Search Results (7,917)

Airborne LiDAR (ALS) data have been extensively utilized for aboveground biomass (AGB) estimation; however, the high acquisition costs make it challenging to attain wall-to-wall estimation across large regions. Some studies have leveraged ALS data as intermediate variables to amplify sample sizes, thereby reducing costs and enhancing sample representativeness and model accuracy, but the cost issue remains in larger-scale estimations. Satellite LiDAR data, offering a broader dataset that can be acquired quickly with lower costs, can serve as an alternative intermediate variable for sample expansion. In this study, we employed a three-stage up-scaling approach to estimate forest AGB and introduced a method for quantifying estimation uncertainty. Based on the established three-stage general-hierarchical-model-based estimation inference (3sGHMB), an RK-3sGHMB inference method is proposed to make use of the regression-kriging (RK) method, and then it is compared with conventional model-based inference (CMB), general hierarchical model-based inference (GHMB), and improved general hierarchical model-based inference (RK-GHMB) to estimate forest AGB and uncertainty at both the pixel and forest farm levels. This study was carried out by integrating plot data, sampled ALS data, wall-to-wall Sentinel-2A data, and airborne P-SAR data. The results show that the accuracy of CMB ($R_{\mathit{adj}}^2$ = 0.37, RMSE = 33.95 t/ha, EA = 63.28%) is lower than that of GHMB ($R_{\mathit{adj}}^2$ = 0.38, RMSE = 33.72 t/ha, EA = 63.53%), while it is higher than that of 3sGHMB ($R_{\mathit{adj}}^2$ = 0.27, RMSE = 36.58 t/ha, EA = 60.43%). Notably, RK-GHMB ($R_{\mathit{adj}}^2$ = 0.60, RMSE= 27.07 t/ha, EA = 70.72%) and RK-3sGHMB ($R_{\mathit{adj}}^2$ = 0.55, RMSE = 28.55 t/ha, EA = 69.13%) demonstrate significant accuracy enhancements compared to GHMB and 3sGHMB. For population AGB estimation, the precision of the proposed RK-3sGHMB (p = 94.44%) is the highest, providing that there are sufficient sample sizes in the third stage, followed by RK-GHMB (p = 93.32%) with sufficient sample sizes in the second stage, GHMB (p = 90.88%), 3sGHMB(p = 88.91%), and CMB (p = 87.96%). Further analysis reveals that the three-stage model, considering spatial correlation at the third stage, can improve estimation accuracy, but the prerequisite is that the sample size in the third stage must be sufficient. For large-scale estimation, the RK-3sGHMB model proposed herein offers certain advantages. Full article

Asthma has long been associated with increased susceptibility to viral respiratory infections, leading to significant exacerbations and poorer clinical outcomes. Contrarily and interestingly, emerging data and research surrounding the COVID-19 pandemic have shown that patients with asthma infected with SARS-CoV-2 experienced decreased severity of disease, lower hospitalization rates, as well as decreased morbidity and mortality. Research has shown that eosinophils could enhance immune defense against viral infections, while inhaled corticosteroids can assist in controlling systematic inflammation. Moreover, reduced ACE-2 expression in individuals with asthma may restrict viral entry, and the Th2 immune response may offset the Th1 response typically observed in severe COVID-19 patients. These factors may help explain the favorable outcomes seen in asthmatic patients during the COVID-19 pandemic. This review highlights potential protective mechanisms seen in asthmatic patients, including eosinophilia, the use of inhaled corticosteroids, reduced ACE-2 expression, and a dominate Th2 immune response. Such a study will be helpful to better manage patients with asthma who have contracted COVID-19. Full article

