Search Results (910)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

UAV platforms equipped with RTK positioning and LiDAR sensors are increasingly used for landslide monitoring, offering frequent, high-resolution surveys with broad spatial coverage. In this study, we applied high-frequency UAV-based monitoring to the active Baldiola earthflow (Northern Apennines, Italy), integrating 10 UAV–LiDAR and photogrammetric surveys, acquired at average intervals of 14 days over a four-month period. UAV-derived orthophotos and DEMs supported displacement analysis through homologous point tracking (HPT), with robotic total station measurements serving as ground-truth data for validation. DEMs were also used for multi-temporal DEM of Difference (DoD) analysis to assess elevation changes and identify depletion and accumulation patterns. Displacement trends derived from HPT showed strong agreement with RTS data in both horizontal (R² = 0.98) and vertical (R² = 0.94) components, with cumulative displacements ranging from 2 m to over 40 m between April and August 2024. DoD analysis further supported the interpretation of slope processes, revealing sector-specific reactivations and material redistribution. UAV-based monitoring provided accurate displacement measurements, operational flexibility, and spatially complete datasets, supporting its use as a reliable and scalable tool for landslide analysis. The results support its potential as a stand-alone solution for both monitoring and emergency response applications. Full article

Background: Despite the growing demand for amyloid PET quantification, practical challenges remain. As automated software platforms are increasingly adopted to address these limitations, we evaluated the reliability of commercial tools for Centiloid quantification against the original Centiloid Project method. Methods: This retrospective study included 332 amyloid PET scans (165 [¹⁸F]Florbetaben; 167 [¹⁸F]Flutemetamol) performed for suspected mild cognitive impairments or dementia, paired with T1-weighted MRI within one year. Centiloid values were calculated using three automated software platforms, BTXBrain, MIMneuro, and SCALE PET, and compared with the original Centiloid method. The agreement was assessed using Pearson’s correlation coefficient, the intraclass correlation coefficient (ICC), a Passing–Bablok regression, and Bland–Altman plots. The concordance with the visual interpretation was evaluated using receiver operating characteristic (ROC) curves. Results: BTXBrain (R = 0.993; ICC = 0.986) and SCALE PET (R = 0.992; ICC = 0.991) demonstrated an excellent correlation with the reference, while MIMneuro showed a slightly lower agreement (R = 0.974; ICC = 0.966). BTXBrain exhibited a proportional underestimation (slope = 0.872 [0.860–0.885]), MIMneuro showed a significant overestimation (slope = 1.053 [1.026–1.081]), and SCALE PET demonstrated a minimal bias (slope = 1.014 [0.999–1.029]). The bias pattern was particularly noted for FMM. All platforms maintained their trends for correlations and biases when focusing on subthreshold-to-low-positive ranges (0–50 Centiloid units). However, all platforms showed an excellent agreement with the visual interpretation (areas under ROC curves > 0.996 for all). Conclusions: Three automated platforms demonstrated an acceptable reliability for Centiloid quantification, although software-specific biases were observed. These differences did not impair their feasibility in aiding the image interpretation, as supported by the concordance with visual readings. Nevertheless, users should recognize the platform-specific characteristics when applying diagnostic thresholds or interpreting longitudinal changes. Full article

Wave deformation and sediment transport nearest the shoreside are among the main reasons for sand erosion and beach profile changes. In particular, identifying the areas of incident-wave breaking and longshore current generation parallel to the shoreline is important for understanding the morphological changes of coastal beaches. In this study, a two-phase incompressible flow model along with a sandy sloping topography was employed to investigate the wave deformation and longshore current generation areas in a circular wave basin model. The finite volume method (FVM) was implemented to discretize the governing equations in cylindrical coordinates, the volume-of-fluid method (VOF) was adopted to differentiate the air–water interfaces in the control cells, and the zonal embedded grid technique was employed for grid generation in the cylindrical computational domain. The water surface elevations and velocity profiles were measured in different wave conditions, and the measurements showed that the maximum water levels per wave were high and varied between cases, as well as between cross-sections in a single case. Additionally, the mean water levels were lower in the adjacent positions of the approximated wave-breaking zones. The wave-breaking positions varied between cross-sections in a single case, with the incident-wave height, mean water level, and wave-breaking position measurements indicating the influence of downstream flow variation in each cross-section on the sloping topography. The cross-shore velocity profiles became relatively stable over time, while the longshore velocity profiles predominantly moved in the alongshore direction, with smaller fluctuations, particularly during the same time period and in measurement positions near the wave-breaking zone. The computed velocity profiles also varied between cross-sections, and for the velocity profiles along the cross-shore and longshore directions nearest the wave-breaking areas where the downstream flow had minimal influence, it was presumed that there was longshore-current generation in the sloping topography nearest the shoreside. The computed results were compared with the experimental results and we observed similar characteristics for wave profiles in the same wave period case in both models. In the future, further investigations can be conducted using the presented circular wave basin model to investigate the oblique wave deformation and longshore current generation in different sloping and wave conditions. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

