Search Results (9,486)

Against the backdrop of high-quality urbanization in cities, the rapid expansion of metro networks has led to severe spatial mismatches in land use around station areas, which seriously restricts the full exertion of the comprehensive benefits of the transit-oriented development (TOD) model. Taking 139 operational metro stations in Xi’an in 2024 as the research sample, this study constructs a multi-objective land use optimization model with the richness of public services, transportation accessibility and population distribution balance as the three core maximization objectives. A hierarchically adaptive improved NSGA-III algorithm is proposed, with the following four key technical optimizations implemented: multi-dimensional adaptive reference point adjustment, design of real-integer hybrid coding genetic operators, construction of an enhanced multi-criteria environmental selection mechanism, and dynamic regulation of algorithm iteration. Experimental results show that the performance of the improved algorithm is significantly superior to that of the traditional NSGA-III algorithm: the values of the three core objectives are increased by 59.58%, 12.94% and 7.35% respectively compared with the original data; the algorithm achieves stable convergence after 25 iterations, with the convergence efficiency improved by 30%. The obtained Pareto optimal front features good uniformity (U = 0.92) and coverage (C = 0.95), and all the 80 non-dominated solutions meet all constraint conditions, with the solution set highly coupled with the urban functional zoning and spatial planning of Xi’an. This study proposes a zoned, prioritized and phased hierarchical land use optimization strategy for the areas around metro stations in Xi’an. The research findings provide a replicable research framework and methodological reference for the TOD practice and land use optimization of metro station areas in other rapidly urbanizing central cities in China and developing countries worldwide with the characteristic of rapid rail transit expansion. Full article

Substantial tracts of fallow farmland remain unutilized across southwestern China throughout winter and spring. To explore a high-yield planting pattern for utilizing such fallow land, a cereal–legume intercropping experiment was conducted in Chengdu in 2021–2022 and in 2022–2023. This involved five different intercropping ratios of oat (Avena sativa) and common vetch (Vicia sativa) including 100:0, 75:25, 50:50, 25:75, and 0:100 based on seed number per unit area. The relative density, LER (land equivalent ratio), hay yield, nutritional composition and in vitro fermentation characteristics were assessed. The study revealed that the combination of oat and common vetch led to a significant enhancement in the production performance over the monocultures. At the flowering stage, the most balanced interspecific competition was observed at a ratio of 50:50. The ratio of 50:50 had the higher LER in the mixture—from 1.018 to 1.873—, which was significantly higher than the other two intercropping ratios in 2021–2022. At the flowing development stage in 2021–2022, the harvesting of mixed crops at the 50:50 ratio resulted in a significant higher crude protein yield, 1454.7 kg/hm², than the other intercropping ratios. As the growth stage continued, the mixture hay neutral detergent fiber and acid detergent fiber contents increased, while the relative feed value and crude fat content decreased. The soluble sugar content increased with the prolongation of the growth stage and peaked at the jointing stage, and decreased with the decrease in the proportion of oat in the mixture. Additionally, the gas production showed an overall decreasing trend with the increase in the proportion of common vetch. The dry matter degradation rate in the mixture hay was overall higher than that of the monocultures, and the NH₃-N content showed an overall trend of increasing with the decrease with the intercropping ratio of oat. Consequently, the 50:50 ratio may be recommended as an oat-common vetch intercropping ratio suitable for utilizing fallow fields in southwestern China from October to April to produce high-quality forage. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

Climate-induced drought and intensifying land-use pressures threaten ecosystem services and agricultural productivity, particularly in regions with distinctive soil and ecological characteristics. Alabama’s Black Belt, defined by its clay-rich soils and shaped by a legacy of plantation agriculture, uneven land tenure, and persistent socioeconomic disadvantage, is increasingly vulnerable to these interacting stressors. This study analyzes long-term (2000–2023) spatiotemporal patterns of Land Use Land Cover (LULC) change and vegetation response to drought to inform sustainable resource management. Multi-temporal Landsat imagery and National Land Cover Database (NLCD) products were used to quantify LULC dynamics. At the same time, vegetation condition and moisture stress were assessed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI). Drought conditions were evaluated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates temperature-driven evaporative demand. Results indicate substantial landscape change, including declines in deciduous forest (−17.78%) and pasture/hay (−13.17%), alongside increases in medium-intensity developed land (+20.25%) and evergreen forest (+10.62%). Declining NDVI and NDMI values indicate increasing vegetation stress, particularly during prolonged droughts. Vegetation response exhibited a weak relationship with SPI (R = 0.37) but a stronger association with SPEI (R = 0.59), underscoring the importance of accounting for atmospheric water demand. These findings highlight the growing vulnerability of Black Belt ecosystems to coupled climate and land-use pressures and provide insights to strengthen climate-resilient agricultural management. Full article

