Search Results (265)

Flood susceptibility maps are essential tools for identifying high-risk areas. However, traditional approaches often face limitations in spatial resolution and adaptability under changing climatic conditions, particularly in data-scarce regions. This study addresses these limitations through a data-driven geospatial approach that integrates high-resolution Earth Observation and Geographic Information Systems (GIS) data to improve flood susceptibility assessment in a small river basin in Romania. Ten flood conditioning factors were analyzed, including Elevation, Slope, Topographic Wetness Index (TWI), Topographic Position Index (TPI), Profile Curvature, Aspect, Soil Texture, Distance to the River, Normalized Difference Vegetation Index (NDVI), and Soil Moisture. Historical flood extent data extracted from PlanetScope imagery were used for model training and validation. Two statistical methods, Frequency Ratio (FR) and Weight of Evidence (WoE), were applied to map flood susceptibility at a 12.5 m resolution. Results indicate that both models captured the spatial variability of flood-prone areas, but WoE achieved higher predictive performance (AUC = 0.945) than FR (AUC = 0.876), while FR tended to underestimate flood-prone zones. Half of the basin falls within low to very low susceptibility classes, whereas high and very high susceptibility together occupy about 25–29% of the basin and concentrate along river corridors in the central and southern sectors, overlapping with built-up areas. Consequently, about 38% (WoE) and 30% (FR) of the total built-up area fall within high and very high susceptibility classes. The results demonstrate that integrating high-resolution open-source Earth Observation data with statistical modeling provides a reliable, transferable framework for flood susceptibility assessment and land-use planning in data-scarce environments. Full article

Long-term assessment of vegetation recovery in mining-disturbed landscapes is essential for ecological restoration and sustainable land management. This study assessed fractional vegetation cover (FVC) dynamics from 2000 to 2024 in a hilly coal-mining region of the middle Yellow River Basin using Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. Annual FVC was retrieved from maximum-value NDVI composites using a pixel dichotomy model. Theil–Sen trend analysis, the Mann–Kendall test, lag-1 autocorrelation assessment, coefficient of variation (CV), partial correlation analysis, and the geographical detector model were combined to quantify vegetation recovery, interannual variability, and hydroclimatic–topographic associations. To avoid ambiguity in spatial interpretation, statistics were calculated for the full study region, coalfield polygons, and the surrounding non-mining area. FVC increased across 67.78% of the full study region, 65.42% of the coalfield polygons, and 68.66% of the surrounding non-mining area. High-FVC zones expanded from 33.50% in 2000–2004 to 71.56% in 2020–2024. Because significant positive lag-1 autocorrelation occurred in 33.36% of valid pixels, nominal Mann–Kendall significance was interpreted cautiously. Low- and very-low-CV classes dominated the coalfields, while high-variability pixels were localized monitoring priorities. Precipitation was broadly positively associated with FVC, whereas the independent temperature effect was weak and mostly non-significant. Actual evapotranspiration (AET) had the highest explanatory power in the full geographical-detector model, but an AET-excluded sensitivity analysis showed that temperature, elevation, and precipitation remained important explanatory variables. Thus, AET should be interpreted as an integrated vegetation–water–energy coupling indicator rather than a fully independent causal driver. These findings support restoration-priority identification and sustainable land management in hilly mining regions. Full article

Improving water use efficiency (WUE) is a crucial approach to addressing agricultural water scarcity and promoting the sustainability of agricultural ecosystems. Although extensive research has been conducted on the factors associated with cropland WUE, the relationship between cropland fragmentation and WUE remains poorly understood. This study focused on northern China and employed an integrated “size–shape–configuration” framework to measure cropland fragmentation from 2005 to 2020. A geographically weighted regression (GWR) model was then applied to analyze the spatially varying relationships between cropland fragmentation and WUE. The results showed that over half of northern China experienced intensified cropland fragmentation. Noticeable spatial heterogeneity was observed, with relatively low fragmentation in the North China Plain and the Northeast China Plain and higher levels in the topographically complex northwestern region. Throughout the study period, the size-based fragmentation index (FI_size) consistently exhibited higher mean values than the shape-based (FI_shape) and configuration-based (FI_config) indices. The mean cropland WUE across the 1027 analysis units increased from 0.962 to 1.071, although 220 units—mainly distributed in the three northeastern provinces—experienced a decline. Among the analysis units with statistically significant local coefficients, FI_config was predominantly negatively associated with WUE, whereas FI_size and FI_shape were predominantly positively associated with WUE, with the positive associations mainly concentrated in the North China Plain. Moreover, FI_config exhibited larger absolute local coefficient magnitudes and greater spatial variability than the other two fragmentation dimensions. Environmental factors also showed distinct spatial associations with WUE, with annual precipitation and NDVI being predominantly positively associated with WUE, whereas soil erodibility and slope were mainly negatively associated. These findings highlight the need for region-specific management of cropland landscape patterns. This study provides preliminary quantitative evidence of the relationships between cropland fragmentation and WUE, offering valuable insights for improving WUE and promoting sustainable agricultural management. Full article

