Accurate prediction of urban land use changes at fine spatial scales is essential for developing healthy and sustainable cities, yet traditional simulation models struggle to capture local dynamics due to limited availability of fine-grained data and insufficient complexity in modeling urban systems. To address these limitations, we propose a novel approach that leverages advances in pre-trained vision-language foundation models combined with spatial dynamic modeling to forecast detailed urban land use patterns. Specifically, we collected a spatially dense collection of street view images (SVIs) throughout Shenzhen, China, and applied UrbanCLIP, a specialized vision-language prompting framework, to perform zero-shot inference of urban land use directly from images without labeled datasets and model retraining. The resulting fine-grained classifications delineate eight distinct urban land use types, producing a detailed urban functional map. These high-resolution patterns were then integrated into a spatial dynamic model enhanced by polynomial regression to simulate urban evolution toward 2035. This approach effectively captures neighborhood influences, socioeconomic drivers, and urban planning policies. Our simulation provides actionable insights for sustainable development in Shenzhen by identifying areas for balanced growth, targeted infrastructure investments, and ecological preservation. Compared to conventional methods, our methodology significantly improves predictive accuracy and spatial granularity. By incorporating foundation models, our approach addresses traditional data constraints, offering scalable and robust tools for informed urban governance and decision-making. • Proposed a VLM-enhanced framework to predict fine-grained urban land use changes. • Achieved zero-shot land use inference based on street view images. • Produced high-resolution simulations of Shenzhen's urban dynamics toward 2035.

Soil water response to extreme drought-rewetting events under typical land use patterns in a rainfed farmland system

The impact of land use change awareness on the psychological adaptation of migrant communities in Khulna city

Riparian zone transformation in South Africa: Evaluating changes, causes, and the role of legal frameworks

Effective climate change mitigation through land-based strategies requires maximizing soil carbon (C) sequestration across land uses and management practices. Yet, land-use and management impacts on soil C and nitrogen (N) fractions and their distribution in soil profiles in water-limited environments remain elusive. We evaluated various labile and stable pools of soil C and N at 0–15, 15–30, and 30–60 cm depths under four long-term land uses – continuous alfalfa and tall fescue systems, a conventionally managed annual cropping system, and cottonwood orchard – to understand land use-driven changes in soil C fractions in different depths, and total profile C sequestration. Results showed that C and N fractions were allocated differently across depth layers, suggesting divergent mechanisms of C sequestration at different depths. Perennial systems increased labile and stable C pools, specifically at 0–15 cm, thereby supporting biologically mediated soil organic C (SOC) sequestration. The annual cropping system accumulated greater mineral-associated organic C (MAOC) and inorganic C at 30–60 cm, mediated by the physicochemical pathway of C formation and stabilization. At 0–60 cm, soil inorganic N and potentially mineralizable N (PMN) were 2.4–7.3 and 3.8–8.4 times higher, respectively, under annual crops than other land uses. The profile distribution of labile N and C fractions relative to MAOC played a crucial role in SOC sequestration in various depths. The potential of arid soils to sequester C varied with vegetation type and land use. Management practices should focus on optimizing the distribution of labile C and N throughout the profile to promote microbial activity and enhance soil C sequestration. • Soil profile C and N distribution varied with diverse land-use systems. • Soil mineralizable C and N significantly influenced depth-wise organic C distribution. • Perennial crops promoted soil C storage in surface soils via biological processes. • Annual cropping supported the physicochemical pathway to sequester C in subsurface soils.

Microplastics influence organic carbon depletion in macroaggregates and soil structural stability in the Yanhe catchment

Land-use change and surface warming in Uganda’s oil-rich Albertine region (1995–2025): A geodetector analysis

Traditional village protection and sustainable development: Knowledge graph construction and hotspot evolution

Agricultural and urban land use intensifies riverine GHG emissions across continents.

• 20 + years of data from 74 experiments reveal ski slope impacts on alpine hydrology. • Surface runoff is higher for ski slopes, indicating altered hydrological behaviour. • Random forest analysis shows surface runoff depends on multiple variables. • Key surface runoff drivers include land use, geology, soil, and topography. • Sustainable soil and land-use management can reduce ski slope runoff. This study investigates the surface runoff behaviour in 12 ski regions in the Eastern Alps based on data from 74 rainfall simulation experiments. The dataset includes information on hydrological responses, water storage, and infiltration. By applying a random forest regression model, surface runoff coefficients were linked to site-specific hydrological variables, providing insights into drivers of surface runoff. Results indicate higher surface runoff coefficients on ski slopes (0.57) compared to reference areas (0.07) within ski resorts that were not modified for skiing activities. Moreover, our findings suggest that geological variables are the strongest predictors of surface runoff on ski slopes. On ski slopes the surface runoff response is also influenced by a combination of hydrological variables, including mechanical disturbances from slope preparation and land use practices. For the reference areas, soil and land use variables play a more pronounced role. These findings underscore the importance of sustainable soil management and restoration strategies to mitigate the impacts of ski slope construction on runoff regimes and to maintain hydrological stability in Alpine regions.

