Search Results (13,972)

Land Use and Land Cover (LULC) changes significantly influence flood hazard, especially in rapidly urbanizing areas like the Wadi Ibrahim watershed in Makkah, Saudi Arabia. This study analyzed the impacts of historical (2001–2025) and projected (2037) LULC changes on floods using remote sensing, GIS, and hydrological modeling with 30 m DEM and Landsat data. Urban growth was assessed from 2001, 2013, and 2025 maps, and future scenarios were simulated with the MOLUSCE plugin in QGIS using Cellular Automata–Artificial Neural Network (CA-ANN) techniques. Hydrological simulations were used to examine changes in flood discharge and response to LULC transitions. The results revealed substantial urban expansion, with built-up areas increasing from 12 km² (11%) in 2001 to 28.7 km² (26%) in 2025 and projected to reach 31.9 km² (28.3%) by 2037. The corresponding impervious surface fraction rose from 11% to 28% over the same period. Hydrological modeling for 50-, 100-, and 200-year return periods reveals a significant escalation in flood response, with peak discharge (Q_p) increasing by up to 12% and runoff volume (V) by approximately 9% between 2001 and 2037. The LULC classification using the Random Forest algorithm demonstrated strong and reliable performance, achieving an average Kappa (κ) value of 0.86, indicating almost perfect agreement. Overall, the findings underscore the need for sustainable land management to reduce flood risk in rapidly growing arid regions. Full article

The primary aim of this study is to identify the driving mechanisms behind long-term water-level changes and drought–flood transitions in Dongting Lake. To achieve this, we employed methods including the Improved Standardized Water Level Index (ISWI), Mann–Kendall test, Sen’s slope estimator, and a random forest–SHAP model to analyze hydro-meteorological data from 1992 to 2023. The results demonstrate a significant overall decline and spatial heterogeneity in water levels, alongside a systemic shift in the regional pattern from flood-dominated conditions to frequent droughts with intense drought–flood abrupt alternations. Crucially, during the critical autumn water recession period, runoff anomalies from the Yangtze River’s three outlets emerged as the dominant factor driving water-level changes, far exceeding the influence of local precipitation. Furthermore, a recent downward shift in the water level–discharge relationship indicates that under identical inflow conditions, water levels are now 1.5 to 2.0 m lower than in previous decades. These general findings highlight that critical-period inflow reductions and altered boundary hydrodynamic conditions mutually amplify low-water-level risks, providing a scientific reference for adaptive water resource management in complex river-connected lakes. Full article

Misiones, Argentina, holds one of the largest remnants of the Atlantic Forest, with almost 1.4 million hectares of native forest, representing a critical landscape for sustainable biodiversity conservation. However, connectivity across this ecoregion is increasingly threatened by habitat conversion, landscape fragmentation, and poaching pressures that extend beyond protected area boundaries, undermining long-term sustainability of wildlife populations. Using conservation detection dogs, we located, collected, and genetically confirmed 198 scats belonging to four game species: 20 lowland tapir (Tapirus terrestris), 72 white-lipped peccary (Tayassu pecari), 55 collared peccary (Pecari tajacu), and 51 Azara’s agouti (Dasyprocta azarae). Analyses examining species-specific habitat associations emphasized the importance of extending inference beyond point locations to encompass species’ home ranges, with native forest consistently identified as a key component of habitat use. The high prevalence of scats in mosaics of human-modified habitats outside protected areas, especially along their borders, underscores the importance of managing these areas as part of a broader sustainable landscape matrix. While native forest fragments outside of protected areas may serve as important refugia supporting species persistence, their contribution to sustainable management depends on reducing poaching pressure across these landscapes. There is an urgent need to expand antipoaching efforts beyond protected areas and across the Atlantic Forest in the Green Corridor of Misiones while preventing ongoing deforestation and the expansion of monoculture plantations. Achieving sustainable wildlife management in this region will require integrated strategies that promote sustainable land use, conservation planning, and rural development. Full article

