Search Results (694)

Peste des Petits Ruminants (PPR) is a highly contagious viral disease of small ruminants and remains a major threat to food security and rural livelihoods across Africa, the Middle East, and Asia. In the Mediterranean, uneven outbreak reporting and intense spatial clustering hinder the identification of regions where environmental and anthropogenic conditions favor disease occurrence. This study applied an interpretable machine-learning ecological niche modeling framework to characterize PPR spatial suitability in North Africa. A merged outbreak dataset (n = 744) was compiled from the Food and Agriculture Organization (FAO) EMPRES-i and the World Animal Health Information System (WAHIS) databases for 2005–2026. Outbreak locations were linked to environmental and anthropogenic predictors, spatially thinned, and paired with randomly sampled pseudo-absences at a 1:1 ratio. After correlation-based screening and Boruta feature selection, four classifiers were compared under five-fold spatial block cross-validation: a generalized linear model (GLM), a support vector machine (SVM), Random Forest (RF), and extreme gradient boosting (XGBoost). All models showed good discriminatory performance. Random Forest (RF) and extreme gradient boosting (XGBoost) yielded the highest area under the receiver operating characteristic curve value (AUC = 0.94). Random Forest achieved the highest specificity, XGBoost achieved the highest sensitivity, and the support vector machine showed the most even sensitivity–specificity tradeoff among the machine-learning classifiers. Sheep density, mean diurnal temperature range, temperature seasonality, and human population density were consistently the dominant drivers. Predicted PPR suitability based on reported outbreaks was concentrated along the North African coastal belt and low across most arid inland regions. These findings suggest that passive surveillance is likely to be most informative in coastal production systems where host density, environmental suitability, and reporting opportunity overlap. At the same time, areas of lower reported-outbreak suitability should not be interpreted as disease-free and may require complementary active surveillance approaches. Full article

Integrating existing tree-level information into harvester operator decision-making can significantly enhance precision forest management, particularly with respect to biodiversity preservation and climate-smart adaptation. During harvester operations, a primary challenge lies in positioning the machine with sufficient accuracy in real time to relate a priori individual-tree-level reference information to the operator. We propose a lightweight procedure using tree-to-tree matching to continuously register a real-time tree map collected from a harvester (or another mobile laser scanning system) to a precomputed reference map derived from an airborne laser scanner (ALS). We assess the robustness of the method using simulated tree maps and validate its real-world performance in experiments using a LiDAR-equipped harvester performing a thinning operation in a boreal forest. In simulations, registration was found to be robust up to a moderate tree density of approximately 1700 ha⁻¹, even when using a reference map with a lower positional accuracy and higher error rates than in our harvester experiments. Using real-world data from the thinning operation, the registration method was demonstrated to successfully mitigate meter-scale positioning drifts remaining in the LiDAR-inertial trajectory. After the continuous registration procedure, the positioning error was reduced to the level of 0.5 m, constrained by the accuracy of the prior map derived from sparse ALS data with ∼5 transmissions/m². Importantly, the registration procedure was shown to update in real time (at most 20 ms update time for stands with densities of at most 2000 ha⁻¹, after an initial computational phase. Notable features of the registration procedure are its low memory consumption, fast runtime and capacity to accurately position the harvester despite LiDAR-inertial positioning drift. While these results demonstrate the potential for real-time operation, full implementation requires the development of real-time tree detection and estimation of tree attributes. Full article

The energy utilization of residual woody biomass is a relevant strategy for the decentralized energy transition and local waste management in rural areas. The objective of this study was to characterize (physically, chemically, and energetically) five types of residual biomass: pine branches, huinumo (this material refers to the long, thin pine needles that, after drying and falling, form a layer on the forest floor), cherry branches and leaves, and grass waste generated in the community of San Francisco Pichátaro, Michoacán, Mexico, in order to evaluate its viability for the production of densified solid biofuels. A comprehensive analysis was conducted, including moisture content, higher heating value, proximate characterization, structural chemical analysis (using the Van Soest method), elemental CHONS analysis, ash microanalysis (by ICP-OES), and a multicriteria analysis with normalized energy and compositional indicators. The results showed that huinumo and cherry leaves were the most outstanding biomasses, presenting the highest heating values (20.7 MJ/kg) and low moisture and ash contents. Pine branches obtained the most balanced results, characterized by their equilibrium in fixed carbon and lignin, as well as their low potassium content. The multicriteria analysis showed that there is no absolute optimal biomass; however, it indicates that pine branches and huinumo are the most robust feedstocks for the production of briquettes or pellets. The results confirm the significant technical and environmental potential of local lignocellulosic residues for the production of solid biofuels and for contributing to sustainable energy solutions at the local scale. Full article