Most remote-sensing assessments within China’s large-scale forestry engineering regions have relied primarily on greenness indicators, leaving productivity recovery and remaining restoration potential insufficiently characterized. Here, we assessed vegetation greenness and productivity recovery status, together with habitat-constrained remaining recovery potentials, within China’s Eight Forestry Engineering Regions from 2001 to 2025 using long-term Normalized Difference Vegetation Index (NDVI) and net primary productivity (NPP) datasets and climatic, topographic, and soil variables. Both NDVI and NPP increased significantly across all regions, with overall trends of 0.0029 yr⁻¹ and 3.38 g C m⁻² yr⁻¹ per year, respectively. The middle Yellow River shelterbelt region showed the strongest increasing trend, with 94.4% and 98.4% of vegetated pixels exhibiting significant increases in NDVI and NPP, respectively. The sliding-window similar-habitat model revealed that most regions have already approached their habitat-constrained potential states, though substantial remaining potential persisted in parts of the Three-north shelterbelt program and ecotonal areas. Greenness and productivity recovery potentials were positively correlated (Pearson r = 0.637), yet 14.0% of the vegetated area exhibited low greenness but high-productivity remaining potential, indicating that apparent greening does not necessarily translate into equivalent productivity recovery. These findings highlight the importance of jointly evaluating vegetation structural and functional recovery using greenness and productivity indicators. They also provide a scientific basis for differentiated restoration assessment and management within China’s large-scale forestry engineering regions. Full article

The position of urban road networks along the spectrum from geometric order to organic growth reflects the long-term tension between institutional planning and adaptive accommodation to local conditions, yet systematic quantification of this continuum in large historical urban datasets remains absent. This study digitizes road network data for 256 Qing Dynasty county cities using GIS methods and proposes a Geometric–Organic Index (GOI), a composite measure formed by equal-weight averaging of four sub-indicators: cross-junction ratio (CrossR), orientation regularity (OrientR), block regularity (BlockR), and connectivity ratio (ConnR). The results show that the GOI follows a continuous unimodal distribution (mean 0.346, median 0.341, n = 220, representing cities for which all four sub-indicators including block regularity could be computed), confirming that the geometric–organic dimension constitutes a continuum rather than a binary classification. Across the 182 cities for which both GOI values and external city wall regularity scores are simultaneously available, wall regularity correlates significantly with road network GOI (Pearson r = 0.320, p < 0.001). Mediation analysis reveals that topographic relief influences road network structure indirectly through wall regularity (Sobel z = −2.15, p = 0.032; Bootstrap 90% CI excludes zero), establishing city walls as morphological templates through which environmental constraints are transmitted to the urban interior. Primal-graph-based syntax-style validation on 173 cities (the 182-city set restricted to n_nodes ≥ 10 and available road files) further shows that GOI correlates significantly with intelligibility (r = 0.403, p < 0.001) and synergy (r = 0.463, p < 0.001), while mean local integration correlates negatively with GOI (r = −0.349), revealing a structural trade-off between global order and local efficiency. The equal-weight GOI scheme proves robust across 624 weighting combinations (Kendall $\tau$ = 0.928 for near-equal-weight combinations), and a global spatial autocorrelation test (Moran’s I = 0.137, p = 0.001) indicates weak spatial clustering without undermining the principal conclusions. This study provides the first large-sample empirical test of the morphological transmission hypothesis linking a city’s outer boundary to its interior road network, and offers a transferable quantitative framework for urban morphological typology. Full article