Accurate simulation of karst spring discharge is critical for sustainable water resource management, yet it remains a significant challenge due to the inherent complexity, heterogeneity, and non-linearity of karst systems. While machine learning models have been increasingly applied to this problem, standalone algorithms often struggle to simultaneously capture complex temporal dependencies and maintain robust generalization. This study provides a comprehensive comparative assessment of five state-of-the-art machine learning (ML) models for forecasting the daily discharge of the Jósva Spring, located in the World Heritage Aggtelek karst area. The main goal of the study is to determine which modern machine learning approach can most accurately forecast the daily discharge of the Jósva Spring using meteorological data and the discharge of a hydraulically connected upstream spring. This is motivated by the need for a reliable operational prediction tool for complex karst aquifers, the improved water-resource management in a climate-sensitive region, and a lack of comparative studies evaluating multiple ML paradigms on the same karst system. The study also aimed at comparing the predictive performance of five state-of-the-art ML models to identify the most accurate and robust model and to understand the predictability of the karst system by analyzing feature importance, lag effects, and temporal dependencies. Three tree-based ensemble models (Random Forest, XGBoost, and Extra Trees) and two deep learning architectures (a Bidirectional Long Short-Term Memory network, BiLSTM, and a novel Hybrid XGBoost–BiLSTM model) were trained using a five-year (2015–2019) daily dataset comprising rainfall, temperature, and upstream discharge. The modeling framework was designed for synchronous simulation (lead time = 0 days), estimating concurrent downstream discharge using upstream and meteorological measurements from the same time step. A rigorous feature-engineering workflow was implemented based on statistical characterization, correlation analysis, and time-series diagnostics. Models were trained on 80% of the dataset and evaluated on an independent 20% test set. The results demonstrate that the proposed Hybrid XGBoost-BiLSTM model achieved the highest predictive accuracy on the unseen test data (R² = 0.74, NSE = 0.74, RMSE = 716.35 L/min). While the standalone tree-based models, particularly XGBoost (R² = 0.66), also exhibited strong and competitive performance, the hybrid architecture provided a consistent and measurable improvement across all evaluation metrics. The hybrid model’s success is attributed to its synergistic design, which leverages the powerful feature extraction and refinement capabilities of XGBoost to provide a more informative input space for the BiLSTM, thereby enhancing its ability to capture complex temporal dependencies while mitigating overfitting. Feature importance analysis confirmed that upstream discharge at a 3-day lag was the most critical predictor, highlighting the system’s hydraulic connectivity. This research provides clear, evidence-based guidance showing that hybrid machine learning architectures, which integrate the strengths of different modeling paradigms, represent the most effective approach for developing robust and reliable operational prediction tools for complex karst aquifers. Full article

Crown-level tree species semantic segmentation enables fine-grained forest inventory and management. Current high-precision tree species classification typically relies on multi-source remote sensing data, the acquisition and processing of which remain costly for large-area applications, making low-cost unmanned aerial vehicle (UAV) RGB imagery an attractive option for large-scale forest mapping. However, in heterogeneous forests, complex canopy structures and the limited spectral discriminability of low-cost UAV RGB imagery make 2D appearance cues alone insufficient for reliable species discrimination, crown delineation, and accurate separation of adjacent crowns. This often leads to inter-class confusion, blurred crown boundaries, and poor recognition of small crowns. To address these limitations, this paper proposes MonoCrown (MCrown), which strengthens geometric and contextual representation for distinguishing visually similar species and delineating crowns from single-temporal UAV RGB imagery. To compensate for the insufficiency of appearance cues, MCrown introduces monocular depth inferred offline from the same RGB image as a frozen geometric prior, and integrates cross-window global–local attention (CW-GLA), bidirectional cross-modal attention (BiCoAttn), and depth-adaptive injection (DAI) to capture long-range dependencies and promote complementary use of appearance and geometric features, especially for small crowns with similar visual patterns in complex scenes. To validate the method’s effectiveness, a crown-level UAV RGB dataset covering approximately 40 km² was constructed. Systematic comparative experiments were conducted on the proposed dataset and on public benchmarks, supporting the effectiveness of the proposed approach across ten dominant classes, especially for small crowns and visually similar categories. Its mean Intersection over Union (mIoU) and overall accuracy (OA) reached 74.1% and 87.3%, respectively. The method achieves high-precision crown-level tree species semantic segmentation using single-temporal UAV RGB as the sole acquired modality, while monocular depth inferred from the same RGB image serves only as a frozen geometric prior, without requiring multispectral, multi-temporal, or active-sensor acquisitions. This offers a practical solution for crown-level tree species mapping in heterogeneous forests. Full article

