Lidar Detection Research Articles

Impact of lidar data assimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan

Accurate simulations of planetary boundary layer (PBL) processes in numerical weather models are vital for air quality predictions. Advanced remote sensing techniques, such as lidar detection and ranging (lidar), can provide aerosol information with high temporal and vertical resolutions in the PBL. In this study, a lidar data assimilation system was developed based on the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter (WRF-LETKF) framework coupled with the Community Multiscale Air Quality (CMAQ) model. The objective was to investigate the impact of lidar data assimilation on PBL prediction and the subsequent influence on PM2.5 prediction for a high-air-pollution event.The fine particulate matter (PM2.5) profiles retrieved from two micropulse lidar observations in northern and central Taiwan were assimilated in the WRF-LETKF system. Three numerical experiments, BASE (with a nudging strategy), CTRL (with an ensemble framework), and LDA (with assimilation of lidar-retrieved PM2.5 profiles), were conducted for a high-air-pollution episode. The BASE simulation overestimates the wind speed, which also leads to PM2.5 underestimation. The CTRL and LDA simulations are able to improve the wind fields and enhance the PM2.5 accumulation. With a strong error correlation between the lidar-retrieved PM2.5 concentration and the wind fields, the LDA simulation effectively corrects the wind flow from the surface to the PBL top, which further adjusts the PM2.5 transport processes and leads to results that agree well with observations.

CALIOP-Based Quantification of Central Asian Dust Transport

Central Asia is one of the most important sources of mineral saline dust worldwide. A comprehensive understanding of Central Asian dust transport is essential for evaluating its impacts on human health, ecological safety, weather and climate. This study first puts forward an observation-based climatology of Central Asian dust transport flux by using the 3-D dust detection of Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP). The seasonal difference of transport flux and downstream contribution are evaluated and compared with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Central Asian dust can be transported not only southward in summer under the effect of the South Asian summer monsoon, but also eastward in other seasons under the control of the westerly jet. Additionally, the transport of Central Asian dust across the Pamir Plateau to the Tibetan Plateau is also non-negligible, especially during spring (with a transport flux rate of 150 kg m−1 day−1). The annual CALIOP-based downstream contribution of Central Asian dust to South Asian (164.01 Tg) is 2.1 times that to East Asia (78.36 Tg). This can be attributed to the blocking effect of the higher terrain between Central and East Asia. Additionally, the downstream contributions to South and East Asia from MERRA-2 are only 0.36 and 0.84 times that of CALIOP, respectively. This difference implies the overestimation of the wet and dry depositions of the model, especially in the low latitude zone. The quantification of the Central Asian dust transport allows a better understanding of the Central Asian dust cycle, and supports the calibration/validation of aerosol-related modules of regional and global climate models.

Flash resonance Raman lidar for SO2 gas leak detection

We present a demonstration of flash resonance Raman lidar for leak detection of SO2 gas. An optical parametric oscillator (OPO) with a deep UV tuning range around 217.0 nm corresponding to an electronic absorption band of SO2 gas has been utilized to monitor the 3D leakage of SO2 gas occurring 2 m away and to explore the wavelength dependence of the resonance Raman effect for SO2 gas. We believe this new efficient gas sensing technique provides an attractive alternative to conventional 3D lidar sensing based on the mechanical scan.

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection.

Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m2 and maximum detectable depth of 1.7–1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.

Dark/bright band of a melting layer detected by coherent Doppler lidar and micro rain radar

Observation of a melting layer using a 1.55 µm coherent Doppler lidar (CDL) is first presented during a stratiform precipitation event. Simultaneous radar measurements are also performed by co-located 1.24 cm micro rain radar (MRR) and 10.6 cm Doppler weather radar (DWR). As a well-known bright band in radar reflectivity appears during precipitation, an interesting dark band about 160 m below that in lidar backscattering is observed. Due to the absorption effect, the backscattering from raindrops at 1.55 µm is found much weaker than that at short wavelengths usually used in direct detection lidars. However, the CDL provides additional Doppler information which is helpful for melting layer identification. For example, a spectrum bright band with broadened width and sign conversion of skewness is detected in this case. After a deep analysis of the power spectra, the aerosol and precipitation components are separated. The fall speed of hydrometeors given by CDL is found smaller than that of MRR, with the differences of approximately 0.5 m/s and 1.5 m/s for the snow and rainfall, respectively. To illustrate the influence of absorption effect, simulations of the backscatter coefficient and extinction coefficient of aerosol and rainfall are also performed at the wavelength range of 0.3 ∼ 2.2 µm using the Mie theory.

