Search Results (329)

This study aimed to quantify and assess the spatial distribution of ²³⁸U, ²³²Th, and ⁴⁰K in the soils of the Espora Hydroelectric Power Plant (Espora HPP) and Queixada Small Hydroelectric Power Plant (Queixada SHPP) watershed (model hydraulic development areas) and their relationship with the geological, chemical, physical, and biological aspects of the soil. The study areas are located in the Corrente River drainage basin, in the southwestern portion of the state of Goiás, Brazil. Radionuclides were quantified using a PGIS-2 portable gamma spectrometer, with measurements taken at 21 sampling points. Soil samples were collected from the surface layer (0–20 cm) for particle-size and chemical analyses. The results indicated that the average radionuclide contents in the soils were 64.49 Bq/kg for ⁴⁰K, 45.44 Bq/kg for ²³⁸U, and 4.53 Bq/kg for ²³²Th. When comparing these values with the global average established by UNSCEAR, it was observed that ²³²Th and ⁴⁰K concentrations were below the global reference, whereas ²³⁸U concentration exceeded the world average of 33 Bq/kg. Particle-size characterization revealed significant variability in soil texture, with sand content ranging from 51.46 to 90.91%, clay content from 7.45 to 30.64%, and silt content from 1.64 to 17.90%. Organic matter content had an average of 10.09 g/kg, while soil pH ranged from 4.67 to 6.54. The results of this study have demonstrated the relevance of integrating radiometric and geochemical data for assessing environmental safety in hydroelectric development areas. The approach adopted can support monitoring programs and decision-making processes related to soil management and land-use planning in regions influenced by hydraulic infrastructures. Full article

Accurate modelling of reflection, transmission, absorption, and emission at material interfaces is essential for infrared imaging, rendering, and the simulation of optical and sensing systems. This need is particularly pronounced across the short-wave to long-wave infrared (SWIR–LWIR) spectrum, where many materials exhibit dispersion- and wavelength-dependent attenuation described by complex refractive indices. In this work, we introduce a unified formulation of the full Fresnel equations that directly incorporates wavelength-dependent complex refractive-index data and provides physically consistent interface behaviour for both dielectrics and conductors. The approach reformulates the classical Fresnel expressions to eliminate sign ambiguities and numerical instabilities, resulting in a stable evaluation across incidence angles and for strongly absorbing materials. We demonstrate the model through spectral-rendering simulations that illustrate realistic reflectance and transmittance behaviour for materials with different infrared optical properties. To assess its suitability for thermal-infrared applications, we also compare the simulated long-wave emission of a heated glass sphere with measurements from a LWIR camera. The agreement between measured and simulated radiometric trends indicates that the proposed formulation offers a practical and physically grounded tool for wavelength-parametric interface modelling in infrared imaging, supporting applications in spectral rendering, synthetic data generation, and infrared system analysis. Full article

LuTan-1(LT-1) is the first Chinese civil L-band satellite constellation for geohazard observation, comprising LT-1A and LT-1B satellites. By employing interferometric altimetry and differential deformation measurement technologies, it achieves high-precision topographic mapping and establishes sub-millimeter-level deformation monitoring capabilities. To meet the high-precision measurement requirements for applications such as topographic surveying and deformation monitoring, this study systematically evaluates four categories of image quality metrics—geometric, radiometric, and polarimetric characteristics, as well as orbital and baseline quality—based on in-orbit test data from the twin satellites. The test results demonstrate that all image quality indicators of the LT-1 SAR satellites meet the design specifications, confirming that the imagery can provide robust spatial technical support for applications including geological hazard monitoring, land resource investigation, earthquake assessment, disaster prevention and mitigation, fundamental surveying and mapping, and forestry monitoring. Full article

Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with additional networks or losses, complicating training and deployment. We propose a data-centric framework, Hybrid Object-Based Augmentation and Histogram Matching (Hybrid OBA–HM), which improves cross-domain building segmentation without modifying the backbone architecture or using target-domain labels. The proposed framework comprises two stages: (i) object-based augmentation to increase structural diversity and building coverage, and (ii) histogram-based normalization to mitigate radiometric discrepancies across domains. Experiments on OpenEarthMap and cross-city transfer among three KOMPSAT-3A scenes show that Hybrid OBA–HM improves F1-scores from 0.808 to 0.840 and from 0.455 to 0.652, respectively, while maintaining an object-level intersection over union of 0.89 for replaced buildings. Domain-indicator analysis further reveals larger gains under stronger radiometric and geometric mismatches, indicating that the proposed framework strengthens cross-domain generalization and provides practical guidance by relating simple domain diagnostics (e.g., brightness/color and orientation mismatch indicators) to the expected benefits of augmentation and normalization when adapting to new domains. Full article

