Search Results (425)

Regional ecological security patterns provide a spatial basis for coordinating ecosystem conservation, restoration, and sustainable land management in ecologically fragile basins. However, the joint consideration of ecosystem multifunctionality, landscape connectivity, and existing protected areas remains limited in many ecological security pattern studies. Taking the Inner Mongolia section of the Yellow River Basin (IMYRB) as the study area, this study assessed nine provisioning, regulating, and cultural ecosystem services using the 2020 land-use map and other spatial datasets. Ecological sources were identified through multi-service hotspot analysis, while ecological corridors and pinch points were delineated using the minimum cumulative resistance model and circuit theory; the identified sources were further compared spatially with existing nature reserves. The analysis identified 33 ecological sources covering 34,264.28 km² (23.47% of the basin) and 80 ecological corridors. A pronounced east–west contrast was evident: ecological sources in the east were generally larger and more aggregated, whereas those in the west were smaller and more fragmented. Key pinch points were concentrated in narrow linkage zones between adjacent ecological sources in the eastern IMYRB, indicating critical connectivity bottlenecks. Only 9.7% of the ecological source area overlapped with existing nature reserves, revealing a substantial conservation gap. These findings highlight the need to prioritize the protection of uncovered ecological sources and maintain critical connectivity zones, providing a spatial basis for protected-area optimization and ecological restoration planning in the IMYRB. Full article

Accurate estimation of irrigation water use is essential for agricultural water accounting and water-resource allocation in large irrigated districts, yet existing statistics are usually available only as aggregated administrative totals and cannot adequately characterize seasonal and spatial differences in field-applied water. In this study, a soil moisture profile response-driven framework was developed to estimate spring and summer irrigation water use in the Hetao Irrigation District, a typical large-scale irrigated region in the upper Yellow River Basin. Soil property zones were first delineated using K-means clustering based on field capacity, wilting point, available water capacity, bulk density, porosity, and electrical conductivity, and season-specific soil profile response layers were identified through bootstrap stability tests using 0–100 cm daily soil moisture changes. A net water inflow response was then constructed by integrating soil water storage change, precipitation, and evapotranspiration, and six estimation models were compared, including a soil-water-response conversion model, historical-management baseline models, and recent-management baseline models. Model calibration was conducted for 2016–2021, and independent testing was performed for 2022. Spatial allocation constraints were further introduced to ensure consistency between total estimated irrigation volume and pixel-scale irrigation depth patterns. Results showed that the optimal soil profile stratification was 0–20/20–60/60–100 cm for spring irrigation and 0–20/20–50/50–100 cm for summer irrigation, indicating clear seasonal differences in profile response. For spring irrigation, the recent three-year management baseline model performed best, with a training-period RMSE of 8.8 mm and MAPE of 8.5%, and a testing-period RMSE of 8.4 mm, bias of −2.7%, and R² of 0.97. For summer irrigation, the historical-median management baseline model was most robust, with a training-period RMSE of 8.1 mm and MAPE of 14.0%, and a testing-period RMSE of 4.4 mm, bias of −9.4%, and R² of 0.96. Spatially, spring irrigation depths increased from 2019 to 2022 and were higher in WLBH, JFZ, and YJ, whereas summer irrigation depths were generally lower and more concentrated in western and central sub-irrigation districts. The proposed framework provides a practical approach for linking soil moisture profile response, management-based volume constraints, and spatially explicit irrigation mapping in large-scale irrigated regions. Full article

Land subsidence in soft-soil urban transport hubs arises from the complex coupling of natural geology and intensive anthropogenic activities, yet its spatial differentiation mechanisms and seasonal drivers remain poorly understood in high-development core areas. This study develops a progressive analytical framework integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), GeoDetector, Gaussian Mixture Model (GMM), and Singular Spectrum Analysis (SSA) to investigate spatiotemporal deformation patterns and driving mechanisms in the Shanghai Hongqiao Transport Hub Core Area from 2015 to 2024 using 209 Sentinel-1A images. Validation against official subsidence contours yields a Pearson correlation coefficient of 0.697 (p < 0.001) and an RMSE of 4.23 mm, confirming good spatial pattern agreement. Urban functional zones and construction stages are identified as the dominant influencing factors, with their interaction exhibiting notable bi-factor enhancement. Six distinct deformation response types are delineated via GMM, and two opposing seasonal signals are distinguished: near-instantaneous precipitation-driven surface loading on shallow soft soil and temperature-driven thermoelastic expansion of built structures. These findings may inform differentiated subsidence management and offer a transferable workflow for analogous soft-soil urban areas. Full article