Monitoring and analyzing reservoir landslides are essential for predicting and mitigating geohazards, which are crucial for maintaining sustainability and supporting socio-economic development in reservoir areas. High spatiotemporal resolution is vital for effective reservoir landslide monitoring and analysis. For this purpose, we improved the resolution of the differential interferometric synthetic aperture radar (DInSAR) technique by fusing two-path deformation results from an overlapping Sentinel-1 area. First, we summarized the mathematical ratio relationship between deformation from the two paths. Second, time-series linear interpolation and time-reference difference removal were applied to the two separate deformation results of time-series DInSAR. Third, a ratio algorithm was adopted to fuse the deformation of the two paths into one integrated time-series result. The standard deviations of the deformation before and after fusion were similar, confirming the accuracy of the fusion results and feasibility of the method. From the integrated deformation, we analyzed the hydraulic impact, mechanisms, and physical processes associated with four reservoir landslides in the Three Gorges Reservoir area of China, accounting for rainfall and water-level data. The comprehensive analysis presented herein provides new insights on the hydraulic mechanisms of reservoir landslides and verifies the efficacy of this new integrated method for landslide investigation and monitoring. Full article

Analyzing the spatial and temporal trends in cropping patterns and intensity on a larger scale is essential for implementing timely policy decisions and strategies in response to climate change and variability. By converting cropping intensity estimates, we can compute net and gross production values, indirectly indicating food security status in the study region. This study compared the utility of optical (MOD13Q1) and SAR (Sentinel 1A) datasets for determining cropping patterns and associated intensity estimates across multiple agricultural seasons from 2019 to 2023, with spatial resolutions of 250 m and 20 m, respectively. The analysis revealed that the highest and lowest gross cropped areas using Sentinel 1A data were 55.85 lakh hectares (2022–2023) and 52.88 lakh hectares (2019–2020), respectively. For MODIS data, the highest and lowest gross cropped areas were 62.07 lakh hectares (2022–2023) and 56.87 lakh hectares (2019–2020). Similarly, the highest and lowest net sown areas using Sentinel 1A data were 43.71 lakh hectares (2022–2023) and 41.76 lakh hectares (2019–2020), and for MODIS data, the values were 48.81 lakh hectares (2022–2023) and 46.39 lakh hectares (2019–2020), respectively. Regardless of the datasets used, the highest gross and net cropped areas were reported in Tiruvannamalai district and the lowest in Kanchipuram district. Thiruvarur district reported the highest cropping intensity, while Sivagangai district had the lowest. Among all seasons, the rabi season accounted for the maximum area, followed by the kharif and summer seasons. The study concluded that single cropping (51%) was the dominant cropping pattern in Tamil Nadu, followed by double cropping (31%) and triple cropping (17%) in both datasets. Sentinel 1A data showed better performance in estimating gross and net cropped areas than optical data, with deviations ranging from 7.02% to 11.01%, regardless of the year and cropping estimates derived. The results indicated that the spatial resolution of the datasets was not a significant factor in determining cropping patterns and intensity on a larger scale. However, this may differ for smaller study areas. Full article

Long synthetic aperture radar (SAR) offers the advantage of achieving higher resolution by utilizing longer synthetic aperture times, which makes it a promising technology for ocean observation in the future. However, compared to SAR systems with shorter synthetic aperture times, it suffers more severely from issues such as image defocusing, blurring and artifacts during the observation of maritime targets, due to motion errors. To improve the quality of SAR imaging against motion errors in long synthetic aperture time scenarios, this paper proposes a combined motion compensation (MOCO) method based on subaperture processing. The method first divides the full aperture data into several subapertures. Within each subaperture, the platform is assumed to move at approximately constant velocity. The major imaging step is then combined with two motion compensation operations, which are performed individually within each subaperture. Then, the processed subaperture data are stitched together, and finally, the residual errors are compensated by the third MOCO, resulting in the final image. By simulating maritime observation targets with point targets, simulation results demonstrate that the proposed MOCO algorithm effectively reduce the influence of motion errors, suppress the sidelobe interference to the imaging, and improve the focusing accuracy. Compared with other classical MOCO algorithms, the ISLR_r and ISLR_a metrics show improvements of 0.2662 and 0.8170 dB, respectively. Further verification of the proposed method is conducted by processing the imaging results of measured sea surface data. The proposed algorithm produces clearer wave textures and achieves better imaging performance on targets such as ships in the sea. This result validates the effectiveness and superiority of the proposed method. The proposed method effectively addresses the need for high-precision motion error compensation in high-resolution imaging within long synthetic aperture time system. Full article