Timber sale prices are influenced by multiple tract, product, and transaction characteristics. This study evaluates the effects of species composition, product class, sale method, harvest type, timber quality, and average tree diameter on timber stumpage prices using timber sale records from Alabama between 1 January 2010 and 31 December 2019. Prices were modeled on a per weight unit basis using a generalized linear model with a Gamma distribution and logarithmic link. Results indicate that larger average diameters were consistently associated with higher prices across most product classes. Harvest type also influenced prices, with salvage operations yielding prices approximately 8.3% lower than thinning operations. Timber quality had a moderate effect: good-to-excellent quality timber sold for about 4.8% higher prices than poor-to-fair quality timber. Sale method was an important determinant of price outcomes. Negotiated sales generated significantly lower prices than sealed-bid sales, averaging approximately 17% lower overall. However, interaction analysis revealed that negotiated sales produced higher prices for mixed hardwood sawtimber, likely reflecting the diverse end uses of these products. Regional effects were also evident, with higher prices observed in the southwestern portion of the state, likely due to proximity to the Port of Mobile and associated export markets. These findings highlight the importance of both tract and transaction characteristics in determining timber prices and provide guidance for landowners and forest managers when selecting sale strategies and management practices. Full article

Urban forest management practices generate substantial amounts of twig biomass that is commonly treated as waste, despite its potential as a source of bioactive compounds. Biological and chemical properties of methanolic extracts of 19 urban forest tree and shrub species were assessed using a multidisciplinary approach combining high-performance thin-layer chromatography (HPTLC) and HPTLC-DPPH bioautography with spectrophotometric determination of radical scavenging activity (RSA), total phenolic content (TPC), inhibition assays of skin aging-related enzymes (tyrosinase and elastase), and testing against skin pathogens Staphylococcus aureus and Pseudomonas aeruginosa. The results revealed marked differences in biological activity among extracts, driven primarily by specific phytochemical profiles. Torminalis glaberrima (Gand.) Sennikov & Kurtto (108.8 ± 6.6 μmol TE/mL) and Paliurus spina-christi Mill. (106.6 ± 1.6 μmol TE/mL) exhibited the highest RSA, correlating with elevated TPC. Acer campestre L. (51.6 ± 9.1%) showed the strongest elastase inhibition. The most pronounced tyrosinase inhibition was observed for Torminalis glaberrima (39.0 ± 3.5%), indicating a significant contribution of TPC. In contrast, the strongest antibacterial activity was recorded for Acer platanoides L. and Carpinus betulus L., despite their lower TPC values, suggesting the contribution of non-phenolics. Phenolic zones (R_F 0.10, 0.28, 0.57, 0.58) were identified as putative markers of the observed bioactivities. Overall, twigs emerge as an underexplored source with considerable potential for natural cosmetics development, warranting further investigations. Full article

