Search Results (52)

The primary measurement involves detecting tiny (picometer-level) pathlength fluctuations between satellites using heterodyne laser interferometry for space-based gravitational wave detection. The interference of two laser beams with a MHz-level frequency difference produces a MHz beat-note, in which the gravitational wave signal is encoded in the phase of the beat-note. The phasemeter then performs micro-radian accuracy phase measurement and communication information demodulation for this beat-note. To mitigate the impact of phase modulation, existing solutions mostly alleviate it by reducing the modulation depth and optimizing the structure of the pseudo-random noise (PRN) codes. Since the phase modulation is not effectively separated from the phase of the beat-note phase measurement, it has a potential impact on the phase extraction of the micro-radian accuracy of the beat-note. To solve this problem, this paper analyzes the influence mechanism of phase modulation on beat-note phase measurement and proposes a method to separate the modulated phase based on complex rotation. The beat-note is processed by complex conjugate rotation, which can effectively eliminate the PRN modulated phase. Simulation and analysis results demonstrate that this method can significantly enhance the purity of the measured phase in the beat-note while maintaining the ranging and communication functions. Targeting the application of the micro-radian phasemeter in space-based gravitational wave detection, this study presents the reconstruction and separation method of the ranging and communication phase in beat-note, which also provides a new direction for the final selection of modulation depth in the future. Full article

Coastal areas have been the target of interdisciplinary research aiming to support studies related to their socio-economic and ecological value and their role in protecting backshore ecosystems and assets from coastal erosion and flooding. Some of these studies focus on either onshore or inshore areas using sensors and collecting valuable information that remains unknown and untapped by other researchers. This research demonstrates how satellite, aerial, terrestrial and marine remote sensing techniques can be integrated and inter-validated to produce accurate information, bridging methodologies with different scope. High-resolution data from Unmanned Aerial Vehicle (UAV) data and multispectral satellite imagery, capturing the onshore environment, were utilized to extract underwater information in Coral Bay (Cyprus). These data were systematically integrated with hydroacoustic including bathymetric and side scan sonar measurements as well as ground-truthing methods such as drop camera surveys and sample collection. Onshore, digital elevation models derived from UAV observations revealed significant elevation and shoreline changes over a one-year period, demonstrating clear evidence of beach modifications and highlighting coastal zone dynamics. Temporal comparisons and cross-section analyses displayed elevation variations reaching up to 0.60 m. Terrestrial laser scanning along a restricted sea cliff at the edge of the beach captured fine-scale geomorphological changes that arise considerations for the stability of residential properties at the top of the cliff. Bathymetric estimations derived from PlanetScope and Sentinel 2 imagery returned accuracies ranging from 0.92 to 1.52 m, whilst UAV reached 1.02 m. Habitat classification revealed diverse substrates, providing detailed geoinformation on the existing sediment type distribution. UAV data achieved 89% accuracy in habitat mapping, outperforming the 83% accuracy of satellite imagery and underscoring the value of high-resolution remote sensing for fine-scale assessments. This study emphasizes the necessity of extracting and integrating information from all available sensors for a complete geomorphological and marine habitat mapping that would support sustainable coastal management strategies. Full article

A new method of producing metal powders for additive manufacturing by the atomization of free-falling melt streams using pulsed cross-flow gaseous shock or detonation waves is proposed. The method allows the control of shock/detonation wave intensity (from Mach number 4 to about 7), as well as the composition and temperature of the detonation products by choosing proper fuels and oxidizers. The method is implemented in laboratory and industrial setups and preliminarily tested for melts of three materials, namely zinc, aluminum alloy AlMg5, and stainless steel AISI 304, possessing significantly different properties in terms of density, surface tension, and viscosity. Pulsed shock and detonation waves used for the atomization of free-falling melt streams are generated by the pulsed detonation gun (PDG) operating on the stoichiometric mixture of liquid hydrocarbon fuel and gaseous oxygen. The analysis of solidified particles and particle size distribution in the powder is studied by sifting on sieves, optical microscopy, laser diffraction wet dispersion method (WDM), and atomic force microscopy (AFM). The operation process is visualized by a video camera. The minimal size of the powders obtained by the method is shown to be as low as 0.1 to 1 μm, while the maximum size of particles exceeds 400–800 μm. The latter is explained by the deficit of energy in the shock-induced cross-flow for the complete atomization of the melt stream, in particular dense and thick (8 mm) streams of the stainless-steel melt. The mass share of particles with a fraction of 0–10 μm can be at least 20%. The shape of the particles of the finest fractions (0–30 and 30–70 μm) is close to spherical (zinc, aluminum) or perfectly spherical (stainless steel). The shape of particles of coarser fractions (70–140 μm and larger) is more irregular. Zinc and aluminum powders contain agglomerates in the form of particles with fine satellites. The content of agglomerates in stainless-steel powders is very low. In general, the preliminary experiments show that the proposed method for the production of finely dispersed metal powders demonstrates potential in terms of powder characteristics. Full article