This investigation examines the optical degradation of 260 nm and 265 nm UVC LEDs subjected to varying drive current conditions, simulating real-world deployment in consumer and professional disinfection systems. The primary aim was to assess lifetime trends and degradation behaviour based exclusively on radiometric and spectral data. A total of 24 devices (12 per wavelength group) were operated for 2000 h under a broad range of thermally stabilised current levels, from low-standby to maximum-rated operation. The results demonstrated distinct current-dependent ageing characteristics, wherein, for the tested device sets and operating conditions, 260 nm LEDs exhibited faster optical power degradation than the investigated 265 nm LEDs under nominal drive conditions. Notably, a moderate current derating of approximately 20% resulted in a more than fourfold increase in L70 lifetime and over a threefold extension in the number of effective disinfection cycles. Despite a stable spectral power distribution throughout ageing, significant statistical variation in lifetime metrics (L90, L80, L70, L50) was observed even among identically operated devices, underscoring the need for population-level reliability qualification. Optical lifetime estimates based on empirical model fitting indicated that the Ruschel logarithmic function most accurately captured the long-term degradation trends for the analysed datasets. These findings provide practical guidance for the design of durable and efficient UVC LED systems within the investigated device class and operating regimes, supporting sustained germicidal performance and long-term operational reliability across diverse use cases. Full article

Ensuring good air quality in indoor environments of historical and artistic significance is essential not only for protecting valuable artworks but also for safeguarding human health. While many studies in this field tend to focus on the preservation of cultural heritage, fewer have addressed the impact on visitors and worshippers. Yet, places such as museums, galleries, churches, and other religious sites attract large numbers of people, making indoor air quality a key factor for their well-being. This study focused on evaluating air quality within the Santuario della Beata Vergine dei Miracoli in Saronno, Italy, a religious site that welcomes large numbers of visitors and worshippers each year. A detailed analysis of particulate matter was conducted, including chemical characterization by ICP-MS for metals, ion chromatography for water-soluble ions, and thermal–optical analysis for the carbonaceous fraction, as well as assessments of size distribution and radiometric properties. The results indicated overall good air quality conditions: concentrations of heavy metals were below levels of concern (<35 ng m⁻³), and gross alpha, beta, and ¹³⁷Cs activity concentrations remained below the minimum detectable thresholds. Hence, no significant health risks were identified. Full article

Accurate mapping of hurricane-induced damage is essential for guiding rapid disaster response and long-term recovery planning. This study evaluates the Three-Dimensional Multi-Attributes, Multiscale, Multi-Cloud (3DMASC) framework for semantic classification of pre- and post-hurricane Light Detection and Ranging (LiDAR) data, using Mexico Beach, Florida, as a case study following Hurricane Michael. The goal was to assess the framework’s ability to classify stable landscape features and detect damage-specific classes in a highly complex post-disaster environment. Bitemporal topo-bathymetric LiDAR datasets from 2017 (pre-event) and 2018 (post-event) were processed to extract more than 80 geometric, radiometric, and echo-based features at multiple spatial scales. A Random Forest classifier was trained on a 2.37 km² pre-hurricane area (Zone A) and evaluated on an independent 0.95 km² post-hurricane area (Zone B). Pre-hurricane classification achieved an overall accuracy of 0.9711, with stable classes such as ground, water, and buildings achieving precision and recall exceeding 0.95. Post-hurricane classification maintained similar accuracy; however, damage-related classes exhibited lower performance, with debris reaching an F1-score of 0.77, damaged buildings 0.58, and vehicles recording a recall of only 0.13. These results indicate that the workflow is effective for rapid mapping of persistent structures, with additional refinements needed for detailed damage classification. Misclassifications were concentrated along class boundaries and in structurally ambiguous areas, consistent with known LiDAR limitations in disaster contexts. These results demonstrate the robustness and spatial transferability of the 3DMASC–Random Forest approach for disaster mapping. Integrating multispectral data, improving small-object representation, and incorporating automated debris volume estimation could further enhance classification reliability, enabling faster, more informed post-disaster decision-making. By enabling rapid, accurate damage mapping, this approach supports sustainable disaster recovery, resource-efficient debris management, and resilience planning in hurricane-prone regions. Full article

