Search Results (148)

The identification of unmarked graves is important in archaeology, forensics, and cemetery management, but invasive methods are often restricted due to ethical or cultural concerns. This necessitates the use of non-invasive geophysical techniques. Our study demonstrates the potential of induced polarization (IP) imaging as a non-invasive remote sensing technique specifically suited for detecting and characterizing unmarked graves. IP leverages changes in the electrical properties of soil and pore water, influenced by the accumulation of organic matter from decomposition processes. Measurements were conducted at an inactive cemetery using non-invasive textile electrodes to map a documented grave from the early 1990s, with a survey design optimized for high spatial resolution. The results reveal a distinct polarizable anomaly at a 0.75–1.0 m depth with phase shifts exceeding 12 mrad, attributed to organic carbon from wooden burial boxes, and a plume-shaped conductive anomaly indicating the migration of dissolved organic matter. While electrical conductivity alone yielded diffuse grave boundaries, the polarization response sharply delineated the grave, aligning with photographic documentation. These findings underscore the value of IP imaging as a non-invasive, data-driven approach for the accurate localization and characterization of graves. The methodology presented here offers a promising new tool for archaeological prospection and forensic search operations, expanding the geophysical toolkit available for remote sensing in culturally and legally sensitive contexts. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

The detection of archeological sites in satellite imagery is often hindered by environmental constraints such as vegetation cover and variability in meteorological conditions, which affect the visibility of subsurface structures. This study aimed to develop predictive models for assessing archeological site visibility in satellite imagery by integrating vegetation indices and meteorological data using machine learning techniques. The research focused on megalithic tombs associated with the Funnel Beaker culture in Poland. The primary objective was to create models capable of detecting archeological features under varying environmental conditions, thereby enhancing the efficiency of field surveys and reducing associated costs. To this end, a combination of vegetation indices and meteorological parameters was employed. Key indices—including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Archeological Index (NAI)—were analyzed alongside meteorological variables such as wind speed, temperature, humidity, and total precipitation. By integrating these datasets, the study evaluated how environmental conditions influence the visibility of archeological sites in satellite imagery. The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. The highest predictive accuracy was achieved during periods of high soil moisture variability and fluctuating weather conditions. These findings enabled the development of visibility prediction maps, guiding the optimal timing of aerial surveys and minimizing the risk of unsuccessful data acquisition. The results underscore the effectiveness of integrating meteorological data with satellite imagery in archeological research. The proposed approach not only improves site detection but also reduces operational costs by concentrating resources on optimal survey conditions. Furthermore, the methodology is applicable to diverse archeological contexts, enhancing the capacity to locate and document heritage sites across varying environmental settings. Full article

Effective trail management is essential for preventing environmental degradation and promoting sustainable recreational use. This study proposes a GIS-based, explainable machine learning framework for predicting forest trail degradation using exclusively environmental variables, eliminating the need for costly visitor monitoring data that remains unavailable in most operational forest settings. Field surveys conducted in Geumjeongsan, South Korea, classified trail segments as degraded or non-degraded based on physical indicators such as erosion depth, trail width, and soil hardness. Environmental predictors—including elevation, slope, trail slope alignment (TSA), topographic wetness index (TWI), vegetation type, and soil texture—were derived from spatial analysis. Three machine learning algorithms (Binary Logistic Regression, Random Forest, and Gradient Boosting) were systematically compared using confusion matrix metrics and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). Random Forest (RF) was selected for its strong performance (AUC-ROC = 0.812) and seamless integration with SHAP (SHapley Additive exPlanations) for transparent interpretation. Spatial block cross-validation achieved an AUC-ROC of 0.729, confirming robust spatial generalization. SHAP analysis revealed vegetation type as the most significant predictor, with hardwood forests showing higher degradation susceptibility than mixed forests. A susceptibility map generated from the RF model indicated that 40.7% of the study area faces high to very high degradation risk. This environmental-only approach enables proactive trail management across data-limited forest systems globally, providing actionable insights for sustainable trail maintenance without requiring visitor use data. Full article

