Search Results (6,407)

Canopy base height (CBH) is a key descriptor of forest vertical structure and an essential input for fire behavior modeling and ecosystem assessments, yet it remains difficult to retrieve reliably from satellite observations. Spaceborne waveform LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) mission provides detailed information on vertical vegetation structure through relative height (RH) metrics, but existing CBH studies have largely relied on empirically selected percentiles or indirect calibration approaches. Here, we present a physically informed framework for CBH estimation that interprets the full GEDI RH profile as a continuous representation of vertical energy distribution and identifies CBH as a structural transition within this profile. Three RH-based approaches—the first-derivative, clustering-threshold, and crown-length methods—were evaluated against independent UAV LiDAR observations. Among them, the clustering-threshold approach achieved the best agreement with UAV-derived CBH (R² = 0.71, RMSE = 1.27 m) and was selected for regional-scale mapping. Sparse GEDI-derived CBH samples were further integrated with Sentinel-2 optical data using a gradient boosting regression model to generate wall-to-wall CBH maps for the Jiagedaqi District, northeastern China, achieving an RMSE of 1.01 m against independent validation data. The results demonstrate that CBH can be retrieved directly from GEDI RH metrics without requiring region-specific airborne LiDAR calibration of the GEDI-based CBH retrieval itself, while UAV LiDAR is used only for independent validation. By advancing the interpretation of spaceborne waveform LiDAR for structural boundary detection, this study expands the utility of GEDI data for large-scale mapping of fire-relevant forest structural attributes. Full article

This paper focuses on the analysis of long-term land surface temperature (LST) dynamics and land-use changes in the city of Košice, Slovakia, during the period 1985–2025. The analysis is based on multi-temporal Landsat satellite imagery processed within a geographic information system (GIS) environment. Non-parametric statistical methods, including the Mann–Kendall trend test and the Theil–Sen slope estimator, were applied at the pixel level to identify the direction, magnitude, and statistical significance of long-term trends. Land-use changes were evaluated using CORINE Land Cover data together with the NDVI and NDBI spectral indices. The results revealed a statistically significant increase in land surface temperature across almost the entire urban area, with the mean LST increasing by 5.83 °C between 1985 and 2025. The analysis also confirmed a strong positive correlation between built-up areas and LST values, whereas vegetation cover exhibited a significant cooling effect represented by a strong negative correlation with surface temperature. Spatial analysis identified pronounced warming hotspots concentrated mainly in industrial and newly urbanized areas, while vegetation-stabilized zones showed lower warming intensity or localized cooling trends. The findings highlight the dominant influence of urbanization processes on the city’s thermal regime and emphasize the importance of urban vegetation as a key adaptation element for mitigating the surface urban heat island effect. The study also illustrates the added value of integrating remote sensing data, GIS tools, and pixel-based trend analysis in the assessment of long-term changes in the urban thermal environment of medium-sized Central European cities. The results provide a spatial basis for climate adaptation planning and future assessments of urban thermal comfort and environmental quality. Full article

The urban heat island (UHI) effect has become a prominent ecological and energy challenge amid rapid urbanization. This study comprehensively examined the spatiotemporal dynamics of UHI intensity in built-up areas across 216 Chinese cities spanning five climatic zones from 2000 to 2020 and quantified UHI-triggered energy consumption, as well as revealing its driving mechanisms. The results showed a significant increasing trend in UHI intensity across China’s urban built-up areas during summer days, summer nights, and winter nights from 2000 to 2020, with corresponding annual growth rates of 10.23, 5.61, and 5.08 km²·°C·a⁻¹, respectively. However, winter daytime UHI intensity declined dramatically from 4.72 °C in 2000 to −10.21 °C in 2020, which can be attributed to the reduction in socioeconomic activities during the COVID-19 period. UHI intensity intensified significantly across all climate zones, with the largest increases observed in the middle temperate zone and warm temperate zone, reaching 127.23 km²·°C and 116.04 km²·°C, respectively. Spatially, 39.8% of the 216 cities exhibited a significant increasing trend in UHI intensity, while only 2.8% showed a decreasing trend. After 2005, the contribution of large cities to UHI intensity continued to rise, reaching 54% in 2020. This study estimated UHI-induced energy consumption in terms of standard coal equivalent, with the northern and middle subtropical zones jointly accounting for over 61.9% of the annual average consumption. Regression results confirmed that impervious surface expansion served as the dominant positive driver of UHI, while vegetation coverage exerted a strong cooling effect. These findings can facilitate the formulation of region-specific UHI mitigation and energy conservation policies for cities under different climatic conditions and at diverse development scales. Mechanistic analysis further revealed that variations in impervious surface area dominated the rise in UHI intensity, whereas changes in the normalized difference vegetation index exerted a significant mitigating effect. These findings provide a solid scientific basis for targeted UHI mitigation and energy-saving management strategies for cities across different climate zones and urban scales. Full article