Radiometric calibration of multispectral imagery plays a critical role in the determination of vegetation-related features. This radiometric calibration strongly depends on a proper sensor configuration when acquiring images, the shutter speed being a critical parameter. The objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3) on a homogeneous field of turfgrass (Cynodon dactylon L.—Poaceae) and on the vegetation index (VI) values calculated from them, under different solar radiation conditions. For this purpose, 10 shutter speed configurations were tested in field campaigns with variable solar radiation values. The main results demonstrated that the reflectance in the green spectral region was more sensitive to shutter speed than that of the red and NIR spectral regions, particularly under high solar radiation conditions. Moreover, VIs calculated using the green band were more sensitive to slow shutter speeds, thus presenting a higher probability of providing meaningless artifact values. In conclusion, this study provides shutter speed recommendations under different illumination conditions to optimize the reflectance and the VI sensitivity within the image, which can be applied as a simple method to optimize image acquisition from unmanned aerial vehicles under varying solar radiation conditions. Full article

Monitoring land use and land cover (LULC) transformations is essential for understanding socio-ecological dynamics. This study assesses structural shifts in Romania’s landscapes between 1990 and 2018 by integrating algorithmic complexity, fractal analysis, and Grey-Level Co-occurrence Matrix (GLCM) texture analysis. Multi-year maps were used to compute Kolmogorov complexity, fractal measures, and 15 GLCM metrics. The measures were compiled into a unified matrix, and temporal trajectories were explored with principal component analysis and k-means clustering to identify inflection points. Informational complexity and Higuchi 2D decline over time, while homogeneity and angular second moment rise, indicating greater local uniformity. A structural transition around 2006 separates an early heterogeneous regime from a more ordered state; 2012 appears as a turning point when several indices reach extreme values. Strong correlations between fractal and texture measures imply that geometric and radiometric complexity co-evolve, whereas large-scale fractal dimensions remain nearly stable. The multi-index approach provides a replicable framework for identifying critical transitions in LULC. It can support landscape monitoring, and future work should integrate finer temporal data and socio-economic drivers. Full article

Fourier transform spectrometers (FTSs) are cornerstone instruments in Earth observation space missions, effectively monitoring atmospheric gases in missions such as Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), and Infrared Atmospheric Sounding Interferometer (IASI). It will also be the core instrument of Meteosat Third Generation—Sounding (MTG-S) and the future Earth Explorer (EE) mission Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM). Building on this legacy, the European Space Agency (ESA) has developed an FTS instrument and an inverse model designed to estimate the radiometric and spectral performance from a set of instrumental parameters. The model and its validation using in-flight measurements of the FTS instrument Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA)-Lite are described in this paper. The results indicate that the difference between the model predictions and the measured signal is less than 2% relative to the average of the measurements. Moreover, we can correctly predict the instrument’s radiometric gain and offset and reconstruct a scientific science spectrum. This model can be utilised effectively to evaluate the radiometric performance of future FTS missions. Full article

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy. Full article

Above-ground biomass in agroforestry refers to the total mass of living vegetation, primarily trees and shrubs, integrated into agricultural landscapes. It plays a key role in climate change mitigation by capturing and storing carbon. Accurate estimation of above-ground biomass in agroforestry systems requires effective drone deployment and sensor management. This study presents a detailed methodology for biomass estimation using Unmanned Aircraft Systems, based on an experimental campaign conducted in the Campania region of Italy. Multispectral drone platforms were used to generate calibrated reflectance maps and derive vegetation indices for biomass estimation in agroforestry landscapes. Integrating field-measured tree attributes with remote sensing indices improved the accuracy and efficiency of biomass prediction. Following the assessment of mission parameters, flights were conducted using a commercial drone to demonstrate consistency of results across multiple altitudes. Terrain-follow mode and high image overlap were employed to evaluate ground sampling distance sensitivity, radiometric performance, and overall data quality. The outcome is a defined process that enables agronomists to effectively estimate above-ground biomass in agroforestry landscapes using drone platforms, following the procedure outlined in this paper. Predictive performance was evaluated using standard model metrics, including R², RMSE, and MAE, which are essential for replicability and comparison in future studies. Full article

