Search Results (550)

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

The design of aircraft components is a complex process that must simultaneously account for environmental impact, manufacturability, cost and structural performance to meet modern regulatory requirements and sustainability objectives. When these factors are integrated from the early design stages, the approach transcends traditional eco-design and becomes a genuinely sustainability-oriented design methodology. This study proposes a sustainability-driven design framework for aircraft components and demonstrates its application to a fuselage panel consisting of a curved skin, four frames, seven stringers, and twenty-four clips. The design variables investigated include the material selection, joining methods, and subcomponent thicknesses. The design space is constructed through a combinatorial generation process coupled with compatibility and feasibility constraints. Sustainability criteria are evaluated using a combination of parametric Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) regression models, parametric Finite Element Analysis (FEA), and Random Forest surrogate modeling trained on a stratified set of simulation results. Two methodological pathways are introduced: 1. Cluster-based optimization, involving customized clustering followed by multi-criteria decision-making (MCDM) within each cluster. 2. Global optimization, performed across the full decision matrix using Pareto front analysis and MCDM techniques. A stability analysis of five objective-weighting methods and four normalization techniques is conducted to identify the most robust methodological configuration. The results—based on a full cradle-to-grave assessment that includes the use phase over a 30-year A319 aircraft operational lifetime—show that the thermoplastic CFRP panel joined by welding emerges as the most sustainable design alternative. Full article

Improving fuel and energy efficiency in agricultural tillage is critical for sustainable farming and reducing environmental impacts. In this study, the effects of forward speed and tillage depth on the fuel efficiency parameters of a tractor–chisel plough combination were investigated under controlled field conditions on clay soil. Specific fuel consumption (SFC), fuel consumption per unit area (FCPA), and overall energy efficiency (OEE) were evaluated at four forward speeds (0.6, 0.95, 1.2 and 1.4 m·s⁻¹) and four tillage depths (15, 19.5, 23 and 26.5 cm). SFC ranged from 0.519 to 1.237 L·kW⁻¹·h⁻¹, while OEE varied between 7.918 and 18.854%. Higher forward speeds significantly reduced fuel consumption and improved energy efficiency, whereas deeper tillage increased fuel use and reduced efficiency. Optimal operation occurred at speeds of 1.2–1.4 m·s⁻¹ and shallow to medium depths. Five machine learning algorithms: Polynomial Regression (PL), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR), were applied to model fuel efficiency parameters. RFR achieved the highest accuracy for predicting SFC, while PL performed best for FCPA and OEE, with the mean absolute percentage error (MAPE) below 2%. Models such as PL and RFR excel in data structures dominated by nonlinear relationships. These results highlight the potential of machine learning to guide data-driven decisions for fuel and energy optimization in tillage, promoting more sustainable mechanization strategies and resource-efficient agricultural production. Full article

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

This study presents an integrated, cost-aware artificial intelligence (AI) meta-modelling framework for mine-to-mill optimisation that couples high-fidelity simulation with data-driven predictive modelling. Using over three million scenarios generated in the Integrated Extraction Simulator (IES), the framework quantifies how upstream design parameters such as burden, spacing, hole diameter, and explosive density propagate through screening, crushing, stockpiling, and grinding to affect downstream costs and throughput. Random Forest-based meta-models achieved predictive accuracies above 90%, enabling the rapid evaluation of technical and financial trade-offs across the mining value chain. Stage-wise cost models were formulated for drilling, blasting, comminution, and material handling to link process variables to costs per tonne. The results reveal clear non-linear cost responses: finer fragmentation reduces the total comminution cost despite higher explosive expenditure, while SAG mill load and speed exhibit U-shaped cost relationships with distinct optimal operating windows. By combining physics-based simulations, machine learning, and cost integration, the framework transforms traditional stage-wise optimisation into a holistic, financially informed decision-support system. The proposed methodology supports real-time, AI-enabled digital twins capable of adaptive mine-to-mill optimisation, paving the way for more efficient and sustainable resource extraction. Full article

Arid and semi-arid soils represent extreme habitats where microbial life is constrained by high temperature, low water availability, salinity, and nutrient limitation, yet these ecosystems harbor unique bacterial communities that sustain key ecological processes. To explore the diversity and functional potential of prokaryotic assemblages in Algerian drylands, we compared soils from three contrasting sites: The Oasis of Djanet (RM1), the hyper-arid Tassili of Djanet desert (RM2), and the semi-arid El Ouricia forest in Sétif (RM3). Physicochemical analyses revealed strong environmental gradients: RM2 exhibited the highest pH (8.66), electrical conductivity (11.7 dS/m), and sand fraction (56%), whereas RM3 displayed the greatest moisture (10.9%), organic matter (7.6%), and calcium carbonate (20.7%) content, with RM1 generally showing intermediate levels. High-throughput 16S rRNA gene sequencing generated >60,000 effective reads per sample with sufficient coverage (>0.99). Alpha diversity indices indicated the highest bacterial richness and diversity in RM2 (Chao1 = 3144, Shannon = 10.0), while RM3 showed lower evenness and the dominance of a few taxa. Across sites, 66 phyla and 551 genera were detected, dominated by Actinobacteriota (38–45%) and Chloroflexi (13–44%), with Proteobacteria declining from RM1 (17.5%) to RM3 (3.3%). Venn analysis revealed limited overlap, with only 58 operational taxonomic units shared among all sites, suggesting highly habitat-specific communities. Predictive functional profiling (PICRUSt2, Tax4Fun, FAPROTAX) indicated metabolism as the dominant functional category (≈50% of KEGG Level-1), with carbohydrate and amino acid metabolism forming the metabolic backbone. Notably, transport functions (ABC transporters), lipid metabolism, and amino acid degradation pathways were enriched in RM2–RM3, consistent with adaptation to osmotic stress, nutrient limitation, and energy conservation under aridity. Collectively, these findings demonstrate that Algerian arid and semi-arid soils host diverse, site-specific bacterial communities whose functional repertoires are strongly shaped by soil chemistry and climate, highlighting their ecological and biotechnological potential. Full article

