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Remote Sensing
Volume 17
Issue 22
10.3390/rs17223670
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification

by
Shouzheng Zhu
1,
Ceyuan Wang
1,2,
Yangyang Zhang
1,2,
Wenhang Yang
1,2,
Xu Liu
1,2,
Liu Yang
1,2,
Senyuan Wang
1,
Tongxu Zhang
1,
Xin He
1,*,
Chenhui Hu
1,
Siliang Li
1,
Zhao Cui
1,
Yuwei Chen
1,
Chunlai Li
1 and
Jianyu Wang
1,3
1
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(22), 3670; https://doi.org/10.3390/rs17223670
Submission received: 1 August 2025 / Revised: 31 October 2025 / Accepted: 6 November 2025 / Published: 7 November 2025
Versions Notes

Abstract

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages.
Keywords: methane source traceability; leakage quantification algorithm; unmanned aerial vehicle; multiple error sources; remote sensing and sensors methane source traceability; leakage quantification algorithm; unmanned aerial vehicle; multiple error sources; remote sensing and sensors

Share and Cite

MDPI and ACS Style

Zhu, S.; Wang, C.; Zhang, Y.; Yang, W.; Liu, X.; Yang, L.; Wang, S.; Zhang, T.; He, X.; Hu, C.; et al. Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification. Remote Sens. 2025, 17, 3670. https://doi.org/10.3390/rs17223670

AMA Style

Zhu S, Wang C, Zhang Y, Yang W, Liu X, Yang L, Wang S, Zhang T, He X, Hu C, et al. Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification. Remote Sensing. 2025; 17(22):3670. https://doi.org/10.3390/rs17223670

Chicago/Turabian Style

Zhu, Shouzheng, Ceyuan Wang, Yangyang Zhang, Wenhang Yang, Xu Liu, Liu Yang, Senyuan Wang, Tongxu Zhang, Xin He, Chenhui Hu, and et al. 2025. "Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification" Remote Sensing 17, no. 22: 3670. https://doi.org/10.3390/rs17223670

APA Style

Zhu, S., Wang, C., Zhang, Y., Yang, W., Liu, X., Yang, L., Wang, S., Zhang, T., He, X., Hu, C., Li, S., Cui, Z., Chen, Y., Li, C., & Wang, J. (2025). Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification. Remote Sensing, 17(22), 3670. https://doi.org/10.3390/rs17223670

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