Search Results (1,258)

This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios. Full article

Rapid, sensitive, and environmentally sustainable spectrophotometric methods for the determination of nitrite (${{\mathrm{N}\mathrm{O}}_2}^{-}$) in environmental specimens are proposed. The presented procedures are grounded in the diazotization of sulphathiazole (STZ), followed by coupling with rhodanine (RDN) or 7-hydroxycoumarin (7-HC) in an alkaline medium, and the results were studied. This reaction gave an intense soluble red color at 504 nm and a pale red color at 525 nm for RDN and 7-HC, respectively. The conditions producing the maximum performance and other important analytical criteria in relation to the proposed procedures were investigated to enhance their sensitivity. Beer’s law was abided by for ${{\mathrm{N}\mathrm{O}}_2}^{-}$ over the concentration ranges of 0.08–2.0 µg mL⁻¹ and 0.04–2.4 µg mL⁻¹ using RDN and 7-HC, respectively. The lower limit of detection (LLOD), lower limit of quantification (LLOQ), molar absorptivity (ε), and Sandell’s sensitivity were calculated as follows: 0.0303 µg mL⁻¹, 0.0918 µg mL⁻¹, 4.20 × 10⁴ L mol⁻¹ cm⁻¹, and 1.63 × 10⁻⁶ µg cm⁻² (in the case of RDN); and 0.0387 µg mL⁻¹, 0.1172 µg mL⁻¹, 6.90 × 10⁴ L mol⁻¹ cm⁻¹, and 1.00 × 10⁻⁶ µg cm⁻² (in case of 7-HC). Furthermore, the ecological implications were assessed using three green assessment methodologies: Analytical Eco-Scale (ESA), Analytical GREEnness metric (AGREE), and Green Analytical Procedure Index (GAPI). Thus, our proposed procedures are fully validated and implemented in order to carry out ${{\mathrm{N}\mathrm{O}}_2}^{-}$ quantification in the selected ecological samples (water and soil samples). Full article

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Detecting small objects in Satellite Laser Ranging (SLR) CCD images is critical yet challenging due to low signal-to-noise ratios and complex backgrounds. Existing frameworks often suffer from high computational costs and insufficient feature extraction capabilities for such tiny targets. To address these issues, we propose the Gaussian-Matched Fusion Network (GMF-Net), a lightweight and high-precision detector tailored for SLR scenarios. The core scientific innovation lies in the Gaussian-Matched Convolution (GMConv) module. Unlike standard convolutions, GMConv is theoretically grounded in the physical Gaussian energy distribution of SLR targets. It employs multi-directional heterogeneous sampling to precisely match target energy decay, enhancing central feature response while suppressing background noise. Additionally, we incorporate a Cross-Stage Partial Pyramidal Convolution (CSPPC) to reduce parameter redundancy and a Cross-Feature Attention (CFA) module to bridge multi-scale features. To validate the method, we constructed the first dedicated SLR-CCD dataset. Experimental results show that GMF-Net achieves an mAP@50 of 93.1% and mAP@50–95 of 52.4%. Compared to baseline models, parameters are reduced by 26.6% (to 2.2 M) with a 27.4% reduction in computational load, demonstrating a superior balance between accuracy and efficiency for automated SLR systems. Full article

We assessed the prevalence of three helminthic zoonoses—echinococcosis, fasciolosis and the taeniosis/cysticercosis complex—among residents of the Chucuito Health Network (Puno Health Region, Peru) over four years (2018–2021). Sera (n = 910) were analysed by ELISA to detect pathogen-specific antibodies, following national protocols. Echinococcosis predominated, whereas fasciolosis and taeniosis/cysticercosis occurred at comparatively low levels. Prevalence ranged from 4.4–9.2% for echinococcosis, 1.1–4.9% for fasciolosis, and 1.1–2.7% for taeniosis/cysticercosis across the four years. Prevalence varied significantly between years, with a notable upsurge in echinococcosis in 2021. These findings underscore the need for integrated control and prevention measures grounded in a One Health framework that recognises the interconnections between human, animal and environmental health. Priority actions include strengthened health education programmes, improved hygiene and sanitation practices, and enhanced rural health infrastructure, alongside coordinated epidemiological surveillance and environmental management. Such measures are essential to mitigate the burden of zoonotic disease in vulnerable high-Andean communities. Full article

Light detection and ranging (LiDAR) has been widely used in navigation and environmental perception owing to its excellent beam directivity and high spatial resolution. Among its modalities, single-photon (photon-counting) LiDAR offers higher detection sensitivity at long ranges and under weak-return conditions and has therefore attracted considerable attention. However, this high sensitivity also introduces substantial background counts into the raw measurements; without effective filtering, downstream tasks such as image reconstruction and target recognition are hindered. In this work, a 128-line single-photon LiDAR system for port-environment perception was designed, and a histogram-based statistical filtering engineering solution was proposed. The algorithm incorporates distance-based piecewise adaptive parameterization and adjacent-channel fusion while maintaining a small memory footprint and facilitating deployment. Field experiments using datasets collected in Qingdao and Shanghai demonstrated good denoising performance at ranges up to 2.4 km. In simulation experiments using synthetic data with ground truth, an F1 score of 0.9091 was achieved by RA-ACF HSF, outperforming the baseline methods DBSCAN (0.6979) and ROR (0.7500). The proposed system and method provide a practical engineering solution for maritime navigation and port-environment perception. Full article

