Bridger Photonics (United States)

Snow albedo modulates net solar radiation, influencing snowmelt timing and magnitude, which is critical to watershed ecosystems and downstream communities. Snow grain size, a primary control on albedo, can be derived from near infrared lidar reflectance, a potentially valuable supplement to altimetry data. However, converting raw lidar intensity to reflectance is complex, with several existing methods but no comprehensive accuracy comparison to date. To address this gap, we leverage data from the Airborne Coastal Observatory (ACO), a paired lidar and imaging spectrometer platform, collected on May 15, 2021, over the snow-covered Place Glacier in the Coast Mountains of British Columbia, Canada. Three methods used to derive reflectance from the Riegl 1064 nm lidar were assessed, including: (1) an independent range and incidence angle correction (LR IC ); (2) the vendor product that accounts for range (LR riegl ); and (3) the vendor product with an additional incidence angle correction (LR rieglθ ). Radiative transfer modeling and lidar reflectance were used to derive grain size and the associated theoretical clean snow albedo. Reflectance and property retrievals were assessed against those derived from the imaging spectrometer (Specim Fenix). Reflectance magnitude of LR IC and LR rieglθ were similar in reflectance magnitude to the spectrometer but displayed greater variability. Grain sizes were underestimated (∼136 μm) producing a median relative error in albedo of 2%. LR riegl was positively biased, with greater underestimation of grain size (455 μm) and higher albedo error (6%). Results highlight the necessity of incorporating incidence angle corrections when using lidar for surface reflectance retrievals in mountainous terrain and further demonstrate the potential of lidar intensity for high-resolution snow grain size estimation. • We compare snow surface properties from lidar intensity to imaging spectroscopy. • Spatial comparison over a mountain glacier included variable snow conditions. • Range and incidence angle corrections produced comparable reflectance. • Manufacturer-provided reflectance (range corrected) compared poorly. • Relatively small errors in reflectance produced larger errors in grain size.

The near-infrared (NIR) albedo of snow is controlled by optical snow grain size (ropt). Therefore, characterizing the spatial and temporal variability in ropt at the snow surface is critical for understanding melt timing and magnitude for water availability, and Earth's energy budget towards future climates. While numerous studies have demonstrated estimates of ropt via optical instruments that span scales from in situ to satellites, they leverage differing retrieval techniques, radiative transfer models, and modeled snow grain shapes. Variation in these factors cause tremendous uncertainty in ropt retrievals, yet a thorough evaluation has yet to be conducted. To address this knowledge gap we conducted a laboratory bidirectional reflectance study, using NIR hyperspectral imaging (NIR-HSI) to retrieve grain size metrics for a wide variety of snow microstructures and evaluate them against micro-CT benchmarks. Towards enhanced ropt retrieval accuracy, we sought to determine 1) the optimal modeled snow grain shape, 2) the best-performing radiative transfer model, and 3) variability associated with retrieval techniques, spanning broadband, narrowband, multispectral, and hyperspectral approaches. Our results for optimizing grain shape parameters align with existing studies for the TARTES model, and we offer first recommendations for the SNICAR model. The retrieval technique also displayed considerable variation with the hyperspectral residual method performing best. Multispectral and single-band techniques were comparable to their hyperspectral counterparts at times, but this was attributed to the idealized laboratory conditions and high instrument signalto-noise ratio. Following shape-optimization, the SNICAR and TARTES models produced the best results (median absolute error of 15.6 -17.4%, depending on technique), outperforming the AART model and the Random Mixture model. Towards a more direct comparison with albedo estimate error, we also evaluated the square root of ropt retrievals; median absolute error values ranged from 7.9 -26.2% depending on model and technique, with most pairings resulting in values < 15%. Our results demonstrate that the accuracy of ropt retrievals is highly sensitive to the choice of retrieval technique, radiative transfer model, and grain shape parameters. To minimize error, each of these factors should be carefully selected in the context of the specific measurement. As NIR-HSI instruments and other NIR detectors become increasingly affordable and their resolution improve, the findings presented here provide guidance for improved ropt and snow albedo mapping across ground-based, aerial, and satellite platforms.

