Search Results (6,256)

Natural water content affects many geotechnical parameters and geological properties of soils, which can reduce cohesion and friction, leading to potential failures in structures such as foundations, retaining walls, and slopes. Identification of the water content helps in designing effective drainage and water management systems to prevent flooding and erosion. In tunnel engineering, soil water content plays an important role as the stability of the tunnel face depends on it. This research solves the problem of classifying soil images depending on the natural water content by computer vision technology. First, laboratory soil tests were carried out, and the relationship between the amount of torque on the screw conveyor and the moisture content of the soil was established; photographs of the soil at different conditions were taken at each step of the experiment. Second, the resulting dataset after preprocessing was processed by convolutional neural network algorithms during deep learning; the transfer learning technique was used to obtain better results. As a result, seven algorithms were obtained that allow classifying the soil images, which can later be used to optimize the tunnel construction process. The best classification ability is demonstrated by the algorithm based on the DenseNet architecture (accuracy 0.9302 and loss 0.1980). The proposed model surpasses traditional approaches due to its increased automation and processing speed. Laboratory tests can be carried out only once for one type of soil in order to determine the boundaries of water content for classes labeling, after which only a cheap camera is required from the equipment to transmit new images for processing by the algorithm. Full article

Agricultural waste poses significant environmental, economic, and social challenges globally, with estimates indicating that 10–50% of agricultural products are discarded annually as waste. This review explores strategies for managing agricultural waste to mitigate its adverse impacts and promote sustainable development. Agricultural residues, such as those from sugarcane, rice, and wheat, contribute to pollution when improperly disposed of through burning or burying, contaminating soil, water, and air. However, these residues also represent untapped resources for bioenergy production, composting, mulching, and the creation of value-added products like biochar, bioplastics, single-cell protein and biobased building blocks. The paper highlights various solutions, including integrating agricultural waste into livestock feed formulations to reduce competition for human food crops, producing biofuels like ethanol and biodiesel from lignocellulosic materials, and adopting circular economy practices to upcycle waste into high-value products. Technologies such as anaerobic digestion for biogas production and gasification for synthesis gas offer renewable energy alternatives and ample feedstocks for gas fermentation while addressing waste management issues. Composting and vermicomposting enhance soil fertility, while mulching improves moisture retention and reduces erosion. Moreover, the review emphasizes the importance of policy frameworks, public-private partnerships, and farmer education in promoting effective waste management practices. By implementing these strategies, agricultural waste can be transformed into a resource, contributing to food security, environmental conservation, and economic growth. Full article

This study investigates urban warming in Rajshahi City, Bangladesh, by examining changes in land surface temperature (LST) from 1990 to 2023 and exploring its relationship with key biophysical factors. LST was derived from Landsat thermal imagery, and both spatial and temporal variations were analyzed using Geographic Information Systems (GIS). Key biophysical indices, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Bareness Soil Index (NDBSI), were calculated using corresponding Landsat satellite sensors, and they evaluated the impact of LULC types (vegetation, water, soil, and built-up areas) on thermal variations. LULC was derived following the Support Vector Machine classification technique. The Urban Thermal Field Variance Index (UTFVI) was employed to assess surface urban heat island (SUHI) effects, warming conditions, ecological stress, and thermal comfort zones. Spatial trend and hotspot analyses of LST change were performed using spatial trend analysis and the Getis-Ord Gi* statistic, respectively. Linear regression analysis examined the relationship between LST and biophysical indices. Results show that winter mean LST increased by 2.66 °C during the 33-year period, with maximum LST rising by 4.29 °C. The most significant warming occurred in central-northern, central-western, and south-eastern zones. The rise in LST and the growing intensity of SUHI effects are largely due to urban growth, especially where green spaces and water bodies have been replaced by impervious surfaces. Hotspot analysis identified clusters of high-temperature zones, while UTFVI analysis confirmed a marked expansion of strong heat island conditions, especially in central urban areas. Linear regression results showed notable links between LST and key biophysical variables, where higher LST values were commonly linked to greater built-up density and declines in vegetation cover and surface water. Overall, the results highlight the need for better urban planning approaches such as increasing green cover, using permeable materials, and adopting strategies that can adapt to climate impacts. This study presents a framework for analyzing urban climate dynamics that can be adapted to other rapidly growing cities, aiding efforts to promote sustainable development and build urban resilience. Full article

