Search Results (193)

The deformation of surrounding soil primarily governs the behavior of underground structures. Consequently, variations in their external geometry significantly affect their overall seismic response. Moreover, large soil deformations and structural uplift caused by liquefaction severely threaten their seismic safety. While most previous studies have focused on conventional rectangular subway stations, the seismic performance of novel varying-span structures remains largely unexplored. In this study, nonlinear dynamic time-history analyses are conducted to investigate the soil–structure interaction (SSI) of large unequal-span subway stations in liquefiable sites. Furthermore, the seismic responses of both the structure and the surrounding soil are systematically evaluated under various burial depths of the liquefiable layer. Finally, a U-shaped foundation reinforcement method is proposed to mitigate structural uplift. The results show that unequal-span structures suppress liquefaction in lateral soil, whereas significant liquefaction occurs beneath the base slab and cantilevered middle slabs. The burial depth of the liquefiable layer has a negligible effect on the liquefaction state directly under the center span. Regarding structural response, global uplift follows a spatial pattern that peaks at the center span and gradually attenuates laterally. Although the proposed U-shaped reinforcement effectively reduces both total and differential uplift, it does not fundamentally change the underlying liquefaction mechanism. Specifically, reinforcing the soil under cantilevered sections minimizes differential uplift while enhancing the overall economic efficiency of the seismic design. These findings provide a scientific basis for optimizing the seismic resilience of complex underground structures, contributing to the development of resource-efficient and disaster-resilient urban underground infrastructure in liquefaction-prone regions. Full article

Urban green infrastructure plays a crucial role in enhancing environmental resilience in cities, particularly in arid regions characterized by water scarcity, soil salinity, and high climatic stress. However, arid coastal cities remain insufficiently studied with regard to spatially explicit assessments of the structure and dynamics of green infrastructure. This study evaluates the state and spatial organization of urban green infrastructure in Aktau, Kazakhstan, over the period 2015–2025, with the most recent satellite observations obtained in June 2025. Sentinel-2 satellite imagery was used to calculate seasonal Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values, and zonal statistics were applied to assess intra-urban differentiation across functional zones. In addition, inventory-based indicators—Green Planting Density (GPD), Structural Composition of Greenery (SCG), and Protective Green Infrastructure (PGI)—were integrated to complement the remote sensing analysis. The results indicate a moderate overall increase in mean NDVI values (from 0.21 to 0.28), with the most significant growth observed in central and coastal areas (ΔNDVI = +0.12; ΔSAVI = +0.21), while industrial and newly developed zones exhibit only limited changes. Despite these localized improvements, the spatial configuration of green infrastructure remains fragmented, reflecting a persistent center–periphery asymmetry in urban greening. These results underline the importance of irrigation practices and spatially targeted greening strategies for improving vegetation conditions in arid urban environments. The proposed integrated approach combining satellite-derived vegetation indices and inventory-based indicators can serve as a useful tool for monitoring urban green infrastructure and supporting evidence-based planning in arid coastal cities. Full article

Increasing frequency and intensity of extreme precipitation events, together with altered soil saturation dynamics, have significantly increased the occurrence of shallow landslides. These processes are closely linked to climate change and increasingly affect mountainous and hilly regions worldwide, where rainfall-induced pore pressure variations and transient infiltration govern slope instability. Despite growing recognition of climate-driven slope failures, most conventional geomechanical analyses still rely on static assumptions and simplified boundary conditions, which are insufficient to capture the pronounced temporal variability of hydro-climatic forcing. To address this gap, this study introduces a conceptual and methodological framework for a proactive landslide cadaster, developed within the Climate Adaptive Resilience Evaluation (CARE) framework. Rather than serving as a static inventory of past events, the proposed cadaster functions as a structured, updatable repository of climate–geomechanical parameters that directly support advanced landslide analyses. The core innovation lies in the formalization of the climate–geomechanical interface, which enables the transformation of climatic and hydrological variables into parameters directly applicable in geomechanical modeling. These parameters encompass climatic, hydrological, geomechanical, and thermo-hydraulic processes and are assigned to spatially referenced locations, complemented by documented landslide occurrences. Their spatial distribution forms a network of reference points that allows interpolation, continuous updating, and reuse across multiple analyses. In this way, the cadaster becomes a proactive, process-based data infrastructure, serving as the foundational input for scenario-based landslide susceptibility, hazard, and risk assessments within the CARE analytical workflow. The conceptual framework is illustrated through an example from Slovenia, focusing on the Visole area near Maribor, where selected data types and workflow steps are presented for demonstration purposes. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along highways and other linear transport corridors remains comparatively underexplored despite its potentially important role as a carbon sink. Here, we integrate field-measured AGB samples with GF-2 high-resolution satellite imagery to evaluate the suitability of multiple remote-sensing predictors and machine-learning algorithms for estimating AGB in highway roadside vegetation. Six remote-sensing variables were used as predictors, including four vegetation indices (Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index (EVI), and Modified Soil-Adjusted Vegetation Index (MSAVI) and two-band ratios (B342 and B12/34). Five regression models—multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost)—were developed and systematically compared under both single-variable and multi-variable scenarios. Model performance was evaluated using five-fold cross-validation, with the coefficient of determination (R²) and the root mean square error (RMSE) as metrics of evaluation. The results indicate that the RF model under the multi-variable scenario achieved the best overall performance, with a training R² of 0.83 and a testing RMSE of 0.84 kg·m⁻², substantially outperforming the other linear and non-linear models. The optimal RF model was further applied to GF-2 imagery to produce a spatially explicit AGB map for a 32 km highway segment and a 30 m roadside buffer on both sides, yielding an estimated total aboveground biomass of 566.97 t for the corridor. These findings demonstrate that combining high-resolution remote sensing with machine-learning approaches can effectively improve AGB estimation for linear roadside vegetation systems, providing technical support for ecological monitoring, roadside greening management, and carbon accounting for transport infrastructure. Full article

