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Deployment-invariant probability of detection characterization for aerial LiDAR methane detection
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2024-09-30 , DOI: 10.1016/j.rse.2024.114435 Michael J. Thorpe, Aaron Kreitinger, Dominic T. Altamura, Cameron D. Dudiak, Bradley M. Conrad, David R. Tyner, Matthew R. Johnson, Jason K. Brasseur, Peter A. Roos, William M. Kunkel, Asa Carre-Burritt, Jerry Abate, Tyson Price, David Yaralian, Brandon Kennedy, Edward Newton, Erik Rodriguez, Omar Ibrahim Elfar, Daniel J. Zimmerle
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2024-09-30 , DOI: 10.1016/j.rse.2024.114435 Michael J. Thorpe, Aaron Kreitinger, Dominic T. Altamura, Cameron D. Dudiak, Bradley M. Conrad, David R. Tyner, Matthew R. Johnson, Jason K. Brasseur, Peter A. Roos, William M. 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 1) light levels received by the remote sensor due to variations in terrain albedo or other factors, 2) spatial density of remote sensing measurements, or 3) variation in individual sensor performance. 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 - a source that does not spatially overlap with a methane plume originating from another source location. 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 1.0 and GML 2.0, respectively) 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.
更新日期：2024-10-03
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