In this study, we examined the microclimates at eight entrances to a karst system distributed between an elevation of 812 and 906 m in Southern Spain. The karst system, characterised by subvertical open tectonic joints that form narrow shafts, developed on the slope of a mountainous area with a Mediterranean climate and strong chimney effect, resulting in an intense airflow throughout the year. The airflows modify the entrance temperatures, creating a distinctive pattern in each opening that changes with the seasons. The objective of this work is to characterise the outflows and find simple temperature-based parameters that provide information about the karst interior. The entrances were monitored for five years (2017–2022) with temperature–humidity dataloggers at different depths. Other data collected include discrete wind measurements and outside weather data. The most significant parameters identified were the characteristic temperature (T_y), recorded at the end of the outflow season, and the rate of cooling/warming, which ranges between 0.1 and 0.9 °C/month. These parameters allowed the entrances to be grouped based on the efficiency of heat exchange between the outside air and the cave walls, which depends on the rock-boundary geometry. This research demonstrates that simple temperature studies with data recorded at selected positions will allow us to understand geometric aspects of inaccessible karst systems. Dynamic high-airflow cave systems could become a natural source of evidence for climate change and its effects on the underground world. Full article

The continuous monitoring of forest vegetation conditions is of the utmost importance. The commonly used tools for assessing vegetation conditions are the normalized difference vegetation index (NDVI) and its successor—the enhanced vegetation index (EVI). In this study, the NDVI and EVI were coupled with the data on the number of dead trees removed during sanitation felling in an area of 13,780 km² during the period 2015–2022. In order to determine which satellite-borne index best represents the actual condition of vegetation in forests of the European temperate zone, the classes of the trend in changes in the NDVI and EVI were compared with the respective trends in the volume of dead trees, following the assumption that a positive trend in the spectral index values should be reflected by a negative trend in the volume of dead trees, and vice versa. The analyses were carried out for pixels within the all-species mask in the study area and for pixels representing individual tree species. NDVI is a good predictor of forest vegetation in the European temperate zone and is substantially better than EVI. Spatially, NDVI yields more pixels showing a negative slope for the trend in changes in the spectral index values, while EVI seems to overestimate the number of positive slopes. A larger number of negative slopes in the trend in changes in NDVI seems to agree with the increasing volume of dead trees in the analysed period. Comparing the detected trend class masks for spectral indices and the multi-annual course of dead trees, in 12 out of 16 cases, the slopes of the trend in changes in NDVI agree with the slopes of the trend in the volume of dead trees, while for EVI, this number is reduced to 9. In addition, NDVI reflects the condition of coniferous tree species, Scots pine and Norway spruce, substantially better. Full article

Niche complementarity is suggested to be a main driver of productivity overyielding in diverse environments due to enhanced resource use efficiency and reduced competition. Here, we combined multiple different approaches to demonstrate that niche overlap is the most likely cause to explain a lack of overyielding of three tree species when grown in different species combinations. First, in an experimental planting we found no relationship between productivity and species diversity for leaf, wood, or root production (no slope was significantly different from zero), suggesting a lack of niche differences among species. Second, data extracted from the United States Department of Agriculture Forest Inventory and Analysis revealed that the species do not significantly co-occur in natural stands (p = 0.4065) as would be expected if coexistence was common across their entire range. Third, we compared trait differences among our species and found that they are not significantly different in multi-dimensional trait space (p = 0.1724). By combining multiple analytical approaches, we provide evidence of potential niche overlap that precludes coexistence and a positive diversity–productivity relationship between these three tree species. Full article