Forest fires are becoming increasingly prevalent and destructive in many regions of the world, posing significant threats to biodiversity, ecosystems, human settlements, climate, and the economy. The United States of America (USA), Australia, Canada, Greece and Portugal are five regions that have experienced enormous forest fires. One way to reduce the size and rage of forest fires is by decreasing the amount of flammable material in forests. This can be achieved using autonomous Unmanned Ground Vehicles (UGVs) specialized in vegetation cutting and equipped with Artificial Intelligence (AI) algorithms to identify and differentiate between vegetation that should be preserved and material that should be removed as potential fire fuel. In this paper, an innovative study of forest vegetation detection, classification and 3D localization using ground vehicles’ RGB and depth images is presented to support autonomous forest cleaning operations to prevent fires. The presented work, which is a continuation of a previous research, presents a method for 3D objects localization in the real-world using Deep Learning Object Detection (DLOD) combined with an RGB-D camera. It presents and compares results of eight recent high-performance DLOD architectures, YOLOv5, YOLOv7, YOLOv8, YOLO-NAS, YOLOv9, YOLOv10, YOLO11 and YOLOv12, to detect and classify forest vegetation in five classes: “Grass”, “Live vegetation”, “Cut vegetation”, “Dead vegetation”, and “Tree-trunk”. For the training of the DLOD models, our custom dataset acquired in dense forests in Portugal is used. A methodology that combines the best DLOD trained for vegetation detection and classification and an RGB-D camera for the 3D localization of the classified detected objects in the real-world. The presented methods are employed in an Unmanned Ground Vehicle (UGV) to localize forest vegetation that needs to be thinned for fire prevention purposes. A key challenge for autonomous forest vegetation cleaning is the reliable discrimination of objects that need to be identified to reach the goal of fire prevention using autonomous unmanned ground vehicles in dense forests. With the obtained results, forest vegetation is precisely detected, classified and localized using the DL models and the localization method presented. Also, the fastest DLOD architecture to train is YOLOv5, and the fastest to infer are YOLOv7 and YOLOv12. The innovation presented is the detection, classification, and 3D localization of the vegetation using DLOD architectures, in real-time, with a localization error of the real-world object in width, height and depth under 21.4, 20.7 and 11%, respectively, using only a depth camera and a processing unit. The 3D localized objects are defined as parallelepiped geometrical shapes. The methodology for vegetation detection, classification and localization presented in this paper is highly suitable for future autonomous forest vegetation cleansing, specialized using unmanned ground vehicles. Full article

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

In complex sedimentary environments, the identification of thin sandbodies and the accurate prediction of their thickness remain challenging, particularly when relying on a single analytical approach. Taking the lower sub-member of the fourth member of the Shahejie Formation (Es₄^L) in the Chenguanzhuang area of the Dongying Depression as a case study, this study proposes a quantitative prediction method that integrates sedimentary facies constraints with machine learning-based seismic multi-attribute fusion. Based on core observations, well log data, and 3D seismic datasets, the study area is subdivided into two zones: Zone I (shallow-water delta front) and Zone II (shore–shallow lake). Sensitive attributes for each zone are optimized using Pearson correlation analysis and hierarchical clustering, and five machine learning models—SVR, Random Forest, MLP, Ridge Regression, and Lasso Regression—are systematically evaluated. The MLP model is selected for Zone I, achieving R² values of 0.856 and 0.936 for the training and test sets, respectively, whereas Ridge Regression combined with leave-one-out cross-validation (LOOCV) is adopted for Zone II to mitigate overfitting caused by limited well data, yielding R² values of 0.864 and 0.779. Compared with conventional linear regression (R² = 0.45), the proposed approach significantly improves the accuracy of quantitative sandbody prediction, providing a reliable geological basis for hydrocarbon exploration and an effective technical framework for similar complex sedimentary environments. Full article

Cunninghamia lanceolata Lamb. occupies a significant role in artificial forests globally, making its sustainable management crucial for terrestrial forest ecology. We experimentally determined soil physicochemical properties and the shrub and herb diversity of different age classes of Cunninghamia lanceolata plantations in Southwest China in 2023. The Mantel tests, RDA, and PLS-SEM were used to analyze the effects of stand factors on soil fertility and shrub and herb diversity. Shrub and herb diversity, as well as soil physicochemical properties, vary significantly across age classes in Cunninghamia lanceolata plantations. The maximum values of organic carbon, total nitrogen, total phosphorus, and available silicon were observed in the mature forest (36.62 g/kg, 1.90 g/kg, 0.53 g/kg, and 84.33 mg/kg, respectively), while the minimum values were found in the middle-aged forest (17.77 g/kg, 0.81 g/kg, 0.34 g/kg, and 53.70 mg/kg). TPH was the most influential stand factor. TBH was strongly correlated with RDA1 (r = 0.821, p < 0.001); soil organic carbon, total nitrogen, total phosphorus, and available silicon were negatively correlated with stand density. In this study, we propose a detailed age class-based thinning plan with strong implementability: cultivating large-diameter timber, maintaining soil fertility and understory plant diversity, and being friendly to forest farm management personnel. This approach could enhance biodiversity and ecosystem stability in Cunninghamia lanceolata plantations and serves as a reference for the sustainable management and operation of the Cunninghamia lanceolata forest ecosystem. Full article

