbanização.

The variation of structural and compositional characteristics of urban forests is influenced by the urban landscape heterogeneity and several biotic, abiotic, and human factors. Urban forests provide numerous ecosystem and social benefits key to the wellbeing of citizens and to enhance environmental conditions in cities. However, the quantity and quality of these services are determined by the urban forest structure, composition, and spatial variation. There has been little research on the heterogeneity in the urban forest structure and composition across the entire urbanized area of Mexico City, one of the largest and most populated cities in North America. This study explores urban forest composition, diversity and structure across the entire urbanized area and within six urban land uses and the 16 boroughs of Mexico City using tree data from 500 fixed-area plots of 400 m2 distributed across the city. Alfa and beta diversity analysis, and analysis of variance revealed differences in tree diversity and structure within land uses and boroughs. Green areas had higher basal area but less species richness than the residential and the commercial-residential land-use types. The lower values of basal area and canopy cover were found in the boroughs in the east part of the city, and the highest species richness was in boroughs in the south. Land use types and boroughs are ecologically heterogeneous units (β = 0.5, β = 0.6, respectively) and urban forest planning needs to consider their specific conditions. The higher proportion of non-native species found in this study highlights the need to diversify prioritizing native species.

Cloud-based Earth observation platforms, such as data cubes, enable reproducible analyses of long-term satellite time series for climate and urban studies. In parallel, Essential Climate Variables (ECVs) provide a standardised framework for monitoring climate dynamics, with urban land cover and temperature being particularly relevant in historic urban contexts. This study analyses long-term trends and statistical associations between satellite-based ECVs and urbanisation indicators within the Historic Urban Landscape (HUL) of Sfax (Tunisia) from 1985 to 2021. Using the Digital Earth Africa (DEA) data cube, we derived six urban spectral indices (USIs), land surface temperature, air temperature at 2 m, wind characteristics, and precipitation from Landsat and ERA5 reanalysis data. An automated and reproducible Python-based workflow was implemented to assess USI behaviour, evaluate their performance against the Global Human Settlement Layer (GHSL), and explore spatio-temporal co-variations between urbanisation and climate variables. Results reveal a consistent increase in air and surface temperatures alongside a decreasing precipitation trend over the study period. The USIs demonstrate comparable accuracy levels (≈88–90%) in delineating urban areas, with indices based on SWIR and NIR bands (NDBI, BUI, NBI) showing the strongest statistical associations with temperature variables. Correlation and multivariate regression analyses indicate that temporal variations in USIs are more strongly associated with air temperature than with land surface temperature; however, these relationships reflect statistical co-variation rather than causality. By integrating satellite-based ECVs within a data cube framework, this study provides an operational methodology for long-term monitoring of urban-climate interactions in historic Mediterranean cities, supporting both climate adaptation strategies and the objectives of the UNESCO HUL approach.

ABSTRACT Water quality is a stern issue in urban areas because it is indispensable for the sustenance of life. The present study aimed to determine the spatial trends with time in water quality attributed to transitioning anthropogenic land‐use/cover in a Himalayan city—Dehradun, India—through the Weighted Arithmetic Water Quality Index (WQI). A total of 36 samples from nine surface aquifers during four seasons were analyzed for physicochemical and biological water quality parameters and compared with standards prescribed by BIS—Bureau of Indian Standards and WHO—World Health Organization. The water quality categorization during the monsoon (good–moderate), post‐monsoon (poor–very poor), and the winter and premonsoon seasons (poor–unfit) reveals the temporal trends. Further, WQI values indicate that 25% of the water samples (urban) were in the very poor–unfit category, 52% in poor (semi‐urban), and 22.2% in good status (rural), with elevated values for Coliform bacteria at all locations. Unsustainable anthropogenic activities, namely, discharge of wastewater, indiscriminate dumping of solid waste, and fecal matter, are the main factors, which have negatively impacted the water quality of these rivulets. Land‐cover analyses from 2002 to 2023 observed an increase in settlement—urban built‐up (+7.5%), cultivated land (+7.5%), and barren land (+4.4%), whereas grassland (−8%), forest (−12%), and water bodies (−0.11%) decreased in terms of area coverage, with a slight decline in land surface temperature regimes attributed to the aerosol optical depth variations. The management of water resources (catchment basin conservation) in the Dehradun region is a major concern pertaining to urban land use planning and the mandate of Sustainable Development Goals (SDGs) 6 and 11.