Search Results (239)

Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering polarized image and a laser LiDAR point cloud, and the corresponding system is constructed. Based on the extraction of pixel coordinates from the 3D LiDAR point cloud, the method adds information on the polarization degree and polarization angle of the micro-light polarization image, as well as on the reflective intensity of each point of the LiDAR. The mapping matrix of the radar point cloud to the pixel coordinates is made to contain depth offset information and show better fitting, thus optimizing the 3D point cloud converted from the micro-light polarization image. On this basis, algorithms such as 3D point cloud fusion and pseudo-color mapping are used to further optimize the matching and fusion procedures for the micro-light polarization image and the radar point cloud, so as to successfully realize the alignment and fusion of the 2D micro-light polarization image and the 3D LiDAR point cloud. The experimental results show that the alignment rate between the 2D micro-light polarization image and the 3D LiDAR point cloud reaches 74.82%, which can effectively detect the target hidden behind the glass under the low illumination condition and fill the blind area of the LiDAR point cloud data acquisition. This study verifies the feasibility and advantages of “polarization + LiDAR” fusion in low-light glass scene reconnaissance, and it provides a new technological means of covert target detection in complex environments. Full article

To address the poor thermal performance and low output efficiency of conventional solid-state microchip lasers, this study proposes and implements a bonded Nd:YAG/Cr⁴⁺:YAG laser based on fiber splitter pumping. Experimental results demonstrate that at a 4.02 mJ pump pulse energy and a 100 Hz repetition rate, the system achieves four linearly polarized output beams with an average pulse energy of 0.964 mJ, a repetition rate of 100 Hz, and an optical-to-optical conversion efficiency of 23.98%. The energy distribution ratios for the upper-left, lower-left, upper-right, and lower-right beams are 22.61%, 24.46%, 25.50%, and 27.43%, with pulse widths of 2.184 ns, 2.193 ns, 2.205 ns, and 2.211 ns, respectively. As the optical axis distance increases, the far-field spot pattern transitions from a single circular profile to four fully separated spots, where the lower-right beam exhibits beam quality factors of M_x² = 1.181 and M_y² = 1.289. Simulations at a 293.15 K coolant temperature and a 4.02 mJ pump energy reveal that split pumping reduces the volume-averaged temperature rise in Nd:YAG by 28.81% compared to single-beam pumping (2.57 K vs. 3.61 K), decreases the peak temperature rise by 66.15% (6.97 K vs. 20.59 K), and suppresses peak-to-peak temperature variation by 78.6% (1.34 K vs. 6.26 K). Compared with existing multi-beam generation methods, the fiber splitter approach offers integrated advantages—including compact size, low cost, high energy utilization, superior beam quality, and elevated damage thresholds—and thus shows promising potential for automotive multi-point ignition, multi-beam single-photon counting LiDAR, and laser-induced breakdown spectroscopy (LIBS) online analysis. Full article

Accurate detection of the atmospheric boundary layer (ABL) is important for weather forecasting, urban air quality monitoring, and agricultural and ecological protection. In this study, we propose a new method for enhancing ABL height detection accuracy by integrating multi-channel polarized lidar signals at 355 nm and 532 nm wavelengths. Radiosonde observations and ERA5 reanalysis are used to validate the lidar-derived results. By calculating the gradients of signals of different wavelengths and weighted fusion, the position of the top of the boundary layer is identified, and corresponding weights are assigned to signals of different wavelengths according to the signal-to-noise ratio of the signals to obtain a more accurate atmospheric boundary layer height. This method can effectively mitigate the influence of noise and provides more stable and accurate ABL height estimates, particularly under complex aerosol conditions. Three case studies of ABL height detection over the Beijing region demonstrate the effectiveness and reliability of the proposed method. The fused ABLHs were found to be consistent with the sounding data and ERA5. This research offers a robust approach to enhancing ABL height detection and provides valuable data support for meteorological studies, pollution monitoring, and environmental protection. Full article

Conventional techniques to measure drone heights from the ground, including global navigation satellite systems (GNSSs), barometers, acoustic sensors, and LiDAR, are limited by their measurement ranges, an inability to directly obtain the height from the ground, or poor concealment. To overcome these shortcomings, we propose the use of GNSS reflectometry (GNSS-R) to determine a drone’s height from the ground. We conducted experiments over farmland and an urban road using a drone that carried an upward-looking right-hand circularly polarized (RHCP) antenna, a downward-looking left-hand circularly polarized (LHCP) antenna, and an intermediate frequency (IF) data collector to test the performance. Three flights were conducted in a bare soil scenario, a sparse apple orchard scenario, and an urban road scenario. A software-defined receiver was used to process the IF signal data to compute the one-dimensional time-delay-dependent power peak positions of the direct and reflected GNSS signals. Based on these peak positions, the path delay measurements between the direct and reflected signals were derived per second based on the BDS B1C and B2a, GPS C/A, and L5 signals. The drone heights were then retrieved. The results showed that the drone height retrieval accuracy could reach approximately 0.5–2 m. Full article