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. Four main geological indicators were identified by examining the attributes of these factors and their association to outburst intensity. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K Nearest Neighbors (KNN), Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision support tool for mine executives to prevent and control outburst incidents. Full article

Blue economy aims to bring prosperity to coastal communities whilst also protecting natural ocean resources for future generations. But how can this vision be put into practice, especially in communities in which dependence on natural resources is high, and food and livelihood security are key concerns? This paper examines two cases of community-led nature-based enterprise in Kenya in a search for solutions to this challenge: fisheries reform through market access and gear sustainability; mangrove forest conservation and community development using carbon credit revenues. I use a ‘diverse economies framework’ for the first time in blue economy contexts to delve into the heterogeneous relations at work and in search of insights that can be applied in multiple contexts. Analysed through key informant interviews and field observation, the cases reveal a complex assemblage of institutions, knowledges, technologies, and practices within which enterprises operate. Whilst the enterprises featured are still relatively new and developing, they suggest a direction of travel for a community-led sustainable blue economy that both supports and benefits from nature recovery. The insights gained from this diverse economies analysis lead us to appreciate a sustainable blue economy as a rediscovered and reinvigorated relationship of reciprocity between society and nature—one that nurtures place-based nature-based livelihoods and nature recovery together, and which embodies a set of values and ethics shared by government, communities, and business. Full article

Efficient phosphorus recovery is of great significance for sustainable wastewater management and resource recycling. While chemical precipitation is widely used, its effectiveness under complex multi-factor conditions remains challenging to predict and optimize. This study compiled a multidimensional dataset from recent experimental literature, encompassing key operational parameters (reaction time, temperature, pH, stirring speed) and dosages of three metal precipitants (Al³⁺, Ca²⁺, Fe³⁺) to systematically evaluate and benchmark phosphorus recovery performance across these distinct systems, six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gaussian Process Regression (GPR), Elastic Net, Artificial Neural Network (ANN), and Partial Least Squares Regression (PLSR)—were developed and cross-validated. Among them, the GPR model exhibited superior predictive accuracy and robustness. ($R^2$ = 0.69, $RMSE$ = 0.54). Beyond achieving high-fidelity predictions, this study advances the field by integrating interpretability analysis with Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP). These analyses identified distinct controlling factors across systems: reaction time and pH for aluminum, Ca²⁺ dosage and alkalinity for calcium, and phosphorus loading with stirring speed for iron. The revealed factor-specific mechanisms and synergistic interactions (e.g., among pH, metal dose, and mixing intensity) provide actionable insights that transcend black-box prediction. This work presents an interpretable Machine Learning (ML) framework that offers both theoretical insights and practical guidance for optimizing phosphorus recovery in multi-metal systems and enabling precise control in wastewater treatment operations. Full article

Modern port operations face increasing challenges from rapidly changing weather and environmental conditions, requiring accurate short-term forecasting to support safe and efficient maritime activities. This study presents a sensor fusion-based machine learning framework for real-time multi-target nowcasting of wind gust speed, sustained wind speed, and wind direction using heterogeneous data collected at the Port of Livorno from February to November 2025. Using an IoT architecture compliant with the oneM2M standard and deployed at the Port of Livorno, CNIT integrated heterogeneous data from environmental sensors (meteorological stations, anemometers) and vessel-mounted LiDAR systems through feature-level fusion to enhance situational awareness, with gust speed treated as the primary safety-critical variable due to its substantial impact on berthing and crane operations. In addition, a comparative performance analysis of Random Forest, XGBoost, LSTM, Temporal Convolutional Network, Ensemble Neural Network, Transformer models, and a Kalman filter was performed. The results show that XGBoost consistently achieved the highest accuracy across all targets, with near-perfect performance in both single-split testing (R² ≈ 0.999) and five-fold cross-validation (mean R² = 0.9976). Ensemble models exhibited greater robustness than deep learning approaches. The proposed multi-target fusion framework demonstrates strong potential for real-time deployment in Maritime Autonomous Surface Ship (MASS) systems and port decision-support platforms, enabling safer manoeuvring and operational continuity under rapidly varying environmental conditions. Full article

