Search Results (60)

A dual-polarization energy selective surface (ESS) with low insertion loss (IL) and high shielding effectiveness (SE) based on a dual-resonant equivalent circuit topology was proposed for high-intensity radiation field (HIRF) protection in this paper. The design principle was elucidated through an equivalent circuit model and translated into a physical ESS implementation. It consists of two resonant rings, vertically arranged and loaded with diodes, along with two lumped capacitors. Simulation and measurement results demonstrate that the IL is less than 3 dB when in the OFF state in a working frequency band, and the SE exceeds 20 dB when in the ON state. Moreover, the ESS’s dual-polarization, low cost, and easy-to-design characteristics hold great promise for broad applications in protecting communication and radar systems in complex electromagnetic environments. Full article

Due to the increased age of the global population, the proportion of the elderly population continues to rise. The safety of the elderly living alone is becoming an increasingly prominent area of concern. They often miss timely treatment due to undetected falls or illnesses, which pose risks to their lives. In order to address this challenge, the technology of indoor abnormal behavior detection has become a research hotspot. This paper systematically reviews detection methods based on sensors, video, infrared, WIFI, radar, depth, and multimodal fusion. It analyzes the technical principles, advantages, and limitations of various methods. This paper further explores the characteristics of relevant datasets and their applicable scenarios and summarizes the challenges facing current research, including multimodal data scarcity, risk of privacy leakage, insufficient adaptability of complex environments, and human adoption of wearable devices. Finally, this paper proposes future research directions, such as combining generative models, federated learning to protect privacy, multi-sensor fusion for robustness, and abnormal behavior detection on the Internet of Things environment. This paper aims to provide a systematic reference for academic research and practical application in the field of indoor abnormal behavior detection. Full article

Monitoring the amount of waste in open landfill sites in developing countries is important from the perspective of building a sustainable society and protecting the environment. Some landfill sites provide information on the amount of waste in reports and news articles; however, in many cases, the survey methods, timing, and accuracy are uncertain, and there are many sites for which this information is not available. In this context, monitoring the amount of waste using satellite data is extremely useful from the perspective of uniformity, objectivity, low cost, safety, wide coverage area, and simultaneity. In this study, we developed a method for calculating the relative volume of waste at 15 landfill sites in six developing countries using time-series digital surface model (DSM) data from the satellite optical sensor, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which has accumulated more than 20 years of observational data. Unnecessary variations between images were reduced by bias correction based on a reference area around the site. In addition, by utilizing various reported values, we introduced a method for converting relative volume to absolute volume and converting volume to weight, enabling a direct comparison with reported values. We also evaluated our method compared with the existing method for calculating changes in waste volume based on TanDEM-X DEM Change Map (DCM) products. The findings of this study demonstrated the efficacy of the employed method in capturing changes, such as increases and stagnation, in the amount of waste deposited. The method was found to be relatively consistent with reported values and those obtained using the DCM, though a decrease in accuracy was observed due to the depositional environment and the absence of data. The results of this study are expected to be used in the future for technology that combines an optical sensor and synthetic aperture radar (SAR) to monitor the amount of waste. Full article

Radar-based human behavior recognition has significant value in IoT application scenarios such as smart healthcare and intelligent security. However, the existing unimodal perception architecture is susceptible to multipath effects, which can lead to feature drift, and the issue of limited cross-scenario generalization ability has not been effectively addressed. Although Wi-Fi sensing technology has emerged as a promising research direction due to its widespread device applicability and privacy protection, its drawbacks, such as low signal resolution and weak anti-interference ability, limit behavior recognition accuracy. To address these challenges, this paper proposes a dynamic adaptive behavior recognition method based on the complementary fusion of radar and Wi-Fi signals. By constructing a cross-modal spatiotemporal feature alignment module, the method achieves heterogeneous signal representation space mapping. A dynamic weight allocation strategy guided by attention is adopted to effectively suppress environmental interference and improve feature discriminability. Experimental results show that, on a cross-environment behavior dataset, the proposed method achieves an average recognition accuracy of 94.8%, which is a significant improvement compared to the radar unimodal domain adaptation method. Full article

