Search Results (603)

The background for this study is the limitations of the conventional approach of using deforestation area multiplied by biomass densities or emission factors. We demonstrated how TanDEM-X and GEDI data can be combined to estimate forest Above Ground Biomass (AGB) change at the national scale for Tanzania. The results can be further recalculated to estimate CO₂ emissions and removals from the forest. We used repeated short wavelength, InSAR DEMs from TanDEM-X to derive changes in forest canopy height and combined this with GEDI data to convert such height changes to AGB changes. We estimated AGB change during 2012–2019 to be −2.96 ± 2.44 MT per year. This result cannot be validated, because the true value is unknown. However, we corroborated the results by comparing with other approaches, other datasets, and the results of other studies. In conclusion, TanDEM-X and GEDI can be combined to derive reliable temporal change in AGB at large scales such as a country. An important advantage of the method is that it is not required to have a representative field inventory plot network nor a full coverage DTM. A limitation for applying this method now is the lack of frequent and systematic InSAR elevation data. Full article

Ammonia (NH₃) emissions affect the environment, climate and human health and originate mainly from agricultural sources like synthetic nitrogen fertilizers. Accurate and replicable measurements of NH₃ emissions are crucial for research, inventories and evaluation of mitigation measures. There exist specific application limitations of NH₃ emission measurement techniques and a high variability in method performance between studies, in particular from small plots. Therefore, the aim of this study was the assessment of measurement methods for ammonia emissions from replicated small plots. Methods were evaluated in 18 trials on six sites in Germany (2021–2022). Urea was applied to winter wheat as an emission source. Two small-plot methods were employed: inverse dispersion modelling (IDM) with atmospheric concentrations obtained from Alpha samplers and the dynamic chamber Dräger tube method (DTM). Cumulative NH₃ losses assessed by each method were compared to the results of the integrated horizontal flux (IHF) method using Alpha samplers (Alpha IHF) as a micrometeorological reference method applied in parallel large-plot trials. For validation, Alpha IHF was also compared to IHF/ZINST with Leuning passive samplers. Cumulative NH₃ emissions assessed using Alpha IHF and DTM showed good agreement, with a relative root mean square error (rRMSE) of 11%. Cumulative emissions assessed by Leuning IHF/ZINST deviated from Alpha IHF, with an rRMSE of 21%. For low-wind-speed and high-temperature conditions, NH₃ losses detected with Alpha IDM had to be corrected to give acceptable agreement (rRMSE 20%, MBE +2 kg N ha⁻¹). The study shows that quantification of NH₃ emissions from small plots is feasible. Since DTM is constrained to specific conditions, we recommend Alpha IDM, but the approach needs further development. Full article

Hundreds of Japanese features of war (field positions, tunnels, and fortifications) were constructed in Hong Kong during World War II. However, most of them were poorly documented and were left unknown but still in relatively good condition because of their durable design, workmanship, and remoteness. These features of war form parts of Hong Kong’s brutal history. Conservation, at least in digital form, is worth considering. With the authors coming from multidisciplinary and varied backgrounds, this paper aims to explore these features using a scientific workflow. First, we reviewed the surviving archival sources of the Imperial Japanese Army and Navy. Second, airborne LiDAR data were used to form territory digital terrain models (DTM) based on the Red Relief Image Map (RRIM) for identifying suspected locations. Third, field expeditions of searching for features of war were conducted through guidance of Global Navigation Satellite System—Real-Time Kinetics (GNSS-RTK). Fourth, the found features were 3D-laser scanned to generate mesh models as a digital archive and validate the findings of DTM-RRIM. This study represents a reverse-engineering effort to reconstruct the planned Japanese defense tactics of guerilla fight and Kamikaze grottos that were never used in Hong Kong. Full article

This research aims to characterize sediment dynamics in the Rupinaro catchment, a uniquely terraced and human-shaped basin in Italy’s Liguria region, employing geomorphometric methods to unravel sediment connectivity in a landscape vulnerable to shallow landslides. Within a scenario-based approach, we utilized high-resolution LiDAR-derived digital terrain models (DTMs) to calculate the Connectivity Index, comparing sediment dynamics between the original terraced landscape and a virtual natural scenario. To reconstruct a pristine slope morphology, we applied a topographic roughness-based skeletonization algorithm that simplifies terraces into linear features to simulate natural hillslope conditions and remove anthropogenic structures. The analysis was carried out considering diverse targets (e.g., hydrographic networks, road networks) and the effect of land use. The results reveal significant differences in sediment connectivity between the anthropogenic and natural morphologies, with implications for erosion and landslide susceptibility. The findings reveal that sediment connectivity is moderately higher in the scenario without terraces, indicating that terraces function as effective barriers to sediment transfer. This highlights their potential role in mitigating landslide susceptibility on steep slopes. Additionally, the results show that roads exert a stronger influence on the Connectivity Index, significantly altering flow paths. These modifications appear to contribute to increased landslide susceptibility in adjacent areas, as reflected by the higher observed landslide density within the study region. Full article