Earthwork volume calculation is a fundamental process in civil engineering and construction, requiring high-precision terrain data to assess ground stability encompassing load-bearing capacity, susceptibility to settlement, and slope stability and to ensure accurate cost estimation. However, seasonal and environmental constraints pose significant challenges to surveying. This study employed unmanned aerial vehicle (UAV) photogrammetry and light detection and ranging (LiDAR) mapping to evaluate the accuracy of digital terrain model (DTM) generation and earthwork volume estimation in densely vegetated areas. For ground extraction, color-based indices (excess green minus red (ExGR), visible atmospherically resistant index (VARI), green-red vegetation index (GRVI)), a geometry-based algorithm (Lasground (new)) and their combinations were compared and analyzed. The results indicated that combining a color index with Lasground (new) outperformed the use of single techniques in both photogrammetric and LiDAR-based surveying. Specifically, the ExGR–Lasground (new) combination produced the most accurate DTM and achieved the highest precision in earthwork volume estimation. The LiDAR-based results exhibited an error of only 0.3% compared with the reference value, while the photogrammetric results also showed only a slight deviation, suggesting their potential as a practical alternative even under dense summer vegetation. Therefore, although prioritizing LiDAR in practice is advisable, this study demonstrates that UAV photogrammetry can serve as an efficient supplementary tool when cost or operational constraints are present. Full article

Coastal environments are fragile ecosystems threatened by various factors, both natural and anthropogenic. The preservation and protection of these environments, and in particular, the sand dune systems, which contribute significantly to the defense of the inland from flooding, require continuous monitoring. To this end, high-resolution and high-precision multitemporal data acquired with various techniques can be used, such as, among other things, the global navigation satellite system (GNSS) using the network real-time kinematic (NRTK) approach to acquire 3D points, UAS-based structure-from-motion photogrammetry (SfM), terrestrial laser scanning (TLS), and handheld mobile laser scanning (HMLS)-based light detection and ranging (LiDAR). These techniques were used in this work for the 3D survey of a portion of vegetated sand dunes in the Caleri area (Po River Delta, northern Italy) to assess their applicability in complex environments such as coastal vegetated dune systems. Aerial-based and ground-based acquisitions allowed us to produce point clouds, georeferenced using common ground control points (GCPs), measured both with the GNSS NRTK method and the total station technique. The 3D data were compared to each other to evaluate the accuracy and performance of the different techniques. The results provided good agreement between the different point clouds, as the standard deviations of the differences were lower than 9.3 cm. The GNSS NRTK technique, used with the kinematic approach, allowed for the acquisition of the bare-ground surface but at a cost of lower resolution. On the other hand, the HMLS represented the poorest ability in the penetration of vegetation, providing 3D points with the highest elevation value. UAS-based and TLS-based point clouds provided similar average values, with significant differences only in dense vegetation caused by a very different platform of acquisition and point of view. Full article

Few hydrogeological studies have focused on possible per- and poly-fluoroalkyl substance (PFAS) contamination in groundwater with particular attention to the role of hydraulic interconnections and to the interdigitations present between shallow and deep aquifer layers in heterogeneous alluvial systems. In general, deeper groundwater is considered chemically safer and less impacted by contamination, especially in multilayer aquifers characterized by low permeability apparently confining horizons. Therefore, this research analyzed PFAS in groundwater at depths ranging from 20 to 120 m below ground level, combining stratigraphic, hydrogeological, and chemical data with GIS mapping to identify industrial activities potentially contributing to PFAS contamination using the cross-checking methodology. During the second survey, the monitoring network was extended along a hydrogeological transect, including two springs located upstream and downstream of the deep wells, to assess PFAS concentration in shallow groundwater and the possible transfer along the groundwater flow path. The intra-site comparative analysis reveals, for the same sampling locations, a differentiation in the PFAS profiles detected across the two monitoring campaigns, indicating a temporal evolution in the chemical composition. Furthermore, chemical results show the presence of PFAS exclusively in deep monitoring wells, confirming a spatially heterogeneous distribution within the aquifer system. These results highlight both the temporal and spatial evolution of PFAS concentration, suggesting a complex contaminant migration pathway along preferential gravel and sand horizons in deeper aquifer layers. The conceptual hydrogeological model confirmed hydraulic interconnections among aquifer layers and identified zones of higher vulnerability to contamination. The analysis of possible PFAS migration pathways at the basin scale raised some questions about the influence of wells features and management practices on PFAS distribution in shallow and deep groundwater. The findings of this research contribute to environmental sustainability, providing initial insights for measuring and managing the presence and pathways of PFAS in deep alluvial aquifers. Full article