Accurate estimation of river discharge is fundamental for water resources management, flood forecasting, and drought monitoring in the Amazon River Basin. Rating curves, which relate water level (stage) to discharge, are the primary tool for streamflow estimation. This study evaluates traditional curve-fitting methods and machine learning algorithms for modeling rating curves at the two largest gauging stations in the Amazon River: Itacoatiara and Óbidos. The analysis is based on 70 stage–discharge measurements at Itacoatiara (2008–2023) and 176 measurements at Óbidos (1968–2023). Five modeling approaches were compared: Power Law, Linear Regression, Decision Tree, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed against official baseline rating curves maintained by Brazil’s National Water Agency (ANA) and the Geological Survey of Brazil (SGB/CPRM) using Root Mean Square Error (RMSE), coefficient of determination (r²), Mean Bias Error (MBE), Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE). Results indicate that ensemble-based machine learning methods, particularly XGBoost (RMSE = 7463 m³/s, NSE = 0.973 at Itacoatiara; RMSE = 18,378 m³/s, NSE = 0.872 at Óbidos), outperformed traditional methods. However, the Decision Tree exhibited overfitting that could not be resolved through pruning, depth limitation, or other strategies given the sample size. Traditional methods such as the optimized Power Law remain practical and transparent alternatives for operational use. The findings suggest that machine learning can complement traditional approaches for improving rating curve accuracy in large tropical rivers, with K-fold cross-validation used to assess variability and performance. Full article

Tree mechanical stability is essential for forest management and urban safety. Although static pulling tests are currently the standard for non-destructive advanced risk assessments, these tests have significant methodological limitations. Large trees require high applied forces to produce measurable signals, which poses safety risks and causes equipment wear. Conversely, structurally compromised ancient, veteran, or dead trees (snags) may yield poor signal-to-noise ratios at low loads, leading to unstable model fits and unreliable safety factor extrapolations. Additionally, standard inclinometers often experience interference from motion-induced accelerations. This study introduces a high-resolution, low-noise measurement approach that resolves small basal inclinations and stem bending responses. This method uses laser-based tracking to monitor stem bending, torsion, and inclination under mechanical load. Experimental data were collected by combining traditional pulling tests with this novel system, as well as by conducting a pilot study that monitored tree movement during low-strength wind gusts. The proposed method enables more precise characterization of the initial load-response curve. Improving the signal-to-noise ratio at lower force levels allows for more robust safety extrapolations. When combined with a 3D LiDAR scan, the method can reveal deviations from the theoretical bending line in order to locate internal defects and variations in wood properties. These findings bridge a critical gap in tree risk assessment by improving the applicability of static testing to massive trees, as well as ecologically valuable yet structurally vulnerable snags and ancient and veteran trees. Full article

The expansion of digital technologies in dairy farming (precision dairy farming) has created new opportunities for the systematic use of data, which can lead to more efficient production processes. This study aimed to develop and evaluate models for predicting daily milk yield from dairy cows during summer. This yield was modeled at the individual level, with days in milk and parity group included as baseline covariates in all analyses. Three feature-set scenarios were defined and evaluated, in which the temperature–humidity index (THI) and milk yield history were added to the baseline variables either separately (Scenarios 1 and 2) or jointly (Scenario 3). Performance was evaluated using walk-forward validation, and feature selection was nested within each iteration’s training window. The performance of the linear mixed model (LMM) was then compared with two machine learning models, random forest (RF) and gradient boosting machine (GBM), within the same experimental framework. In Scenario 3, all three models showed similar fits (R² = 0.92 and concordance correlation coefficient = 0.96), although the GBM model yielded a smaller error (root mean square error [RMSE] = 2.07 ± 0.22, mean absolute error [MAE] = 1.39 ± 0.12) than the RF model (RMSE = 2.10 ± 0.23, MAE = 1.45 ± 0.13) and the LMM (RMSE = 2.15 ± 0.22, MAE = 1.41 ± 0.10). Overall, adding the THI and recent milk yield history to the baseline variables improved short-term prediction accuracy in this dataset, with the GBM model showing the smallest error. These results can support farmers and herd managers in predicting short-term milk yield under heat stress conditions and making timely management decisions. Full article