Field data, as the basis for planning and designing forest roads, must have high spatial accuracy. Classical (using a theodolite and a level) and modern (based on total stations and GNSSs) surveying methods are used in current field data survey for forest road design. This study analyzed the spatial accuracy of classical and modern surveying methods, the accuracy of spatial data recorded using a UAV equipped with an RGB camera at different flight altitudes, and the accuracy of lidar data of the Republic of Croatia. This study was conducted on a forest area where salvage logging was carried out, which enabled the use of a GNSS receiver in RTK mode as a reference method. The highest RMSE values of the spatial coordinates were recorded for measurements obtained with the classical surveying method (0.89 m) and a total station (0.33 m). The flight altitude of the UAV did not significantly affect the spatial error of the collected data, which ranged between 0.07 and 0.09 m. The cross-terrain slope, as one of the factors that significantly affect the amount of earthworks, did not differ statistically significantly between the methods. The ALS error was strongly influenced by the cross-terrain slope. The authors conclude that the new survey methods (SfM and lidar data) provide high-accuracy data but also draw attention to challenges in their use, such as vegetation and biomass on the ground. Full article

This article presents a methodology for developing a three-dimensional terrain model based on numerical data in the form of a point cloud, with an emphasis on reducing mesh surface errors and using a surface smoothing factor. Initial surface generation was based on a point cloud with a square mesh, and an adopted algorithm for mesh conversion to the input form for the computer aided design (CAD) environment was presented. The use of a bilinear interpolation algorithm was proposed to reduce defects in the three-dimensional surface created in the reverse engineering process. The terrain mapping accuracy analyses were performed for three samples of different geometry using two available options in the Siemens NX program. All obtained surfaces were subjected to shape deviation analysis. For each of the analyzed surfaces, changing the smoothing factor from 0% to 15% did not cause significant changes in accuracy depending on the method adopted. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 6.16%, and in the Variable Density (VD) method, it was within the range of 5.01–6%. For steep regions, in the UD method, it was 6.25%, and in the VD method, it was within the range of 5.39–6.47%, while for concave–convex regions, in the UD method, it was 6.5% and in the VD method, it was within the range of 4.96–6.36%. For a smoothing factor value of 20%, a sudden increase in the inaccuracy of the shape of the obtained surface was observed. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 69.84%, and in the Variable Density (VD) method, it was 71.62%. For steep regions, in the UD method, it was 76.07%, and in the VD method, it was 80.94%, while for concave–convex regions, in the UD method, it was 56.08%, and in the VD method, it was 62.38%. The developed methodology provided high accuracy in the reproduction of numerical data that can be used for further analyses and manufacturing processes, such as 3D printing. Based on the obtained data, three fused deposition model (FDM) prints were made, presenting each of the analyzed types of terrain geometry. Only FDM printing was used, and other technologies were not verified. Full article

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions. Full article

As a core enabler of the emerging low-altitude economy, UAV networks face significant security risks during operation, including malicious node infiltration and data tampering. Existing trust management schemes suffer from deficiencies such as strong reliance on infrastructure, insufficient capability for multi-dimensional trust evaluation, and vulnerability to collusion attacks. To address these issues, this paper proposes a group decision-making (GDM)-enabled dynamic trust management method, termed GDM-DTM, for low-altitude UAV networks. GDM-DTM comprises four core parts: Subjective Consistency Evaluation, Objective Consistency Evaluation, Global Consistency Evaluation, and Self-Proof Consistency Evaluation. Furthermore, the method integrates a Dynamic Trust Adjustment Mechanism with multi-attribute trust computation, enabling efficient trust evaluation independent of ground infrastructure and thereby facilitating effective malicious UAV detection. The experimental results demonstrate that under identical conditions with a malicious node ratio of 30%, GDM-DTM achieves an accuracy of 85.04% and an F-score of 91.66%. Compared to the current state-of-the-art methods, this represents an improvement of 6.04 percentage points in accuracy and 3.71 percentage points in F-score. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

The Brazilian Amazon, a global biodiversity hotspot, faces escalating anthropogenic pressures and climate change, underscoring the urgent need to identify priority areas for ecological restoration and sustainable forest use. This study applied a topoclimatic zoning methodological framework in the Legal Amazon to evaluate the environmental suitability of 12 native tree species across anthropogenically altered landscapes. Species occurrence data were compiled from the RADAMBRASIL Project, GBIF, Herbaria, and forest inventory literature. Climatic, topographic, and geographic variables (1961–2022) informed the zoning model. Our findings reveal that species such as Dinizia excelsa Ducke (81%) and Handroanthus albus (Cham.) Mattos (78%) exhibit exceptionally high topoclimatic suitability. Conversely, Simarouba amara Aubl. (37%) and Schizolobium parahyba (Vell.) S.F.Blake var. amazonicum (Huber ex Ducke) Barneby (46%) showed the lowest proportions in high-potential areas, suggesting their greater ecological breadth or specific niche requirements in altered zones. Principal Component Analysis (PCA) indicated strong correlations between high-potential areas and Af3, Am3, and Aw4 climatic subtypes. This study offers a replicable, evidence-based model for prioritizing species and locations, significantly supporting sustainable silviculture and enhancing the long-term resilience of Amazonian forests in the face of climate change. Full article