Buried oil and gas pipelines, the critical arteries of global energy infrastructure, are increasingly vulnerable to severe geological hazards such as reverse faulting, yet their structural integrity is often pre-compromised by stochastic corrosion damage accumulated during service. However, quantifying the coupled impact of spatial corrosion heterogeneity and large ground deformation remains a formidable challenge due to the complex nonlinearities involved in soil–structure interactions and wall thinning. This study establishes a probabilistic assessment framework integrating random field theory, nonlinear finite element analysis, and a generative conditional diffusion model to characterize realistic 2D non-Gaussian corrosion morphologies. The numerical results reveal a significant geometric stiffening effect induced by internal pressure, where moderate operating levels effectively suppress cross-sectional distortion by counteracting the Brazier effect. Consequently, this mechanism facilitates a fundamental transition in failure modes from localized tensile rupture to ductile buckling, significantly extending the critical fault displacement threshold. Furthermore, probabilistic fragility analysis demonstrates that the spatial dispersion of pitting, rather than just average wall thinning, governs the initiation of premature failure. Mechanistic analysis indicates that high internal pressure, while providing pneumatic support, exacerbates tensile strain localization at corrosion pits, leading to a heightened probability of premature rupture under minor fault deformations, a critical hazard that traditional deterministic models significantly underestimate. These findings provide a quantitative theoretical foundation for the reliability-based design and maintenance of energy lifelines traversing active tectonic zones. Full article

Systematic, spatially explicit tree inventories are increasingly implemented in cities worldwide, as they are crucial for evidence-based green infrastructure planning. Currently, different approaches are adopted, which differ in methodological framework and parameter standardization, limiting comparative assessments and coordinated monitoring. This study presents a replicable protocol for a field-based digital street tree census, applied in a densely built central area and in a low-density suburban area of Rome. Field surveys documented a set of 15 parameters, including species identity, dendrometric and tree pit parameters, acquired using open-source QGIS/QField tools. Subsequent analysis evaluated floristic diversity, population structure, and climate suitability at the neighborhood scale, enabling the identification of context-specific vulnerabilities. The testing of the methodology shown in this work involved 13,017 georeferenced tree pits, pointing out substantial pit restoration needs and insufficient soil conditions in the most densely urbanized area, whereas the suburban area shows optimal conditions with extensive road verge green spaces. Joint interpretation of the considered parameters reveals that high floristic diversity alone does not guarantee climate resilience: high-diversity neighborhoods can exhibit substantial non-climate-resilient species and limited alignment with local species recommendations, demonstrating that comprehensive evaluation of street tree populations requires integrated analysis. The operationalized protocol establishes a replicable, municipally scalable methodological framework, providing policymakers with fine-scale, actionable insights enabling differentiated urban forestry strategies addressing both infrastructure deficits and long-term species climate suitability. Full article