The conflict between exploitation of coal resources and environmental protection is highly pronounced in the Wanli mining area, located in the arid and semi-arid region of Inner Mongolia, China. The impact of mining operations has led to varying degrees of surface subsidence, which further threatens the ecological environment as coal extraction continues. The Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique offers significant advantages over traditional subsidence monitoring methods, particularly in complex terrain with vertical and horizontal valleys. This approach enables large-scale, low-cost, and all-weather monitoring. Based on 64 Sentinel-1A SAR images from 2018 to 2023, this study aims to promptly identify the location, deformation degree, and evolution characteristics of mining-induced subsidence within the study area using SBAS-InSAR techniques. The results indicate that the area affected by mining-induced subsidence covers 109.73 km², with a maximum cumulative subsidence of 283.41 mm and a maximum subsidence velocity of 46.45 mm/y. Additionally, during the field verification, 29 ground fractures, predominantly located along the precipitous borders of subsidence areas, were identified, validating the credibility of the monitoring results. This study demonstrates that SBAS-InSAR technology remains highly effective in the erosional terrain of the Loess Plateau. The monitoring data can help in-production mining to accurately identify the characteristics and patterns of surface subsidence induced by coal mining operations. It provides reliable policymaking data support and makes significant contributions to optimize cost-efficiency and guide targeted monitoring efforts in subsequent management work of the Wanli mining area as well as other mining areas. Full article

Ground-based global navigation satellite system (GNSS) and remote sensing interferometric synthetic aperture radar (InSAR) techniques have proven to be very useful for deformation monitoring. GNSS provides high-precision data but only at a limited number of points, whereas InSAR allows for a much denser distribution of measurement points, though only in areas with high and consistent signal backscattering. This study aims to integrate these two techniques to overcome their respective limitations and explore their potential for effective monitoring of critical infrastructure, ensuring the protection of people and the environment. The proposed approach was applied to monitor deformations of the shoulder structures of the MOSE (MOdulo Sperimentale Elettromeccanico) system, the civil infrastructure designed to protect Venice and its lagoon from high tides. GNSS data were collected from 36 continuous GNSS (CGNSS) stations located at the corners of the emerged shoulder structures in the Treporti, San Nicolò, Malamocco, and Chioggia barriers. Velocities from February 2021/November 2022 to June 2023 were obtained using daily RINEX data and Bernese software. Three different processing strategies were applied, utilizing networks composed of the 36 MOSE stations and eight other continuous GNSS stations from the surrounding area (Padova, Venezia, Treviso, San Donà, Rovigo, Taglio di Po, Porto Garibaldi, and Porec). InSAR data were sourced from the European ground motion service (EGMS) of the Copernicus program and the Veneto Region database. Both services provide open data related to the line of sight (LOS) velocities derived from Sentinel-1 satellite imagery using the persistent scatterers interferometric synthetic aperture radar (PS-InSAR) approach. InSAR velocities were calibrated using a reference CGNSS station (Venezia) and validated with the available CGNSS data from the external network. Subsequently, the velocities were compared along the LOS at the 36 CGNSS stations of the MOSE system. The results showed a strong agreement between the velocities, with approximately 70% of the comparisons displaying differences of less than 1.5 mm/year. These findings highlight the great potential of satellite-based monitoring and the effectiveness of combining GNSS and InSAR techniques for infrastructure deformation analysis. Full article

Trees are indispensable to ecosystems, yet mortality rates have been increasing due to the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, the need to monitor, assess, and predict tree mortality has become increasingly urgent to better address climate change and protect forest ecosystems. Over the past few decades, remote sensing has been widely applied to vegetation mortality observation due to its significant advantages. Here, we reviewed and analyzed the major research advancements in the application of remote sensing for tree mortality monitoring, using the Web of Science Core Collection database, covering the period from 1998 to the first half of 2024. We comprehensively summarized the use of different platforms (satellite and UAV) for data acquisition, the application of various sensors (multispectral, hyperspectral, and radar) as image data sources, the primary indicators, the classification models used in monitoring tree mortality, and the influence of tree mortality. Our findings indicated that satellite-based optical remote sensing data were the primary data source for tree mortality monitoring, accounting for 80% of existing studies. Time-series optical remote sensing data have emerged as a crucial direction for enhancing the accuracy of vegetation mortality monitoring. In recent years, studies utilizing airborne LiDAR have shown an increasing trend, accounting for 48% of UAV-based research. NDVI was the most commonly used remote sensing indicator, and most studies incorporated meteorological and climatic factors as environmental variables. Machine learning was increasingly favored for remote sensing data analysis, with Random Forest being the most widely used classification model. People are more focused on the impacts of tree mortality on water and carbon. Finally, we discussed the challenges in monitoring and evaluating tree mortality through remote sensing and offered perspectives for future developments. Full article