The cessation of recurrent anthropogenic activities can promote vegetation succession. In this paper, we report a case study of passive restoration of the herbaceous plant vegetation associated with cork oaks carried out by citizens in collaboration with local farmers in a suburban area of Rome (Italy). A sampling design has been carried out in two comparable patches using replicated plots: (i) a first patch corresponding to the passive restored area, evolving from an uncultivated field towards a cork oak forest, where the mowing activity was stopped in 2017, and (ii) a second patch corresponding to an uncultivated land periodically mowed as a control. We recorded 24 plant species in the restored patch and 9 in the control patch. The Shannon-Wiener diversity index was significantly higher in the restored patch when compared to the control. Whittaker diagrams, graphically representing evenness, showed significant differences among plotted values. The Chao 2 richness estimators evidence the differences between patches (52.17 species vs. 9), graphically observed in the sample rarefaction curves. An analysis in the 2017–2025 period showed a substantial increase in NDVI values in the restored patch (from 0.18 in 2017 to 0.28 in 2025; approximately +54% relative to 2017; mean NDVI increased from 0.181 in 2017 to 0.29 in 2025), indicating an increase in cover/biomass associated with the post-2017 restoration of the area. Suspending mowing, both humidity (due to the reduction in grass cover) and nutrients increase, and the pH is reduced (Ellenberg indices): it is possible that the young oak trees are comparatively more effective cation exchangers. Therefore, only a few years after mowing was suspended, we observed a marked recovery not only of the dominant cork oak component but also of the herbaceous species (Vulpio-Dasypyretum villosi association). Even young, isolated cork oak trees can act as nurse plants (or keystone structures), supporting many species and creating microhabitats for shade-tolerant plants. This passive restoration began when local citizens and a school asked landowners to stop mowing in an area where cork oaks were naturally regenerating, making it an example of autonomous citizen-led environmental management. Full article

Grassland lake basins are mostly located in arid and semi-arid regions and represent typical ecologically fragile zones. As a representative inland lake in the cold and arid region of northern China, Hulun Lake serves as a crucial node for maintaining the ecological balance of the Hulunbuir grassland. Studying its ecological carrying capacity is particularly key to implementing the philosophy of a holistic approach to the management of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Based on data from 2018 to 2024 across four cities (banners, districts) in the Hulun Lake basin, this study constructs an evaluation system to measure ecological carrying capacity across three dimensions—ecosystem support, human activity pressure, and socio-economic response—using the Pressure–State–Response (PSR) model. Spatial analysis and geodetector methods are employed to explore its spatiotemporal differentiation and influencing factors. The findings are as follows: (1) The ecological carrying capacity in the Hulun Lake basin exhibits a significant spatial differentiation pattern, characterized by a gradient of “high in the east, low in the west; high in pastoral areas, low in urban areas.” (2) The overall trend in ecological carrying capacity shows a slow increase amid fluctuations, but the carrying capacity level remains relatively low. (3) The core driving forces of ecological carrying capacity primarily stem from the dimensions of population quality and infrastructure, while the direct influence of agricultural production is relatively limited. (4) Transportation infrastructure plays a strongly influential role as a driving mechanism of ecological carrying capacity in the Hulun Lake basin. Its synergy with factors such as education, information, and industry significantly affects both the ecosystem support capacity and the socio-economic responses of the basin. This study provides a reference for ensuring the ecological security of the Hulun Lake basin. Full article