Permafrost thickness serves as a critical indicator of hydrogeological conditions in cold regions and significantly influences the safety of engineering infrastructure. Due to the combined effects of climate, ecology, and human activities, the thermal characteristics and spatial distribution of permafrost in the Greater and Lesser Khingan Mountains of Northeast China exhibit high complexity, rendering existing permafrost thickness estimation methods largely inapplicable in this region. We developed an integrated estimation framework that bridges the gap between limited deep ground temperature measurements and regional-scale mapping. To overcome the scarcity of deep borehole (>20m) data, a physical-statistical inversion method was employed to derive permafrost base depths from shallow borehole temperature profiles, thereby expanding the foundational dataset to 104 representative sites. Integrating these ground observations with satellite-derived products (e.g., MODIS NDVI) and auxiliary environmental covariates (e.g., DEM-based topography and gridded climatic data), a Random Forest algorithm (RF) was applied to generate a 1 km-resolution permafrost thickness distribution map across Northeast China with a classification accuracy of 0.74. The results indicate that the average permafrost thickness in the study area is 47.71 ± 10 m, exhibiting a spatial pattern of thicker in the north and west, thinner in the south and east, and greater in mountainous areas than in plains. The top three influencing factors of permafrost thickness are atmospheric precipitation, surface thawing degree days (TDDs), and topographic position index (TPI), revealing that the thickness of discontinuous permafrost in northeastern China is primarily governed by local factors such as soil moisture, represented by the thick permafrost existed under a small patch of ground surface. This study provides a new methodological framework for estimating permafrost thickness in regions with limited ground temperature gradient measurement in deep boreholes. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

Urban street trees provide essential environmental and social benefits, yet their vitality is often challenged by adverse urban conditions such as traffic emissions, impervious surfaces, and limited soil moisture. Conventional street tree management relies heavily on labor-intensive field inspections, making large-scale and repeatable assessment difficult. To address this limitation, this study proposes a GeoAI-based framework that integrates high-resolution aerial imagery, multispectral satellite data, and deep learning–based semantic segmentation to automatically delineate individual street trees and derive a remote sensing-based vitality proxy. Street trees are detected from orthorectified aerial imagery using semantic segmentation models, and vegetation indices—including NDVI, NDRE, and NDMI—are extracted from multispectral satellite imagery. An area-weighted object–pixel matching strategy is applied to associate spectral indicators with individual crowns across multi-resolution datasets. A composite vitality proxy is then constructed by integrating chlorophyll- and moisture-related indices. The results reveal spatial variability in spectral vitality signals across different urban environments. Trees along major road corridors tended to exhibit lower chlorophyll- and moisture-related indicators, while those near parks, riverfront walkways, and recently developed residential areas generally showed higher values. NDMI provided complementary insights into moisture-related stress that were not fully reflected by chlorophyll-based indices. Although the proposed vitality proxy is not a substitute for field-based diagnosis, the overall framework offers a scalable approach for citywide screening and prioritization of potentially stressed trees, supporting data-informed urban green infrastructure management within smart-city planning contexts. Full article

Ground subsidence in urban areas often reflects hidden failures within water and sewer infrastructure, amplified by hydrologic and seasonal conditions. This study analyzes 303 documented subsidence incidents in Gyeonggi Province, South Korea, from 2018 to 2024, focusing on infrastructure-related causes and their spatial and seasonal patterns. Incident records were reviewed to identify root causes, geographic distribution, seasonal trends, and impacts, including human injury and vehicle damage. Descriptive analysis showed that sewer pipe damage (39.3%) was the leading cause, followed by poor compaction or backfilling (22.8%) and excavation-related damage (14.2%). Subsidence linked to sewer systems occurred disproportionately during the summer monsoon, highlighting interactions between rainfall, pipe deterioration, and soil erosion. Statistical analysis using the Extended Fisher’s Exact Test revealed significant associations between subsidence causes and seasonality, vehicle damage, and regional location, but no significant link with human injury. Defective pipe construction contributed to elevated regional vulnerability, particularly in eastern municipalities, while excavation-related incidents were more common in spring. These results underscore the need for seasonally adaptive inspections, targeted rehabilitation of aging water and sewer networks, and region-specific asset management. By connecting subsurface failures with hydrologic conditions and infrastructure performance, this study offers data-driven insights to enhance proactive water infrastructure management and urban resilience. Full article

