EROS in Action - CoNED with Dean Gesch and Jeff Danielson

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Detailed Description

USGS/EROS Physical Scientist Dean Gesch and USGS/EROS CoNED Applications Project Chief Jeff Danielson discuss the Coastal National Elevation Database (CoNED), its acquisition and its uses.
 

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Image Dimensions: 1920 x 1080

Date Taken:

Length: 00:04:15

Location Taken: Sioux Falls, SD, US

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Steven Young - Writer/Producer

Transcript

CoNED stands for Coastal National Elevation Database and that is a geospacial product based on elevation data and as its name implies it's really focused on the land water interface so right where the water meets the land along the ocean coastlines we take the near shore water depth information or what we call the bathymetry and we take the land side elevation data or what we call the topography and merge those into a seamless elevation model across the land/water interface that is the product that we call CoNED. That's the base elevation information we call a TBDEM and that stands for Topographic Bathymetric Digital Elevation Model. - We use a lot of airborne LIDAR which stands for Light Detection and Ranging and most of that data is acquired by USGS through partners. It's airborne based. Roughly Along the coast we have many years of repeat coverage of LIDAR to help us make these integrated models. On the processing side we go through a process of taking the point clouds, creating very detailed land water mass from those point clouds, gridding the data using different types of interpolation, making sure all this data is properly converted in the same vertical horizontal reference frame and then merging it together with the bathemetry data in the same fashion. The bathymetry data is all from like SONAR, mostly acoustic SONAR, track lines, hydrographic surveys, but we also have recently explored using bathymetry from satellite data so satellite-derived bathymetry from Landsat 8 and also WorldView-3 where we can correlate the radiance values from a satellite with actual water depth information - One of the main uses for it is for sea level rise modeling and sea level rise assessment. Obviously in coastal areas low relief coastal areas the population and resources that reside and exist in those areas are vulnerable to sea level rise. You need to know the height of the land. When the water rises where is that water going to go? It's going to go in certain places, it's not going to go in certain places, so these coastal elevation models are very useful for that. Other impacts of raised water levels doesn't have to be just from sea level rise that can be from storms storm surges or more episodic events versus kind of long-term chronic conditions that I would call sea level rise so for instance hurricanes, a hurricane storm surge, the modeling that's done of how a particular hurricane or the storm surge associated with a particular hurricane is going to propagate onto the land surface needs to know very specifically what the bathymetry or the water depth is near the shore because that has a big effect on how that storm surge wave if you will propagates onto the land surface - Down in Louisiana CoNED has become the primary elevation data set for their statewide master planning. That program is all about the restoration the wetlands and also looking at trying to mitigate hazards such as storm surge and flooding and they've chosen CoNED as the base DEM for their planning which drives all their modeling such as geomorphic and wetland restoration modeling. Our goal is to be able to take those high-resolution wetland extent boundaries and try and correlate that with LCMAP to look at the Landsat time series over wetlands and see how that compares to the lighter mapping so that that's one thing that CoNED is doing. CoNED is doing all kinds of structure for motion research using both drones and satellite data so we're gonna start using WorldView-3 data to look at mapping topography from those satellite systems directly. The CoNED project also does a lot with bluff mapping so we've actually devised some methodologies to map bluffs and cliffs using LIDAR data and also point cloud data.