Search Results (6,299)

An automated air-conditioner cleaning system was developed as a retrofit solution for conventional split-type units to reduce residual moisture in the evaporator section and suppress post-shutdown microbial accumulation. The system was integrated with an 18,000 BTU h⁻¹ air-conditioner and implemented using an Arduino-based closed-loop control platform with temperature and relative humidity monitoring. After shutdown, the indoor fan was operated under low-, medium-, or high-speed conditions to remove retained moisture from the cooling coil. System performance was evaluated in an 18 m³ test room through measurements of electrical consumption, operating cost, relative humidity, and microbial contamination in room air and on the evaporator coil before and after system installation. Low-speed operation showed the lowest current demand, power consumption, and electricity cost, with corresponding values of 0.36 ± 0.01 A, 79.2 ± 0.8 W, and 0.47 THB per 150 min. Post-shutdown humidity reduction was achieved under all tested conditions, while the high-speed mode provided the fastest drying response, reducing relative humidity to approximately 60% within 120 min. In the room air, the greatest reduction in airborne fungi after shutdown was observed at low speed, whereas the greatest reduction in airborne bacteria was observed at medium speed. On the evaporator coil, the strongest bacterial suppression was obtained at low speed, where the bacterial count after 24 h decreased from 633.33 ± 34.27 CFUs before installation to below the detection limit after installation. These results indicate that the proposed system reduced moisture retention and microbial contamination with minimal energy consumption. Full article

Airblast sprayers remain the dominant pesticide delivery system in California citrus; however, mechanistic characterization of spray transport and off-target fate under realistic field-scale atmospheric variability remains limited. Regulatory airblast drift assessments in the United States (U.S.) currently rely on a sparse, dormant-apple canopy representation, despite substantial structural differences from foliated citrus canopies that may influence drift behavior. To address this gap, this study quantified airblast spray drift in a commercial citrus orchard across multiple downwind distances under varied daytime meteorological conditions and evaluated the influence of distance and weather variables on measured drift. Airborne and sedimentation drift were measured from a conventional axial-fan airblast sprayer operating at 10.3 bar, 5.1 km·h⁻¹, and 935 L·ha⁻¹ in a 4.0 m tall mandarin (Citrus reticulata) orchard using a U.S. Environmental Protection Agency (EPA)-approved, International Organization for Standardization (ISO) standard 22866-aligned protocol. Drift collectors (n = 2688), including flat cards, artificial foliage, and horizontal and vertical string samplers, were deployed from 33 m upwind to 183 m downwind of the orchard edge. Airborne drift measurements showed no significant vertical stratification or near-field decay between 8 m and 23 m downwind (p > 0.05), indicating rapid plume homogenization following canopy exit. In contrast, sedimentation drift declined sharply within 30 m and attenuated logarithmically with distance, governed by progressive droplet depletion and plume dilution. Estimated drift cessation distances were 127.5 m for artificial foliage and 182.1 m for horizontal string samplers. Drift magnitude varied significantly among trials (p < 0.05), reflecting sensitivity to meteorological variability. Multiple linear regression identified wind direction, wind speed, and atmospheric pressure as significant predictors of downwind deposition (p < 0.05), whereas air temperature and relative humidity primarily influenced drift through evaporative control of droplet lifetime. Collectively, these results demonstrate that spray drift from foliated citrus canopies is substantially attenuated relative to dormant-canopy scenarios. Although not intended to define regulatory buffer distances, the high-resolution dataset generated provides mechanistically interpretable parameterization inputs for next-generation airblast drift models, supporting improved representation of canopy interactions, plume evolution, and meteorological modulation in regulatory exposure assessments. Full article