Climate change drives shifts in forest vegetation zones, and the major tree species of warm–temperate evergreen broad-leaved forests in South Korea are also expected to undergo changes in their potential distributions under future climate conditions. This study applied a Committee Averaging (CA) ensemble species distribution model to Quercus acuta, Machilus thunbergii, Quercus glauca, and Castanopsis sieboldii to project changes in their potential distributions under four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) across two future periods (2050s and 2090s). To compare interspecific differences in response more clearly, we applied a two-tier threshold scheme that distinguished potential habitat (agreement ≥0.6) from highly suitable habitat (>0.8), and we concurrently conducted a Multivariate Environmental Similarity Surface (MESS) analysis to assess predictive uncertainty arising from extrapolation into future climates. The CA ensemble models showed excellent predictive performance (AUC 0.947–0.989; TSS 0.837–0.943). For Q. acuta, M. thunbergii, and Q. glauca, potential habitat expanded consistently across all SSP scenarios, and highly suitable habitat shifted northward into parts of the central and Gangwon regions. In contrast, both the potential habitat and the highly suitable habitat of C. sieboldii contracted under most future scenarios. These results demonstrate that even species belonging to the same warm–temperate evergreen broad-leaved forest community can respond differently to future climate. The two-tier threshold scheme applied in this study was effective not only for assessing whether distributions expand but also for delineating and evaluating climatically stable core habitats. Although our findings need to be interpreted in light of the uncertainty associated with extrapolation into future climates, they can serve as useful baseline data for establishing climate-change-adaptive forest conservation and species-specific management strategies. Full article

The concentrations of 18 chemical elements were determined in the upper soil horizons within the landscapes of river basins in the Eastern Caucasus, using the Ulluchay, Sulak, Sunzha, Samur, Shuraozen (Russia), Karachay, and Atachay (Azerbaijan) rivers as case studies. This research aims to provide an ecological and geochemical assessment of soil conditions in the mountain river basins of the Eastern Caucasus. The ecological status of the soils is largely governed by elevated concentrations of such elements as Zn, Ni, Cu, Mo, As, and Cr, which exhibit both accumulation tendencies and potential toxicity. Environmentally unfavorable areas were identified through an integrated scoring assessment that incorporates the values of four ecological and geochemical indices: the modified contamination factor (mCf), the Pollution Load Index (PLI), the Potential Ecological Risk Index (PERI), and the total contamination index (Zc). According to each index, more than half of the study area is classified as uncontaminated. Low PLI values were recorded for 54% of the sampling sites, and low mCf values for 63%. Based on PERI and Zc, 82% of the sampling sites are categorized as uncontaminated. The integral scoring assessment enabled the delineation of more than a dozen environmentally unfavorable areas, with the highest concentrations observed in the Atachay and Karachay basins, spatially extensive in the Sunzha basin. The formation of environmentally unfavorable zones in terms of soil contamination is primarily driven by natural factors, including lithological conditions, climatic features, complex topography, and the directions of waterborne and mechanical migration. Anthropogenic factors contribute to a lesser extent to the development of high-contamination zones and exert only localized influences near major settlements. The results can be applied to mitigate public health risks and to promote sustainable development of mountain river basins. Targeted measures are proposed for the sustainable management of contaminated areas, including restrictions on agricultural activities and the use of drinking water sources. Full article