Algal lectin Griffithsin (GRFT) is a well-known mannose-binding protein which has broad-spectrum antiviral activity against several important infectious viruses including HIV, HCV, and SARS-CoV-2. Therefore, GRFT has been brought great attention to antiviral therapeutic development. In this report, we have tested GRFT’s activity against the lethal Ebola virus in vitro and in vivo. Our data have shown that the IC₅₀ value is about 42 nM for inhibiting Zaire Ebola virus (EBOV) infection in vitro. The preliminary in vivo mice model using mouse-adapted EBOV has also shown a certain efficacy for delayed mortality compared to the control animals. A GRFT pull-down experiment using viral particles demonstrates that GRFT can bind to N-glycans of EBOV. Thus, it can be concluded that GRFT, through binding to viral glycans, may block Ebola virus infection and has potential for the treatment of Ebola virus disease (EVD). Full article

Lakes are a crucial component of inland water bodies, and changes in their water levels serve as key indicators of global climate change. Traditional methods of lake water level monitoring rely heavily on hydrological stations, but there are problems such as regional representativeness, data stability, and high maintenance costs. The satellite altimeter is an essential tool in lake research, with the Synthetic Aperture Radar (SAR) altimeter offering a high spatial resolution. This enables precise and quantitative observations of lake water levels on a large scale. In this study, we used Sentinel-3A SAR Radar Altimeter (SRAL) data to establish a more reasonable lake height inversion algorithm for satellite-derived lake heights. Subsequently, using this technology, a systematic analysis study was conducted with Qinghai Lake as the case study area. By employing regional filtering, threshold filtering, and altimeter range filtering techniques, we obtained effective satellite altimeter height measurements of the lake surface height. To enhance the accuracy of the data, we combined these measurements with GPS buoy-based geoid data from Qinghai Lake, normalizing lake surface height data from different periods and locations to a fixed reference point. A dataset based on SAR altimeter data was then constructed to track lake surface height changes in Qinghai Lake. Using data from the Sentinel-3A altimeter’s 067 pass over Qinghai Lake, which has spanned 96 cycles since its launch in 2016, we analyzed over seven years of lake surface height variations. The results show that the lake surface height exhibits distinct seasonal patterns, peaking in September and October and reaching its lowest levels in April and May. From 2016 to 2023, Qinghai Lake showed a general upward trend, with an increase of 2.41 m in lake surface height, corresponding to a rate of 30.0 cm per year. Specifically, from 2016 to 2020, the lake surface height rose at a rate of 47.2 cm per year, while from 2020 to 2022, the height remained relatively stable. Full article

Multiple-Input Multiple-Output (MIMO) radar enjoys the advantages of a high degree of freedom and relatively large virtual aperture, so it has various forms of applications in several aspects such as remote sensing, autonomous driving and radar imaging. Among all multiplexing schemes, Time-Division Multiplexing (TDM)-MIMO radar gains a wide range of interests, as it has a simple and low-cost hardware system which is easy to implement. However, the time-division nature of TDM-MIMO leads to the dilemma between the lower Pulse Repetition Interval (PRI) and more transmitters, as the PRI of a TDM-MIMO system is proportional to the number of transmitters while the number of transmitters significantly affects the resolution of MIMO radar. Moreover, a high PRI is often needed to obtain unambiguous imaging results for MIMO-SAR 3D imaging applications on a fast-moving platform such as a car or an aircraft. Therefore, it is of vital importance to develop an algorithm which can achieve unambiguous TDM-MIMO-SAR 3D imaging even when the PRI is low. Inspired by the motion compensation problem associated with TDM-MIMO radar imaging, this paper proposes a novel imaging algorithm which can utilize the phase shift induced by the time-division nature of TDM-MIMO radar to achieve unambiguous MIMO-SAR 3D imaging. A 2D-Compressed Sensing (CS)-based method is employed and the proposed method, which is called HPC-2D-FISTA, is verified by simulation data. Finally, a real-world experiment is conducted to show the unambiguous imaging ability of the proposed method compared with the ordinary matched-filter-based method. The effect of velocity error is also analyzed with simulation results. Full article