While accurate athlete load monitoring is crucial for preventing injury and optimizing performance, the commonly used session rating of perceived exertion training load or competition load method faces limitations due to compliance issues related to missing subjective data self-reported by the athlete and the subsequent challenges in imputing the sessional rating of perceived exertion (sRPE) component, an average value for a training or competition session. This study investigated the imputation of missing RPE scores from the mechanical work and from a Speed–Deceleration–Contact (SDC) model. A total of 1002 datasets were collected from women’s rugby sevens competitions. Using either the mechanical work or SDC, linear regression and random forest imputation models were assessed at different missingness levels and their results compared to those of a common method of daily team mean substitution (DTMS) using an ANOVA of the accuracy by the model type and missingness. The statistical equivalence was evaluated for true and imputed sRPE scores by the model and strategy. Significant interactions between the model type and missingness were found, with all the imputed scores being deemed statistically equivalent. From the ANOVA, DTMS was found to be the poorest-performing model and the random forest model was the best. However, the best-performing model was not superior to previously reported imputation approaches, which confirms the difficulty in using subjective measures of the load when missing data is a prevalent issue in team sports. Practitioners are encouraged to critically evaluate any method of imputation for an athlete’s load. Full article

The goal of this study was to develop a GIS-based Decision Support Model for selecting the best timber harvesting systems on steep terrain. The model combines multiple layers, each representing an important factor in mechanized logging. These layers are used to create a final map that functions as a spatially explicit Decision Support Model that helps decide which machines are best suited for different forest areas. A key idea of this study is to consider not only operational criteria (slope, ruggedness, wetness, and road accessibility), but also a fundamental silvicultural aspect, i.e., the assessment of tree growth classes to enable the integration of silvicultural deliberations into timber harvest planning. The data used for this model come from orthophoto image and a Digital Terrain Model (DTM). The operational factors were analyzed using GIS tools, while the silvicultural aspects were assessed using the deep learning algorithm DeepForest and tree growth equations (allometric functions). The model was tested by comparing its results with field data taken in a Norway Spruce stand in South Tyrol/Italy. The findings show that the model reliably evaluates operational factors. For silvicultural aspects, it tends to underestimate the number of small trees, but provides a good representation of tree size classes within a forest stand. The innovation of this method is that it relies on low-cost, open-source tools instead of expensive 3D scanning devices. Full article

In the processing of industry front-end waste, the reactor plays a critical role as a key piece of equipment, making its operational status monitoring essential. However, in practical applications, issues such as equipment aging, data transmission failures, and storage faults often lead to data loss, which affects monitoring accuracy. Traditional methods for handling missing data, such as ignoring, deleting, or interpolation, have various shortcomings and struggle to meet the demand for accurate data under complex operating conditions. In recent years, although artificial intelligence-based machine learning techniques have made progress in data imputation, existing methods still face limitations in capturing the coupling relationships between the sequential and channel dimensions of time series data. To address this issue, this paper proposes a time series decoupling-based data imputation model, referred to as the Decomposite-based Transformer Model (DTM). This model utilizes a time series decoupling method to decompose time series data for separate sequential modeling and employs the proposed MixTransformer module to capture channel-wise information and sequence-wise information, enabling deep modeling. To validate the performance of the proposed model, we designed data imputation experiments under two fault scenarios: random data loss and single-channel data loss. Experimental results demonstrate that the DTM model consistently performs well across multiple data imputation tasks, achieving leading performance in several tasks. Full article

Dynamic transmission models (DTMs) have been used to estimate various aspects of the public health impact of varicella vaccination programs. The aim of this study was to validate the predictions of a DTM—developed using the typical approach to varicella modeling—using real-world data from a country with a long-term universal varicella vaccination (UVV) program and to assess the sensitivity of the predictions to changes in model input parameters. A compartmental, age-stratified DTM was developed using the settings corresponding to the existing UVV program in Germany. The model-predicted total number of varicella cases followed the same trend as observed in the reported data. The agreement between the simulations’ results and the data was the highest for the age group most exposed to varicella (0–5 years old), while for other age groups, a decline in accuracy was observed. Sensitivity analyses identified the input parameters having a crucial impact on the model’s long-term predictions. The results supported the reliability of the DTM for assessing the impact of varicella vaccination programs over the first decades after their introduction and provided an insight into how certain parameters and assumptions influence the model output and thus require careful evaluation in the studies of future varicella vaccination programs. Full article

Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains. Full article