Forest management and tree felling in the stand change the structural characteristics, which causes changes in the microclimate conditions. The microclimate is a key in sustainable forest management because soil temperature and moisture regimes regulate nutrient cycling in forest ecosystems. The aim of this research was to determine the changes in air and soil temperatures in pedunculate oak forest stands in different stages of shelterwood that stimulate natural regeneration. The research was conducted in pedunculated oak forests in Spačva area. The microclimatic parameters were measured in a mature old forest stand without shelterwood cutting and in stands with preparatory cut, seed cut, and final cut. The intensity of shelterwood had an impact on the amplitudes and values of air and soil temperatures. The highest average air temperature was in the stand with a preparatory cut. Extreme values of air and soil temperatures were measured in the stands with a final cut. The highest air and soil temperature amplitudes were in the stand with a final cut, with the exception of most of the winter, when the highest soil temperature amplitude was in the stand with a seed cut. The highest number of icy, cold, and hot days was in the stand with a final cut. SARIMA models establish that the difference between microclimatic parameters is not accidental. Full article

Accurate identification of mattic epipedon degradation is critically important for addressing ecological issues such as the weakening of alpine grassland carbon sink capacity and reduced soil and water conservation. However, efficient and rapid methods for its detection remain limited. This study aimed to clarify the hyperspectral response mechanisms of mattic epipedon degradation and, based on hyperspectral technology, to construct models for identifying degraded mattic epipedon and screen preprocessing methods suitable for different moisture conditions. The results showed the following: (1) The XGBoost model with preprocessing using multiplicative scatter correction combined with second derivative transformation (MSC+SD) performed best, achieving an identification accuracy and Kappa coefficient of 0.85 and 0.82, respectively. The characteristic bands were concentrated in the visible light range (446–450 nm) and short-wave infrared range (2134 nm, 2267–2269 nm), which are closely related to the spectral responses of organic carbon and mineral components. (2) Spectral reflectance was significantly negatively correlated with moisture content, and model accuracy decreased as moisture content increased. (3) After correction using the EPO algorithm, the model accuracy for the high-moisture group improved by 13.2–16.7%, whereas that for the low-moisture group (<15%) decreased by 7.5%, verifying 15% moisture content as the critical threshold for water interference. This study elucidated the impact mechanism of moisture on the hyperspectral characteristics of the mattic epipedon. The established MSC+SD-XGBoost model adapts to varying moisture conditions, providing technical support for the rapid monitoring of mattic epipedon degradation and holding significant practical value for carbon management in alpine ecosystems. Full article

Ochradenus baccatus Delile (Resedaceae) is a widely distributed desert shrub known for its remarkable growth form plasticity, growing either independently or as a facultative climber on other vegetation. Despite its ecological adaptability, the drivers underlying its dual growth strategy remain poorly understood in arid ecosystems. This study aimed to investigate the growth form plasticity of O. baccatus across diverse ecological gradients in Saudi Arabia and identify key environmental and floristic factors influencing its climbing and independent forms. Field surveys were conducted from 2020 to 2024 across 103 sites, using stratified random sampling. At each site, vegetation data were collected using 50 × 50 m quadrats, and species composition, life form percentage, and O. baccatus behavior were recorded. Results revealed clear ecological separation between behaviors. Climbing individuals were associated with higher elevations, greater tree and shrub cover, and moderate soil fertility, while independent individuals were broadly distributed in herbaceous and open habitats. Diversity indices (Shannon, Simpson, evenness) increased with altitude, particularly in climbing habitats. PERMANOVA confirmed significant differences in species composition between behaviors (p = 0.0001), and SIMPER analysis identified species like Haloxylon salicornicum and Zygophyllum album as key contributors in climbing habitats. Indicator species analysis revealed behavior-specific taxa, while CCA demonstrated that rainfall, soil moisture, and temperature were the strongest environmental predictors of growth behavior. This study highlights the ecological flexibility of O. baccatus and the role of environmental filtering and plant community structure in shaping its growth strategy. These results have implications for the growth form plasticity of desert plants and can be applied to vegetation management and restoration in arid ecosystems. Full article

Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. The optimal model is selected to simulate landslide susceptibility maps under four return periods (5, 10, 20, and 50 years). Additionally, the mean decreases in the Gini and SHAP values are employed to identify the most significant factors contributing to landslides. The results indicate the following: (1) Effective antecedent precipitation is the most influential factor in landslide occurrence, ranging from one to two times higher than other factors. (2) Most meteorological stations in the study area show antecedent effective precipitation that follows a lognormal distribution, mainly in coastal areas, with a secondary fit to the general extreme value distribution. The spatial distribution of antecedent effective precipitation is more prominent in the coastal and western mountainous regions, with lower values that then increase with longer return periods in central areas. (3) The XGBoost model achieves the best performance, with an area under the curve of 0.96 and an accuracy of 89.02%. (4) The landslide susceptibility maps for the four return periods reveal three high-risk zones: the southern coastal mountains, the western Zhejiang mountains, and the areas surrounding the hilly region of Shaoxing to Taizhou in central Zhejiang. This study provides dynamic decision-making support for the prevention and control of rainstorm-induced landslide risks. Full article