The degradation of wetlands and forests is still a threat to the supply and recovery of ecosystem services in the tropics. Studies comparing restoration measures and ecosystem service recoveries are fragmented. This study investigated the spatial extent and drivers of wetland/forest degradation, and assessed the effects of restoration measures on the recovery of ecosystem services and resilient livelihoods. A cross-sectional household survey was conducted targeting households adjacent to restored and unrestored wetland/forest ecosystems. The data was analyzed using a Binary Logistic regression to characterize earlier and recovered ecosystem services between forest and wetland ecosystems. High spatial-resolution optical satellite imagery from the Airbus constellation was obtained and analyzed to examine wetland and forest degradation. Our findings revealed that the spatial extent of degraded land under wetlands and forests decreased between 2023 and 2025. Ecosystem service degradation was primarily driven by chronic poverty, excessive water abstraction, population growth, burning practices, overharvesting of resources, overgrazing, cultivation, infrastructure development, and the invasion of alien species (p < 0.05). The counteractive ecosystem restoration activities undertaken included mobilization and sensitization of communities on wetland restoration, wetland demarcation, revegetation, establishment of flood control measures, and provision of alternative livelihoods (p ≤ 0.05). The multiple direct and indirect ecosystem service recoveries reported were provisioning services (increases in pasture, enhanced livestock production, increased soil productivity, health-related benefits from crops and livestock products) and regulating services (improved water quality/quantity). The ecosystem service recoveries were more significant in the restored wetlands than the forests. The indicators of enhanced ecosystem-based resilient livelihoods included increased household incomes, higher livestock yields, increased crop productivity, improved health from crop/livestock products, improved water quality/quantity, and enhanced scenic beauty and tourism (p < 0.05). The restoration activities in degraded wetland systems had more potential to facilitate full recovery of the wetland ecosystem compared to the absence of interventions. This evidence highlights the need to restore high-ecological-sensitive ecosystems to sustain the delivery of ecosystem services for community and environmental resilience. Full article

The development of urban green infrastructures (UGI) is considered among the main nature-based solutions for climate mitigation in cities; however, the role of soils in the carbon (C) balance of UGI ecosystems remains largely overlooked. Urban green spaces are typically dominated by constructed Technosols, created by adding organic materials on top of former natural or agricultural subsoils. The combined effects of land-use history and current UGI management result in a high spatial variation of soil organic carbon (SOC) stocks and soil CO₂ emissions. Our study aimed to explore this variation for the case of Wageningen University campus. Developed on a former agricultural land, the campus area includes green spaces dominated by trees, shrubs, lawns, and herbs, with well-documented management practices for each vegetation type. Across the campus area (~32 ha), a random stratified topsoil sampling (n = 90) was conducted to map the spatial variation of topsoil (0–10 cm) SOC stocks. At the key sites (n = 8), representing different vegetation types and time of development (old, intermediate, and recent), SOC profile distribution was analyzed including SOC fractionation in surface and subsequent horizons, as well as the dynamics in soil CO₂ emissions, temperature, and moisture. Topsoil SOC contents on campus ranged from 1.1 to 5.5% (95% confidence interval). On average, SOC stocks under trees and shrubs were 10–15% higher than those under lawns and herbs. The highest CO₂ emissions were observed from soil under lawns and coincided with a high proportion of labile SOC fraction. Temporal dynamics in soil CO₂ emissions were mainly driven by soil temperature, with the strongest relation (R² = 0.71–0.88) observed for lawns. Extrapolating this relationship to the calendar year and across the campus area using high-resolution remote sensing data on surface temperatures resulted in a map of the CO₂ emissions/SOC stocks ratio, used as a spatial proxy for C turnover. Areas dominated by recent and intermediate lawns emerged as hotspots of rapid C turnover, highlighting important differences in the role of various UGI types in the C balance of urban green spaces. Full article

Rice is a vital staple food crop worldwide and also one of the major sources of greenhouse gas (GHG) emissions, generating substantial methane (CH₄) and nitrous oxide (N₂O). As one of the key management practices for rice production, the GHG mitigation potential of water management has attracted extensive attention, whereas its global scalability remains to be further investigated. Based on 15,458 global observations of field experimental data, we employed advanced machine learning methods to quantify the GHGs and soil carbon sequestration of global rice systems around 2020. Furthermore, we identified the optimal spatial distribution of GHG mitigation for five rice water management practices (continuous flooding (CF), flooding–midseason drainage–reflooding (FDF), alternate wetting and drying irrigation (AWD), flooding–midseason drainage–intermittent irrigation (FDI), and rainfed cultivation (RF)) through scenario simulation, under the premise of no yield reduction. The results of machine learning simulation showed that optimizing water management could reduce global rice greenhouse gas emissions by 39.17%, equivalent to 340.46 Mt CO₂ eq, while increasing rice yields by 3.55%. This study provides valuable insights for the optimization of agricultural infrastructure and the realization of agricultural sustainable development. Full article

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions. Full article