In regions where severe topographic constraints coincide with intensive human activity, the mechanisms underlying landscape ecological risk (LER) and its spatial differentiation remain poorly understood. In particular, the nonlinear responses and threshold effects arising from the combined influence of complex natural gradients, urban expansion, and policy interventions have not been adequately characterized, limiting effective regional ecological management and policy formulation. Taking the Taiyuan section of the Fen River Basin as the study area, this study constructed an LER index using land-use data for 2014, 2019, and 2024. Landscape metrics and spatial autocorrelation analyses were used to characterize the spatiotemporal evolution of LER, and a LightGBM-SHAP model with spatial block cross-validation was employed to identify the nonlinear effects of natural and socioeconomic drivers. A four-quadrant zoning framework integrating current risk state and driver sensitivity was then developed. The results showed that: (1) LER followed a fluctuating trajectory, rising from 2014 to 2019 and declining from 2019 to 2024, with evident spatial differentiation. Low- and relatively low-risk zones dominated about 71% of the area, while medium- to high-risk zones clustered mainly in the northeast, south, and parts of the northwest; high-risk agglomerations gradually contracted. (2) LER was driven by both natural and socioeconomic factors, with natural factors playing the stronger role. Slope, NDVI, elevation, and GDP were the key drivers. (3) The effects of these drivers were strongly nonlinear: slope increased risk at 5–13° but reduced it above 13°; NDVI displayed an inverted U-shaped relationship, with the strongest positive contribution at 0.60–0.75.; and elevation shifted from a positive to negative contribution near 1200 m. Based on these results, the framework coupling risk state and driver sensitivity delineated differentiated management units, providing fine-scale spatial guidance for ecological protection, restoration, and development control in topographically constrained river-valley basins. Full article

Quarry waste piles are heterogeneous engineered embankments that are susceptible to slope instability, yet early detection of pre-failure surface changes remains challenging due to complex surface conditions and measurement uncertainty. This study presents an integrated remote sensing-based framework for monitoring quarry waste pile instability by combining multi-temporal change detection with scale-dependent surface roughness analysis. The original contribution of the proposed framework lies in linking displacement-based change detection with multi-scale characterization of surface roughness, enabling both observed surface movement and topographical conditions associated with developing instability to be evaluated within a unified monitoring approach. Multi-epoch Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) and photogrammetric point clouds were acquired before and after documented failure events at an active quarry site at active quarry sites located northeast of Montreal, Quebec, Canada. The regional climatic conditions, characterized by seasonal freeze–thaw cycles, rapid snowmelt, and periods of heavy rainfall, can promote water infiltration and elevated pore-water pressures, thereby increasing the susceptibility of these heterogeneous waste piles to slope instability. A standardized workflow was implemented, including precision alignment using a Recursive Iterative Closest Point (R-ICP) registration strategy, vegetation filtering with a multiscale CANUPO classifier, and uncertainty quantification through a Level of Detection (LoD) analysis. The resulting LoD thresholds were 10–15 cm for LiDAR-to-LiDAR comparisons and 34–36 cm for mixed-sensor datasets. Multi-scale roughness analysis revealed that zones which later experienced instability exhibited consistently higher and more heterogeneous roughness than adjacent stable areas within a well-defined linear scale range. A roughness-based A/D indicator enabled objective delineation of hazardous zones prior to failure. Post-failure monitoring showed surface smoothing following major displacement, followed by renewed roughness increases associated with secondary movements. These results demonstrate that scale-dependent roughness provides complementary information to displacement-based change detection, enabling potentially unstable areas to be identified and prioritized before substantial displacement becomes evident. The integrated framework can assist quarry managers in targeting field inspections and monitoring efforts toward higher-risk areas and support earlier preventive actions to reduce slope-failure risk. Full article

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries. Full article

Municipal records of human–wildlife conflict are widely available but remain underused for identifying conflict-prone urban interfaces and supporting preventive management. This study compared reported human–wild boar (Sus scrofa) conflict patterns and the land-use composition of high-density report zones in Olsztyn, Poland, in 2010 and 2020. Municipal records of wild boar presence, complaints, and interventions were matched to street-network features and integrated with harmonized land-use data in a geographic information system. Kernel density estimation delineated high-density report zones, Getis–Ord Gi* analysis characterized street-segment clustering, and land-use overlay and descriptive compositional metrics quantified differences between years. High-density zones shifted from an extensive, peripheral configuration in 2010 to a markedly more compact pattern integrated into the urban fabric in 2020. Proportional shares increased for transport (+12.64 percentage points), residential (+10.12), and recreational areas (+2.40), while industrial and wasteland areas (−13.13), grasslands (−6.33), and forests (−4.85) decreased. These findings indicate a reorganization of reported conflict geography, not changes in wild boar abundance, habitat preference, movement behavior, or synurbization. Routinely collected municipal records can support targeted monitoring, traffic-risk mitigation, waste management, green-space planning, and preventive urban wildlife management. Full article