The formation and development of gullies are pervasive drivers of hillslope degradation, yet forecasting where and at what elevation gullies begin remains challenging. This study proposes a morphometric–energetic framework to anticipate gully-initiation zones in catchments developed on low-permeability lithologies and limited tectonic control across contrasting climatic and geomorphic settings. Using GIS analyses and morphometric parameters, with some derived from hypsometric curves, our objective is to link basin-scale morphology and energy distribution to the propensity for linear incision, thereby defining a statistically representative initiation belt and stream network positions most susceptible to gully initiation. The study results show that the altitudinal range most susceptible to gully development is at the mean basin’s elevation, and that this range can be associated with an energy potential (Şen’s “Energy Index”) similar to those used to calculate hydroelectric potential in a river basin. Furthermore, the study highlights that the contributing area required to activate these erosive processes varies within fairly narrow limits, between 1 and 3 ha. The framework is designed to be quantitative, transferable among landscapes, and parsimonious in data requirements, even if applicable, as mentioned, in basins with low-permeability lithology and limited tectonic control, and as a first-level predictive tool. By prioritizing diagnostics that can be computed from standard topographic datasets, the approach aims to support land-use planning and sediment-risk mitigation, offering a practical pathway for early identification and management of areas vulnerable to gullying. Full article

To study the soil moisture dynamics and rainfall infiltration characteristics of karst sloping farmland and their driving factors, an abandoned farmland was selected for this study, and five monitoring points (from the foot, S1, of the slope to the top, S5) were set along the terrain gradient. The volumetric water content data of the 0–40 cm soil layer was obtained through in situ monitoring for one year. The infiltration characteristics were quantified in combination with a staining tracer test, and the soil properties were determined. The results showed that the soil moisture content increased with the deepening of the soil layer, and there was significant slope differentiation. The moisture content in the downhill slopes (S1, S2) was significantly higher than that in the uphill slopes (S4, S5), and the annual average value of S5 was 27.4% lower than that of S1. The moisture difference (Δθ, the difference in moisture content between hillslope and flatland) changed from positive to negative from the foot of the slope to the top, indicating that moisture was transported downward along the slope surface. A dye tracer showed that from S1 to S5, the water transport pathway gradually shifted from exhibiting deeper vertical penetration and narrower lateral spread to showing shallower vertical penetration and wider lateral spread. The preferential flow index decreased from 46.6 ± 2.3% to 34.7 ± 2.1%, indicating a progressive reduction in rapid vertical channeling, while the lateral flow index reached its peak (21.4 ± 2.7%) in the middle of the slope (S3), suggesting enhanced horizontal water redistribution at this position. Correlation analysis indicated that soil bulk density was extremely significantly negatively associated with infiltration capacity, while capillary porosity, non-capillary porosity, total porosity, organic matter, and high aggregate content were extremely significantly positively associated with infiltration capacity. These results revealed that the topographic gradient affected soil moisture and water infiltration paths by regulating soil physical properties in this karst forest ecosystem. It should be noted that the research results are only applicable to one slope and should not be directly extended to all karst slope agricultural landscapes. Full article

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

Vegetation is an important component of ecosystems and plays an important role in carbon balance, water balance, and energy conversion. The spatial and temporal changes in the normalized difference vegetation index (NDVI), water resources, and hydrometeorological factors in southwest China between 2003 and 2020 were investigated using multisource remote sensing data. Correlation analyses were performed to assess the correlation among NDVI, water resource changes, and hydrometeorological factors with different time lags. A stepwise regression model with different lag times was constructed to clarify the effects of four topographic factors and eight climatic factors on NDVI, and the following conclusions were obtained: (1) NDVI increased from 2003 to 2020, and the increase became obvious after 2012. (2) NDVI was considerably affected by alterations in the soil water content caused by natural changes. The correlation of NDVI with evapotranspiration and precipitation was high, followed by NDVI’s correlation with surface temperature. The spatial distribution of the positive correlation between NDVI and evapotranspiration and NDVI and precipitation was relatively consistent, and a positive correlation was observed in most parts of Southwest China. (3) The hydrometeorological factors mainly affected NDVI with a lag of 0–1 month, and the correlation was high in western Sichuan and most of Yunnan. In Yunnan, Available Water Capacity (AWC) affected NDVI with a lag of 0–2 months; the lag was 0–1 month in western Yunnan and 1–2 months in eastern Yunnan. (4) In terms of different vertical heights, the NDVI in the regions with altitudes higher than 3000 m was affected by climate change, especially evapotranspiration and precipitation. (5) Digital Elevation Model (DEM), Latitude (Lat), Evapotranspiration (ET), Precipitation (PRCP), Land Surface Temperature (LST), and NDVI were closely related in the construction of stepwise regression models with different lag times. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