Nearshore sandbars are dynamic features that characterize shallow morphobathymetry and vary over a wide range of geometries and temporal lifespans. Nearshore sandbars influence beach geometry by altering the energy of incoming waves; thus, monitoring the evolution of sandbars is a fundamental approach in effective coastal planning. Due to several natural and technical limitations related to shallow seafloor mapping, there is a significant gap in the availability of high-resolution, shallow bathymetric data for monitoring the dynamic behaviour of nearshore sandbars effectively. This study introduces a novel image-processing technique that produces time series of pseudo-bathymetric data by utilizing multi-temporal (monthly) drone imagery, and it provides an assessment of local morphodynamics at a sandy beach in the southeast Mediterranean. The technique is called standardized-ratio bathymetric index (SRBI), and it transforms natural-colour drone imagery to pseudo-bathymetric data by applying an empirical formula used for satellite-derived bathymetry. This technique correlates well with laser altimetry depth measurements; however, it does not require in situ depth data for implementation. The resulting pseudo-bathymetric data allows for extracting cross-shore profiles and delineating the sandbar crest with 4 m horizontal accuracy. Stacking of temporal profiles allowed for the quantification of the sandbar’s crest and trough changes at different alongshore sections. The main findings suggest that the nearshore crescentic sandbar at Episkopi Beach (north Crete) shows strong seasonality regarding net offshore migration that is promoted by enhanced wave action during winter months. In addition, the crescentic sandbar is susceptible to morphology arrestment during prolonged weeks of low wave action. The average migration rate during winter is 10 m.month⁻¹, with some sections exhibiting a maximum of 60 m.month⁻¹. This study (a) offers a novel remote-sensing approach, suitable for nearshore seafloor monitoring with low computational complexity, (b) reveals sandbar geometry and temporal change in superior detail compared to other observational methods, and (c) advances knowledge about nearshore sandbar monitoring in the Mediterranean region. Full article

Precise information on forest canopy height (FCH) is critical for forest carbon stocks estimation and management, but mapping continuous FCH with satellite data at regional scale is still a challenge. By fusing ICESat-2, Sentinel-1/2 images and ancillary data, this study aimed to develop a workflow to obtain an FCH map using a machine learning algorithm over large areas. The vegetation-type map was initially produced by a phenology-based spectral feature selection method. A forest characteristic-based model was then proposed to map spatially continuous FCH after a multivariate quality control. Our results show that the overall accuracy (OA) and average F1 Score (F1) for eight main vegetation types were more than 90% and 89%, respectively, and the vegetation-type map agreed well with the census areas. The forest characteristic-based model demonstrated a greater potential in FCH prediction, with an R-value 60.47% greater than the traditional single model, suggesting that the addition of the multivariate quality control and forest structure characteristics could positively contribute to the prediction of FCH. We generated a 30 m continuous FCH map by the forest characteristic-based model and evaluated the product with about 35 km² of airborne laser scanning (ALS) validation data (R = 0.73, RMSE = 2.99 m), which were 45.34% more precise than the China FCH, 2019. These findings demonstrate the potential of our proposed workflow for monitoring regional continuous FCH, and will greatly benefit accurate forest resources assessment. Full article