The origin of humans on Earth is closely linked to understanding how ancient populations dispersed into adjacent territories. Traditionally, studies have identified landscape and climatic changes as the primary factors in this dispersal. However, we propose that regional tectonic and geodynamic factors also played a significant role in shaping these movements. To analyze this phenomenon, we employed several primary methods, including radiometric dating, magnetostratigraphy, paleomagnetic correlation, isotope–oxygen analysis, tectonothermal studies, gravity mapping, paleobiogeographic assessment, lithofacies analysis, and event and cyclic stratigraphy. Our research indicates that the Akchagylian hydrospheric maximum, which reached up to +200 m, significantly limited the early dispersal of hominins from Africa to Eurasia. The migration corridor was shaped by tectonic activity between the Dead Sea Transform and the boundary of the Mesozoic Terrane Belt carbonate platform. We argue that, during the early stages of hominin evolution in East Africa, the Levantine Corridor (LC) had not yet developed into an optimal route for dispersal, either tectonically or paleogeographically. Suitable habitats for early hominins emerged only after the regression at the end of the Middle Gelasian, around two million years ago, when sea level fell by approximately 200 m, leading to the dissection of the coastal high plateau of the Eastern Mediterranean. We therefore suggest that the LC became established only after the termination of the Akchagylian transgression and the subsequent landscape reconfiguration of the Eastern Mediterranean. Our integrated analysis, combining paleomagnetic, structural, tectonic, and event stratigraphy data, indicates that the age of the renowned ‘Ubeidiya site in northern Israel is several thousand years older than previously thought. This paleogeographic impact had not been considered in earlier studies. Considering the diverse and complex factors that governed hominin dispersal from Africa into Eurasia within this multifaceted region, we propose that the scope of research should be broadened. Our detailed study of the Carmel area, located northeast of the Levantine Corridor and influenced by it during the Pleistocene, indicates that this region was inundated during the early phases of hominin migration out of Eastern Africa. Besides this, we have conducted an integrated geological–geophysical landscape analysis of the central part of the Israeli coastal plain. Full article

The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious calibration techniques are limited by their calibration frequency, making them insufficient for continuous monitoring requirements. To address this challenge, the present study proposes a spectral-angle difference correction-based cross-calibration approach, using the Landsat 8/9 Operational Land Imager (OLI) as the reference sensor to calibrate the HJ-2A/2B CCD sensors. This method improves both radiometric accuracy and temporal frequency. The study utilizes cloud-free image pairs of HJ-2A/2B CCD and Landsat 8/9 OLI, acquired simultaneously at the Dunhuang and Golmud calibration sites between 2021 and 2024, in combination with atmospheric parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset and historical ground-measured spectral reflectance data for cross-calibration. The methodology includes spatial matching and resampling of the image pairs, along with the identification of radiometrically stable homogeneous regions. To account for sensor viewing geometry differences, an observation-angle linear correction model is introduced. Spectral band adjustment factors (SBAFs) are also applied to correct for discrepancies in spectral response functions (SRFs) across sensors. Experimental results demonstrate that the cross-calibration coefficients differ by less than 10% compared to vicarious calibration results from the China Centre for Resources Satellite Data and Application (CRESDA). Additionally, using Sentinel-2 MSI as the reference sensor, the cross-calibration coefficients were independently validated through cross-validation. The results indicate that the radiometrically corrected HJ-2A/2B 16m-MSI CCD data, based on these coefficients, exhibit improved radiometric consistency with Sentinel-2 MSI observations. Further analysis shows that the cross-calibration method significantly enhances radiometric consistency across the HJ-2A/2B 16m-MSI CCD sensors, with radiometric response differences between CCD1 and CCD4 maintained below 3%. Error analysis quantifies the impact of atmospheric parameters and surface reflectance on calibration accuracy, with total uncertainty calculated. The proposed spectral-angle correction-based cross-calibration method not only improves calibration accuracy but also offers reliable technical support for long-term radiometric performance monitoring of the HJ-2A/2B 16m-MSI CCD sensors. Full article

This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile-based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI—Moisture Index and NDSII_1—Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4 − B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma-radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 76.41% and 85.55% spatial overlap accuracy, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture-controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit the temporal availability of usable scenes. NDGRI offers a rapid, cost-effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi-arid and arid regions. Full article