Continuous flight auger piles (CFAPs) are highly versatile and productive deep foundation elements. Known for their execution speed, low noise, and minimal vibration, they are extensively used in Brazil, particularly for urban projects or environmentally sensitive areas. Technologically, they employ a Real-Time Operation System (RTOS) to control the execution energy for each drilled pile. When used effectively, this energy-based monitoring system can provide information that replaces or correlates with other challenging-to-measure variables, accommodating the impact of various exogenous variables on a pile’s execution and performance. Foundation designers often define one or more characteristic lengths for different pile groups, considered representative for each group despite uncertainties and morphological changes along the terrain. Hence, considering an energy-based control, which enables an individual assessment for each pile, is beneficial given soil’s complexity, which can vary significantly even within a small area. By determining the optimal execution energy, individualized stopping criteria for piles can be established, directly influencing costs and productivity and enhancing reliability. The present paper proposes a methodological workflow to automate the necessary calculations for execution energies, correlate them with bearing capacities measured by load tests or estimated from standard soil surveys, and predict the execution energy and corresponding stopping criteria for the drilling depth of each pile. This study presents a case study to illustrate the methodology proposed, accounting for a real construction site with multiple piles. It shows that considering fixed-length piles may not favor safety, as the energy-based analysis revealed that some piles needed longer shafts. This study also shows that for the 316 CFAPs analyzed with depths ranging from 8 to 14 m, a total of 564 m of pile shafts was unnecessary (which accounted for more than 110 m³ of concrete), indicating that cost optimization is possible. Overall, these analyses improve design safety and reliability while reducing execution costs. The results demonstrate that execution energy can serve as a proxy for subsurface resistance, correlating well with NSPT values and bearing capacity estimations. The methodology enables the individualized assessment of pile performance and reveal the potential for improving the reliability and cost-effectiveness of the geotechnical design process. Full article

International markets are placing increasing importance on sustainability benchmarks that encompass not only environmental but also social and economic dimensions. This study investigates whether cooperative wineries, particularly those composed of small-scale producers, can meet these growing demands. Sixteen winegrowers from a cooperative winery in the Douro region of Portugal were surveyed using indicators aligned with the National Sustainability Certification Benchmark for the Wine Sector (RNCSSV). The survey captured practices from the 2022/2023 season to assess readiness for certification and examine viticultural practices affecting sustainability. Results highlight structural challenges: 57% of respondents operate as family-run businesses, often with informal organizational practices—only one-third of which met the 50% certification threshold. Nevertheless, there is evidence of a transition toward sustainable viticulture. Many producers reported soil cover practices and reduced herbicide use (19% no longer apply them), with positive implications for soil conservation and yield stability, particularly where water is available. Despite constraints in data detail, particularly regarding pesticide use and field practices, the study provides a solid empirical basis for targeted sustainability efforts. These findings may support the development of simplified tools and tailored strategies to foster sustainability transitions in cooperative winegrowing contexts. Full article

High-enthalpy geothermal resources in volcanic settings often lack clear surface manifestations, requiring integrated, data-driven approaches to identify hidden reservoirs. In this study, we apply a multivariate clustering technique—genetic K-Means clustering (GKMC)—to a comprehensive soil gas dataset collected from 1050 sampling sites across the ~100 km² Garehagua mining license, located in the southern rift zone of Tenerife (Canary Islands). The survey included diffuse CO₂ flux measurements and concentrations of key soil gases (He, H₂, CH₄, O₂, N₂, Ar isotopes, and ²²²Rn, among others). Statistical-graphical analysis using the Sinclair method allowed for an objective classification of geochemical anomalies relative to background populations. The GKMC algorithm segmented the dataset into geochemically coherent clusters. One cluster, defined by elevated CO₂, helium, and ²²²Rn levels, showed a clear spatial correlation with inferred tectonic lineaments in the southern rift zone. These anomalies are interpreted as structurally controlled conduits for the ascent of deep magmatic-hydrothermal fluids. The findings support the presence of a concealed geothermal system structurally constrained in the southern region of Tenerife. This study demonstrates that integrating GKMC clustering with soil gas geochemistry offers a robust methodology for detecting hidden geothermal anomalies. By enhancing anomaly detection in areas with subtle or absent surface expression, this approach contributes to reducing exploration risk and provides a valuable decision-support tool for targeting future drilling operations in volcanic terrains. Full article

