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Ken Vierra

High Accuracy Trace Gas Measurements from a Lightweight UAV

Recently, numerous studies have contested the global impact of fugitive emissions from oil and gas operations on the methane budget.  Consequently, precise quantification of gas leaks is needed to better constrain the total methane burden from this source.  In the past, high precision insitu analyzers mounted in a research aircraft have been used to circle the suspected leak and quantify the plume.  While this is a robust method, it is costly, and missions cannot be deployed quickly.  Quick deployment is needed because leaks can be transient, variable, and a huge safety hazard.  A lightweight UAV is uniquely suited to both fly through a plume generated by a leak, and deploy quickly at little cost. However, the UAV platform is too small to carry the 70-pound analysis system used on a plane.

NOAA GMD has developed a unique sampling system, called the Active AirCore, in which a pump compresses air into a 100-meter long, small diameter tube at a constant flow rate. This long tube acts as an “atmospheric tape recorder” by storing the sample stream in the long tube. When the sample is analyzed, the data is a “play back” of the air the UAV flew through while in flight.  The sampler can be removed from the UAV, and immediately analyzed using an ultra-high precision trace gas analyzer like those used on aircraft.  This new sampling system provides the same level of accuracy as an analyzer mounted on a larger aircraft, but is light enough to be flown on a UAV.  Because the measurements are done in a van at the flight location, data analysis and quantification of the leak is near-real time.  This near-real-time analysis allows highly flammable natural gas leaks to be quickly identified and quantified providing information that will help evaluate the safety and potential economic losses at an individual oil and gas production site.

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Development of an Autonomous Payload for Detection of Seals and Polar Bears on Sea Ice

Polar bears and Ice-associated seals (bearded, ringed, spotted, and ribbon seals) are key components of Arctic marine ecosystems and are important resources for coastal Alaska Native communities. Reliable abundance estimates for ice seals are needed for developing sound management decisions under the Marine Mammal Protection Act and extinction risk assessments under the Endangered Species Act. The animals’ broad and patchy geographic distributions and rapidly changing sea ice habitat make these species particularly challenging to study.

An autonomous payload is required to integrate UAS into surveys of ice-associated mammals, in order to improve the efficiency and human safety in gathering essential data for NOAA stewardship. Moving from occupied aircraft to long-range UAS operations will require an efficient and “smart” payload to collect images needed for abundance estimation and habitat analysis while providing situational awareness to the pilot in command.

The Alaska Fisheries Science Center’s Marine Mammal Lab is developing a system that can run advanced machine learning algorithms on-board the aircraft to process multispectral imagery in real time, minimizing the collection of extraneous imagery that requires burdensome data storage, management, and processing.  Algorithm development is utilizing a neural network approach known as YOLO, which processes imagery at a rate of 60-100 frames per second.  Over 1.8 million color and thermal images are being used to train YOLO to detect animals on the sea ice and classify detections to species. This algorithm will be tested in-flight during April 2019.

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Nighttime Fire Observations eXperiment (NightFOX)

Biomass burning produces major impacts on local and regional air quality which potentially plays an interactive role in climate change. A capable small, fixed-wing unmanned aircraft system (sUAS) can serve as an ideal platform for measurements of biomass burning emissions, plume distribution, fire extent and perimeter, and supporting meteorological data, especially at night when manned aircraft typically do not operate. The NOAA UASPO-funded Nighttime Fire Observations eXperiment (NightFOX) project aims to develop and deploy a sUAS observation system utilizing two modular and easily exchangeable payloads. One payload will provide in situ measurements of CO2, CO and fine- and coarse-mode aerosol size distributions in biomass burning plumes for characterization of fire combustion efficiency and emissions. A filter sampler will collect bulk aerosol samples for off-line composition analysis. The second payload will be flown over the fire to make remote sensing measurements of fire perimeter and fire radiative power using visible and short-, mid-, and long-wavelength IR observations. The multi-spectral remote sensing data will be used to provide sub-pixel information for comparison with satellite fire observations, and along with measured meteorological parameters, will be used to inform, test, and improve the WRF-SFIRE fire-atmosphere model.

Prototype in-situ and remote-sensing instrument payloads have been developed and are operational. Initial test flights with the payloads have recently been conducted. The performance of the prototype payloads has proven satisfactory and new versions are currently under development that will be used for the NIghtFOX operational deployment to study western wildfires next summer during the NOAA/NASA FIREX-AQ mission. Preliminary data processing algorithms for the remote sensing observations have been developed based on test flight results. A nighttime high-altitude FAA COA was obtained and a nighttime flight to an altitude of 2000 ft (0.61 km) was conducted on November 08, 2018, as a stepping stone to the 1 km design altitude for remote sensing operations.