Strong heterogeneity and ambiguous seismic responses hinder reliable sandstone thickness prediction when using a single seismic attribute in the lower sandstone interval of the Talang Akar Formation (hereafter abbreviated as the LTAF interval) in the B gas field, South Sumatra Basin. To address this challenge, we propose a seismic attribute fusion and reservoir sweet-spot prediction framework based on a multiscale convolutional neural network (CNN) integrated with a self-attention module. Multiple seismic attribute volumes are organized as multi-channel 2D attribute slices, and parallel convolutions with kernel sizes of 3 × 3, 5 × 5, and 7 × 7 are employed to capture spatial features ranging from thin-bed boundaries and channel morphology to sand-body assemblage distribution. The self-attention module explicitly models inter-attribute dependencies and performs adaptive weighted fusion to suppress noise and emphasize informative attributes. The network adopts a dual-output design, producing (i) a sandstone thickness prediction map at the same spatial resolution as the input and (ii) attribute importance scores for quantitative attribute selection and geological interpretation. Using 3D seismic data and well-constrained thickness labels, the proposed model achieves an R² of 0.8954, outperforming linear regression (R² = 0.8281) and random forest regression (R² ≈ 0.8453). The learned importance scores indicate that amplitude-related attributes (e.g., RMS amplitude and maximum amplitude) contribute most to thickness prediction, whereas frequency- and energy-related attributes show relatively lower contributions, which is consistent with bandwidth-limited resolution effects. Overall, the proposed framework unifies attribute fusion, thickness prediction, and interpretability within a single model, providing practical support for fine reservoir characterization and development optimization in heterogeneous sandstone reservoirs. Full article

This study presents a newly conducted comprehensive investigation into the lateral torsional buckling (LTB) behavior of un-bonded pre-stressed (PS) thin-walled steel I-beams subjected to non-uniform bending moments, combining a numerical study with a machine learning (ML) approach and experimental validation. Despite extensive prior work, no exact analytical solution exists particularly for non-uniform bending or can be extremely complicated, as the resulting differential equations contain variable coefficients particularly under non-uniform bending due to the complexity of the PS system. To overcome this limitation, a numerical study using finite element (FE) analysis is first conducted with emphasis on the key geometric and pre-stressing parameters, including unbraced beam length, tendon eccentricity, deviators configurations, and pre-stressing force, to evaluate the LTB behavior. The FE modeling was then validated against experimental testing to ensure the accuracy and reliability of the FE solutions. Subsequently, a comprehensive dataset is generated using FE simulations to train the ML models aimed at predicting the LTB resistance of the PS system. This study presents three ML approaches, including support vector regression (SVR), random forest (RF) and least-square boosting (LSBoost), and their optimal hyperparameters are determined using Bayesian optimization (BO) to enhance the prediction performance. The results indicate that the LTB capacity predicted by the Bayesian-optimized ML models achieve high predictive accuracy and are in close agreement with numerical FE simulations, thereby highlighting their potential in capturing the complex, underlying non-linear interactions influencing the buckling behavior of the PS structural system. Accordingly, the proposed framework offers a robust and effective predictive tool for evaluating LTB resistance, particularly under non-uniform bending where exact analytical solutions are not available, and for supporting the design and assessment of PS steel structures. Full article

In the face of intensifying climate disruptions, coastal landscapes like Necoclí in Colombia’s Department of Antioquia are sites of both vulnerability and resilience. This paper examines how thick mapping acts as a methodology for decentralized spatial planning and a practice of revolutionary care by amplifying youth voices and fostering situated climate adaptation. Drawing from a participatory mapping process co-developed with young people, we reflect on how community-based approaches can trigger territorial restructuring from the bottom up. Through storytelling, visual documentation, and collective drawing, the mapping process brought to light lived experiences and local ecological knowledge that are often excluded from technocratic spatial integration strategies. These thick maps function as tools for sub-local territorial agency, allowing youth to reconnect with their landscapes while providing municipal administrations with the granular data needed for equitable spatial development. The paper explores how this form of mapping challenges top-down adaptation narratives and enables more inclusive planning for just futures by centering the territorial dimensions of climate risk. Our findings reveal a profound divergence in territorial perception: while older settlers maintain a narrative of loss tied to a lush, forested past, children’s drawings expose an internalized ecological thinning, characterized by the absence of native flora and the threatening proximity of a rising sea. Ultimately, this study demonstrates how thick mapping contributes to socio-ecological transitioning by bridging the gap between national climate policies and the spatial expression of local needs in frontline communities. Full article