This study investigates the use of the radar vegetation index (RVI) derived from Sentinel-1 synthetic aperture radar (SAR) data for landslide detection. Traditional landslide detection methods often rely on the Normalized Difference Vegetation Index (NDVI) derived from optical imagery, which is susceptible to limitations imposed by weather conditions (clouds, rain) and nighttime. In contrast, SAR data, acquired by Sentinel-1, provides all-weather, day-and-night coverage. To leverage this advantage, we propose a novel approach utilizing RVI, a vegetation index calculated from SAR data, to identify non-vegetated areas, which often indicate potential landslide zones. To enhance the accuracy of non-vegetated area classification, we employ the high-performing EfficientNetV2 deep learning model. We evaluated the classification performance of EfficientNetV2 using RVI derived from Sentinel-1 SAR data with VV and VH polarizations. Experiments were conducted on SAR imagery of the Iburi district in Hokkaido, Japan, severely impacted by an earthquake in 2018. Our findings demonstrate that the classification performance using RVI with both VV and VH polarizations significantly surpasses that of using VV and VH polarizations alone. These results highlight the effectiveness of RVI for identifying non-vegetated areas, particularly in landslide detection scenarios. The proposed RVI-based method has broader applications beyond landslide detection, including other disaster area assessments, agricultural field monitoring, and forest inventory. Full article

Permafrost serves as a crucial indicator of global climate change. Its degradation significantly influences Earth’s surface systems, including hydrology, soil, climate, ecosystems, and even civil construction. The distribution of permafrost in the Mongolian Plateau (MP) has an important influence in North Asia and even the Euro-Asia continent. This study provides a comprehensive review of the current state of permafrost degradation and its influence on MP, including climate change and human activities. Remote sensing technologies for permafrost monitoring, including optical remote sensing data models and InSAR technology, are also reviewed. This paper outlines eight future research directions by exploring the latest advancements and technical challenges in permafrost monitoring in the region. These include fundamental investigations of the permafrost zone; evaluation of permafrost effects on ecosystems; hydrology and water resources research; assessment and engineering of freeze–thaw hazards; sustainable regional development in permafrost zones; remote sensing monitoring techniques for permafrost; inter-regional comparative and collaborative research; and data sharing and standardization for permafrost research. This study provides valuable insights into the progress of permafrost degradation not only in the MP but also as a reference for related permafrost studies in other mid-to-high latitudes regions. Full article

SAR data from the lunar spaceborne Reconnaissance Orbiter’s (LRO) Mini-RF and Chandrayaan-1’s Mini-SAR provide valuable insights into the properties of the lunar surface. However, public lunar SAR data products are not properly registered and are limited by localization issues. Existing registration methods for Earth SAR have proven to be inadequate in their robustness for lunar data registration. And current research on methods for lunar SAR has not yet focused on producing globally registered datasets. To solve these problems, this article introduces a robust automatic registration method tailored for S-band Level-1 Mini-RF and Mini-SAR data with the assistance of lunar DEM. A simulated SAR image based on real lunar DEM data is first generated to assist the registration work, and then an offset calculation approach based on normalized cross-correlation (NCC) and specific processing, including background removal, is proposed to achieve the registration between the simulated image, and the real image. When applying Mini-RF images and Mini-SAR images, high robustness and good accuracy are exhibited, which produces fully registered datasets. After processing using the proposed method, the average error between Mini-RF images and DEM references was reduced from approximately 3000 m to about 100 m. To further explore the additional improvement of the proposed method, the registered lunar SAR datasets are used for further analysis, including a review of the circular polarization ratio (CPR) characteristics of anomalous craters. Full article