Coastal sand dunes, shaped by aeolian and marine processes, are critical to natural ecosystems and human societies, making their morphological monitoring essential for effective conservation. However, large-scale, high-precision monitoring of topographic change remains a persistent challenge, a challenge that advanced sensing technologies can address. In this study, we propose an integrated, sensor-based approach using a UAV-mounted light detection and ranging (LiDAR) system, combined with a GNSS-RTK positioning unit and a novel ground control point (GCP) design to acquire high-resolution topographic data. Field surveys were conducted at four time points between October 2022 and February 2023 in the Tottori Sand Dunes, Japan. The digital elevation models (DEMs) derived from LiDAR point clouds achieved centimeter-level accuracy, enabling reliable detection of subtle topographic changes. Analysis of DEM differencing revealed that wind-driven sand deposition and erosion resulted in elevation changes of up to 0.4 m. These results validate the efficacy of the UAV-LiDAR sensor system for high-resolution, multitemporal monitoring of coastal sand dunes, highlighting its potential to advance the development of environmental sensing frameworks and support data-driven conservation strategies. Full article

Low-altitude Unmanned Aerial Vehicle (UAV) detection using LiDAR range images faces persistent challenges. These include sparse features for long-range targets, large scale variations caused by viewpoint changes, and severe interference from complex backgrounds. To address these issues, we propose an improved detection framework based on YOLOv10. First, we design a Swin-Conv hybrid module that combines sparse attention with deformable convolution. This module enables the network to focus on informative regions and adapt to target geometry. These capabilities jointly strengthen feature extraction for sparse, long-range targets. Second, we introduce Attentional Feature Fusion (AFF) in the neck to replace naïve feature concatenation. AFF employs multi-scale channel attention to softly select and adaptively weight features from different levels, improving robustness to multi-scale targets. In addition, we systematically study how the viewpoint distribution in the training set affects performance. The results show that moderately increasing the proportion of low-elevation-view samples significantly improves detection accuracy. Experiments on a self-built simulated LiDAR range-image dataset demonstrate that our method achieves 88.96% mAP at 54.2 FPS, which is 4.78 percentage points higher than the baseline. Deployment on the Jetson Orin Nano edge device further validates the model’s potential for real-time applications. The proposed method remains robust under noise and complex backgrounds. The proposed approach achieves an effective balance between detection accuracy and computational efficiency, providing a reliable solution for real-time target detection in complex low-altitude environments. Full article

The growing threat of unauthorized drones to ground-based critical infrastructure necessitates efficient ground-to-air surveillance systems. This paper proposes a lightweight framework for UAV detection and altitude measurement from a fixed ground perspective. We introduce Ghost-YOLOv11n, an optimized detector that integrates GhostConv modules into YOLOv11n, reducing computational complexity by 12.7% while achieving 98.8% mAP_0.5 on a comprehensive dataset of 8795 images. Deployed on a LuBanCat4 edge device with Rockchip RK3588S NPU acceleration, the model achieves 20 FPS. For stable altitude estimation, we employ an Extended Kalman Filter to refine measurements from a monocular ranging method based on similar-triangle geometry. Experimental results under ground monitoring scenarios show height measurement errors remain within 10% up to 30 m. This work provides a cost-effective, edge-deployable solution specifically for ground-based anti-drone applications. Full article

Ground settlement is readily induced by shield–tail gaps formed during tunneling, where soil loss must be compensated through backfill grouting. However, improper grouting control may trigger tunnel uplift, segment misalignment, and, after solidification, problems such as voids, cracking, and water ingress. Ensuring construction safety and long-term serviceability requires both reliable detection of grouting effectiveness and a mechanistic understanding of grout diffusion. This review systematically synthesizes sensing technologies, diffusion modeling, and intelligent data interpretation. It highlights their interdependence and identifies emerging trends toward multimodal joint inversion and real-time grouting control. Non-destructive testing techniques can be broadly categorized into geophysical approaches and sensor-based methods. For synchronous detection, vehicle-mounted GPR systems and IoT-based monitoring platforms have been explored, although studies remain sparse. Theoretically, grout diffusion has been investigated via numerical simulation and field measurement, including the spherical diffusion theory, columnar diffusion theory, and sleeve-pipe permeation grouting theory. These theories decompose the diffusion process of the slurry into independent movements. Nevertheless, oversimplified models and sparse monitoring data hinder the development of universally applicable frameworks capable of capturing diverse engineering conditions. Existing techniques are further constrained by limited imaging resolution, insufficient detection depth, and poor adaptability to complex strata. Looking ahead, future research should integrate complementary non-destructive methods with numerical simulation and intelligent data analytics to achieve accurate inversion and dynamic monitoring of the entire process, ranging from grout diffusion and consolidation to defect evolution. Such efforts are expected to advance both synchronous grouting detection theory and intelligent and digital-twin tunnel construction. Full article

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures. Full article