Propagation characteristics of partially coherent twisted Laguerre-Gaussian beam in atmospheric turbulence with anisotropy

Analytical formulas for average intensity of partially coherent twisted Laguerre-Gaussian beam (PCTLGB) propagating through anisotropic atmospheric turbulence are derived based on the extended Huygens–Fresnel principle. Our results indicate that PCTLGB gradually evolves from dark hollow distribution to flat-topped distribution and finally degenerates to Gaussian distribution both in free space and atmospheric turbulence. We also find that the effect of atmospheric turbulence on propagation characteristics for PCTLGB can be effectively reduced by adjusting twisted factor, topological charge and beam order. An important result is that PCTLGB has stronger anti-turbulence ability when vortex phase and twisted phase have the same chirality, and it can resist atmospheric turbulence more effectively than the beam without twisted phase under the same conditions. Our results will be useful in free space optical communication, lidar detection and imaging.

MAGE: Multisource Attention Network With Discriminative Graph and Informative Entities for Classification of Hyperspectral and LiDAR Data

Land use and land cover (LULC) classification plays a significant role in Earth observation tasks. Nowadays, we can observe the same scene with multiple heterogeneous sensors. Combining diverse information therein for multisource joint classification has become a promising research topic in the remote sensing community. For example, the fusion of hyperspectral image (HSI) and lidar detection and ranging (LiDAR) data has been under active research. The current methodology for HSI and LiDAR joint classification tends to ignore the topological relationship between pixels, limiting the effectiveness of feature extraction and fusion. Another obstacle to satisfactory performance is the scarcity of annotated data. To overcome the above challenges, this article proposes a multisource attention network called MAGE to improve the collective classification. We use a semi-supervised graph transductive module to underline the relevance among pixels by explicitly constructing a multimodal adjacency matrix. Specifically, MAGE designs a self-supervised feature extraction module for pre-training, mitigating the dependence on annotated samples and alleviating the common overfitting and over-smoothing problems encountered by the deep graph neural network (GNN). The experimental results of three standard datasets, i.e., MUUFL, Trento, and Houston, demonstrate the effectiveness of the proposed approach. In particular, MAGE achieves an overall accuracy of 95.26% and an average accuracy of 96.27% on the challenging MUUFL dataset, surpassing the state-of-the-art methods. The code and models are publicly available at https://github.com/d1x1u/MAGE.

Polarized Light Scattering of Particulate Matter in Detection of Oceanographic Profiling LiDAR

Polarized Light Scattering of Particulate Matter in Detection of Oceanographic Profiling LiDAR

Full-Waveform Classification and Segmentation-Based Signal Detection of Single-Wavelength Bathymetric LiDAR

Single-wavelength bathymetric LiDAR (532 nm) can provide seamless meter- and submeter-scale DEMs of both the terrestrial surface and seafloor. However, the mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and non-terrestrial waveforms from the original waveforms without auxiliary information, and flexible detection of peaks based on a new FW theoretical model. A novel FW signal-detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FW are divided into 5 categories for subsequent signal detection by utilizing a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition and a flexible signal detection framework. In order to search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than a generalized Gaussian model optimized using the Levenberg–Marquardt algorithm (LM-GGM) and RLD algorithm.

Single - Photon Lidar for Canopy Detection with a Multi-Channel Si Spad at 1064 Nm

Single - Photon Lidar for Canopy Detection with a Multi-Channel Si Spad at 1064 Nm