Background: Mental well-being is an important yet often overlooked component of cardiometabolic health. Dietary habits may influence psychological outcomes, but evidence among high-risk populations in Central and Eastern Europe remains limited. This study investigated the association between dietary behaviors and mental well-being among adults with cardiometabolic risk in Hungary. Methods: A cross-sectional analysis was conducted using data from the European Health Interview Survey (EHIS) 2019. The study included 2785 adults with cardiometabolic high risk (obesity, hypertension, or hypercholesterolemia). Mental well-being was assessed using the WHO-5 Well-Being Index and categorized as poor (≤50) or better (>50). Dietary habits, sociodemographic factors, and lifestyle factors were analyzed. Weighted multivariable logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: Overall, 25.9% of participants had poor mental health. In multivariable analyses, low intake of vegetables (OR = 1.15), fruits (OR = 1.55), fruit juice (OR = 1.26), and fish (OR = 1.17), as well as inadequate water intake (OR = 1.38), were each independently associated with higher odds of poor mental health after adjustment for sex, education, income levels, self-perceived health status, physical activity, and alcohol consumption. Conclusions: Healthier dietary behaviors, particularly higher consumption of vegetables, fish, and adequate hydration, are associated with better mental well-being among individuals with cardiometabolic risk. These results underscore the need for comprehensive intervention strategies that simultaneously address physical health and psychological well-being among vulnerable populations. Full article

Urban–rural fringes within contaminated regions frequently exhibit severe socio-environmental fragmentation and territorial stigmatization. This study evaluates the implementation of a Successional Agroforestry System (SAFS) in the “Land of Fires” (Southern Italy), which is conceptualized as a multifunctional socio-ecological infrastructure. Adopting a six-year longitudinal case study design (2019–2025), the research utilizes the Gioia methodology to triangulate retrospective field records and systematic monitoring with iterative qualitative narratives. Semi-quantitative and retrospective ecological evaluations indicate that the established multi-layered vertical stratification improved proxy indicators of structural complexity and soil functionality. Estimated soil surface coverage increased from 5.0 ± 1.2% to 85.0 ± 4.3%, while proxy vegetation density rose from 4.8 ± 1.2 to 36.4 ± 4.7 plants/m² (p < 0.001). Beyond these biophysical trends, the intervention catalyzed a “narrative inversion,” transitioning the site from a stigmatized wasteland to a socio-ecological hub that fostered a significant increase in community engagement (from 6.2 ± 1.4 to 34.8 ± 6.5 participants per event). By integrating agroecological practices with the EcoFoodFertility framework, the project highlights the potential of localized interventions to support primary environmental prevention strategies aligned with a One Health paradigm. The findings suggest that this SAFS represents a scalable model for territorial re-signification, offering transferable insights for aligning ecological restoration with social innovation in degraded peri-urban landscapes in accordance with Nature-Based Solutions (NBSs) and European Green Deal objectives. Full article

Accurate extraction of transmission towers from Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) point clouds is a prerequisite for overhead transmission line (OTL) acceptance. This task remains challenging because tower points are heavily entangled with ground, vegetation, conductors, and insulators, especially in complex terrain. To address this issue, we propose a shape prior-guided coarse-to-fine framework for tower extraction from UAV LiDAR point clouds. First, candidate tower regions are localized from the scene point cloud through preprocessing, near-ground suppression, and density-based clustering. Second, the least-disturbed central body of each candidate tower is identified in a slice-wise manner and used to estimate the tower orientation and four principal structural axes. Third, side-view and front-view structural envelopes are progressively inferred to suppress non-tower points around the tower body and tower head. Finally, a base-constrained filtering strategy is introduced to remove residual ground and low-vegetation points within the tower footprint. Experiments conducted on multiple OTL datasets acquired in different regions of China, including plains and mountainous areas, demonstrate that the proposed method achieves robust and efficient tower extraction across diverse scenarios. The results indicate that explicit structural priors offer a promising complement to feature-driven and data-intensive approaches, particularly in scenarios with limited annotated data and strict real-time requirements. The proposed method processes scene point clouds containing tens to hundreds of millions of points, with an average extraction time of approximately 100 to 300 s per tower depending on scene density. Full article