The Liuwan burial complex is the largest known prehistoric clan-based cemetery in the upper Yellow River region, making its preservation vital for Chinese cultural heritage and sustainable local development. To address threats from unregulated agricultural activities and illegal looting, this study proposes a non-invasive preventive protection approach. Surface-visible tombs were identified using low-altitude UAV imagery and deep learning models (YOLOv8n, YOLOv5n, RT-DETR-l, and Hyper-YOLO). By incorporating environmental factors such as elevation, slope, aspect, distance to water, Topographic Wetness Index, and Topographic Position Index, potential tomb distributions were modeled on the Biomod2 platform and key environmental drivers were analyzed. Hyper-YOLO achieved the highest identification accuracy (94.4%). The optimal model, EMwmean (TSS = 0.492, AUC = 0.798), showed that high-potential tomb areas are mainly concentrated in the central region, with tombs preferring elevations of 1964–1978 m, south-facing slopes, and slopes of 13.14–19.19°. This study demonstrates the feasibility of using deep learning to identify surface-visible tombs and predict their potential distributions based on environmental characteristics, thereby providing priority references for heritage protection in Liuwan rather than a definitive inventory of all subsurface remains or cultural phases. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

As the “last mile” of emergency management, rural emergency response capability vulnerability assessment is crucial for strengthening emergency systems. A three-dimensional vulnerability assessment framework was developed from a risk–capacity matching perspective, comprising exposure, sensitivity, and adaptive capacity. Taking four typical rural areas as case studies, we applied the comprehensive vulnerability index, the coupling coordination degree model, and the obstacle degree model to quantify vulnerability, analyze risk–capability matching, and identify obstacle factor patterns. The results show that (1) the quality of risk–capability matching determines the level of vulnerability; (2) high coupling produces a dual amplification effect, whose direction depends on matching quality; and (3) economic foundations set the upper resource limit for capacity building, while topographical conditions shape baseline risk pressure. The interaction of these two factors drives the spatial distribution of obstacle factors across villages. This study positions rural emergency response capacity as a core safety dimension for sustainable development, thereby providing a robust foundation for rural sustainability. Full article

Transparent and comparable evaluation of ecological restoration outcomes is essential for advancing performance-based environmental governance and ESG-aligned ecological compensation. However, existing grassland monitoring approaches in semi-arid regions often rely on single vegetation indices, which fail to capture ecosystem structure, functional recovery, and temporal dynamics. To address these limitations, this study proposes a process-oriented Restoration Index (RI) based on multi-source remote sensing data. By integrating spectral, textural, and phenological indicators, together with topographic and climatic factors, derived from Sentinel-2 and Landsat time-series imagery, the framework characterizes vegetation productivity, community structure, and seasonal ecological processes within a unified analytical framework. A case study in the Xilingol grassland of Inner Mongolia shows that different management strategies, including grazing exclusion, reseeding, and rotational grazing, are associated with distinct restoration trajectories and recovery performance. The results indicate that the RI captures both spatial heterogeneity and temporal evolution of ecosystem recovery, while the normalization procedure improves the relative comparability of restoration assessment results within the adopted framework. Quantitative evaluation shows positive agreement with field observations, providing preliminary support for the applicability of the approach within the study area. Overall, the RI framework provides a scalable and policy-relevant basis for ecological restoration assessment and may support ecological compensation evaluation, environmental auditing, and more transparent restoration governance. Full article

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article