2019 NOAA OCM Unmanned Aerial System Lidar DEM: Great Bay NERR

In April 2019, Quantum Spatial (QSI) was contracted by The National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management (OCM) in partnership with the Great Bay National Estuarine Research Reserve (GBNERR) to collect high resolution Light Detection and Ranging (LiDAR) data in the spring of 2019 for four research sites within the GBNERR in New Hampshire. These four sites are “Sentinel Sites” within the GBNERR where vegetation and elevation parameters have previously been continuously monitored and surveyed using traditional methods that require extensive person hours and manual labor.

The bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and anthropogenic features removed. It is derived from NIR LiDAR data using TIN processing of the ground point returns. Some elevation values have been interpolated across areas in the ground model where there is no elevation data (e.g. over water, under dense vegetation). Air Shark collected the NOAA Great Bay UAS LiDAR data, on behalf of Quantum Spatial, for the NOAA Office for Coastal Management between 05/01/2019 and 05/09/2019.

In addition to these bare earth Digital Elevation Model (DEM) data, the lidar point data that these DEM data were created from, are also available. A link to the lidar point data metadata record is provided in the Related Items section of this metadata record.

Data were collected to aid NOAA & GBNERR in assessing the viability of using non-invasive remote sensing survey techniques to monitor these sentinel sites and reduce the impacts of traditional survey techniques on the marsh environment.

Any conclusions drawn from the analysis of this information are not the responsibility of Quantum Spatial, Great Bay NERR, NOAA, the Office for Coastal Management or its partners.

Data is available online for bulk and custom downloads.

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Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations.

Non-vegetated Vertical Accuracy was assessed using 20 ground check points. These check points were not used in the calibration or post processing of the LiDAR point cloud data.

The Non-vegetated Vertical Accuracy (NVA) of this dataset, tested at 95% confidence level is 0.082 m. Please see the LiDAR data report for a discussion of the statistics related to this dataset.

Flight plans are designed with sufficient sidelap to ensure there are no gaps between flightlines. Shaded relief images have been visually inspected for gaps.

LiDAR data has been collected and processed for all areas within the project study area.

Data was examined at a 1:2000 scale. Relative accuracy of the flightlines was assessed in Microstation using TerraMatch.

In some areas of heavy vegetation or forest cover, there may be relatively few ground points in the LiDAR data. TINing the points produces large triangles and hence the elevations may be less accurate within such areas. Elevation values for open water surfaces should be considered approximate due to the increase of laser noise seen over water, frequent lack of returns, and the overall dynamic nature of water.

Shaded relief images have been visually inspected for data errors such as pits, border artifacts, and shifting. LiDAR flightlines have been examined to ensure consistent elevation values across overlapping flightlines. The Root Mean Square Error (RMSE) of line to line relative accuracy for this dataset is 0.025 m. Please see the LiDAR data report for a discussion of the statistics related to this dataset.

Data is backed up to tape and to cloud storage.

Air Shark collected Light Detection and Ranging (LIDAR) data on behalf of Quantum Spatial in Great Bay NERR for the NOAA Office for Coastal Management (OCM).

NOAA OCM received the data and ingested it into the Digital Coast Data Access Viewer for distribution.