Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform

Atmospheric CO2 plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO2 vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO2 detection due to its characteristics of high precision, high time resolution, and high spatial resolution. Ground-based CO2-DIAL can provide the continuous observations of the vertical profile of CO2 concentration, which can be highly significant to gaining deeper insights into the rectification effect of CO2, the ratio of respiration photosynthesis, and the CO2 dome in urban areas. A set of ground-based CO2-DIAL systems were developed by our team and highly accurate long-term laboratory experiments were conducted. Nonetheless, the performance suffered from low signal-to-noise ratio (SNR) in field explorations because of decreasing aerosol concentrations with increasing altitude and surrounding interference according to the results of our experiments in Wuhan and Huainan. The concentration of atmospheric CO2 is derived from the difference of signals between on-line and off-line wavelengths; thus, low SNR will cause the superimposition of the final inversion error. In such a situation, an efficient and accurate denoising algorithm is critical for a ground-based CO2-DIAL system, particularly in field experiments. In this study, a method based on lifting wavelet transform (LWT) for CO2-DIAL signal denoising was proposed. This method, which is an improvement of the traditional wavelet transform, can select different predictive and update functions according to the characteristics of lidar signals, thereby making it suitable for the signal denoising of CO2-DIAL. Experiment analyses were conducted to evaluate the denoising effect of LWT. For comparison, ensemble empirical mode decomposition denoising was also performed on the same lidar signal. In addition, this study calculated the coefficient of variation (CV) at the same altitude among multiple original signals within 10 min and then performed the same calculation on the denoised signal. Finally, high-quality signal of ground-based CO2-DIAL was obtained using the LWT denoising method. The differential absorption optical depths of the denoised signals obtained via LWT were calculated, and the profile distribution information of CO2 concentration was acquired during field detection by using our developed CO2-DIAL systems.

[1]  Savita Gupta,et al.  Image Denoising Using Wavelet Thresholding , 2002, ICVGIP.

[2]  Luca Fiorani,et al.  Volcanic CO2 detection with a DFM/OPA-based lidar. , 2015, Optics letters.

[3]  Zhishen Liu,et al.  Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method , 2006 .

[4]  Chikao Nagasawa,et al.  Development of 1.6  μm DIAL using an OPG/OPA transmitter for measuring atmospheric CO2 concentration profiles. , 2017, Applied optics.

[5]  Jeffrey Y. Beyon,et al.  Coherent differential absorption lidar measurements of CO2. , 2004, Applied optics.

[6]  Pieter P. Tans,et al.  Vertical profiles of CO2 above eastern Amazonia suggest a net carbon flux to the atmosphere and balanced biosphere between 2000 and 2009 , 2010 .

[7]  Dengxin Hua,et al.  Improvement of the signal to noise ratio of Lidar echo signal based on wavelet de-noising technique , 2013 .

[8]  Patrick Oonincx,et al.  Second generation wavelets and applications , 2005 .

[9]  Mike Burton,et al.  A new frontier in CO2 flux measurements using a highly portable DIAL laser system , 2016, Scientific Reports.

[10]  De-Shuang Huang,et al.  Antinoise approximation of the lidar signal with wavelet neural networks. , 2005, Applied optics.

[11]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .

[12]  I. Fung,et al.  Observational Contrains on the Global Atmospheric Co2 Budget , 1990, Science.

[13]  F. Gibert,et al.  2-μm Ho emitter-based coherent DIAL for CO(2) profiling in the atmosphere. , 2015, Optics letters.

[14]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[15]  Xin Ma,et al.  On-line wavelength calibration of pulsed laser for CO2 DIAL sensing , 2014 .

[16]  Gwénolé Quellec,et al.  Adaptive Nonseparable Wavelet Transform via Lifting and its Application to Content-Based Image Retrieval , 2010, IEEE Transactions on Image Processing.

[17]  Xin Ma,et al.  Feasibility Study of Multi-Wavelength Differential Absorption LIDAR for CO2 Monitoring , 2016 .

[18]  Wei Guo,et al.  Noise Smoothing for Nonlinear Time Series Using Wavelet Soft Threshold , 2007, IEEE Signal Processing Letters.

[19]  Xin Ma,et al.  A nonlinear merging method of analog and photon signals for CO2 detection in lower altitudes using differential absorption lidar , 2017 .

[20]  Syed Ismail,et al.  Development of a Ground-based 2-Micron Differential Absorption Lidar System to Profile Tropospheric CO 2 , 2006 .

[21]  Xin Ma,et al.  Method for wavelength stabilization of pulsed difference frequency laser at 1572 nm for CO(2) detection lidar. , 2015, Optics express.

[22]  Philippe Ciais,et al.  Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO2 , 2007, Science.

[23]  Xin Ma,et al.  Sensitivity of on-line wavelength during retrieval of atmospheric CO 2 vertical profile , 2015 .

[24]  ATMOSPHERIC LIDAR NOISE REDUCTION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION , 2012 .

[25]  J. Pereira,et al.  Climate Change 2014: Impacts, Adaptation and Vulnerability: Part B: Regional Aspects: Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2015 .

[26]  I. Johnstone,et al.  Threshold selection for wavelet shrinkage of noisy data , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Wei Gong,et al.  Anti-noise algorithm of lidar data retrieval by combining the ensemble Kalman filter and the Fernald method. , 2013, Optics express.

[28]  L. V. G ATTI,et al.  Vertical profiles of CO2 above eastern Amazonia suggest a net carbon flux to the atmosphere and balanced biosphere between 2000 and 2009 , 2010 .

[29]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[30]  Richard G. Baraniuk,et al.  Improved wavelet denoising via empirical Wiener filtering , 1997, Optics & Photonics.

[31]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[32]  P. T. Woods,et al.  Measurements of toluene and other aromatic hydrocarbons by differential-absorption LIDAR in the near-ultraviolet , 1992 .

[33]  R. Wells,et al.  Smoothness Estimates for Soft-Threshold Denoising via Translation-Invariant Wavelet Transforms , 2002 .

[34]  Ergun Erçelebi,et al.  Electrocardiogram signals de-noising using lifting-based discrete wavelet transform , 2004, Comput. Biol. Medicine.

[35]  Pradeep Kumar,et al.  Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets , 2013, Signal Image Video Process..