This study focuses on modelling the spatial dependence of airborne particle contamination using Response Surface Methodology (RSM), with consideration of its implications for technical cleanliness and employee health. The analysis is based on two measurement campaigns conducted in an industrial production hall, where particle concentrations were recorded across multiple size fractions using a TROTEC PC220 device. The results demonstrate that RSM effectively captures nonlinear relationships and spatial gradients, enabling the identification of local extrema and contamination hotspots. Statistical analysis confirmed a significant influence of spatial coordinates on particle concentration across all fractions, with finer particles exhibiting stronger spatial dependence, consistent with aerosol behaviour in indoor environments. Quadratic model terms revealed stable hotspot regions persisting even after corrective measures, indicating persistent contamination sources or structural factors. Residual analysis suggested additional unmodeled local sources or transport mechanisms. Based on the integration of RSM and multi-fraction analysis, a mechanistic contamination model (source–transport–receptor framework with deposition processes) is proposed, linking particle behaviour with surface contamination and potential human exposure. The approach enables data-driven, localised contamination control and supports optimisation of technical cleanliness and occupational health conditions. Full article

High-resolution LiDAR and individual-tree modeling are generating increasing volumes of tree-level spatial data, including coordinates, tree height, and diameter at breast height (DBH). However, the lack of scalable and spatially explicit identifiers still limits the organization and integration of tree records in digital twin forest systems. This paper presents a GeoSOT-based framework for assigning position-linked identifiers to standardized tree observation records. The proposed code is used as a spatial anchor for record organization, candidate retrieval, and lifecycle-oriented management, rather than as a direct label of biological tree identity. The framework is implemented through a Yukon-based workflow for spatial storage and GeoSOT-code attachment, with a Bigtable-style schema described for time-stamped record organization. In a Mengjiagang forest farm case study, 604 treetop observations were extracted from airborne-LiDAR-derived canopy height models. Perturbation tests, boundary stress testing, controlled candidate matching, and a prototype retrieval benchmark show that fine-level GeoSOT codes are sensitive to positional uncertainty, whereas coarser levels combined with target-cell and adjacent-cell retrieval provide more stable candidate filtering with compact candidate sets under controlled experimental conditions. These results suggest that GeoSOT-based coding can support tree-observation record organization and candidate matching in digital twin forest systems. Independent cross-source identity validation and deployed database-level benchmarking should be addressed using real multi-source datasets and operational database environments. Full article

Platinum group elements (PGEs), especially platinum (Pt), palladium (Pd), and rhodium (Rh), are analyzed as emerging airborne contaminants in urban environments. This study aimed to monitor the spatial and temporal distribution of PGEs in urban air and to evaluate their potential as indicators of traffic-related emissions. The paper presents a five-year monitoring of Pt, Pd, and Rh mass concentrations in airborne particulate matter collected from three urban locations (North, Center, and South) with different traffic loads in Zagreb, Croatia. Weekly samples were digested in acid under high temperature and high pressure, and analyzed using inductively coupled plasma mass spectrometry (ICP-MS). At the monitoring location South, mass concentrations of all PGEs were generally 20–40% higher than at other locations, consistent with its higher traffic density. The PGEs showed seasonal variability, with 40–60% higher mass concentrations in winter and autumn than in spring and summer. The spatial and temporal distribution of PGE mass concentrations across urban locations demonstrates their potential as indicators of traffic-related activity. Palladium mass concentrations were consistently the highest, as a result of its increased use in modern catalytic converters. These findings underscore the relevance of long-term PGE monitoring for understanding urban atmospheric pollution dynamics within changing environmental conditions. Full article

The water–land classification is fundamental for shoreline extraction and coastal habitat mapping, which is the basis of a comprehensive assessment and ecosystem-based coastal zone management. This study aims to separate water and land for coastal zones by taking advantage of both high-resolution satellite imagery and airborne lidar point clouds. Considering physical principles of optical remote sensing and lidar, we developed a prior knowledge-based localization classification approach that eliminates the need for collecting training sets and handling temporal differences across multiple data sources. Our approach first created the initial classification using the WorldView-2 (WV2) Normalized Difference Water Index. Then, the Connected Components Labeling algorithm was used to create a non-overlapping partition of the working area. The third step involved processing the water blocks using prior land cover knowledge. Finally, we used lidar point clouds to refine the initial water blocks and their neighboring areas. This classification approach showed promising results along Matagorda Bay, Texas, an approximately 2449 km² area that is covered by 26 WV2 images and 1568 lidar tiles. Full article