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.

Aerial LiDAR measurements at 7474 oil and gas production facilities in the Permian Basin yield a measured methane emission rate distribution extending to the detection sensitivity of the method, 2 kg/h at 90% probability of detection. Emissions are found at 38.3% of facilities scanned, a significantly higher proportion than reported in lower-sensitivity campaigns. LiDAR measurements are analyzed in combination with measurements of the heavy tail portion of the distribution (&gt;600 kg/h) obtained from an airborne solar infrared imaging spectrometry campaign by Carbon Mapper (CM). A joint distribution is found by fitting the aligned LiDAR and CM data. By comparing the aerial samples to the joint distribution, the practical detection sensitivity of the CM 2019 campaign is found to be 280 kg/h [256, 309] (95% confidence) at 50% probability of detection for facility-sized emission sources. With respect to the joint model distribution and its confidence interval, the LiDAR campaign is found to have measured 103.6% [93.5%, 114.2%] of the total emission rate predicted by the model for equipment-sized emission sources (~2 m diameter) with emission rates above 3 kg/h, whereas the CM 2019 campaign is found to have measured 39.7% [34.6%, 45.1%] of the same quantity for facility-sized sources (150 m diameter) above 10 kg/h. The analysis is repeated with data from CM 2020-21 campaigns with similar results. The combined distributions represent a more comprehensive view of the emission rate distribution in the survey area, revealing the significance of previously underreported emission sources at rates below the detection sensitivity of some emissions monitoring campaigns.

Active stabilization of tunable lasers enables coherent FMCW ladar with sub-mm range resolution, which opens up new application areas in ladar imaging and metrology. Demonstrations, applications, and benefits of the technology are reviewed.

Bridger Photonics has been researching and developing a ladar system based on heterodyne detection for imaging through brownout and other DVEs. There are several advantages that an FMCW ladar system provides compared to direct detect pulsed time-of-flight systems including: 1) Higher average powers, 2) Single photon sensitive while remaining tolerant to strong return signals, 3) Doppler sensitivity for clutter removal, and 4) More flexible system for sensing during various stages of flight. In this paper, we provide a review of our sensor, discuss lessons learned during various DVE tests, and show our latest 3D imagery.

We present a miniature non-mechanical zoom camera using deformable MOEMS mirrors. Bridger Photonics, Inc. (Bridger) in collaboration with Montana State University (MSU), has developed electrostatically actuated deformable MEMS mirrors for use in compact focus control and zoom imaging systems. Applications including microscopy, endomicroscopy, robotic surgery and cell-phone cameras. In comparison to conventional systems, our MEMS-based designs require no mechanically moving parts. Both circular and elliptical membranes are now being manufactured at the wafer level and possess excellent optical surface quality (membrane flatness &lt; &lambda;/4). The mirror diameters range from 1 - 4 mm. For membranes with a 25 &#956;m air gap, the membrane stroke is 10 &#956;m. In terms of the optical design, the mirrors are considered variable power optical elements. A device with 2 mm diameter and 10 &#956;m stroke can vary its optical power over 40 diopters or 0.04mm^&and;(-1). Equivalently, this corresponds to a focal length ranging from infinity to 25 mm. We have designed and demonstrated a zoom system using two MOEMS elements and exclusively commercial off-the-shelf optical components to achieve an optical zoom of 1.9x with a 15&deg; full field of view. The total optical track length of the system is 36 mm. The design is approximately 30 mm x 30 mm x 20 mm including the optomechanical housing and image sensor. With custom optics, we anticipate achieving form factors that are compatible with incorporation into cell phones.

We report a precision LADAR system that can achieve 35 µm FWHM range-peak-width resolution and 86 nm range precisions for absolute distance and length metrology and precision 3D LADAR imaging applications.