NOAA
UAS Program

Welcome
to the

Research Areas

Evaluate observing strategies

Address critical data gaps

Facilitate UAS application

Evaluate ship-launched UAS technology and infrastructure

Develop extended visual line of sight operations

Analyze the value of high-altitude observations

Develop UAS CONOPS for conducting pinniped surveys in remote regions

UAS Program Mission

To facilitate UAS applications and utilization

Accelerate transition of UAS capabilities from research to operations

Provide expertise and resources for UAS research and development

Internal NOAA FY2019

Request for UAS Proposals

Vision: To fully exploit UAS capabilities to meet NOAA’s mission requirements



News

Drone Training for NOAA Shipboard Operation

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Drone Training for NOAA Shipboard Operation

ARTICLE AND FIGURES PROVIDED BY: CAPT Brian Taggart, NOAA (ret) NOAA Affiliate - Earth Resources Technology NOS/NGS/OCS

The National Geodetic Survey Remote Sensing Division and Office of Coast survey, recently trained seven NOAA ship officers and Navigation Response Team members on drone operations at the NOAA Marine Operations Center in Newport, OR. The successful two-day training included classroom instruction and hands-on flights focused on vessel-based research and mapping missions.

Nighttime Fire Observations eXperiment (NightFOX) Update

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Nighttime Fire Observations eXperiment (NightFOX) Update

ARTICLE / FIGURES PROVIDED BY: DR. RU-SHAN GAO (EARTH SYSTEM RESEARCH LABORATORY / CHEMICAL SCIENCES DIVISION)

Biomass burning produces major impacts on local and regional air quality and 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.

On 31 July 2019, the NightFOX remote sensing payload onboard a Black Swift Technologies S2 UAS was used to monitor a prescribed burn in Boulder County, CO. The experiment was very successful, producing a fire map and demonstrating the capability and usefulness of the system (see associated figures and video). For the next step we plan to deploy the system to make measurements over real wildfires in the western US in August and September 2019.

This project is funded by the NOAA UAS Program Office, and includes a partnership between NOAA ESRL/CSD and the University of Colorado Boulder.

Chequamegon Heterogenous Ecosystem Energy-Balance Study (CHEESEHEAD'19)

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Chequamegon Heterogenous Ecosystem Energy-Balance Study (CHEESEHEAD'19)

ARTICLE / FIGURES PROVIDED BY: BRUCE BAKER AND TEMPLE LEE

The NOAA Air Resources Laboratory (ARL) Atmospheric Turbulence and Diffusion Division (ATDD), supported by the UAS Program Office, is participating in the Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD'19) campaign near Park Falls, Wisconsin (Figure 1). The aim of CHEESEHEAD'19 is to study interactions and feedbacks between the land surface and atmosphere and to improve how these interactions are represented in weather and climate models.

During three week-long campaigns in July, August, and September 2019, NOAA ATDD is operating two small Unmanned Aircraft Systems (sUAS). ATDD is using a DJI S-1000 (Figure 2) to obtain in-situ temperature and moisture measurements, along with land surface temperature measurements from a downward-pointing infrared camera, in the vicinity of 30-m (100-ft) towers installed in the CHEESEHEAD domain by partners from the National Center for Atmospheric Research (NCAR) (Figure 3). These towers are instrumented with a myriad of instruments to sample different meteorological variables (e.g., temperature, moisture, and wind), as well as exchanges of heat and moisture between the land surface and overlying atmosphere. These tower measurements, combined with the sUAS measurements, are then used to estimate the variability in heat exchange in the region surrounding the tower. Within a ~ 500 x 500 m area surrounding the tower, there is significant variability in temperature, with differences on the order of 10 °C over this area (Figure 3).

In addition to the DJI S-1000, ATDD is also operating a Meteomatics SSE (Figure 4) during CHEESEHEAD. This platform is used for obtaining vertical profiles of temperature, moisture, and wind. In the example from the morning of 12 July, ATDD performed 4 flights adjacent to one of the NCAR meteorological towers. These flights show the growth and evolution of the atmospheric boundary layer (i.e., the lowest part of the atmosphere directly affected by the surface), as well as increase in near-surface moisture (Figure 5). During the August and September CHEESEHEAD campaigns, ATDD will fly the Meteomatics adjacent to the NOAA Global Monitoring Division (GMD) 447-m Park Falls tall tower, which is outfitted with an array of meteorological measurements at multiple heights, to evaluate wind speeds and wind directions derived from the Meteomatics.

Fusion of LiDAR and Hyperspectral Imagery to Monitor Wetland Restoration Benefiting Salmon

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Fusion of LiDAR and Hyperspectral Imagery to Monitor Wetland Restoration Benefiting Salmon

ARTICLE / FIGURES PROVIDED BY: DR. G. CURTIS ROEGNER, NOAA FISHERIES

Effective restoration of wetlands from anthropogenic stress is a critical research priority worldwide, and in the Pacific Northwest of the US there is a heightened relevance for supporting recovery of listed and endangered salmon. Wetland vegetation communities are especially important for shelter and as a source of invertebrate prey preferred by juvenile salmon during migration to the ocean. While many new restoration projects have commenced in recent years, often lacking is the means for evaluation of the restoration effectiveness. This evaluation includes quantification of the trajectory of physical systems and vegetation communities from initial states towards those more beneficial to desired outcomes (e.g. fish survival). Typical wetland/estuarine vegetation and topographic surveys are expensive, time-consuming, and restricted in spatial and temporal cover, difficulties that until now have limited evaluation of restoration trajectories toward recovery.

This project is supported by funding from the UAS Program Office, and includes a partnership between NOAA Fisheries, Pacific Northwest National Laboratories, RykaUAS, and the National Park Service, has developed a UAS for remote sensing of vegetation types using a 110-band imaging spectrometer (BaySpec OCI) flown on a DJI Matrice 600 hexacopter. We established a library of ground-truthed “spectral signatures” from wetland plant species and analytical routines allowing for output of categorized maps and statistical metrics. The next phase of the project entails integrating a LiDAR (RIEGL miniVUX-1UAV) instrument for determining topography-vegetation species relationships and to track landform changes as restoration projects evolve over time. Fusing the vegetation and topographic data offers a means for the rapid and comprehensive assessment of habitat metrics with minimal additional ground truthing, and provides methods to evaluate the effectiveness of management actions.

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