Severe and unpredictable wind conditions significantly disrupt flight safety, mission planning, and scheduling. Traditional wind forecasting methods rely on low-resolution mesoscale models or resource-intensive instrumentation. This study evaluates the accuracy of 40 m Large-Eddy Simulations (LESs), nested within a mesoscale framework, to better resolve hazardous wind phenomena over GrandSKY, North Dakota, the first large-scale commercial Uncrewed Aircraft System (UAS) test park in the United States, serving as a hub for UAS innovation and Beyond Visual Line of Sight operations. Using low-altitude airborne observations from Meteodrone flights, satellite data, and ground-based measurements, we assess the model’s accuracy in predicting wind speed and direction during both summer and winter. Results demonstrate that the 40 m LES provides improved predictions of wind gust variability compared to the 1 km forecast, and the impact on flight safety is quantified. The LES also reveals notable discrepancies in UAS flyability predictions, which result in up to a 17% reduction in operational windows during the summer. This study’s novelty lies in using a 40 m resolution LES nested within a 1 km WRF simulation, combined with multi-source observations, to resolve low-altitude turbulence and quantify its impact on UAS operations. A 10–18% correction factor can be applied to TKE (or derived wind variability) in coarser WRF runs to better estimate maximum wind speeds without LES. The findings highlight the potential of high-resolution LES modeling to support reliable UAS operations in weather-sensitive environments, laying the groundwork for broader integration of advanced simulation techniques in national airspace management systems. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

Hyperspectral airborne data combined with machine learning has proven effective for characterizing plant nutritional quality. However, terrain, viewing geometry, and illumination can distort spectral signatures, leading to biased models with limited generalizability for large-scale mapping across farms with a heterogeneous landscape. In this study, we developed a framework for mapping pasture quality using airborne hyperspectral imaging while explicitly accounting for in-field acquisition and environmental effects. Nitrogen content (N%) and metabolizable energy (ME) were used as reference indicators across four hill country farms in New Zealand with contrasting environmental and management conditions. Ground truth was obtained using standard laboratory wet chemistry methods and paired with AisaFENIX airborne hyperspectral data, resulting in 1610 spectral samples derived from 161 spatially independent ground plots. Gaussian Process Regression (GPR) and a one-dimensional convolutional neural network (1D-CNN) were trained and evaluated on an independent test dataset. Both models achieved strong predictive performance (R² > 0.8); however, GPR provided more reliable estimates through predictive uncertainty. Using a 95% confidence interval threshold to mask uncertain predictions increased overall performance (R² > 0.9) and consequently improved the reliability of the mapped outputs. This approach enables spatially explicit pasture nutrient assessment to support precision land management for carbon and nitrogen. Full article

Airborne microplastics (MPs) are increasingly recognized as a pervasive pollutant with potential implications for environmental and human health. Despite growing concern, the influence of seasonal dynamics and environmental conditions on MP distribution remains poorly understood. This study investigates the temporal variability and environmental drivers of MPs across outdoor settings, highlighting how factors such as temperature, wind speeds, and precipitation modulate their behaviors. Using a combination of shielded gravitational deposition sampling (Sigma-2) and bulk deposition sampling over four seasons, coupled with μ-FTIR single particle analysis, we quantified MP abundance, size distribution, morphology, and polymer composition across contrasting environments. Deposition fluxes differed between samplers, with bulk samplers yielding 131–1589 MP/m²/d and Sigma-2 samplers yielding 4208–39,126 MP/m²/d. Multivariate analyses indicate that temperature was significantly correlated with MP loading in the Sigma-2 sampler, whereas precipitation effects were not detectable within the temporal resolution of our dataset. Polymer profiles differed between samplers, with Sigma-2 samples enriched in polyamide (PA) and resin-type particles, and bulk samples containing higher proportions of rubber and acrylate. Spherical and irregular particles were the predominant morphologies across both samplers. Together, these findings provide new insights into the environmental controls governing airborne MP deposition and underscore the need for long-term, meteorology-integrated, and methodologically standardized monitoring strategies to improve exposure assessment and inform mitigation efforts. Full article