To explore the patterns of biomass accumulation and sediment carbon burial indicators in mangrove forests under different thinning intensities, a study was conducted on an 8-year-old Kandelia obovata Sheue & al. plantation on Shupaisha Island, Longwan District, Wenzhou City, Zhejiang Province. Three treatments were designed: no thinning (CK), 20% thinning, and 40% thinning. Stand growth and plant carbon density were evaluated for all three treatments at the initial thinning stage and two years later. Sediment carbon density and organic carbon burial rate were assessed only for CK and 20% thinning. Thinning significantly enhanced mangrove growth and plant carbon storage. Compared with unthinned stands, 20% and 40% thinning treatments significantly increased branch diameter and biomass (p < 0.05). The order of mangrove height was 20% thinning > 40% thinning > CK. The plant carbon densities in the 20% and 40% thinned stands were 16.31 Mg C·ha⁻¹ and 15.30 Mg C·ha⁻¹, respectively, far exceeding that of the control (4.80 Mg C·ha⁻¹). In contrast, sediment carbon responses were negative in the short term. After thinning, the sedimentation rate and organic carbon content in mangrove sediments decreased. Sediment carbon density decreased from 88.10 Mg C·ha⁻¹ in unthinned stands to 85.02 Mg C·ha⁻¹ under 20% thinning, accompanied by a reduction in carbon burial rate. Overall, these two-year results indicate increased plant carbon storage under thinning, whereas measured sediment carbon indicators under moderate thinning declined over the same period. Longer-term monitoring is needed to assess whether these short-term responses translate into net ecosystem carbon consequences. Full article

Secondary broadleaf forests constitute a vital component of China’s forest resources, characterized by diverse ecological functions, strong regeneration capacity, and widespread distribution. They possess significant potential for carbon storage, yet their carbon sink capacity is influenced by multiple factors, including successional stage, tree species composition, soil conditions, and human disturbance levels. However, the response mechanism of carbon sequestration capacity in secondary broadleaf forest ecosystems to thinning intensity remains unclear. This study aims to elucidate the effects of different thinning intensities (0% (CK), 10% (LT), 25% (MT), and 35% (HT)) on soil greenhouse gas (GHG) emissions, vegetation, and soil organic carbon sinks. Results indicate that total GHG emissions increased by 1.9%, 31.86%, and 42.18% under LT, MT, and HT, respectively. Vegetation carbon sequestration decreased by 5.26% and 16.22% under LT and MT, respectively, while increasing by 13.17% under HT. Soil organic carbon sequestration increased by 37.33% under LT, but decreased by 5.89% and 61.41% under MT and HT, respectively. In summary, compared with the control, ecosystem carbon sequestration increased by 30.66% in LT, while decreasing by 32.06% and 71.73% in MT and HT, respectively. Our study indicates that light thinning intensity can enhance the carbon sequestration potential of ecosystems and effectively mitigate climate change. Full article

Under recurring droughts, the southwestern U.S. loses a significant proportion of precipitation as evapotranspiration (ET), suggesting an opportunity to reduce ET via forest thinning. To better understand the potential impacts of thinning on the forest hydrologic cycle, we used sap flow sensors and Bowen ratio stations to measure ET in thinned and non-thinned ponderosa pine (Pinus ponderosa Douglas ex C. Lawson) stands in northern Arizona during the wet year of 2023, where thinning removed 42% of overstory basal area. Although our study site had experienced prolonged drought in previous years, heavy winter snowfall made 2023 a wet year. We correlated sap flow with environmental variables and used principal component analysis to identify the primary drivers of ponderosa pine water use in thinned and non-thinned stands. Results showed that after accounting for tree size, thinned stands had ~20% (~5 L day⁻¹) higher individual-tree water use at daily and weekly temporal scales than non-thinned stands. At the stand level, thinning decreased overstory ET (OET) but increased understory ET (UET), indicating a reallocation of outgoing water fluxes in the water balance. As a result, total ET (sum of OET and UET) decreased from 584 to 516 mm year⁻¹. In the semi-arid forest, this decrease in total ET of 68 mm year⁻¹ (~12% reduction) indicates an ecohydrologically meaningful outcome of forest thinning. In both stands, tree water use was strongly regulated by environmental variables, primarily atmospheric variables such as air temperature and vapor pressure deficit. Overall, our results suggest that thinning can still promote an improved stand-level forest water balance during a wet year and thus may enhance forest resilience under projected increases in heat and aridity in the southwestern U.S. Full article