The escalating global contradiction between water supply and demand has imposed novel imperatives on regional water security within the paradigm of resilient city development. To elucidate the supply–demand dynamics of water provisioning services under heterogeneous institutional contexts, this study selected the Guangdong–Hong Kong–Macao (GHKM) region as the empirical study area. Employing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, we coupled meteorological, land-use/land-cover (LULC), and pedological data to quantify the provisioning of water yield services in 2024. Concurrently, sector-specific water demands—encompassing agricultural, industrial, domestic, and ecological categories—were accounted for using statistical yearbooks. Subsequently, a Supply–Demand Index (SDI) was formulated to delineate the spatial matching patterns. The findings reveal pronounced spatial heterogeneity in water service provisioning; high-value zones are predominantly aggregated along the western and southern coastal belts, whereas low-value zones are dispersed across the northern mountainous terrains. Based on the SDI classification, the study area comprises 12 supply-surplus, 6 supply–demand-equilibrium, and 5 supply-deficit administrative units. Notably, the northern Guangdong mountainous region assumes a critical ecological role in water conservation, whereas the core megalopolises of the Pearl River Delta exhibit an acute dependency on extrinsic water subsidies, with Macao demonstrating a distinct ecological deficit in water provisioning. Furthermore, this study uncovers the overestimation artifact of water yield estimations over urban impervious surfaces, thereby providing a robust empirical foundation for the cross-regional synergistic governance and adaptive management of water resources. Full article

Global climate change is intensifying water scarcity in Mediterranean agroecosystems, demanding a transition from rainfed to irrigated management for high-value crops like vineyards and olive groves. This study introduces a novel hydrologic framework to assess field-scale rainwater harvesting potential across nearly 60,000 individual vineyard and olive grove parcels in continental Portugal. Unlike conventional valley-focused models that delineate catchments at drainage junctions, our approach uses high-resolution digital elevation models and open-source spatial libraries (Python’s Fiona, Rasterio, Whitebox) to link every agricultural pixel to its unique upstream hillslope catchment. We quantify and compare “in-parcel” resources (direct precipitation, Vp) with “ex-parcel” resources (upstream runoff, Vup) under historical (1981–2010) and future (2041–2070) climate scenarios (CMIP6; SSP1-2.6, SSP3-7.0, and SSP5-8.5). A central contribution of this study is the evaluation of water security, defined here as the relative safety buffer between harvested water and the biological irrigation requirements (Vip) prescribed for both cultures in each of seven agroclimatic zones defined across the country. Security categories are based on the ratio (Vp − Vip)/Vip for in-parcel resources and (Vup − Vip)/Vip for ex-parcel resources, where values above zero indicate a sustainable surplus, and negative values signify a state of insecurity. Results demonstrate that ex-parcel resources are significantly more substantial, offering 2.5 to 35 times the potential of in-parcel counterparts. While vineyards currently exhibit high security nationwide, southern olive groves face a critical degradation from “secure” to “insecure” status by 2070 under fossil-fueled pathways (SSP5-8.5), with security indices dropping as low as −33.2 in the southern Alentejo region. This highlights ex-parcel runoff as a vital, underutilized hydrologic buffer that can safeguard Mediterranean agriculture against projected climate-induced deficits. Full article