Soil health is essential for food security, ecosystem stability, and sustainable development, yet its spatial heterogeneity and driving mechanisms remain insufficiently understood at regional scales. This study investigates soil health in Heilongjiang Province, China. A Soil Health Index (SHI) was constructed using eight indicators covering physical, chemical, and biological properties based on multi-source datasets at 1 km spatial resolution. A random forest (RF) model was applied to identify key environmental drivers, and Moran’s I and Getis–Ord Gi* statistics were used to analyze spatial clustering. The results showed that SHI values ranged from 0.19 to 0.70, with a mean of 0.45. The RF model achieved strong performance (R² = 0.6666, RMSE = 0.03184, MAE = 0.02372), significantly outperforming linear regression (R² ≈ 0.17). Significant spatial clustering was observed, where “hotspots” refer to statistically significant clusters of high SHI values, and “coldspots” indicate clusters of low SHI values based on Getis–Ord Gi* analysis. Climate factors (temperature and precipitation) and elevation were the dominant drivers. Significant spatial clustering was observed, with clear hotspot and coldspot patterns. These findings provide spatial evidence for sustainable land-use planning and zonal soil management. However, the analysis is limited by data resolution and model interpretability, which may affect the representation of fine-scale variability. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

Nocturnal urban heat presents significant but understudied risks within tropical megacities, where high humidity and heat storage in built-up areas prevent nighttime thermal recovery and intensify chronic heat stress. This study investigates the nocturnal surface urban heat island (SUHI) dynamics in the Bangkok Metropolitan Region (BMR) over two decades (2003–2023) with a daytime SUHI comparative baseline. We examined long-term thermal variations using MODIS land surface temperature data and Landsat urban–rural classification. The results demonstrate an increase in nighttime land surface temperature (LST) of 0.109, with nocturnal SUHI proving more persistent than its daytime counterpart with a temperature difference as high as 2.0 °C between urban and rural areas during the night. While daytime SUHI peaked at 6.3 °C in April 2011, with the strongest effects during April–May, nocturnal SUHI exhibited less seasonal variability but sustained elevated values throughout the year. Heat-retaining nocturnal hotspots have expanded from central Bangkok to newly developed urban areas. Cross-correlation analysis suggests that El Niño–Southern Oscillation (ENSO) strongly modulates SUHI anomalies, with maximum cross-correlations for a time lag of 3 months. These results suggest the need for urban adaptation strategies that specifically address nocturnal heat, as well as design strategies such as improved ventilation, high-emissivity materials, green infrastructure allowing evapotranspiration, and cooling centers for vulnerable populations to enhance thermal resilience across the BMR. Full article

Cultural and natural heritage are increasingly framed as components of territorial governance rather than isolated conservation elements; yet, a structural gap persists between their strategic recognition in planning documents and their measurable integration into statutory land-use systems that guide spatial decision-making. This gap is particularly pronounced in protected areas, where ecological integrity, cultural and symbolic values, tourism functions, and socio-economic expectations converge within environmentally sensitive landscapes. This study develops and empirically applies a compatibility-based analytical framework that embeds Multi-Criteria Decision Analysis (MCDA) within the statutory spatial planning system of Kozara National Park. The framework combines (i) institutional analysis of legally binding planning instruments, (ii) zoning-aligned analytical units derived from the Special-Purpose Spatial Plan and Management Plan, and (iii) a weighted multi-criteria model incorporating ecological integrity, cultural–historical significance, tourism and recreation capacity under controlled use, and socio-economic feasibility. Climate-related disturbance exposure is incorporated as a planning-relevant modifier of ecological compatibility. Composite compatibility scores under the baseline configuration range from 2.55 to 3.85 across analytical units. Rank correlation analysis suggests a high degree of structural consistency across both alternative weighting configurations relative to the baseline scenario (Spearman’s ρ ≈ 0.90), with only limited rank reordering observed, primarily between the two highest-ranked analytical units. Dispersed low-intensity recreational configurations demonstrate the highest structural robustness, whereas infrastructure-intensive zones exhibit management-dependent compatibility. The findings show how spatial planning in protected areas can operationalize compatibility as a measurable decision-support principle without substituting statutory zoning logic. Full article