In September 2018, NASA launched the ICESat-2 satellite into a 500 km high Earth orbit. It carried a truly unique lidar system, i.e., the Advanced Topographic Laser Altimeter System or ATLAS. The ATLAS lidar is capable of detecting single photons reflected from a wide variety of terrain (land, ice, tree leaves, and underlying terrain) and even performing bathymetric measurements due to its green wavelength. The system uses a single 5-watt, Q-switched laser producing a 10 kHz train of sub-nanosecond pulses, each containing 500 microjoules of energy. The beam is then split into three “strong” and three “weak” beamlets, with the “strong” beamlets containing four times the power of the “weak” beamlets in order to satisfy a wide range of Earth science goals. Thus, ATLAS is capable of making up to 60,000 surface measurements per second compared to the 40 measurements per second made by its predecessor multiphoton instrument, the Geoscience Laser Altimeter System (GLAS) on ICESat-1, which was terminated after several years of operation in 2009. Low deadtime timing electronics are combined with highly effective noise filtering algorithms to extract the spatially correlated surface photons from the solar and/or electronic background noise. The present paper describes how the ATLAS system evolved from a series of unique and seemingly unconnected personal experiences of the author in the fields of satellite laser ranging, optical antennas and space communications, Q-switched laser theory, and airborne single photon lidars. Full article

Hazards associated with natural factors annually result in significant human and economic losses. An accurate and up-to-date assessment of various hazards can limit their impact and bring benefits both in the modeling phase and mostly in the risk mitigation plan stage. The article presents the results of a multi-hazard analysis that considers floods, landslides, and earthquakes carried out in the Ungheni area, located in the eastern part of Romania at the border with the Republic of Moldova. The research focused on producing harmonized hazard maps for the two countries since the area spreads jointly between the two countries. Common geospatial data were used for modeling and risk assessment, such as airborne laser scanners, global navigation satellite systems, rasters, and vectors from analog and digital sources. Among hazards, the flood maps for the studied area, Ungheni, were designed using 2D hydraulic modeling in HECRAS software (version 6.3.1); the landslide maps considered the ArcGis platform following Romanian methodology; and the seismic analysis collected onsite measurements on the built environment. The shared use of geospatial data in modeling the three hazards led to high accuracy of the results and determined their spatial homogeneity. It was observed that only two areas, Mînzătești and Coada Stîncii villages from Ungheni Areal, are highly vulnerable to all three hazards. The research findings, along with mitigation recommendations, have contributed to the development of a more precise action plan for natural hazards events by local authorities and decision-makers. Full article

Forest canopy height is an important indicator of the forest ecosystem, and an accurate assessment of forest canopy height on a large scale is of great significance for forest resource quantification and carbon sequestration. The retrieval of canopy height based on remote sensing provides a possibility for studying forest ecosystems. This study proposes a new method for estimating forest canopy height based on remote sensing. In this method, the GEDI satellite and ICESat-2 satellite, which are different types of space-borne lidar products, are used to cooperate with the Landsat 9 image and SRTM terrain data, respectively. Two forest canopy height-retrieval models based on multi-source remote sensing integration are obtained using a random forest regression (RFR) algorithm. The study, conducted at a forest site in the northeastern United States, synthesized various remote sensing data sets to produce a robust canopy height model. First, we extracted relative canopy height products, multispectral features, and topographic data from GEDI, ICESat-2, Landsat 9, and SRTM images, respectively. The importance of each variable was assessed, and the random forest algorithm was used to analyze each variable statistically. Then, the random forest regression algorithm was used to combine these variables and construct the forest canopy height model. Validation with airborne laser scanning (ALS) data shows that the GEDI and ICESat-2 models using a single data source achieve better accuracy than the Landsat 9 model. Notably, the combination of GEDI, Landsat 9, and SRTM data (R = 0.92, MAE = 1.91 m, RMSE = 2.78 m, and rRMSE = 12.64%) and a combination of ICESat-2, Landsat 9, and SRTM data (R = 0.89, MAE = 1.84 m, RMSE = 2.54 m, and rRMSE = 10.75%). Compared with the least accurate Landsat 9 model, R increased by 29.58%, 93.48%, MAE by 44.64%, 46.20%, RMSE by 42.80%, 49.40%, and the rRMSE was increased by 42.86% and 49.32%, respectively. These results fully evaluate and discuss the practical performance and benefits of multi-source data retrieval of forest canopy height by combining space-borne lidar data with Landsat 9 data, which is of great significance for understanding forest structure and dynamics. The study provides a reliable methodology for estimating forest canopy height and valuable insights into forest resource management and its contribution to global climate change. Full article