Anthropogenic activities on soil layers contribute to reworking and eventual modification, which, in most cases, are detrimental to the soil. Going by the significance of soil to life in many ramifications, it is imperative that its consistent assessment enhances and guides management practices. This study focuses on delineating soil structure alterations using ground-penetrating radar (GPR), a geophysical survey method. The principle of operation and the simplicity of the technique have attracted the choice of the non-destructive testing (NDT) method with a view that it could circumvent the drawbacks that characterized the conventional methods hitherto used for such evaluation. Furthermore, the technique allows for the spatial investigation of the concealing sub-layer of the soil and, thus, informs its choice. A test site was selected on a plain farmland in Kraków, Poland, where some parts of the soil structure distortions were induced using tractor movement, which exerted normal stress from the soil surface layer. Subsequently, GPR measurements were acquired via pre-established profiles on the test site, and soil samples were taken for the laboratory evaluation of some of the soil’s physical properties. An analysis of the field data revealed that zones of distorted soil structures have lower attenuation effects on the GPR signal, with corresponding lower amplitude values compared with the unaltered soil structure zones. Evaluated physical properties such as bulk density and state variables like moisture water contents also show a declining trend from the unaltered soil structure zone to the altered zones. The results have revealed characteristic signatures of the zone of soil structure alterations from GPR scanning that can enhance its identification and characterization in the field and, thus, promote decision making toward the effective utilization and management of soil. Full article

Aquifer health assessment is essential for sustainable groundwater management, particularly in semi-arid regions with challenging geological conditions. This study presents a novel methodology for assessing aquifer health in the Barakar River Basin, a hard-rock terrain, by integrating tree-based classification, deep learning, and the Soil and Water Assessment Tool (SWAT) model. Employing Random Forest, Decision Tree, and Convolutional Neural Network (CNN) models, the research examines 20 influential factors, including hydrological, water quality, and socioeconomic variables, to classify aquifer health into four categories: Good, Moderately Good, Semi-Critical, and Critical. The CNN model exhibited the highest predictive accuracy, identifying 33% of the basin as having good aquifer health, while Random Forest assessed 27% as Critical heath. Pearson correlation analysis of CNN-predicted aquifer health indicates that groundwater recharge (r = 0.52), return flow (r = 0.50), and groundwater fluctuation (r = 0.48) are the most influential positive factors. Validation results showed that the CNN model performed strongly, with a precision of 0.957, Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) of 0.95, and F1 score of 0.828, underscoring its reliability and robustness. Geophysical Electrical Resistivity Tomography (ERT) field surveys validated these classifications, particularly in high- and low-aquifer health zones. This study enhances understanding of aquifer dynamics and presents a robust methodology with broader applicability for sustainable groundwater management worldwide. Full article

Climate change negatively affects agricultural productivity by altering weather patterns, increasing the frequency of extreme events, and disrupting growing seasons. These changes lead to reduced crop yields, soil degradation, and challenges to food security. This research investigates how climate change affects plant cultivation and agricultural farming operations in Khemisset Province of Morocco. A research study based on surveys of 120 farmers combines quantitative and qualitative methods to determine their views about climate change and their adaptive measures. The researchers select their farmers according to land conditions, plant life, and livestock management patterns. The obtained data demonstrate climate parameter deterioration throughout the period extending from 1985 to 2015, which corresponds with meteorological measurements. Climate variability produces adverse environmental effects which negatively affect agricultural output. The Zemmour tribe members and other farmers use different agricultural adaptation strategies, including fertilizer application, rotational cropping, and planting maturation-premature seeds. The research findings highlight the necessity of developing specific adaptation methods that defend agricultural sectors against climate change risks and secure food supplies. This investigation adds to climate resilience knowledge by delivering important findings that guide agricultural sustainability policy development and implementation. Full article