The spatial assessment of soil erosion drivers provides essential information for prioritizing soil conservation areas. This study aims to compare the performance of the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression (GWR) model in explaining and analyzing the spatial variations of soil erosion in the Qara-Su watershed (Ardabil Province, Iran) and identifying the relative roles of the driving factors affecting erosion. To determine the relative importance of factors influencing soil erosion in the Qara-Su watershed, potential soil erosion (A) data and RUSLE model factors, including R, K, LS, C, and P, were collected at 13,845 points within the watershed. Initially, general relationships between erosion and contributing factors were examined using the OLS regression model. Subsequently, to analyze the spatial variability of relationships and identify the relative importance of factors at different locations within the watershed, the GWR model with an adaptive kernel and optimal bandwidth selection based on AICc was employed. The performance of the OLS and GWR models was compared based on fit indices such as R² and Akaike Information Criterion corrected (AICc), and the relative importance of erosion factors was determined based on the mean local GWR coefficients. Results from the RUSLE model indicated an average annual soil erosion of approximately 7.64 tons per hectare, suggesting that the watershed falls into the moderate erosion risk category. According to the GWR model, significant improvements in explaining variations and reducing errors were observed, with higher R² and adjusted R² values (0.62 vs. 0.50) and lower AICc values (3687 vs. 97,848) compared to the OLS model. The local GWR coefficients confirmed spatial non-stationarity and revealed that LS (topography) has the highest importance in mountainous areas. The C factor showed a stronger protective effect in agricultural land-use areas. These results provide a basis for developing targeted strategies to mitigate and manage erosion drivers with higher relative importance and facilitate a better understanding of the causes and mechanisms of soil erosion across the watershed. Full article

The urgency to mitigate greenhouse gas emissions and address the accelerating impacts of climate change has placed renewable energy as a core part of global climate strategies. However, the expansion of renewable infrastructures with a focus on solar systems often generates competition with other land uses, raising concerns about land availability, environmental integrity, and social acceptance. Renewable energy solutions deployment must be aligned with sustainable land-use planning, particularly in diverse and multifunctional landscapes. This study presents a GIS-based Multi-Criteria Decision-Making (MCDM) methodology to identify the most suitable areas for implementing a set of six land-use-based adaptation and mitigation solutions (LAMSs) focused on solar energy. Using Python-based processing algorithms and high-resolution spatial datasets, the methodology integrates technical, environmental, and socioeconomic criteria to generate suitability maps for three different case studies across Europe: Almería (Spain), Valle d’Aosta (Italy), and the Azores (Portugal). Results reveal significant geographical disparities in suitability due to the different land constraints. Almería and the Azores demonstrate high potential for photovoltaic and agrovoltaic farms, while Valle d’Aosta’s mountainous terrain is more limited for these measures. Floating solar and solar land management measures show limited applicability across all sites. The analysis highlights the value of place-based approaches in energy planning and the utility of GIS-MCDM tools to support evidence-based decision-making, enabling context-sensitive deployment of renewable energy infrastructure. Full article

Rail trespassing remains a persistent safety challenge at the system level in the United States, with a 24% increase in incidents within the last decade (2016–2025). Identifying hotspots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study thus creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hotspots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive attributes such as rail miles, grade crossings, population density, and land-use types. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method also identified four structural risk environments, and a Composite Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results indicate that clusters characterized by higher rail infrastructure exposure and mixed land-use environments exhibit the highest CRI values and elevated hotspot probabilities. In contrast, clusters with limited rail infrastructure, including predominantly commercial and rural ZIP codes, show substantially lower risk levels. The findings highlight that trespassing risk is more strongly associated with structural exposure conditions than with isolated historical incident counts. The resulting risk surfaces and hotspots provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies. Full article

This study aims to identify agricultural land suitability in Mardin province, located in the semi-arid Southeastern Anatolia Region of Türkiye. Within this framework, eight ecological criteria were selected to assess agricultural land suitability. Criterion weights were derived from expert judgments using the Analytical Hierarchy Process (AHP), a Multi-Criteria Decision-Making (MCDM) method. The criteria were evaluated within the framework of the five classes used in agricultural land-use suitability, in accordance with the guidelines of the Food and Agriculture Organization of the United Nations (FAO). Based on this classification, maps of the determined criteria were prepared using Geographic Information Systems (GISs), and an agricultural land-use suitability map was produced using a weighted overlay approach. The results indicate that 31.3% of the total land area in Mardin province falls within the highly and moderately suitable classes. For validation, the suitability map was overlaid with the Coordination of Information on the Environment (CORINE) Land Cover (CLC) 2018 data, revealing that 98.8% of highly suitable (S1) areas and 94.6% of moderately suitable (S2) areas correspond to existing agricultural lands. Furthermore, Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) value of 0.815, indicating an acceptable-to-good discrimination ability and confirming the robustness of the model. Full article