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NOAA Evaluates Using Drones for Lidar and Imagery in the National Estuarine Research

Office for Coastal Management (OCM) scientists and their partners tested the utility of private sector drone technology to map marsh habitat in three estuarine research reserves. The team evaluated the quantitative spatial accuracy of both imagery and lidar products, as well as the qualitative gains for habitat mapping in multiple ecosystems.

Obtaining good solid earth elevation data is particularly difficult in dense marsh areas where it is also a critical component to understanding marsh vulnerability to sea level rise. The potential of lidar drone technology to penetrate to the ground with a smaller laser footprint and higher point density could provide a product that is currently unattainable from manned aircraft. Similarly, the detail in imagery that drone technology offers has the potential to provide finer delineations of habitat than the reserves have had from manned imagery. Contract spatial accuracy specifications were set at 10 cm root mean square error (RMSE) vertically for the lidar data and 15 cm RMSE horizontally for the imagery.

During the mission, Quantum Spatial and PrecisionHawk operated the drones, collected the data, and processed it. Staff from OCM and the three reserves (Jacques Cousteau, Grand Bay, and San Francisco Bay) collected independent ground-truth validation data and evaluated the drone deliverables. The two square mile area in San Francisco Bay reserve generated over 380,000 images and had lidar point density of over 400 points per square meter.

The two-year project (Fiscal Years 2016-2018) was funded by NOAA’s Office of Oceanic and Atmospheric Research. The project team includes partners from OCM, Jacque Cousteau NERR, Grand Bay NERR, San Francisco Bay NERR, Wells NERR, Quantum Spatial, and PrecisionHawk.

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Advanced UAS Sensor Development for Marine Mammal Monitoring

In 1963, NOAA Fisheries’ Marine Mammal Laboratory (MML) began to use the mark-recapture method of shear-sampling northern fur seal pups to estimate pup abundance. Presently, these surveys are conducted every two years on St. Paul and St. George Island (Pribilof Islands, Alaska). These trips require up to 22 people to be stationed on the islands for up to three weeks and the presence of scientists on the rookery creates disturbance (authorized by a Federal permits: NMFS/MMPA 14327 and IACUC ANW2013-3). With the help of the UAS Program Office, MML has been collaborating with NOAA’s Aircraft Operations Center (AOC), National Environmental Satellite Data and Information Service (NESDIS), Mystic Aquarium, Aerial Imaging Solutions, and GeoThinkTank (Figure 1) to work on developing a UAS-based approach for conducting northern fur seal abundance surveys.

MML has successfully implemented unoccupied aircraft systems (UAS; i.e., drones) to supplement annual Steller sea lion abundance surveys since 2014. Given the size and relatively more distinct coloration from their background, using a high-resolution mirrorless camera has worked well for capturing images of Steller sea lions (Figure 2). The challenge with developing a similar approach for northern fur seals has been deciphering small black fur seal pups from the black boulder substrate common in the Pribilof Islands—northern fur seals are much harder to count in images!

We have a few objectives for our project to get us closer to our goal: (1) assess a heavy-lift hexacopter with longer flight times and ability to carry heavier payloads, (2) evaluate imaging capabilities of a thermal sensor for northern fur seals, and (3) conduct an on-the-ground assessment of the feasibility of multi-spectral imaging for distinguishing northern fur seals from their background.

In August of 2018 during the shear-sampling surveys on St. George Island, we were able to test the APH-28 hexacopter  (Figure 3) (Aerial Imaging Solutions) mounted with the FLIR DUO Pro R thermal sensor and conduct aerial surveys of a small rookery (Figure 4). We completed redundant surveys of this rookery with this thermal sensor and also with a high-resolution mirrorless digital camera. We will soon count northern fur seals from these two sets of imagery and be able to compare the counts to our traditional ground-survey estimates.

During this same trip, we worked with GeoThinkTank to collect spectral measurements using a handheld spectroradiometer (loaned by NESDIS) of northern fur seals (pups, adult females, and a deceased adult male) and the substrate (rocks, grass, driftwood, etc.) (Figure 5). Collecting measurements like these is a normal procedure for plants and other substrate (e.g., for calibrating satellite imagery), but as far as we know, has never been done for wildlife.

Collecting these spectral measurements in the field in Alaska was made easier by our preliminary trip to Mystic Aquarium in May of 2018. Mystic Aquarium allowed us the opportunity to collect more measurements of northern fur seals (from animals far more cooperative than those we encounter in the wild) and in a more controlled environment to help us streamline our methods for the harsher field conditions in Alaska (Figure 6). These spectral measurements will be used to model a virtual northern fur seal rookery environment to run various aerial survey simulations. This will allow scientists to test various bands beyond the typical four bands customary to off-the-shelf multi-spectral UAS sensors. If optimal bands are identified and multi-spectral imaging is found to be effective, this will guide our next steps towards developing a custom UAS-mounted sensor.

Assessing optimal imaging capabilities will guide sensor selection and further development of an

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