Accurate and spatially detailed soil moisture (SM) data are essential for hydrological research, precision agriculture, and ecosystem monitoring. The NASA’s Soil Moisture Active Passive (SMAP) product offers unprecedented information on global soil moisture. To provide more detailed information about the cropland SM data for the Contiguous United States (CONUS), a 1-km SMAP product has been produced using the THySM model in support of USDA NASS operations. However, the current 1-km product contains substantial data gaps, which poses challenges for applications that require continuous daily data. Data Interpolation Empirical Orthogonal Functions (DINEOF+) is an interpolation technique that uses singular value decomposition (SVD) to address missing data problems. Previous studies have applied DINEOF+ to reconstruct the 1-km daily SM dataset but without further analysis of the reconstruction errors. In this study, we perform a comprehensive validation of DINEOF+ reconstructed SM by using both the original SMAP data and in situ measurements across the CONUS. Our results show that the reconstructed SM closely aligns with the original SM with R² > 0.65 and bias ranging from 0.01 to 0.02 m³/m³. When compared to in situ SM, the mean absolute error (MAE) ranges between 0.01 and 0.04 m³/m³ and the time series correlation coefficient ranges from 0.6 to 0.8. Our findings suggest that DINEOF+ effectively recovers missing data and improves the temporal resolution of SM time series. However, we also note that the accuracy of the reconstructed SM is dependent on the quality of the original SMAP data, emphasizing the need for continued improvements in SM retrievals by satellite. Full article

Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and daily actual evapotranspiration (DAE) by integrating climate data and geophysical insights, with a focus on mountainous areas such as the Adige catchment. The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. Model accuracy is assessed by comparing the similarity of Fuzzy C-Means (FCM) clusters before and after fusion, providing a metric for feature reduction. A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). Furthermore, Kernel density estimation is used to ensure the reproducibility of the RFC-GBR process across large-scale applications. A comparative analysis is conducted across multiple SDL architectures, including LSTM, GRU, TCN, and ConvLSTM, over 50 epochs to better evaluate the beneficial effect of the transformed parameters on model performance and accuracy. Results indicate that adjusted parameter calibration improves model performance in all cases, with better alignment to Wflow ground truth during both wet and dry periods. The proposed model increases the accuracy by 20% to 42% when using simpler SDL models like LSTM and GRU, even with fewer epochs. Full article

Soil respiration (Rs) is a significant contributor to the global carbon cycle, with its two main sources—microbial (heterotrophic, Rh) and plant root (autotrophic, Ra) respiration—being sensitive to various environmental factors. This study investigates the impact of ecosystem disturbances (Ds), including fire, biogenic (insects and pathogens), and harvesting, on soil respiration in Russia’s forest ecosystems. We introduced response factors to account for the effects of these disturbances on Rh over three distinct stages of ecosystem recovery. Our analysis, based on data from case studies, remote sensing data, and the national forest inventory, revealed that Ds increase Rh by an average of 2.1 ± 3.2% during the restoration period. Biogenic disturbances showed the highest impacts, with average increases of 16.5 ± 3.2%, while the contributions of clearcuts and wildfires were, on average, less pronounced—2.0 ± 3.1% and 0.8 ± 3.3%, respectively. These disturbances modify forest soil dynamics by affecting soil temperature, moisture, and nutrient availability, influencing carbon fluxes over varying timescales. This research underscores the role of ecosystem disturbances in altering soil carbon dynamics and highlights the need for improved data and monitoring of forest disturbances to reduce uncertainty in soil carbon flux estimates. Full article

Fissured expansive soils exhibit pronounced moisture-induced swelling, posing significant risks to the stability of geotechnical structures such as dam foundations and core zones. To improve predictive capacity in such environments, this study developed a back-propagation (BP) neural network model to estimate the swelling behavior of fissured expansive soils. The model incorporated four key geotechnical parameters—fissure ratio, dry density, initial moisture content, and overburden pressure—and was implemented in MATLAB using a three-layer feedforward architecture with four inputs, five hidden neurons, and a single output neuron to predict the swelling ratio (increase in specimen height due to water-induced expansion). The model was trained on 81 laboratory-tested samples, with all variables normalized to the range [–1, 1] to ensure numerical stability. Two training algorithms were evaluated: gradient descent with momentum (traingdm) and the Fletcher–Reeves conjugate gradient method (traincgf). The optimal network configuration achieved a mean squared error (MSE) below 0.01, indicating strong predictive accuracy for expansive soil swelling behavior. Comparative results showed that the conjugate gradient algorithm converged nearly 30 times faster than the gradient descent method, while maintaining similar prediction accuracy. Validation on an independent dataset confirmed high agreement with measured swelling ratios. The proposed BP model demonstrates robust generalization and computational efficiency, offering a practical decision-support tool for expansive soil deformation control in dam engineering. Its rapid and accurate predictions make it valuable for Smart City applications such as embankment stabilization, intelligent dam core design, and real-time geotechnical risk assessment. Full article