Traditional angle estimation for sum-and-difference monopulse radar systems is predominantly designed for non-coherent sources or relies on fixed closed-form solutions. However, in the presence of coherent sources, these methods often suffer from performance degradation due to data rank deficiency or unavoidable suppression of target power. To address these limitations, this paper presents a single-snapshot angle estimation method for coherent sources by leveraging the angular super-resolution and ranging capabilities of monopulse radar to achieve 3D imaging in the range-angle domain. The approach utilizes the monopulse ratio spatial steering vector as a search vector and projects the received data onto its orthogonal subspace. By exploiting the coupling characteristics between signal polarization and angle, a cost function is constructed to validate the feedback of the search vector. Theoretical analysis demonstrates that for dual-target scenarios, the cost function reaches its minimum precisely when the search vector aligns with a target’s steering vector, enabling the accurate estimation of both targets’ angles. Furthermore, the polarization-angle coupling constraint reduces the 2D angular search space to a 1D line, significantly lowering computational complexity. Simulation results indicate that the method effectively resolves dual targets under single-snapshot conditions and maintains robust performance even with significant energy disparities. Finally, 3D localization of multiple airborne point targets is achieved by integrating 2D angular information with range data, validating the potential of the method for advanced radar imaging and positioning. Full article

Airborne LiDAR (Light Detection and Ranging) data is widely used in urban modelling and three-dimensional spatial analysis studies. However, the irregular structure of LiDAR point clouds, varying point densities, and class imbalances observed in the datasets make semantic segmentation problematic. This study addresses the four-class semantic segmentation problem (unclassified, vegetation, ground, and building) on aerial LiDAR point clouds, with a particular focus on multi-class segmentation. The Oregon LiDAR Program dataset was obtained through the OpenTopography platform for use in this study. The point cloud data were resampled to 4096 points to ensure a fixed input size; for each point, the X, Y, and Z coordinates, along with the RGB and intensity features, were utilized. Experimental studies compared the proposed method with both baseline models (PointNet, PointNet++ MSG, and VoxelNet Lite) and recent state-of-the-art architectures, including Point Transformer, KPConv, and RandLA-Net. Additionally, the PointNet2 MSG Transformer model was developed based on the PointNet++ MSG architecture and includes a transformer-based feature fusion module. Different loss functions and training configurations were evaluated, and the effects of ensemble learning and test-time augmentation strategies on model performance were analyzed. The experimental results show that the proposed approach achieved a mean Intersection over Union (IoU) of 51.74% and an accuracy of 61.50% on the test dataset. These results demonstrate that combining multi-scale feature extraction with transformer-based feature fusion is an effective approach for semantic segmentation of LiDAR point clouds and multi-class segmentation tasks. Full article

