News

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

Article Provided By: Erin Moreland (NMFS/AFSC/NMML)

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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.

Nighttime Fire Observations eXperiment (NightFOX)

ARTICLE/FIGURE PROVIDED BY: RUSHAN GAO AND TROY THORNBERRY (OAR/ESRL/CSD)

Kenneth Vierra 0 95 Article rating: 4.0

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.

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

Article/Figures Provided By: Kirk Waters (OCM/NOS)

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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.

Advanced UAS Sensor Development for Marine Mammal Monitoring

Article/Figures Provided By: Katie Sweeney (NMFS/AKFSC/NMML)

Kenneth Vierra 0 290 Article rating: 5.0

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

NOAA Evaluates Using Drones to Map Coastline and Nearshore Waters

Article/Figures Provided By: Tim Battista (NCCOS/NOS)

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National Centers for Coastal Ocean Science (NCCOS) scientists and their partners tested the utility of drone technology to map the coastline and nearshore waters of St. Croix in the U.S. Virgin Islands. The team evaluated the quality of land elevation and water depth data acquired by three different drones under a variety of conditions.

Several agencies, including NOAA, need land elevation and water depth data to inform management decisions about the coastal zone. However, many nearshore areas are difficult to access, or are remotely located, making them challenging and expensive to map with existing technologies. Photographs acquired by drones offer a potentially inexpensive and accurate method to fill this data gap at spatial resolutions that far exceed existing technologies. Though, more research is needed to identify optimal drone payloads and parameters, as well as post-processing workflows, before drone technology can be implemented more widely across NOAA.

During the mission, the team mapped six geographic areas in the Buck Island Reef National Monument and the East End Marine Park, collecting over 48,000 digital aerial photos using the DJI S900, 3DR Solo, and DJI Mavic drones. The researchers also collected independent reference data sets to verify the position of the photos and validate the elevations and depths derived from the drone software. The team plans to use data from the mission to develop standard operating procedures for this type of work across NOAA.

The two-year project (Fiscal Years 2017–2019) is funded by NOAA’s Office of Oceanic and Atmospheric Research. The project team includes partners from NCCOS, Oregon State University, Wayne Wright Consulting, NOAA’s Office of Coast Survey, the National Park Service, and the Virgin Islands Department of Planning and Natural Resources.

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