The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article

Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. Our evaluation highlights our method’s robustness, evidenced by closely matched R² and RMSE values across models: KNN (R² of 0.496, RMSE of 5.13 m), RF (R² of 0.510, RMSE of 5.06 m), SVM (R² of 0.544, RMSE of 4.88 m), and XGB (R² of 0.548, RMSE of 4.85 m). The integration of comprehensive feature sets, as opposed to subsets, yielded better results, underscoring the value of using multisource remotely sensed data. Crucially, SHapley Additive exPlanations (SHAP) revealed the multi-seasonal red-edge spectral bands of Sentinel-2 as dominant predictors across models, while volume scattering from UAVSAR emerged as a key driver in tree-based algorithms. This study underscores the complementary nature of multi-sensor data and highlights the interpretability of our models. By offering spatially continuous, high-quality canopy height estimates, this cost-effective, data-driven approach advances large-scale forest management and environmental monitoring, paving the way for improved decision-making and conservation strategies. Full article

Polar vegetation is a critical component of global biodiversity and ecosystem health but is vulnerable to climate change and environmental disturbances. Analysing the spatial distribution, regional variations, and temporal dynamics of this vegetation is essential for implementing conservation efforts in these unique environments. However, polar regions pose distinct challenges for remote sensing, including sparse vegetation, extreme weather, and frequent cloud cover. Advances in remote sensing technologies, including satellite platforms, uncrewed aerial vehicles (UAVs), and sensor fusion techniques, have improved vegetation monitoring capabilities. This review explores applications—including land cover mapping, vegetation health assessment, biomass estimation, and temporal monitoring—and the methods developed to address these needs. We also examine the role of spatial, spectral, and temporal resolution in improving monitoring accuracy and addressing polar-specific challenges. Sensors such as Red, Green, and Blue (RGB), multispectral, hyperspectral, Synthetic Aperture Radar (SAR), light detection and ranging (LiDAR), and thermal, as well as UAV and satellite platforms, are analysed for their roles in low-stature polar vegetation monitoring. We highlight the potential of sensor fusion and advanced machine learning techniques in overcoming traditional barriers, offering a path forward for enhanced monitoring. This paper highlights how advances in remote sensing enhance polar vegetation research and inform adaptive management strategies. Full article

This research aims to estimate long-term aerosol radiative effects by combining radiation and Aerosol Optical Depth (AOD) observations in Barcelona, Spain. Aerosol Radiative Forcing and Aerosol Forcing Efficiency (ARF and AFE) were estimated by combining shortwave radiation measurements from a SolRad-Net CM-21 pyranometer (level 1.5) and AERONET AOD (level 2), using the direct method. The shortwave AFE was derived from the slope between net solar radiation and AOD at 440, 675, 879, and 1020 nm, and the ARF was computed by multiplying the AFE by AOD at six solar zenith angles (20°, 30°, 40°, 50°, 60°, and 70°). Clear-sky conditions were selected from all-skies days by a quadratic fitting. The aerosol was classified to investigate the forcing contributions from each aerosol type. The aerosol classification was based on Pace and Toledano’s thresholds from AOD vs. Ångström Exponent (AE). The GRASP inversions were performed by combined AOD, radiation, Degree of Linear Polarization (DoLP) by zenith angles from the polarized sun–sky–lunar photometer and the elastic signal from the UPC-ACTRIS lidar system. The long-term AFE and ARF are both negative, with an increasing tendency (in absolute value) of +24% (AFE) and +40% (ARF) in 14 years. The yearly AFE varied from −331 to −10 Wm⁻²τ⁻¹, and the ARF varied from −64 to −2 Wm⁻², associated with an AOD (440 nm) from 0.016 to 0.690. The three types of aerosols on clear-sky days are mixed aerosols (61%), desert dust (10%), and urban/industrial-biomass burning aerosols (29%). Combined with Gobbi’s method, this classification clustered the aerosols into four groups by AE analysis (two coarse- and two fine-mode aerosols). Then, the contribution of the aerosol types to the ARF showed that the desert dust forcing had the largest cooling effect in Barcelona (−61.5 to −37.4 Wm⁻²), followed by urban/industrial-biomass burning aerosols (−40.4 to −20.4 Wm⁻²) and mixed aerosols (−31.8 and −24.0 Wm⁻²). Regarding the comparison among Generalized Retrieval of Atmosphere and Surface Properties (GRASP) inversions, AERONET inversions, and direct method estimations, the AFE and ARF had some differences owing to their definitions in the algorithms. The DoLP, used as GRASP input, decreased the ARF overestimation for high AOD. Full article