Global assessments of landslide impact on critical communication infrastructure have become urgent because of rising occurrences related to human activities and climate change. The landslide and glacial slide susceptibility along the Karakoram Highway poses a significant threat to the infrastructure ecosystem, local communities, and the critical China–Pakistan Economic Corridor. This research paper utilized the Small Baseline Subset InSAR technique to monitor the deformation patterns over the past 5 years, yielding high-resolution insights into the terrain instability in this geologically active region. The SBAS time series results reveal that the substantial cumulative deformation in our study area ranges from 203 mm to −486 mm, with annual deformation rates spanning from 62 mm/year to −104 mm/year. Notably, the deformation that occurred is mainly concentrated in the northern section of our study area. The slope’s aspect is responsible for the maximum deformed material flow towards the Karakoram Highway via steep slopes, lost glacial formations, and the climate variations that cause the instability of the terrain. The given pattern suggests that the northern area of the Karakoram Highway is exposed to a greater risk from the combined influence of glacial slides, landslides, and climatic shifts, which call for the increased monitoring of the Karakoram Highway. The SBAS-InSAR method is first-rate in deformation monitoring, and it provides a scientific basis for developing real-time landslide monitoring systems. The line of sight limitations and the complexity and imprecision of weather-induced signal degradation should be balanced through additional data sources, such as field surveys to conduct large slide and glacial slide susceptibility evaluations. These research results support proactive hazard mitigation and infrastructure planning along the China–Pakistan Economic Corridor by incorporating SBAS-InSAR monitoring into the original planning. The country’s trade policymakers and national level engineers can enhance transport resilience, efficiently manage the landslide and glacial slide risks, and guarantee safer infrastructure along this strategic trade route. Full article

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has imposed several medical and economic challenges since its onset in 2019. This is due to its ability to target the respiratory system as well as other organs, resulting in significant impacts and necessitating organ transplants. Our goal is to compile information from the existing literature to investigate how COVID-19 affects outcomes following organ transplantation. A comprehensive literature search was conducted to target studies reporting post-COVID-19 complications. We included 45 studies reporting data related to solid organ transplants, where either the recipient, organ, or donor was affected by SARS-CoV-2. The majority of the included studies concluded that organ transplantation following COVID-19 infection could be performed safely and with similar outcomes to non-COVID-19 patients, regardless of whether the organ, donor, or recipient was affected by COVID-19. No deviation from standard immunosuppression regimens or surgical protocols was necessary either, further re-assuring the feasibility of these transplants as viable treatment options. This applies to organ transplants involving the lungs, kidneys, liver, or heart. However, there was a limited number of studies in some areas, which warrants the need for additional research in order to reach more concrete conclusions pertaining to COVID-19’s effect on organ transplants. Full article

This paper introduces a high-resolution 77–81 GHz mmWave Synthetic Aperture Radar (SAR) imaging methodology integrating low-cost hardware with modified radar signal characteristics specifically for NDT applications. The system is optimized to detect minimal defects in materials, including low-reflectivity ones. In contrast to the existing studies, by optimizing key system parameters, including frequency slope, sampling interval, and scanning aperture, high-resolution SAR images are achieved with reduced computational complexity and storage requirements. The experiments demonstrate the effectiveness of the system in detecting optically undetectable minimal surface defects down to 0.4 mm, such as bonded adhesive lines on low-reflectivity materials with 2500 measurement points and sub-millimeter features on metallic targets at a distance of 30 cm. The results show that the proposed system achieves comparable or superior image quality to existing high-cost setups while requiring fewer data points and simpler signal processing. Low-cost, low-complexity, and easy-to-build mmWave SAR imaging is constructed for high-resolution SAR imagery of targets with a focus on detecting defects in low-reflectivity materials. This approach has significant potential for practical NDT applications with a unique emphasis on scalability, cost-effectiveness, and enhanced performance on low-reflectivity materials for industries such as manufacturing, civil engineering, and 3D printing. Full article

Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments. Full article