Land use and land cover (LULC) change is a major anthropogenic factor influencing flood behavior and hydrological processes. This systematic review synthesizes two decades (2005-2025) of research on hydrological modeling approaches used to assess flood responses under LULC transitions. A total of 114 publications were retrieved from the Scopus database, and after applying PRISMA-based screening, 78 peer-reviewed studies were analyzed using bibliometric and content mapping. The review categorizes hydrological models by spatial scale, process representation, and sensitivity to LULC dynamics. Findings consistently indicate that urban expansion, deforestation, and vegetation loss intensify surface runoff, peak flow, and flood frequency. Despite advancements, significant challenges remain particularly related to data scarcity, model calibration, and the limited integration of socio-economic variables. Emerging tools such as Remote Sensing (RS), Geographic Information Systems (GIS), and machine learning especially within platforms like Google Earth Engine (GEE) enhance LULC detection accuracy and flood prediction capability. The study proposes an integrated decision framework linking bibliometric trends with model selection strategies, enabling researchers to align model choice with data availability and landscape characteristics. Overall, this review emphasizes the importance of interdisciplinary, data-driven modeling to strengthen flood resilience in rapidly transforming land systems.

Spatiotemporal dynamics and suitability of greenhouse agriculture in China: A GEE-based analysis for sustainable land use planning in Henan Province

Different land use types around megacities contribute contrasting heavy metal pollution and health risks in soil-leaf vegetable systems.

Identifying the environmental drivers of the distribution of carbon isotopes in global inland waters.

The interactions between land use and water quality play critical roles in shaping benthic macroinvertebrate biodiversity in rivers. However, existing studies struggle to effectively identify nonlinear interactions between land use and water quality. This study integrates Random Forest and SHapley Additive exPlanations to create a robust framework that identifies the nonlinear interactions among variables. Applied to the Fu River basin, a primary tributary of Poyang Lake, China, our framework identified season-specific drivers: Ammonia Nitrogen and Shannon's diversity landscape index dominated community dynamics in the wet season, while Hydrogen ion concentration and Forest were key in the dry season. Interactive analyses revealed that during the wet season, total phosphorus (TP) and cropland cover formed the most influential pair, with synergistic effects (i.e., combined impact > sum of individual effects). Notably, cropland coverage modulated TP’s impact on benthic diversity: low cropland cover favored positive effects of TP, which diminished as TP concentrations increased to 0.04 mg/L, whereas high cropland cover triggered negative effects that intensified with rising TP and stabilized at 0.10 mg/L. During the dry season, conductivity (Cond) and forest cover emerged as the most impactful pair, also exhibiting synergistic effects. Forest cover modulated Cond’s influence on benthic diversity: under high forest cover, low Cond exerted positive effects that weakened with increasing Cond to 60 μs/cm; under low forest cover, moderate Cond induced increasingly negative effects that plateaued at 80 μs/cm. This study provides a robust approach to decipher context-dependent environmental interactions, offering valuable insights for river ecological conservation and adaptive management. • Decoded nonlinear interactions of water quality/land use on benthic biodiversity in rivers. • NH 3 -N & SHDI/pH & forest dominate the biodiversity during wet/dry season. • TP-Cropland/Conductivity-Forest synergy amplifies impacts during wet/dry season.

The built environment is a critical component in shaping urban areas and their resilience. While existing studies frequently discuss how different built environment elements can enhance a city's capacity to respond quickly and effectively to external shocks, limited research has validated these effects through actual disaster event outcomes. This study aims to empirically investigate the relationship between the built environment and urban resilience, focusing on the context of the COVID-19 pandemic in Singapore. Using urban vitality, approximated by public transit passenger data, as an indicator of the resilience process, we quantified urban resilience through three metrics: robustness, recovery degree, and total performance loss, derived from temporal changes in urban vitality. Multiple linear regression (MLR), spatial lag model (SLM), and geographically weighted regression (GWR) were applied to examine how built environment factors relate to these metrics. The results across models indicate that higher residential density, more diverse land use, and greater distance to CBD are positively associated with resilience, whereas greater transit service is associated with lower resilience. Moreover, GWR explains the highest variations in all resilience metrics compared to MLR and SLM. By mapping spatially varying associations, the findings offer insights to support localized and data-driven planning for building resilient urban environments capable of withstanding and adapting to future shocks. • A new approach assesses built environment's impact on urban resilience. • Urban vitality from human mobility effectively captures resilience processes. • Proposed resilience metrics reveal intra-city disparities in resilience. • Higher density and land use mix are linked to better resilience outcomes. • Proximity to CBD and transit service relate to lower resilience during COVID-19.