Ground segmentation in LiDAR point clouds is a foundational capability for autonomous systems, enabling safe navigation in applications ranging from urban self-driving vehicles to planetary exploration rovers. Reliably distinguishing traversable surfaces in geometrically irregular or sensor-sparse environments remains a critical challenge. This paper introduces a hybrid framework that synergizes multi-resolution polar discretization with sparse convolutional neural networks (SCNNs) to address these challenges. The method hierarchically partitions point clouds into adaptive sectors, leveraging PCA-derived geometric features and dynamic variance thresholds for robust terrain modeling, while a SCNN resolves ambiguities in data-sparse regions. Evaluated in structured (SemanticKITTI) and unstructured (Rellis-3D) environments, two different versions of the proposed method are studied, including a purely geometric method and a hybrid approach that exploits deep learning techniques. A comparison of the proposed method with its purely geometric version is made for the purpose of highlighting the strengths of each approach. The hybrid approach achieves state-of-the-art performance, attaining an F1-score of 95.4% in urban environments, surpassing the purely geometric (91.4%) and learning-based baselines. Conversely, in unstructured terrains, the geometric variant demonstrates superior metric balance (80.8% F1) compared to the hybrid method (75.8% F1), highlighting context-dependent trade-offs between precision and recall. The framework’s generalization is further validated on custom datasets (UMH-Gardens, Coimbra-Liv), showcasing robustness to sensor variations and environmental complexity. The code and datasets are openly available to facilitate reproducibility. Full article

Radio-frequency (RF) microwave wireless power transfer (WPT) offers an efficient means of delivering energy to a wide array of devices over long distances. Previous RF WPT systems faced significant challenges, including complex hardware and control systems, software deficiencies, insufficient rectification power, lack of high-performance substrate materials, and electromagnetic radiation hazards. Addressing these issues, this paper proposes the world’s first watt-level RF WPT system capable of intelligent continuous tracking and occlusion judgment. Our 5.8 GHz band RF WPT system integrates several advanced technologies, such as millimeter-precision lidar, the multi-object image recognition algorithm, the accurate 6-bit continuous beamforming algorithm, a compact 16-channel 32 W high-power transmitting system, a pair of ultra-low axial ratio circularly polarized antenna arrays, ultra-low-loss high-strength ceramic substrates, and a 2.4 W high-power Schottky diode array rectifier achieving a rectification efficiency of $66.8\%$. Additionally, we construct a platform to demonstrate the application of the proposed RF WPT system in battery-free vehicles, achieving unprecedented ${360}^{\circ }$ uninterrupted power supply to the battery-free vehicle. In summary, this system represents the most functionally complete RF WPT system to date, serving as a milestone for several critical fields such as smart living, transportation electrification, and battery-less/free societies. Full article

Tropospheric aerosols play an important role in the notable warming phenomenon and climate change occurring in the Arctic. The accuracy of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) and the distribution of Arctic AOD based on the CALIOP Level 2 aerosol products and the Aerosol Robotic Network (AERONET) AOD data during 2006–2021 were analyzed. The distributions, trends, and three-dimensional (3D) structures of the frequency of occurrences (FoOs) of different aerosol subtypes during 2006–2021 are also discussed. We found that the CALIOP AOD exhibited a high level of agreement with AERONET AOD, with a correlation coefficient of approximately 0.67 and an RMSE of less than 0.1. However, CALIOP usually underestimated AOD over the Arctic, especially in wet conditions during the late spring and early summer. Moreover, the Arctic AOD was typically higher in winter than in autumn, summer, and spring. Specifically, polluted dust (PD), dust, and clean marine (CM) were the dominant aerosol types in spring, autumn, and winter, while in summer, ES (elevated smoke) from frequent wildfires reached the highest FoOs. There were increasing trends in the FoOs of CM and dust, with decreasing trends in the FoOs of PD, PC (polluted continental), and DM (dusty marine) due to Arctic amplification. In general, the vertical distribution patterns of different aerosol types showed little seasonal variation, but their horizontal distribution patterns at various altitudes varied by season. Furthermore, locally sourced aerosols such as dust in Greenland, PD in eastern Siberia, and ES in middle Siberia can spread to surrounding areas and accumulate further north, affecting a broader region in the Arctic. Full article