Ecological compensation is an important policy tool for coordinating ecological protection and economic development and narrowing regional disparities. In China, Linyi, for the first time, applied a cap-and-trade policy to the forestry sector by implementing the Intergovernmental Forest Ecological Indicator Trading Policy (IFEITP)—a new ecological compensation policy—to increase the city’s overall forest coverage. However, the compensation standard for this policy was formulated solely by referring to provincial afforestation subsidy standards, resulting in excessively low indicator trading prices and making the policy difficult to sustain. This paper proposes a technical framework for ecological compensation based on the ecosystem service spillover value (ESSV), aiming to optimize the IFEITP. The results revealed that during the policy implementation period, Linyi’s total ecosystem service value (ESV) increased, and the proportion of ESV provided by forests in each district and county also increased. Under the new framework, there were minor changes in the ecosystem service supply zones and payment zones. The compensation received by supply zones increased by 116.2%, whereas the payments made by payment zones accounted for less than 0.2% of local fiscal revenue. The newly calculated indicator trading price under this framework not only reflects the value of ecosystem services but also remains within the acceptable range of government finances, demonstrating high operability and providing a basis for optimizing the IFEITP. This study offers broader insights for regions with similar ecological and socioeconomic conditions, enabling the application of analogous ecological compensation policies to maintain environmental justice and promote sustainable development. Full article

During the safety planning stage, accurate crash prediction tools are critical for prioritizing countermeasures and allocating resources effectively. Traditional statistical approaches, while long applied in this field, often depend on distributional assumptions that may introduce bias and limit model accuracy. To address these issues, studies have started exploring Machine Learning (ML)-based techniques for crash prediction, particularly for higher functional class roads. However, the application of ML models on two-lane highways remains relatively limited. This study aims to develop an approach to integrate traffic, geometric, and critically, speed-based factors in crash prediction using Random Forest (RF) and SHapley Additive exPlanations (SHAP) techniques. Comparative analysis shows that the RF model improves crash prediction accuracy by up to 25% over the traditional Zero-Inflated Negative Binomial model. SHAP analysis identified AADT, segment length, and average speed as the three most influential predictors of crash frequency, with speed emerging as a key operational factor alongside traditional exposure measures. The strong influence of speed in the RF–SHAP results depicts its critical role in the safety performance of two-lane highways and highlights the value of incorporating detailed operating characteristics into crash prediction models. Overall, the proposed RF–SHAP framework advances roadway safety assessment by offering both predictive accuracy and interpretability, allowing agencies to identify high-impact factors, prioritize countermeasures, and direct resources more efficiently. In doing so, the approach supports sustainable safety management by enabling evidence-based investments, promoting optimal use of limited transportation funds, and contributing to safer, more resilient mobility systems. Full article

Anaerobic digestion of straw is a crucial method for agricultural waste valorization, yet its efficiency is limited by complex factors. This study employed machine learning to predict methane yield and optimize process parameters in biochar-amended straw digestion. A comprehensive dataset integrating experimental and literature data (100 samples, 15 input variables) was constructed, incorporating operational conditions, straw characteristics, and biochar properties (e.g., dosage, particle size, specific surface area, and elemental composition). Prediction models were developed using Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Results indicated that the RF model achieved the best predictive accuracy, with an R² of 0.81 and RMSE of 36.9, significantly surpassing other models. Feature importance analysis identified feeding load, biochar dosage, and biochar carbon content (C%) as the key governing factors, collectively accounting for 65.7% of the total contribution. The model-predicted optimal ranges for practical operation were 15–30 g for feeding load and 5–20 g/L for biochar dosage. This study provides data-driven validation of biochar’s enhancement mechanisms and demonstrates the utility of RF in predicting and optimizing anaerobic digestion performance, offering critical support for sustainable agricultural waste recycling and clean energy generation. Full article

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting. Full article

Despite its global distribution, the impacts of wild pigs on the environment are poorly understood. However, wild boar (Sus scrofa) is recognized as a pest species, causes extensive damage to agriculture, biodiversity, and forests, and contributes to motor vehicle accidents. This study investigates the causes and mechanisms underlying the demographic explosion of wild boar in Italy. The analysis is based exclusively on official datasets from Italian governmental institutes, allowing quantitative correlations between population dynamics, culling rates, and economic impacts. By integrating historical data, population biology, reproductive physiology, and chemical communication, the study reveals that anthropogenic pressures, counterintuitively driven by wildlife management practices, have significantly contributed to population growth. A shift from a K-strategy to an r-strategy in reproductive behavior, induced by sustained control pressure, has led to increased birth rates and accelerated expansion. Disruptions in species homeostasis trigger harmful changes in ecosystem structure and functionality, delineating a model of environmental damage. These findings highlight the urgency of adopting an integrated wildlife management approach that combines conservation biology and physiological principles with targeted operational interventions to prevent further degradation affecting both the species and the ecosystem. Full article