• This paper introduces a new approach to mobility hubs through their transport supply and surrounding context. • The classification process is reproducible, making it applicable to various contexts and for different urban scalables. • The methodology re-adapts the ABC location policy to identify potential locations for Mobility hubs. • The classification system employs a range of indicators, providing a comprehensive perspective from the literature on mobility hubs from both node and place dimensions. • The proposed methodology has been tested in Milan’s first belt area, allowing for a comparative analysis of the current situation and future transformations. Multimodal Mobility Hubs have gained increasing attention as a sustainable approach to promoting environmentally friendly transportation in cities. By co-locating multiple transport services, mobility hubs offer efficient multimodal transfers and address issues such as car dependency, congestion, and unequal access to mobility. Building upon these advantages, this paper introduces a reproducible classification method that evaluates both the transport supply (Node) and the physical and functional characteristics of the surrounding context (Place). The methodology readapts the ABC location policy and the Node–Place classification model to systematically identify existing nodes that can serve as multimodal mobility hubs, combining them with comprehensive indicators derived from the mobility hubs literature. Through this approach, the paper illustrates how open and standardized datasets are used to (i) cluster and score transport nodes based on their multimodal offerings, (ii) analyze land use and urban services in their catchment areas, and (iii) compare current conditions with planned transport and land-use transformations. Additionally, this paper introduces a cross-scale approach to support the localization of potential multimodal mobility hubs, their classification, and insights into their future performance. The proposed framework is tested in Milan’s metropolitan area, where it highlights opportunities to enhance multimodality and, alternatively, provides deeper insights from applying transformations in land use and transport supply. Findings show that this approach is scalable and replicable across diverse urban contexts. Ultimately, the paper contributes to evidence-based policy by offering a tool to guide urban and transport planners in locating, selecting, and upgrading mobility hubs, facilitating more sustainable and inclusive mobility networks.

Holistic assessment of India's water security using coupled climate-human intervention models.

The growing demand for food, energy, and water in resource-constrained regions intensifies land-use conflicts, where solar photovoltaic (PV) expansion often competes with agriculture. Agrivoltaics, the co-location of crop cultivation beneath PV systems, offers a potential dual-use solution to enhance land efficiency. This study presents one of the first agrivoltaic demonstrations in Bangladesh that evaluates the agronomic, economic, and socio-social feasibility of agrivoltaics through a field-based comparative experiment conducted at two solar irrigation pump (SIP) sites in Tetulia, Panchagarh district. A controlled plot design was employed in which selected crops were cultivated under PV panels and in adjacent open-field control plots across two growing seasons (Rabi/winter and Kharif-I/summer). Crop yields were quantitatively measured and compared, and extrapolation analysis was performed to estimate national-scale production potential across approximately 45 ha of existing SIP-covered land. In addition, qualitative data were collected through semi-structured interviews and focus group discussions (FGDs) to assess farmer perceptions and gender dimensions. Results indicate that seven Rabi crops, including tomato, onion, and garlic, experienced yield reductions of 10%-20% under shaded conditions, whereas shade-tolerant ginger and turmeric cultivated in Kharif-I recorded yield increases of 12.3% and 8.7%, respectively. Scaling the pilot findings (0.01 ha) suggests potential seasonal production of nearly 594 t of ginger and turmeric nationwide (45 ha), corresponding to an estimated economic value of approximately US$0.56 million. Qualitative findings revealed strong farmer interest in high-value crop cultivation under PV panels and indicated enhanced women's participation in crop management, post-harvest activities, and contributing to household income diversification. The study demonstrates that agrivoltaics can serve as a climate-smart approach to optimize land use, strengthen food security, and promote renewable energy adoption while creating opportunities for gender-inclusive agricultural practices in rural Bangladesh.

Organophosphate triesters and diesters in the Yellow River Basin (Shandong section): Geographical patterns, land use correlates and compound-specific source apportionment.