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We calculated the Normalized Difference Vegetation Index (NDVI), the Topographic Position Index (TPI), and Diurnal Anisotropic Heat (DAH) to assess vegetation dynamics across different topographic and thermal gradients. Generalized Additive Models (GAM) revealed that tree species exhibited more stable, regular seasonal NDVI trajectories, while shrubs showed moderate fluctuations, and herbaceous species displayed high interannual variability, likely reflecting sensitivity to climatic events. Spatial analysis indicated that trees predominated on steep slopes and higher elevations, herbs were concentrated in low-lying, moisture-retaining areas, and shrubs were more common in areas with higher thermal load. These findings highlight the significant role of terrain and temperature in shaping plant phenology and distribution, underscoring the utility of remote sensing and topographic indices for monitoring ecological processes in complex mountainous environments. Full article

The wind energy resources in the northwestern desert and semi-desert grassland regions of China are abundant. However, the ramifications of large-scale centralized wind farm operations on terrestrial rodents remain incompletely understood. In May and September 2024, we employed a grid sampling method combined with burrow counting and kernel density analysis to investigate the spatial distribution of Alashan ground squirrel (Spermophilus alashanicus) burrows in different wind turbine power zones (control, 750 kW, 1500 kW, 2000 kW, and 2500 kW) at the Taiyangshan wind farm in China. Using generalized additive models and structural equation models, we analysed the relationship between burrow spatial distribution and environmental factors. The results revealed no significant linear correlation between burrow density and turbine layout density, but was significantly positively correlated with turbine power (p < 0.05). The highest burrow density was observed in the 2500 kW zone, with values of 24.43 ± 7.18 burrows/hm² in May and 21.29 ± 3.38 burrows/hm² in September (p < 0.05). The squirrels exhibited a tendency to avoid constructing burrows within the rotor sweeping areas of the turbines. The burrow density distribution exhibited a multinuclear clustering pattern in both May and September, with a northwest–southeast spatial orientation. Turbine power, aspect, and plan convexity had significant positive effects on burrow density, whereas vegetation height had a significant negative effect. Moreover, vegetation height indirectly influenced burrow density through its interactions with turbine power and relief degree. Under the combined influence of turbine power, topography, and vegetation, Alashan ground squirrels preferred habitats in low-density, high-power turbine zones with shorter vegetation, sunny slopes, convex landforms, and minimal disturbance. Full article

Ochroma lagopus, a fast-growing tropical tree species, faces fertilization challenges due to slope heterogeneity in plantations. This study examined 3-year-old Ochroma lagopus at upper and lower slope positions under five treatments: CK (no fertilizer), F1 (600 g/plant), F2 (800 g/plant), F3 (1000 g/plant), and F4 (1200 g/plant) of secondary macronutrient water-soluble fertilizer. Growth parameters and N-P-K stoichiometry were analyzed. Key results: (1) Height increased continuously with fertilizer dosage at both slopes, while DBH peaked and then declined. (2) At upper slopes (nutrient-poor soil), fertilization elevated leaf P but reduced branch N/K and increased root P/K. At lower slopes (nutrient-rich soil), late-stage leaf N increased significantly, with roots accumulating P/K via a “storage strategy”. Stoichiometric thresholds indicated N-K co-limitation (early-mid stage) shifting to P limitation (late stage) on upper slopes and persistent N-K co-limitation on lower slopes. (3) PCA identified F4 (1200 g/plant) and F1 (600 g/plant) as optimal for upper and lower slopes, respectively. This research provides a theoretical basis for precision fertilization in Ochroma lagopus plantations, emphasizing slope-specific nutrient status and element interactions for dosage optimization. Full article

Deep learning has shown significant advantages over traditional spatial interpolation methods in single image super-resolution (SISR). Recently, many studies have applied super-resolution (SR) methods to generate high-resolution (HR) digital bathymetry models (DBMs), but substantial differences between DBM and natural images have been ignored, which leads to serious distortions and inaccuracies. Given the critical role of HR DBM in marine resource exploitation, economic development, and scientific innovation, we propose a frequency-aware texture matching transformer (FTT) for DBM SR, incorporating global terrain feature extraction (GTFE), high-frequency feature extraction (HFFE), and a terrain matching block (TMB). GTFE has the capability to perceive spatial heterogeneity and spatial locations, allowing it to accurately capture large-scale terrain features. HFFE can explicitly extract high-frequency priors beneficial for DBM SR and implicitly refine the representation of high-frequency information in the global terrain feature. TMB improves fidelity of generated HR DBM by generating position offsets to restore warped textures in deep features. Experimental results have demonstrated that the proposed FTT has superior performance in terms of elevation, slope, aspect, and fidelity of generated HR DBM. Notably, the root mean square error (RMSE) of elevation in steep terrain has been reduced by 4.89 m, which is a significant improvement in the accuracy and precision of the reconstruction. This research holds significant implications for improving the accuracy of DBM SR methods and the usefulness of HR bathymetry products for future marine research. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