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas. Full article

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article

Subterranean termites, particularly Odontotermes obesus, cause severe damage to forest nurseries and plantations in arid and semi-arid ecosystems. This study demonstrates the dual functional role of endophytic entomopathogenic fungi, Metarhizium anisopliae and Beauveria bassiana, in termite suppression and induction of plant defense responses. Laboratory bioassays revealed significantly higher virulence of M. anisopliae, with a lower LT₅₀ (lethal time required to cause 50% mortality) of 33.1 h compared to B. bassiana (46.7 h), a steeper probit slope (5.4 ± 0.3), and strong model fit (R² = 0.95), indicating rapid and synchronized mortality. Endophytic colonization varied across host species and application methods, with soil incorporation consistently outperforming foliar inoculation. Maximum colonization (82.5%) was recorded in Tecomella undulata and exceeded 80% in Azadirachta indica under M. anisopliae. Biochemical analyses revealed significant increases in protein (up to 3.5 mg g⁻¹), phenols (3.7 mg g⁻¹), and tannins (2.7 mg g⁻¹). Activity of defense enzymes was significantly enhanced, with catalase reaching 263.5 U mL⁻¹, while Phenylalanine ammonia-lyase and Tyrosine ammonia-lyase exceeded 170 and 198 U mL⁻¹, respectively, indicating activation of antioxidant and phenylpropanoid pathways. Molecular docking analysis further revealed strong interactions between fungal metabolites and termite cellulase, with Bassianin (−8.4 kcal mol⁻¹) and Tenellin (−8.1 kcal mol⁻¹) showing the highest binding affinities. These findings highlight the combined biochemical and molecular mechanisms underlying fungal-mediated termite suppression and plant defense induction, and future research should prioritize transcriptomic validation, rhizosphere microbiome interactions, formulation optimization, and long-term multi-location field evaluation to support sustainable termite management strategies. Full article

Shifting cultivation (SC) is a predominant land use across the tropics, feeding hundreds of millions of marginalized people, causing significant deforestation in tropical regions. A key question is how to realize rapid and large-scale identification of the spatial distribution, cycle numbers, and fallow periods of SC. Building the LandCycler algorithm that fully considers the inter-annual cycle of SC based on Landsat imagery from 1988 to 2020, we identify the distribution and fallow period of SC in Southeast Asia, including Assam in India and Yunnan Province in China. The results show that the LandCycler for the identification of SC is satisfactory (producer’s accuracy 82.12% and user’s accuracy 81.37%), and the accuracy in detecting the average cycle number, and calculating the average fallow period is 83.71%, and 96%, respectively. We found that the total area of SC is as high as 16.79 × 10⁴ km² in Southeast Asia, which uses almost 10% of the total forests. Meanwhile, the average cycle number and the average fallow period of SC are two times and 10 years, respectively. More than 98% of SC has repeated deforestation four times or less. The shorter the distance from settlements and the distance from roads, the larger the cycle number of SC. Although there was no significant correlation between elevation and slope and the cycle number of SC, SCs were mainly distributed at slopes of 18 ± 5° and elevations of 800 ± 300 m. These findings provide effective tools for sustainable agroforestry management as well as for global SC mapping. Full article

This study aimed to enhance the accuracy and interpretability of erosion gully classification within black soil regions by focusing on Changxing Township, Xinxing District, Qitaihe City, Heilongjiang Province as the research site. Utilizing RTK (Real-Time Kinematic) surveying technology, three-dimensional topographic data were collected for 139 actively developing erosion gullies. Key morphological parameters—including gully length, depth, gradient, average top width, average bottom width, and slope gradients on both sides—were extracted to construct interactive features. The variable set was refined through correlation analysis and variance inflation factor (VIF) diagnostics to mitigate multicollinearity. A random forest model was employed as the primary classification approach and benchmarked against logistic regression, support vector machines (SVM), decision trees, and backpropagation neural networks. To address class imbalance, a combination of class weighting, Synthetic Minority Over-sampling Technique (SMOTE), and undersampling methods was implemented. Model tuning and interpretability assessments were performed using cross-validation, grid search optimization, and SHapley Additive exPlanations (SHAP) analysis. The findings demonstrate that the random forest model achieved superior overall performance, with test set accuracy, macro-averaged F1 score, and balanced accuracy values of 0.9143, 0.8087, and 0.8427, respectively. Among imbalance handling techniques, class weighting yielded better results compared to oversampling and undersampling. Feature importance and SHAP analyses identified gully length, average crest width, and their interaction with gully depth as the principal determinants influencing gully grade classification. These results elucidate the synergistic developmental dynamics of gully longitudinal extension, vertical deepening, and lateral widening. The proposed methodology offers valuable technical support for the rapid surveying, classification, and management decision-making processes related to black soil erosion gullies. Full article