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

Predicting landslide displacement is important for geological-hazard early warning. In reservoir areas, displacement evolution is affected by rainfall, reservoir water level, vegetation variation, and the intrinsic non-stationarity of the displacement sequence, which makes accurate prediction difficult for conventional single-sequence models. To address this problem, this study proposes a residual-increment-oriented landslide displacement prediction framework that fuses multi-source monitoring variables. The displacement sequence is first processed into trend and periodic-related fluctuation representations, and the residual increment is used as the prediction target. Rainfall, reservoir water level, and the normalized difference vegetation index (NDVI) are incorporated as external monitoring variables. A cross-branch attention mechanism models interactions among heterogeneous feature branches, and a sparse MoE-based fusion module is introduced to adaptively adjust branch contributions under different deformation conditions. The model predicts the displacement residual increment, from which the final displacement is reconstructed. A case study using the Baishui River (Baishuihe) landslide monitoring dataset was conducted, together with additional validation on the related Bazimen Z110 landslide monitoring dataset and comparisons against conventional recurrent, convolutional, statistical, and Transformer-based baselines. The results show that the proposed model achieves lower RMSE and MAE than the compared methods on the tested datasets. These findings suggest that residual-increment modeling, multi-source monitoring variables, and condition-dependent branch fusion can improve short-term displacement prediction for the tested reservoir-area landslide cases. Full article

Quantifying vegetation ecological quality and ecological water requirement is essential for understanding ecosystem sustainability in arid regions. However, large-scale assessments of vegetation ecological quality and ecological water requirement thresholds remain limited in Central Asia. In this study, we developed a Vegetation Ecological Quality Index (VEQI) for Central Asia based on fractional vegetation cover (FVC) and net primary productivity (NPP) and estimated vegetation ecological water requirement quota (VEWRq) and total vegetation ecological water requirement (VEWR) using the Penman–Monteith method, the soil moisture limitation coefficient (SMLC), and GIS-based spatial analysis. We further examined the spatiotemporal variations in VEQI and VEWR during 2001–2020 and identified VEWRq thresholds corresponding to different VEQI levels. The results showed that (1) the multi-year mean VEQI in Central Asia was 28.46 and exhibited a slight increasing trend during 2001–2020; (2) the annual mean minimum, maximum, and optimal VEWRq were 147.53, 179.71, and 162.52 mm, respectively, corresponding to mean annual VEWR values of 146.98 × 10⁹ m³, 179.04 × 10⁹ m³ and 161.91 × 10⁹ m³, respectively; and (3) VEQI was positively correlated with VEWRq in 89.48% of the vegetation area. The VEWRq threshold increased with vegetation ecological quality. The five VEQI levels in Central Asia, namely very poor, poor, moderate, good, and very good, corresponded to VEWRq thresholds of 28.62–35.96, 88.33–107.81, 190.69–233.32, 362.86–432.81, and 678.59–838.31 mm, respectively. This study provides a remote sensing-based framework for evaluating vegetation ecological quality and quantifying ecological water requirement thresholds in arid regions and offers scientific support for regional ecological management and water resource allocation. Full article

