UAVC: A New Method for Correcting Lidar Overlap Factors Based on Unmanned Aerial Vehicle Vertical Detection

A method to calibrate the overlap factor of Lidar is proposed, named unmanned aerial vehicle correction (UAVC), which uses unmanned aerial vehicles (UAVs) to detect the vertical distribution of particle concentrations. The conversion relationship between the particulate matter concentration and the aerosol extinction coefficient is inverted by the high-altitude coincidence of the vertical detection profiles of the UAV and Lidar. Using this conversion relationship, the Lidar signal without the influence of the overlap factor can be inverted. Then, the overlap factor profile is obtained by comparing the signal with the original Lidar signal. A 355 nm Raman-Mie Lidar and UAV were used to measure overlap factors under different weather conditions. After comparison with the Raman method, it is found that the overlap factors calculated by the two methods are in good agreement. The changing trend of the extinction coefficient at each height is relatively consistent, after comparing the inversion result of the corrected Lidar signal with the ground data. The results show that after the continuously measured Lidar signal is corrected by the overlap factor measured by this method, low-altitude aerosol information can be effectively obtained.

A Novel Lidar Signal Denoising Method Based on Convolutional Autoencoding Deep Learning Neural Network

The lidar is susceptible to the dark current of the detector and the background light during the measuring process, which results in a significant amount of noise in the lidar return signal. To reduce noise, a novel denoising method based on the convolutional autoencoding deep-learning neural network is proposed. After the convolutional neural network was constructed to learn the deep features of lidar signal, the signal details were reconstructed by decoding part to obtain the denoised signal. To verify the feasibility of the proposed method, both the simulated signals and the actually measured signals by Mie-scattering lidar were denoised. Some comparisons with the wavelet threshold denoising method and the variational modal decomposition denoising method were performed. The results show the denoising effect of the proposed method was significantly better than the other two methods. The proposed method can eliminate complex noise in the lidar signal while retaining the complete details of the signal.

Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis

The parametric decomposition of full-waveform Lidar data is challenging when faced with heavy noise scenarios. In this paper, we report a fractional Fourier transform (FRFT)-based approach for accurate parametric decomposition of pulsed Lidar signals with noise corruption. In comparison with other joint time-frequency analysis (JTFA) techniques, FRFT is found to present a one-dimensional Lidar signal by a particular two-dimensional spectrum, which can exhibit the mathematical distribution of the multiple components in Lidar signals even with a heavy noise interference. A FRFT spectrum-processing solution with histogram clustering and moving LSM fitting is designed to extract the amplitude, time offset, and pulse width contained in the mathematical distribution. Extensive experimental results demonstrate that the proposed FRFT spectrum analysis method can remarkably outperform the conventional Levenberg–Marquardt-based method. In particular, it can accurately decompose the amplitudes, time offsets, and pulse widths of the pulsed Lidar signal with a −10-dB signal-to-noise-ratio by mean deviation ratios of 4.885%, 0.531%, and 7.802%, respectively.

Chemical detection of certain greenhouse gases by the LIDAR Technique

The aim of this study is to detect the chemical elements of the greenhouse effect from the LIDAR signal. Using a digital program developed by Fortran language, and based on spectral data. In the present work, The LIDAR sample is clearly contains water vapor and carbon dioxide. According to our results, the content of the sample with methane and the non-detection of nitrogen oxide, due to the absence of its absorption signal in the spectral range of the experimental signal. Carbon dioxide is one of the most dangerous greenhouse gases, our results show that 1 mole of this gas requires 1.45 moles of water vapor.

Estimating the optical extinction of liquid water clouds in the cloud base region

Accurate lidar-based measurements of cloud optical extinction, even though perhaps limited to the cloud base region, are useful. Arguably, more advanced lidar techniques (e.g. Raman) should be applied for this purpose. However, simpler polarisation and backscatter lidars offer a number of practical advantages (e.g. better resolution and more continuous and numerous time series). In this paper, we present a backscatter lidar signal inversion method for the retrieval of the cloud optical extinction in the cloud base region. Though a numerically stable method for inverting lidar signals using a far-end boundary value solution has been demonstrated earlier and may be considered as being well established (i.e. the Klett inversion), the application to high-extinction clouds remains problematic. This is due to the inhomogeneous nature of real clouds, the finite range resolution of many practical lidar systems, and multiple scattering effects. We use an inversion scheme, where a backscatter lidar signal is inverted based on the estimated value of cloud extinction at the far end of the cloud, and apply a correction for multiple scattering within the cloud and a range resolution correction. By applying our technique to the inversion of synthetic lidar data, we show that, for a retrieval of up to 90 m from the cloud base, it is possible to obtain the cloud optical extinction within the cloud with an error better than 5 %. In relative terms, the accuracy of the method is smaller at the cloud base but improves with the range within the cloud until 45 m and deteriorates slightly until reaching 90 m from the cloud base.