This study considers temperature gradients over recent decades near Trujillo, Peru, (8.15 S, 78.95 W) using high-resolution data assimilation. Statistical analyses describe a steep gradient from the cool foggy coast to the warm coastal plains below the Andes. A cross-coast transect is analyzed for seasonal changes in maximum air temperature from SENAMHI station data interpolated with satellite infrared measurements. Weather forecasts aimed at the urban area show a cool bias at higher temperatures and often under-represent the landward increase of 5 °C/10 km, induced by wind-driven upwelling and turbulent heat flux. Morning fog-stratus tends to delay diurnal heating on the beachfront, whereas, a few kilometers inland, warming occurs due to the segregating effect of channeled long-shore winds. Although seasonality is limited near Trujillo, winter exhibits the greatest variance of maximum temperature due to fluctuations of cloud albedo. Regressions of temperature time series onto meteorological fields identify that a subtropical trough/ridge pattern leads to higher winter values due to weaker upwelling, warmer sea temperatures, and reduced fog-stratus. Long-term trends for increased sea/land gradients have implications for the adaptation to climate change. Full article

Water scarcity in semi-arid closed-basin systems is increasingly driven by hydrological and land transformation processes. This study integrates multi-temporal remote sensing and physicochemical data to examine spatio-environmental drivers of surface water decline in Lake Manyara. Normalized Difference Water Index (NDWI) maps derived from dry-season Landsat imagery (July 2015 and July 2025) were used to quantify surface water dynamics, while supervised Maximum Likelihood land use/land cover (LULC) classification provided a characterized existing spatial context of the study area. Physicochemical parameters derived from recent field observations were evaluated using Carlson’s Trophic State Index (TSI). Results indicate a 31.7% reduction in dry-season surface water extent, from 232.4 km² in 2015 to 158.7 km² in 2025, accompanied by a marked spectral shift toward more negative NDWI values, reflecting extensive lakebed exposure. Agricultural expansion and bare land surfaces were spatially associated with stronger negative NDWI patterns (r ≈ −0.64, p < 0.05). Water quality assessment revealed extreme hypereutrophic conditions (TSI = 98.07), characterized by elevated phosphorus, nitrate, and chlorophyll-a, and high ionic concentrations. The findings demonstrate that hydrological contraction, eutrophication, and catchment land transformation are interconnected processes intensifying water scarcity in semi-arid lake systems. Full article

Outbreaks of emerging and re-emerging diseases in both animals and plants are increasing due to climate change, globalization, land-use change, and agricultural intensification. While most pathogen monitoring programs focus on zoonotic threats, wildlife and other organisms in natural habitats can also serve as reservoirs and sentinels for pathogens of agricultural and ecological concern. Plant communities and the pathogens circulating within them are underrepresented in integrated disease monitoring frameworks. This study demonstrates how biodiversity and zoonosis monitoring programs conducted in protected habitats (tallgrass prairies and woodlands) across Illinois, together with insect specimens preserved in biorepositories, can be leveraged to improve knowledge of the identities and ecological associations of a wide range of potential pathogens. We developed an integrative workflow combining taxonomic identification, molecular screening, and epidemiological inference to detect vector-borne plant pathogens from archived insect material. Focusing on Hemiptera (Auchenorrhyncha), we screened specimens for phytoplasmas (Mollicutes), uncultured bacterial plant pathogens transmitted by sap-feeding insects, and characterized host–pathogen associations. At least three distinct phytoplasma strains were detected: ‘Candidatus Phytoplasma asteris’ (16SrI-B), ‘Candidatus Phytoplasma pruni’-related strains (16SrIII), and ‘Candidatus Phytoplasma sacchari’-related strains (16SrXI-H). The latter represents the first documented occurrence of a 16SrXI-H phytoplasma subgroup in Illinois. Overall, we identified five insect specimens harboring phytoplasmas across four preserved sites, all of which were previously unreported for insect–phytoplasma associations. These findings demonstrate the value of existing biodiversity and zoonosis monitoring infrastructures for proactive surveillance of plant pathogens and extending the One Health paradigm to explicitly include natural ecosystems. Full article