Accurate estimation of forest aboveground biomass (AGB) is pivotal for assessing forest carbon sequestration and informing global change studies. Conventional LiDAR-based AGB estimation approaches primarily rely on height and density metrics, which inadequately characterize the complex three-dimensional (3D) structure of forest canopies. This study developed and evaluated a novel method utilizing voxel-based 3D canopy structural metrics derived from airborne LiDAR (ALS) to improve AGB estimation accuracy across diverse forest types. First, voxel-based metrics (Voxel Canopy Height Model (VCHM), canopy volume, and canopy surface area) were extracted from voxelized point clouds. Their distribution patterns across five forest types (Pinus massoniana, Cunninghamia lanceolata, coniferous, broadleaf, and mixed conifer–broadleaf forests) and their correlations with AGB were systematically examined. The results revealed distinct 3D canopy architectures among forest types, with all three voxel metrics showing highly significant positive correlations with AGB; VCHM demonstrated the strongest association. We then constructed two Random Forest models: a baseline model using traditional metrics only, and an enhanced model integrating both traditional and voxel-based metrics. The 10-fold cross-validation indicated that the model incorporating voxel metrics achieved markedly higher accuracy (R² in 0.490–0.684) than the traditional model (R² in 0.480–0.607), representing a relative improvement of 2.1% to 32.7%. The most substantial gain occurred in structurally complex broadleaf forests. The enhanced model was subsequently applied to generate a wall-to-wall AGB map of the study region, yielding a total estimated AGB stock of 8.36 × 10⁶ t, which exhibited a patchy spatial distribution. Pinus massoniana forests accounted for the largest proportion (57.8%) of the total stock. This study demonstrates that voxel-based 3D canopy metrics can more effectively capture forest structural heterogeneity and substantially improve the accuracy of AGB estimation models, particularly for complex forest stands. The findings provide a significant advancement toward precise, stand-scale forest biomass monitoring founded on detailed 3D structural information. Full article

Traditional diagnostic spectroscopy provides a physically interpretable basis for mineral identification. However, how modern classifiers balance spectral and spatial information remains insufficiently understood. This study investigates this issue using CASI airborne hyperspectral data from the Liuyuan area, China. A geologically constrained ground-truth dataset was constructed based on expert knowledge and a semi-automatic Spectral Hourglass workflow. We evaluated representative shallow machine learning methods and deep learning models, including a three-dimensional convolutional neural network (3D-CNN), Vision Transformer (ViT), and SpectralFormer. The Support Vector Machine (SVM) achieved the highest overall accuracy but showed a strong bias toward dominant background classes and failed to reliably detect rare minerals such as jarosite. Deep learning models improved class balance by incorporating broader spectral features. However, excessive spatial aggregation reduced their sensitivity to small and fragmented alteration zones. SpectralFormer models hyperspectral data as ordered spectral sequences and showed more stable performance for spectrally similar and rare minerals. Multi-scale experiments reveal a spectral-dominant discrimination mechanism. Increasing the spectral receptive field improves classification up to an optimal level. In contrast, overly large spatial patches introduce background interference and obscure diagnostic absorption features. These findings highlight the fundamental role of spectral continuity in airborne hyperspectral alteration mineral mapping and clarify the trade-offs involved in integrating spatial context. Full article

Remote sensing is widely used in archaeological prospection to detect surface anomalies (crop marks) indicating buried remains, typically through recognition of visual patterns in high- or very high-resolution imagery acquired by means of satellite, airborne, or drone sensors. In contrast, spectroscopic approaches focusing on variations in spectral signatures still remain rarely applied in archaeological research. This study proposes a technological barrier-free method addressed to archaeologists which is based on pixel-level analysis of the Reflectance Values (RV) and spectral shape variations in the visible, near-infrared and short-wave infrared (VIS-NIR-SWIR) range derived from Sentinel-2 imagery. Spectral signatures are extracted through sampling polygons designed to account for the spatial resolution of the different Sentinel-2 bands and their spatial relationship with the location and size of the archaeological features. The RV method is tested on two Roman archaeological contexts: the ancient city of Telesia Vetere (San Salvatore Telesino, Benevento) and a Roman villa at Podere Colle Agnano (Labro, Rieti) using the full Sentinel-2 archive since 2017. While Telesia has previously been investigated through aerial photo interpretation and archaeological fieldwork, the Roman villa at Labro is documented here for the first time. Results show consistent seasonal repeated spectral separability between areas corresponding to known buried archaeological features and surrounding areas. Similar anomalies were also detected in areas without previously documented remains, thus suggesting the possible presence of buried structures and highlighting the predictive potential of the RV method. Owing to its easiness to use beyond image processing specialism and reliance on open-access data, the method can support archaeological decision-making and guide further investigation with higher-resolution remote sensing data or targeted field surveys, particularly in the framework of preventive archaeology. Full article