Obstacle Detection of LiDAR Based on Optimized DBSCAN

Obstacle Detection of LiDAR Based on Optimized DBSCAN

Spaceborne Environmental Detection Lidar and Its Key Techniques

Spaceborne Environmental Detection Lidar and Its Key Techniques

Model‐guided extremum seeking–case studies

SummaryIn practice, data‐driven control and optimization techniques are applied to address problems in engineering systems of which the model is either unavailable or so complicated that a model‐based analytic design can be hardly carried. Among them, the extremum seeking (ES) is a popular model‐free or data‐driven optimization method that has been effectively applied to provide optimal solutions to various industrial control systems. In this article, a new design philosophy, called the model‐guided ES, which is a special case of model‐guided data‐driven (MGDD) optimization, is presented and demonstrated with two successful case studies. In particular, it is shown that, in these two cases, how models of physical systems, even if imperfect or developed in a data‐driven way instead of the first‐principle based approach, could be integrated together with the conventional ES algorithm to deliver much improved and guaranteed convergence performance and the ultimate bound. It is noted that the first case is for the automotive diesel engine optimization and the second case for the automated regulation of LiDAR detection range. Both cases are successfully validated with experiments.

Theoretical and experimental investigations on speckle suppression of dual-wavelength imaging LiDAR by vibrating the multimode fiber

The multimode fiber (MMF) can be adopted to the fiber-coupling laser transmitting optics system of the array-imaging LiDAR for far-range detection due to the relatively high demand for laser energy which can melt the single-mode fiber. However, the modal noise within the multimode fiber (MMF) can result in severe speckle at the output face which seriously affects the staring imaging LiDAR detection performance. Based on the proposed dual-wavelength LiDAR prototype in which the ICCD detects the 532 nm signal and the array Gm-APD detects the 1064 nm signal, the micro vibrator which is beneficial to the system miniaturization is used for suppressing the speckle. For this application, it’s necessary to investigate the speckle suppression effect to evaluate whether the MMF could be suitable for the LiDAR system or not. To our best knowledge, the multimode speckle suppression for dual-wavelength LiDAR hasn’t been discussed ever, moreover, the Gm-APD speckle suppression research hasn’t been carried out, yet. In this paper, firstly, the principle of the speckle suppression by vibrating the MMF was investigated and the speckle suppression detection models of both detectors were first proposed and discussed. Then, the theoretical analysis was carried out, and it showed that the vibration amplitude of 9 mm and the vibration frequency of higher than 215 Hz could help to get a good suppression results. Finally, the vibrator’s parameters were chosen based on the theoretical results, and the experiments were carried out to investigate the effects of the vibrator’s voltage which affects the vibration frequency and the exposure time on the image contrast of the intensity and the contrast-to-noise ratio (CNR) for ICCD, besides, the Gm-APD’s contrast of triggering probability was also investigated. Experimental results indicated that the vibrator voltage for getting a good speckle suppression was at least 1.5 V, at which the ICCD’s CNR could be enhanced to 1.2 times within 40 ms exposure time and the contrast of intensity of ICCD could be lowered to 0.65 times; besides, the contrast of triggering probability of Gm-APD could be lowered to 0.58 times. Moreover, compared with the speckle suppression results within 5 ms, 40 ms exposure time could help to enhance the CNRs of ICCD to about 3.12 times, lower the contrast of intensity of ICCD and the contrast of the triggering probability of Gm-APD to 0.85 times and 0.84 times, respectively. This paper demonstrates the feasibility of using MMF coupling laser transmitting scheme in the dual-wavelength staring imaging LiDAR, and it provides the experimental and theoretical basis for the system miniaturization and flexible application of dual-wavelength LiDAR.

Potential of spaceborne Brillouin scattering lidar for global ocean optical profiling

The potential of spaceborne Brillouin scattering lidar for generating global ocean optical profiles was studied herein. We analyzed the global distributions of the maximum detectable depths and corresponding optimum wavelengths for spaceborne Brillouin scattering lidar during the day and night, simulated the global vertical profile distributions of the seawater sound speed and Brillouin scattering frequency shift, and discussed the effects of the system parameters and water environment parameters in Case II water on the lidar detection performance and proportion of Brillouin scattering lidar penetrating the upper mixed layer on the global scale. The laser emission wavelength of 490 nm is suitable for detecting open ocean waters, and 540 nm is suitable for detecting coastal waters. The detection depth of the Brillouin scattering lidar operating at night is approximately 10 m greater than that during the day. The vertical profile distributions of the seawater sound and the Brillouin scattering frequency shift decrease as the depth increases from 0 to 200 m in the mid-low latitude regions. The proportions of spaceborne Brillouin scattering lidar penetrating the upper mixed layer in January-February-March, April-May-June, July-August-September, and October-November-December are 75.15%, 76.80%, 59.12%, and 73.10%, respectively. The results indicate that spaceborne Brillouin scattering lidar has great potential for the wide-range and long-term monitoring of upper-ocean water bodies, which would be a good complement to passive satellite ocean color remote sensing technology and the traditional measurement methods of Argo floats, gliders, XBT, and AUV.

Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing

Abstract. Multi-year ground-based remote-sensing datasets were acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites. A highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern midlatitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets in key regions of aerosol–cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary layer coupling, and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds, our study contributes the following new aspects: (1) sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled conditions is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above -15∘C than indicated by prior lidar-only studies at all sites. A virtual lidar detection threshold of ice water content (IWC) needs to be considered in order to bring radar–lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than decoupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below -15∘C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity wave influences, Punta Arenas shows lower fractions of ice-containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and -8∘C. These differences are potentially caused by the contrast in the ice-nucleating particle (INP) reservoir between the different sites.

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

Stabilizing Electro-Optic Modulation Depth With Carrier to Sideband Ratio by Intermediate Optical Beatings

Electro-optic modulation with a constant modulation depth plays important roles in quantum metrology, microwave photonics, and optical precision measurement and sensing, etc. To compensate the continuous drift of the half-wave voltage and maintain the modulation depth, we incidentally generated a reference laser to make intermedia optical beatings with the carrier and +1st sideband, and tightly locked the carrier to sideband ratio by extracting the relative microwave power of those intermedia beat signals using a home-made electronic control module. Experiments of the short-term test for locking bandwidth, the anti-jamming capability, and the long-term stability were demonstrated. As is verified that, the locking bandwidth was estimated up to tens of kHz, and the anti-jamming capability was confirmed despite of a strong hair-dryer agitation. The long-term stability for the modulation depth stabilization showed that the peak-to-peak relative stability for 150-minute was 5.67 × 10−6, and the Allan deviation reached a minimum of 2.19 × 10−7 at an averaging time of 400 s, which is improved by more than four orders of magnitude compared with the open-loop regime. We believe the reported method as well as the designed module is versatile and potential to precision measurement and sensing applications badly requiring a stable modulation depth, such as atom interferometry, optical interferometry and lidar detection.

An Entropy-Based Anti-Noise Method for Reducing Ranging Error in Photon Counting Lidar.

Photon counting lidar for long-range detection faces the problem of declining ranging performance caused by background noise. Current anti-noise methods are not robust enough in the case of weak signal and strong background noise, resulting in poor ranging error. In this work, based on the characteristics of the uncertainty of echo signal and noise in photon counting lidar, an entropy-based anti-noise method is proposed to reduce the ranging error under high background noise. Firstly, the photon counting entropy, which is considered as the feature to distinguish signal from noise, is defined to quantify the uncertainty of fluctuation among photon events responding to the Geiger mode avalanche photodiode. Then, the photon counting entropy is combined with a windowing operation to enhance the difference between signal and noise, so as to mitigate the effect of background noise and estimate the time of flight of the laser pulses. Simulation and experimental analysis show that the proposed method improves the anti-noise performance well, and experimental results demonstrate that the proposed method effectively mitigates the effect of background noise to reduce ranging error despite high background noise.

Evolution properties of partially coherent radially polarized Laguerre–Gaussian vortex beams in an anisotropic turbulent atmosphere

Analytical formulas for the cross-spectral density matrix of a partially coherent radially polarized Laguerre-Gaussian vortex (PCRPLGV) beam in anisotropic atmospheric turbulence are derived based on the extended Huygens-Fresnel principle. The evolution laws of statistical properties of a PCRPLGV beam in turbulence, such as the average intensity, degree of coherence (DOC) and degree of polarization (DOP), are investigated in detail. It is found that the atmospheric turbulence induces degeneration of the intensity distribution of a PCRPLGV beam on propagation, and some new properties, such as self-shaping and self-rotating, will appear on propagation due to vortex phase. In addition, in order to verify our theoretical results, we combined the complex screen method and multi-phase screens method to simulate the propagation of PCRPLGV beam in atmospheric turbulence. And the studies indicate that the simulation results are consistent with the theoretical predictions. Our results will be useful in some potential applications, such lidar detection, remote sensing and free-space optical communications.