Achieving synergistic governance between carbon emissions and ecosystem service value (ESV) in the Yellow River Basin (YRB) remains a significant challenge. Existing studies often rely on a single administrative scale and rarely extend spatial clustering results toward governance-oriented zoning frameworks that support differentiated management. To bridge these gaps, we developed a multi-scale remote sensing framework by integrating the China Land Cover Dataset (CLCD), NPP/VIIRS nighttime light (NTL) imagery, and energy consumption statistics to examine carbon emissions and ESV across the YRB at both county and grid scales. We estimated land-use carbon emissions by integrating emission coefficients with NTL modeling, while ESV was quantified via the equivalent factor method. By coupling dynamic carbon–ESV trends with their spatial association patterns (derived from bivariate LISA), we established a dual-dimensional Trend–Pattern framework for synergistic governance. Our findings reveal a pronounced asymmetry in regional development: while carbon emissions surged from 235 to 1033 million tons, ESV grew by a marginal 0.13% annually. Although carbon emissions and ESV are significantly negatively correlated (p < 0.001), this relationship diverges across scales—weakening at the county level but intensifying at the grid level. Notably, grid-scale analysis identified a contraction of over 56% in “Low carbon–High ESV” synergistic clusters in the upper reaches. This critical signal of declining carbon–ecological synergy was completely obscured by the averaging effect inherent in county-level assessments. Capitalizing on these scale-dependent insights, we delineated seven distinct governance zones, including Trend Control and Ecological Conservation Zones, thereby transitioning from mere spatial description to decision support for zoning-based governance. This study highlights the potential limitations of relying solely on single-tier administrative evaluations and offers a robust, multi-scale decision-support tool for carbon–ecological governance in the YRB. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Traditional soil management relies on “whole-field” averages, which leads to resource waste and environmental degradation under anthropogenic pressures. While combining electromagnetic induction (EMI) and remote sensing is known for digital soil mapping, current approaches lack a unified, automated framework to handle complex multi-source data dependencies for local-scale precision irrigation. To overcome this limitation, this study introduces a novel integrated methodology that couples high-resolution geophysical datasets and remote/proximal sensing through an automated machine learning workflow, capturing dynamic soil–human interaction boundaries more precisely than traditional empirical overlays. The general methodology was tested in a vineyard plot within the Torre Guaceto Natural Reserve (Southern Italy). Spatial datasets from EMI and remote sensing were integrated. Crucially, the K-means clustering algorithm was deployed early in the workflow to optimize the fused datasets and classify the plot into homogeneous zone clusters. The machine learning approach successfully identified two distinct main soil clusters. The spatial boundaries of these zones were rigorously validated using in situ soil moisture data from capacitance sensors, showing a statistically significant variance in volumetric water content between the two zones. This study demonstrates that integrated machine learning workflows can accurately delineate precision agricultural zones without relying on high-cost exhaustive sampling. It is recommended that farmers and managers within sensitive nature reserves adopt this cluster-based Variable Rate Application (VRA) for water and fertilizers to optimize resource efficiency and prevent nutrient leaching into underlying aquifers. Full article

Anthropogenic pressures increasingly threaten ecological connectivity and basin-scale ecological sustainability in peri-urban landscapes. This study proposes a Hybrid Ecological Typology framework for the Büyükçekmece Lake Basin (Istanbul, Türkiye), integrating Morphological Spatial Pattern Analysis (MSPA), Analytic Hierarchy Process (AHP), and Minimum Cumulative Resistance (MCR) analysis within a unified spatial decision-support system. The framework is applied to a 67,627.06 ha basin area, including a 25,976.99 ha terrestrial focus area. The results indicate a structurally heterogeneous landscape dominated by interior habitat zones (85.94%) distributed across 93 core patches. Despite this dominance, ecological connectivity is maintained through a highly fragmented network of 484 landscape elements, where limited bridge (0.08%) and branch (0.62%) structures highlight structural vulnerability. Edge-dominated zones (12.87%) further reflect strong anthropogenic fragmentation pressures. Connectivity analysis identifies 10 key habitat patches with dPC (Probability of Connectivity index) values exceeding 5% and 12 strategic ecological corridors supporting basin-scale ecological flows. The proposed hybrid typology delineates five functional planning categories: conservation areas (22.72%), ecological corridors (1.91%), restoration areas (1.63%), sustainable use areas (0.51%), and controlled development areas (8.11%). Although high-quality habitat cores dominate the basin, ecological connectivity remains spatially constrained, with bottleneck zones (0.89%) concentrated along transportation corridors that significantly reduce landscape permeability. Overall, the findings demonstrate that basin-scale ecological sustainability in peri-urban environments is governed not only by habitat quantity but also by the interaction between spatial configuration and resistance structures. The framework provides a transferable decision-support tool that bridges landscape ecology theory with spatial planning practice for basin management and ecological network design. Full article