Soil moisture is a key variable that affects ecosystem carbon and water cycles and that can directly affect climate change. Remote sensing is the best way to obtain global soil moisture data. Currently, soil moisture remote sensing products have coarse spatial resolution, which limits their application in agriculture, the ecological environment, and urban planning. Soil moisture downscaling methods rely mainly on optical data. Affected by weather, the spatial discontinuity of optical data has a greater impact on the downscaling results. The synthetic aperture radar (SAR) backscatter coefficient is strongly correlated with soil moisture. This study was based on the Google Earth Engine (GEE) platform, which integrated Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and SAR backscattering coefficients and used machine learning methods to downscale the soil moisture product, reducing the original soil moisture with a resolution of 10 km to 1 km and 100 m. The downscaling results were verified using in situ observation data from the Shandian River and Wudaoliang. The results show that in the two study areas, the downscaling results after adding SAR backscattering coefficients are better than before. In the Shandian River, the R increases from 0.28 to 0.42. In Wudaoliang, the R value increases from 0.54 to 0.70. The RMSE value is 0.03 (cm³/cm³). The downscaled soil moisture products play an important role in water resource management, natural disaster monitoring, ecological and environmental protection, and other fields. In the monitoring and management of natural disasters, such as droughts and floods, it can provide key information support for decision-makers and help formulate more effective emergency response plans. During droughts, affected areas can be identified in a timely manner, and the allocation and scheduling of water resources can be optimized, thereby reducing agricultural losses. Full article

Since the Wenchuan earthquake in 2008, Old Beichuan County-town has experienced significant subsidence due to the disruption of the geological environment and the concurrent increase in precipitation. The ongoing land surface deformation poses a threat to the preservation and utilization of the Beichuan National Earthquake Ruins Museum (BNERM), as well as to the safety of urban residents’ lives. However, the evolutionary characteristics of surface deformation in these areas remain largely unexplored. Here, we focused on the BNERM control zone and employed the small-baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique to accurately measure land surface deformation and its spatiotemporal changes. Subsequently, we integrated this data with land cover types and precipitation to investigate the driving factors of deformation. The results indicate a slight overall elevation increase in the study area from June 2015 to May 2023, with deformation rates varying between −35.2 mm/year and 22.9 mm/year. Additionally, four unstable slopes were identified within the BNERM control zone. Our analysis indicates that surface deformation in the study area is closely linked to changes in land cover types and precipitation, exhibiting a seasonal cumulative pattern, and active geological activity may also be a cause of deformation. This study provides invaluable insights into the surface deformation characteristics of the BNERM and can serve as a scientific foundation for the protection of earthquake ruins, risk assessment, early warning, and disaster prevention measures. Full article

In this work, the airborne Synthetic Aperture Radar (SAR) infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) is described. This infrastructure allows IREA-CNR to plan and execute airborne SAR campaigns and to process the acquired data with a twofold aim. On one hand, the aim is to develop research activities; on the other hand, the aim is to support the emergency prevention and management activities of the Department of Civil Protection of the Italian Presidency of the Council of Ministers, for which IREA-CNR serves as National Centre of Competence. Such infrastructure consists of a flight segment and a ground segment that include a multi-frequency airborne SAR sensor based on the Frequency-Modulated Continuous Wave (FMCW) technology and operating in the X- and L-bands, an Information Technology (IT) platform for data storage and processing and an airborne SAR data processing chain. In this work, the technical aspects related to the flight and ground segments of the infrastructure are presented. Moreover, a discussion on the response times and characteristics of the final products that can be achieved with the infrastructure is provided with the aim of showing its capabilities to support the monitoring activities required in a possible emergency scenario. In particular, as a case study, the acquisition and subsequent interferometric processing of airborne SAR data relevant to the Stromboli volcanic area in the Sicily region, southern Italy, are presented Full article

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments. Full article

Reformation of the 3.7–4.0 GHz band to expand 5G communication deployment poses a risk of 5G signals disrupting radar altimeter operation, leading to data loss or inaccuracies. Thus, this paper proposes a guard band protection method to facilitate the coexistence of 5G base stations and radar altimeters operating in the 4.2–4.4 GHz band. To enhance the adjacent channel leakage ratio (ACLR), we implemented spectral regrowth on an oversampled waveform using a high-power amplifier model, filtering out-of-band spectral emissions. The results demonstrated that a 150 MHz guard band enables coexistence, except in the case of the 16-by-16 antenna array in rural environments. Notably, for the 4-by-4 antenna array in urban environments, coexistence can be achieved using a 50 MHz guard band. The proposed mitigation techniques may also be extended to promote coexistence between non-terrestrial networks and 5G communication systems, including satellites, unmanned aerial vehicles, and hot air balloons. Full article

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. Full article

The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA’s operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA’s benefit. STAR’s satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications. Full article

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User’s accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. Full article