Deployment-invariant probability of detection characterization for aerial LiDAR methane detection

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint


Michael J. Thorpe, Aaron Krietinger, Dominic Altamura, Cameron D. Dudiak, Bradley Mark Conrad , David R Tyner, Matthew R. Johnson, Jason K. Brasseur, Peter Roos, William Kunkel , Asa Carre-Burritt, Jerry Abate, Tyson Price, David Yaralian, Brandon Kennedy, Edward Newton, Erik Rodriguez, Omar Ibrahim Elfar, Daniel J. Zimmerle


Accurate detection sensitivity characterization of remote methane monitoring technologies is critical for designing, implementing, and auditing effective emissions monitoring and mitigation programs. Several research groups have developed test methods based on single/double-blind controlled release protocols and regression-based data analysis techniques to create probability of detection (PoD) models for characterizing remote sensor detection sensitivities. The previously created methods and models account for some of the important factors that affect detection sensitivity, such as wind speed, and in the case of Conrad et al. flight altitude. However, these models do not account for other important factors, such as terrain albedo, variation in individual sensor performance, or spatial density of the remote sensing measurements. In this paper, we build on the work of Conrad et al. by introducing a gas concentration noise (GCN) model for Gas Mapping LiDAR aerial methane detection technology that, when combined with wind speed at the emission location, accounts for all significant sensor and environmental parameters that affect detection sensitivity for scenarios involving an isolated emission source resulting in a single methane plume. We incorporate the GCN model into Conrad et al.’s PoD model and apply it to several sets of controlled release data acquired across widely varying deployment and environmental conditions to develop PoD models for Bridger Photonics Inc.’s first- and second-generation (GML 2.0) Gas Mapping LiDAR sensors. Finally, we compare controlled release data acquired by GML 2.0 in different geographic regions and terrain cover types, in different wind conditions, deployed on different aircraft types, and with different flight parameters. Results show that the GML 2.0 PoD model remains valid regardless of the location or conditions under which the sensors are deployed, and the aircraft and flight parameters used for deployment. Based on PoD measurements in 12 production basins across North America, the average 90% PoD emission rate for sites measured by GML 2.0 in 2023 was 1.27 kg/h.



Environmental Monitoring, Environmental Sciences


LiDAR, Remote Sensing, methane, emissions monitoring, detection sensitivity, probability of detection, gas mapping


Published: 2024-01-24 08:36

Last Updated: 2024-01-24 16:36


No Creative Commons license

Additional Metadata

Conflict of interest statement:
Bridger Photonics, Inc. profits from sales of Gas Mapping LiDAR methane emissions monitoring services.

Data Availability (Reason not available):
Data will be made available once paper has passed peer review