Since its launch in 2018, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has provided atmospheric products, including calibrated backscatter profiles and cloud and aerosol layer detection. While not the primary focus of the mission, these products garnered more interest after the end of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data collection in 2023. In comparing the cloud and aerosol detection frequencies from CALIOP and ICESat-2, we find general agreement in the global patterns. The global cloud detection frequencies were similar in June, July, and August of 2019 (64.7% for ICESat-2 and 59.8% for CALIOP), as were the location and altitude of the tropical maximum; however, low daytime signal-to-noise ratios (SNRs) reduced ICESat-2’s detection frequencies compared to those of CALIOP. The ICESat-2 global aerosol detection frequencies were likewise lower. ICESat-2 generally retrieved a higher average global aerosol optical depth compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) over the ocean, but the two were in closer agreement over regions with higher aerosol concentrations such as the Eastern Atlantic Ocean and the Northern Indian Ocean. The ICESat-2 and CALIOP orbital coincidences reveal highly correlated backscatter profiles as well as similar cloud and aerosol layer top altitudes. Future work with machine learning denoising techniques may allow for improved feature detection, especially during daytime. Full article

Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM_2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM_2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM_2.5 concentration. The RSM relevance prediction model (MET-PM_2.5) established a correlation equation between meteorological factors and PM_2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM_2.5 value remained below 75 μg/m³ when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities. Full article

Smoke particles from biomass burning events are typically assumed to be spherical despite previous observations of non-spherical smoke. As such, large uncertainties exist in some physical and optical parameters used in lidar aerosol retrievals, including depolarization and lidar ratio of non-spherical smoke aerosols. In this analysis, using NASA’s Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data during the biomass burning season over Africa from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles to compare with findings of smoke particle non-sphericity from the Cloud-Aerosol Transport System (CATS) lidar. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles, which might otherwise be misclassified as desert dust, polluted dust, or dusty marine by the CALIOP standard aerosol typing algorithm. Then, the relationships between smoke particle sphericity, lidar ratio, and relative humidity are analyzed for CATS and CALIOP observations over Africa. Approximately 18% of smoke layers observed by CALIOP over Africa are non-spherical (depolarization ratio > 0.075) and agree with spatial distributions of non-spherical smoke found in CATS observations. A dependance of lidar ratio on relative humidity was found for layers of spherical smoke over Africa in both CATS and CALIOP data; however, no such dependance was evident for the depolarization ratio and layer relative humidity. While the supplemental smoke aerosol typing algorithm presented in this analysis was targeted only for specific biomass burning regions during biomass burning seasons and is not meant for global operational use, it presents one potential method for improved backscatter lidar aerosol typing. These results suggest that a dynamic lidar ratio, based on layer-relative humidity for spherical smoke, could be used to reduce uncertainties in smoke aerosol extinction retrievals for future backscatter lidars. Full article

Frequency sweeping linearity is essential for Frequency-Modulated Continuous Wave (FMCW) Light Detection and Ranging (LIDAR), as it impacts the ranging resolution and accuracy of the system. Pre-distortion methods can correct for frequency sweeping nonlinearity; however, residual minor nonlinearities can still degrade the system ranging resolution, especially at far distances. Therefore, the precise measurement of minor nonlinearities is particularly essential for long-range FMCW LIDAR. This paper proposes a calibration-free MZI for measuring optical frequency sweeping nonlinearity, which involves alternately inserting two short polarization-maintaining fibers with different delays into one arm of an MZI, and after two rounds of beat collection, the optical frequency sweep curve of the light source is accurately measured for nonlinearity evaluation. Using the proposed method, the nonlinearity of a frequency-swept laser source is measured to be 0.2113%, and the relative nonlinearity is 5.3560 × 10⁻⁵. With the measured frequency sweep curve, we simulate the beat signal and compare it with the collected beat signal in time and frequency domain, to verify the accuracy of the proposed method. A test conducted at 24.1 °C, 30.4 °C, 39.5 °C and 44.0 °C demonstrate the method’s insensitivity to temperature fluctuations. Based on the proposed MZI, a tunable laser is pre-distorted and then used as light source of a FMCW lidar. A wall at 45 m and a building at 1.2 km are ranged by the lidar respectively. Before and after laser pre-distortion, the FWHM of echo beat spectrum are 25.635 kHz and 9.736 kHz for 45 m, 747.880 kHz and 22.012 kHz for 1.2 km. Full article