In this study, we aimed to investigate the occurrence of rare earth elements (REEs) in commonly consumed foods and assess the dietary exposure risks among different age groups in Zhejiang Province. The results showed that tea and shrimp had the highest REE detection rates, reaching 100%. Of all the food categories examined, tea exhibited the highest REE concentrations, significantly exceeding those in other foods. This may be attributed to differences in moisture content, root absorption mechanisms, and processing methods. The concentration pattern of REEs in all samples occurred in the following order: cerium > lanthanum > yttrium > neodymium > neodymium > scandium > praseodymium > gadolinium > dysprosium. The light REEs/heavy REEs (HREEs) ratio was consistently > 2 but remained lower than the ratios observed in the soil and sediments, indicating a potential risk of HREE enrichment. Dietary exposure assessments revealed that the total REE intake among Zhejiang residents was below the established safety threshold (51.3 µg/kg BW/day), with children experiencing the highest exposure (3.71 µg/kg BW/day), primarily due to their lower body weight. In the assessment of individual rare earth elements, Ce exposure in children aged ≤6 years exceeded the toxicological reference value. However, this threshold was established based on studies in pregnant and lactating populations and might not be directly applicable to young children. Therefore, overall dietary exposure to individual REEs remains within safe limits. REE exposure from tea consumption did not pose a health risk, even for habitual tea drinkers. These findings underscore the importance of continuous monitoring of REE accumulation in food and additional research on the potential long-term health effects, even though the current exposure levels of REEs are below the established safety limit. This is especially important considering the bioaccumulative nature of REEs and the limited paucity of toxicological data, particularly in vulnerable populations. Full article

Xinjiang larch (Larix sibirica Ledeb.) is a keystone species in the Altay Mountains, playing a vital role in maintaining ecosystem stability. This study investigates how different soil preparation techniques (ring, strip, and burrow) influence seed germination and seedling establishment by mitigating apomictic allelopathy. Experimental plots were established using artificial seeding and natural seed dispersal at soil depths of 5 cm, 10 cm, and 15 cm. Seedling survival and development were monitored in June, July, and August 2023. The results demonstrated that sod removal significantly enhanced seed germination by reducing allelopathic inhibition, improving seed–soil contact, and increasing moisture retention. Among the techniques, the ring method yielded the highest rates of seedling establishment, particularly when artificial seeding was combined with natural seed dispersal. Although seedling numbers tended to increase with soil depth, the differences were not statistically significant. Temporal dynamics revealed a peak in seedling survival in July, followed by a subsequent decline. These findings highlight the critical role of optimized soil preparation techniques in promoting successful seedling development. The study offers practical guidance for ecological restoration and sustainable forest management in degraded larch ecosystems of the Altay Mountains. Full article

This study investigates the impact of climate variability on wheat production in Algeria’s semi-arid interior plains from 2014 to 2024, aiming to curb the challenges of rainfed wheat cultivation, optimize irrigation, and improve water productivity. The Soil–Water–Atmosphere–Plant (SWAP) model-driven approach refined irrigation scheduling to mitigate climate-induced losses and improve resource efficiency. Using historical climate data, soil properties, and wheat growth observations from the experimental farm of the Technical Institute for Field Crops, the SWAP model was calibrated and validated using one-factor-at-a-time sensitivity analysis, achieving a coefficient of determination (R²) of 0.93 and a Normalized Root Mean Squared Error (NRMSE) of 17.75. Two drought-based irrigation indices, Soil Moisture Drought Index (SMDI) and Crop Water Stress Index (CWSI), guided adaptive irrigation strategies, showing a significant reduction in crop failure during drought periods. Results revealed a strong link between rainfall variability and wheat yield. Adopting a 9-day irrigation interval could increase water productivity to 18.91 kg ha⁻¹ mm⁻¹, enhancing yield stability under varying climatic conditions. The SMDI approach maintained soil moisture during extreme drought, while CWSI optimized water use in normal and wet years. This study integrates SMDI and CWSI into a validated irrigation framework, offering data-driven strategies to enhance wheat production resilience. Findings support sustainable water management and provide practical insights for policymakers and farmers to refine irrigation planning and climate adaptation, contributing to long-term agricultural sustainability. Full article

This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management. Full article