The lifespan of site drilling machines is a critical factor influencing construction projects’ cost, efficiency, and safety. While previous research has identified various factors affecting machine longevity, a significant knowledge gap exists in quantifying the relative importance of these factors and their combined impact, particularly across diverse geographical regions like Saudi Arabia and India. This study addresses this gap by providing a comprehensive risk assessment of the lifespan. The research aims to identify and prioritize the most critical factors impacting lifespan and quantify their contributions to lifespan reduction using correspondence analysis (CA) and the matrix assessment method. A systematic literature review identified 30 risk factors: operational factors, environmental conditions, equipment design and quality, maintenance practices, and operator skill and training. A survey of construction professionals in Saudi Arabia and India, alongside a global perspective, provided data on the probability and impact of each factor. CA and matrix assessment methods were employed to analyze the data, revealing regional variations and commonalities. The results demonstrate that “Operator Training” is consistently a high-impact, high-probability risk across all regions. However, the relative importance of other factors, such as soil conditions and overloading, varies significantly between Saudi Arabia and India. This study introduces the integration of CA and the matrix assessment method to offer a systematic, data-driven approach to the problem. The findings provide actionable insights for construction companies, engineers, and project managers, enabling targeted risk mitigation strategies, optimized maintenance planning, and improved operator training programs. Ultimately, this research contributes to more sustainable, efficient, and cost-effective construction practices by extending the operational life of vital drilling equipment. Full article

With global climate change and the deterioration of the ecological environment, the safety of hydraulic engineering faces severe challenges, among which soil-dwelling termite damage has become an issue that cannot be ignored. Reservoirs and embankments in China, primarily composed of earth and rocks, are often affected by soil-dwelling termites, such as Odontotermes formosanus and Macrotermes barneyi. Identifying soil-dwelling termite damage is crucial for implementing monitoring, early warning, and control strategies. This study developed an improved YOLOv8 model, named MCD-YOLOv8, for identifying traces of soil-dwelling termite activity, based on the Monte Carlo random sampling algorithm and a lightweight module. The Monte Carlo attention (MCA) module was introduced in the backbone part to generate attention maps through random sampling pooling operations, addressing cross-scale issues and improving the recognition accuracy of small targets. A lightweight module, known as dimension-aware selective integration (DASI), was added in the neck part to reduce computation time and memory consumption, enhancing detection accuracy and speed. The model was verified using a dataset of 2096 images from the termite damage survey in hydraulic engineering within Hubei Province in 2024, along with images captured by drone. The results showed that the improved YOLOv8 model outperformed four traditional or enhanced models in terms of precision and mean average precision for detecting soil-dwelling termite damage, while also exhibiting fewer parameters, reduced redundancy in detection boxes, and improved accuracy in detecting small targets. Specifically, the MCD-YOLOv8 model achieved increases in precision and mean average precision of 6.4% and 2.4%, respectively, compared to the YOLOv8 model, while simultaneously reducing the number of parameters by 105,320. The developed model is suitable for the intelligent identification of termite damage in complex environments, thereby enhancing the intelligent monitoring of termite activity and providing strong technical support for the development of termite control technologies. Full article