The event scale method, employed for assessing changes in nitrogen (N) dynamics pre- and post-rain, provides insights into its transport to surface water systems. However, the relationships between N discharge in catchments dominated by different land uses and water quality remain unclear. This study quantified variations in key N components in ponds across forest, paddy field, and orchard catchments before and after six rainfall events. The results showed that nitrate (NO₃⁻-N) was the main N component in the ponds. Post-rainfall, N concentrations increased, with ammonium (NH₄⁺-N) and particulate nitrogen (PN) exhibiting significant elevations in agricultural ponds. Orchard catchments contributed the highest N load to the ponds, while forest catchments contributed the lowest. Following a heavy rainstorm event, total nitrogen (TN) loads in the ponds within forest, paddy field, and orchard catchments reached 6.68, 20.93, and 34.62 kg/ha, respectively. These loads were approximately three times higher than those observed after heavy rain events. The partial least squares structural equation model (PLS-SEM) identified that rainfall amount and changes in water volume were the dominant factors influencing N dynamics. Furthermore, the greater slopes of forest and orchard catchments promoted more N loss to the ponds post-rain. In paddy field catchments, larger catchment areas were associated with decreased N flux into the ponds, while larger pond surface areas minimized the variability in N concentration after rainfall events. In orchard catchment ponds, pond area was positively correlated with N concentrations and loads. This study elucidates the effects of rainfall characteristics and catchment heterogeneity on N dynamics in surface waters, offering valuable insights for developing pollution management strategies to mitigate rainfall-induced alterations. Full article

The mechanism of low- to middle-rank coal seam gas accumulation in the Baode block on the eastern edge of the Ordos Basin is well understood. However, exploration efforts in the Shizuishan area on the western edge started later, and the current understanding of enrichment and accumulation rules is unclear. It is important to systematically study enrichment and accumulation, which guide the precise exploration and development of coal seam gas resources in the western wing of the basin. The coal seam collected from the Shizuishan area of Ningxia was taken as the target. Based on drilling, logging, seismic, and CBM (coalbed methane) test data, geological conditions were studied, and factors and reservoir formation modes of CBM enrichment were summarized. The results are as follows. The principal coal-bearing seams in the study area are coal seams No. 2 and No. 3 of the Shanxi Formation and No. 5 and No. 6 of the Taiyuan Formation, with thicknesses exceeding 10 m in the southwest and generally stable thickness across the region, providing favorable conditions for CBM enrichment. Spatial variations in burial depth show stability in the east and south, but notable fluctuations are observed near fault F1 in the west and north. These burial depth patterns are closely linked to coal rank, which increases with depth. Although the southeastern region exhibits a lower coal rank than the northwest, its variation is minimal, reflecting a more uniform thermal evolution. Lithologically, the roof of coal seam No. 6 is mainly composed of dense sandstone in the central and southern areas, indicating a strong sealing capacity conducive to gas preservation. This study employs a system that fuses multi-source geological data for analysis, integrating multi-dimensional data such as drilling, logging, seismic, and CBM testing data. It systematically reveals the gas control mechanism of “tectonic–sedimentary–fluid” trinity coupling in low-gentle slope structural belts, providing a new research paradigm for coalbed methane exploration in complex structural areas. It creatively proposes a three-type CBM accumulation model that includes the following: ① a steep flank tectonic fault escape type (tectonics-dominated); ② an axial tectonic hydrodynamic sealing type (water–tectonics composite); and ③ a gentle flank lithology–hydrodynamic sealing type (lithology–water synergy). This classification system breaks through the traditional binary framework, systematically explaining the spatiotemporal matching relationships of the accumulated elements in different structural positions and establishing quantitative criteria for target area selection. It systematically reveals the key controlling roles of low-gentle slope structural belts and slope belts in coalbed methane enrichment, innovatively proposing a new gentle slope accumulation model defined as “slope control storage, low-structure gas reservoir”. These integrated results highlight the mutual control of structural, thermal, and lithological factors on CBM enrichment and provide critical guidance for future exploration in the Ningxia Autonomous Region. Full article