This study investigated the effects of Zinc (Zn) content, specifically in the range of 1 wt.% to 7 wt.%, on the powder characteristics, porosity, microstructure, and corrosion behavior of Mg-xZn-0.2Mn alloys produced using selective laser melting (SLM). To evaluate the porosity of the printed parts and various powder attributes, such as size, circularity, void spaces between powders, and inherent imperfections, scanning electron microscopy (SEM) and optical microscopy (OM) were employed. The alloy microstructure, composition, and phase were examined using energy dispersive X-ray (SEM-EDX) and X-ray Diffraction (XRD). The corrosion resistance and degradation behavior were assessed through electrochemical corrosion tests and immersion tests in Hanks’ solution at 37.5 °C, respectively. Finally, OM and SEM-EDX were used to characterize the corrosion products. The findings of this study indicated that the powder size increased with Zn content, maintaining a 0.8 circularity. Powder defects were minimal, with occasional satellite particles. For the SLM-printed samples, it was evident that porosity characteristics could be influenced by Zn content. As Zn content increased, the pore fraction rose from 1.0% to 5.3%, and the pore size grew from 2.2 μm to 3.0 μm. All printed samples consisted of an α-Mg matrix. Additionally, a higher Zn content resulted in more distinct grain boundaries. Corrosion resistance decreased with Zn, leading to more pronounced localized corrosion after immersion in Hanks’ solution. Ca-P was found as white corrosion products on all samples. Full article

Nickel silicides (NiSi) are renowned for their ability to withstand high temperatures and resist oxidation and corrosion in challenging environments. As a result, these alloys have garnered interest for potential applications in turbine blades and underwater settings. However, their high brittleness is a constant obstacle that hinders their use in producing larger parts. A literature review has revealed that incorporating trace amounts of transition metals can enhance the ductility of silicides. Consequently, the present study aims to create NiSi-based powders with the addition of titanium (Ti), boron (B), cobalt (Co), molybdenum (Mo), and vanadium (V) for Additive Manufacturing (AM) through the process of gas atomization. The study comprehensively assesses the microstructure, phase composition, thermal properties, and surface morphology of the produced powder particles, specifically NiSi11.9Co3.4, NiSi10.15V4.85, NiSi11.2Mo1.8, and Ni-Si10.78Ti1.84B0.1. Commonly used analytical techniques (SEM, EDS, XRD, DSC, and laser diffraction) are used to identify the alloy configuration that offers optimal characteristics for AM applications. The results show spherical particles within the size range of 20–63 μm, and only isolated satellites were observed to exist in the produced powders, securing their smooth flow during AM processing. Full article

A new concept of a space-based synchronized reference network is proposed with the development of an optical frequency reference and laser inter-satellite link. To build such time reference, three clock ensemble algorithms, namely the natural Kalman timescale (NKT) algorithm, the reduced Kalman timescale (RKT) algorithm, and the two-stage Kalman timescale (TKT) algorithm are considered. This study analyzes and compares the performance of these algorithms using BDS, GPS, and Galileo satellite clock data from the GFZ GNSS clock corrections, which will be used in constructing future space-based time references. The study shows that the NKT algorithm improves frequency stability by 0.1–0.2 orders of magnitude in the short and medium term. When the satellite clock is mostly a hydrogen clock, the RKT and NKT are close, and the short and medium-term frequency stability slightly increases. In contrast, the TKT algorithm produces a timescale that improves frequency stability by 1–3 orders of magnitude. A quadratic polynomial model predicts the three timescales, with the results indicating that the short-term prediction accuracy of the satellite clock is within 1ns, and the TKT algorithm’s prediction accuracy is 1–2 orders of magnitude higher than that of the NKT and RKT algorithms. With the deployment of next-generation Low Earth Orbit (LEO) satellites equipped with higher-precision clocks, the space-based time reference system will achieve improved accuracy and greater potential for practical applications. Full article

Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree stands is the selection of remote sensing data that vary in temporal, spectral, and spatial resolution, as well as in open and commercial access. For the Tatra Mountains area, which is a unique alpine ecosystem in central Europe, we classified 13 woody species by iterative machine learning methods using random forest (RF) and support vector machine (SVM) algorithms of more than 1000 polygons collected in the field. For this task, we used free Sentinel-2 multitemporal satellite data (10 m pixel size, 12 spectral bands, and 21 acquisition dates), commercial PlanetScope data (3 m pixel size, 8 spectral bands, and 3 acquisitions dates), and airborne HySpex hyperspectral data (2 m pixel size, 430 spectral bands, and a single acquisition) with fusion of the data of topographic derivatives based on Shuttle Radar Topography Mission (SRTM) and airborne laser scanning (ALS) data. The iterative classification method achieved the highest F1-score with HySpex (0.95 RF; 0.92 SVM) imagery, but the multitemporal Sentinel-2 data cube, which consisted of 21 scenes, offered comparable results (0.93 RF; 0.89 SVM). The three images of the high-resolution PlanetScope produced slightly less accurate results (0.89 RF; 0.87 SVM). Full article

Accurate terrain models are critical for studying the formation and development of slot canyons. However, for slot canyon landforms, it is challenging to generate comprehensive and high-resolution morphological data by individual observation due to the inaccessibility of steep walls on either side and the complexity of the field observation environment, such as variable-slope terrain, partial vegetation cover, and lack of satellite signal. Off-the-shelf surveying techniques, including Unmanned Aerial Vehicles (UAV) photogrammetry and Backpack Mobile Laser Scanning (BMLS), facilitate slot canyon surveys and provide better observations. This paper proposes an integrated scheme to generate comprehensive and centimeter-resolution slot canyon terrain datasets (e.g., color point clouds, Digital Elevation Models (DEM), and 3D mesh) using BMLS and fine UAV photogrammetry. The results show that the fine flight of UAVs based on a rough model can avoid collision with obstacles or flying into restricted areas, allowing users to perform tasks faster and safer. Data integration of BMLS and UAV photogrammetry can obtain accurate terrain datasets with a Root Mean Squared Error (RMSE) of point cloud registration of 0.028 m. Such high-resolution integration terrain datasets reduce local data shadows produced solely by individual datasets, providing a starting point to revealing morphological evolution and genesis of slot canyons. Full article

In many cases, we have few optical observations over a short time span, and most of the information generated is too limited to compute a full orbit according to the angles-only method. This study aims to develop a mathematical model to determine the precise orbit from the optical observation data by the least squares method. We have used a set of the Global Navigational Satellite Systems, which are tracked by the Optical Satellite Tracking Station (OSTS) at the National Research Institute of Astronomy and Geophysics (NRIAG), Egypt, to access high-quality predictions for the orbits. We analyzed the orbit predictions from the observations of these satellites that are tracked from seven world stations using the laser ranging method, and the obtained results are compared with orbital elements produced using the Two-Line Element (TLE). The results showed that the orbital prediction accuracy differs for optical observations from laser observations because of the inaccuracy of the NORAD catalog information used; this is due to the difference between the time of observation and the epoch time of TLE. Full article

The Gravity Recovery and Climate Experiment (GRACE) gravity mission has become a leading platform for monitoring temporal changes in the Earth’s global gravity field. However, the usability of GRACE data is severely limited by 11 months of missing data between the GRACE and GRACE Follow-on (GRACE-FO) missions. To date, several approaches have been proposed to fill this data gap in the form of spherical harmonic coefficients (an expression of the Earth’s gravity field, SHCs). However, systematic analysis to reveal the characteristics and consistency of the datasets produced by these latest gap-filling techniques is yet to be carried out. Here, three SHC gap-filling products are systematically analyzed and compared: (1) Combining high–low satellite-to-satellite tracking with satellite laser ranging (SLR) observations (QuantumFrontiers, QF), (2) SLR-based recovery incorporating the GRACE empirical orthogonal function decomposition model proposed by the Institute of Geodesy and Geoinformation at the University of Bonn (hereafter, denoted as IGG), and (3) applying the singular spectrum analysis approach (SSA). The results show that (1) the SHCs of the QF, IGG, and SSA data are consistent up to degree 12; (2) the IGG and SSA data give similar results over the 11 gap months, but the IGG shows a faster increase in the mean ocean water mass and the SSA appears to better capture the interannual variation in the terrestrial water storage; and (3) the noise level increases significantly in the high-degree terms (l > 16) of the QF data, so these data are only applicable for large-scale mass migration research. These results provide a reference for users to select a gap-filling product. Finally, we propose a new scheme based on the triple collocation method to derive a weight matrix to fuse these three datasets into a more robust solution. Full article