Owing to their capacity to conserve water and regulate streamflow, small reservoirs are useful for agriculture, domestic water supply, energy production, industry, flood control, recreation, fisheries, and ecosystem conservation. The presence of these small reservoirs often affects the natural water pathways, but the use of a hydrological model such as the Soil and Water Assessment Tool (SWAT) can help to better apprehend these effects at the watershed scale. Indeed, the SWAT model allows modelers to represent and operate reservoirs by inputting the related parameters while setting the model. However, these reservoir parameters are not automatically generated by the SWAT model algorithms. Subsequently, SWAT users are left alone and must sort out the adequate approach to separately obtain or determine the reservoir parameters. Traditionally, reservoir parameters such as the volumes and surface areas are obtained through in situ hydrographic surveys which are costly and labor demanding. To help SWAT modelers retrieve the input parameters needed for modeling small reservoirs, this paper explicitly presents a spatial analysis procedure using the case study of a small watershed reservoir. In this procedure, the digital elevation model of the watershed is transformed into a triangulated irregular network and turned into contour lines which are used to identify the reservoir surface and volume at the principal and emergency spillways. The retrieved parameters were successfully used to calibrate and validate SWAT simulations of the watershed hydrological behavior. The spatial analysis procedure reported here is a cost-effective alternative to traditional in situ hydrographic surveys and it is useful for addressing watersheds with small reservoirs. The procedure eases the inclusion of reservoirs in SWAT and reduces the risk of model overfitting. Furthermore, the procedure could be useful for developing reservoir elevation–capacity–area curves. Full article

Since 1 January 2024, French local authorities will be required to offer householders a means of recovering biowaste, either as a soil improver or as an energy source. Several criteria influence their choice: cost, availability of operators and equipment, social facilitation, etc. However, greenhouse gas (GHG) emissions are rarely taken into account in the decision-making process. This article compares the emissions of four biowaste recovery systems, differentiated by their process (composting or vermicomposting) and management type (community or industrial). It is based on the carbon footprint method defined by the French Agency for Ecological Transition (ADEME). The assumptions and emission factors come from two sources: a field survey of composting and vermicomposting companies and associations in the Lyon area and a review of the literature on GHG emissions from the decomposition of organic matter. The carbon footprint of the processes was determined by estimating the CO₂ equivalent per ton of composted biowaste. The results show that industrial composting emits the most carbon (CO₂). Depending on whether biogenic carbon is taken into account or not, the ranking of the other three processes changes. When biogenic CO₂ is taken into account, it is the process that has the greatest influence on the result; on the other hand, when biogenic CO₂ emissions are not taken into account, the type of management determines the ranking. These results are discussed in relation to the methodological limitations of the comparison, other biowaste management options and the reduction of biowaste-related emissions. For example, by studying the agricultural use of biowaste compost, the carbon balance could be refined by including the emissions avoided from the production of nitrogen fertiliser. However, environmental assessment is only one of a number of decision-making factors (social, economic, agricultural, etc.) in waste management. Full article

Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on a large scale. The method combines unsupervised and supervised machine learning algorithms. To improve the monitoring accuracy of the PLB regression model, the study used the K-Means algorithm in conjunction with morphological operations to identify potato growth areas. Input data consisted of monthly NDVI from Sentinel-2 and VH bands from Sentinel-1 (covering the year 2021). The identification results were validated on 221 field survey samples with an F1 score of 0.95. To monitor disease severity, we compared seven machine learning models: CART decision trees (CART), Gradient Tree Boosting (GTB), Random Forest (RF), single optical data Random Forest Time series model (TS–RF), single radar data Random Forest Time series model (STS–RF), multi-source data Gradient Tree Boosting Time series model (MSTS–GTB), and multi-source data Random Forest Time series model (MSTS–RF). The MSTS–RF model was the best performer, with a validation RMSE of 20.50 and an R² of 0.71. The input data for the MSTS–RF model consisted of spectral indices (NDVI, NDWI, NDBI, etc.), radar features (VH-band and VV-band), texture features, and Sentinel-2 bands synthesized as a monthly time series from May to September 2021. The feature importance analysis highlights key features for disease identification: the NIR band (B8) for Sentinel-2, DVI, SAVI, and the VH band for Sentinel-1. Notably, the blue band data (458–523 nm) were critical during the month of May. These features are related to vegetation health and soil moisture are critical for early detection. This study presents for the first time a large-scale map of PLB distribution in China with an accuracy of 10 m and an RMSE of 26.52. The map provides valuable decision support for agricultural disease management, demonstrating the effectiveness and practical potential of the proposed method for large-scale monitoring. Full article