Post-fire rehabilitation structures are widely used in Mediterranean burned landscapes to reduce runoff and sediment transfer, yet their ecological associations with early vegetation recovery remain insufficiently documented. This observational study assessed vascular plant composition, species richness, vegetation cover, plant density, aboveground biomass, and soil properties across log barriers, wattles, and log dams in the burned landscape of Ancient Olympia, western Greece. The study area belongs to the humid climatic class of the United Nations Environment Programme (UNEP) aridity framework based on the Thornthwaite aridity index, providing a comparatively wetter Mediterranean post-fire context. Paired depositional and eroded microsites in operationally restored post-fire areas were monitored in 2022 and 2023. The sampling design comprised nine plots and 18 microsites (n = 9 plots, 18 microsites). Generalized estimating equations (GEE), change-score models, principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) were performed to examine associations of monitoring year, microsite condition and rehabilitation structure type with soil and vegetation patterns. A total of 27 vascular plant species belonging to 16 families were recorded. The average vegetation cover increased from 39.17 ± 21.44% in 2022 to 75.11 ± 12.90% in 2023. Model-based marginal estimates with 95% confidence intervals indicated a large positive increase in vegetation cover over this period. Further, rapid early recovery was indicated by large increases in species richness, plant density and biomass. Depositional microsites were associated with stronger recovery signals than eroded ones, characterized by a larger increase in vegetation cover, density, biomass and species richness. Among rehabilitation structures, log dams showed the highest cumulative floristic richness and a broader observed floristic spectrum, although the species-level contingency analysis provided only marginal evidence for structure-associated differences in floristic composition. Changes in selected soil properties including total nitrogen (total N), ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), pH, electrical conductivity (EC), and exchangeable calcium (Ca), magnesium (Mg), and potassium (K), were detected between 2022 and 2023; the multivariate soil pattern was driven primarily by mineral nitrogen, pH, and EC. These findings suggest that, under operational post-fire restoration conditions, rehabilitation structures are associated not only with erosion-control functions but also with microsite differentiation that may shape early plant establishment and biodiversity recovery in Mediterranean burned landscapes. Full article

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

This study aimed to identify areas at risk of deforestation in the Cerrado biome of the Brazilian Midwest (states of Mato Grosso, Mato Grosso do Sul, and Goiás) and to estimate potential soil losses due to water erosion under land-use change scenarios. The methodology integrated the Universal Soil Loss Equation (USLE), spatializing rainfall erosivity (R), soil erodibility (K), topographic factor (LS), and cover-management factor (CP), with the ACEU (Accessibility, Cultivability, Extractability and Unprotected/protection status) model to assess deforestation risk based on accessibility, agricultural suitability, extractive activities, and legal protection status. Results indicated an average soil loss of 0.11 t ha⁻¹ year⁻¹ under natural vegetation cover, with 90% of the area presenting losses below 0.25 t ha⁻¹ year⁻¹. However, 27.5% of the remaining natural cover is located in areas classified as high or very high deforestation risk, indicating significant environmental vulnerability. Simulated scenarios of land-use conversion to pasture and annual crops revealed substantial increases in soil loss, particularly under annual cropping systems, potentially exceeding soil loss tolerance thresholds across millions of hectares. The findings demonstrate that integrating deforestation risk assessment with erosion modeling is a strategic tool for environmental planning, reinforcing the importance of preserving native vegetation to maintain ecosystem services and ensure long-term environmental sustainability. Full article

Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R² = 0.628; biomass: R² = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g⁻¹) than in ‘Favori’ (2.5 mg g⁻¹). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits. Full article

Background: Pregnancy is a vital period during which maternal nutrition profoundly influences both maternal health and fetal development. Carotenoids, predominantly found in fruits and vegetables, are bioactive compounds that enhance antioxidant defenses and facilitate vitamin A metabolism throughout pregnancy. However, assessing carotenoids intake presents challenges due to the lack of dietary assessment tools capable of quantifying individual carotenoids, coupled with limited data from populations in Eastern Europe. Methods: A cross-sectional study involving 621 pregnant women in Romania was conducted to estimate dietary carotenoids intake and investigate associations with dietary patterns and overall diet quality. Dietary data were obtained using the EPIC Food Frequency Questionnaire (EPIC-FFQ), adapted for Romanian populations. A dedicated carotenoid estimation model was developed utilizing the USDA Carotenoid Database. Principal component analysis (PCA) was employed to identify dietary patterns, and diet quality was evaluated using the Diet Quality Index during Pregnancy (DQI-P). Results: The findings revealed significant individual variability. The median intake was highest for β-carotene (2464 μg), and lycopene (1664 μg), followed by lutein and zeaxanthin (908 μg), α-carotene (615 μg), and β-cryptoxanthin (121 μg). The Vegetable-meal pattern exhibited the strongest positive correlation with carotenoids intake, whereas the Energy-dense pattern was primarily associated with vitamin E and tocopherols/tocotrienols, and the Mixed pattern with vitamins A and D. Higher DQI-P scores were consistently correlated with increased carotenoids consumption. Conclusions: Overall, maternal carotenoids intake during pregnancy was frequently insufficient and showed considerable variation among women. A diet rich in vegetables and higher overall diet quality were associated with elevated carotenoids intake levels. These findings enhance the understanding of dietary carotenoids intake among pregnant women in Eastern Europe. Full article