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Topo-bathymetric digital elevation models of the upper Merced and Tuolumne Rivers in California derived from hyperspectral image data and near-infrared LiDAR acquired in 2014
This child data release includes fused topo-bathymetric digital elevation models of the Merced and Tuolumne Rivers in California used to support research on anadromous salmonids. The purpose of this study was to calculate the capacity for reintroduction of salmonids above impassable barriers. Airborne, near-infrared (NIR) LiDAR and hyperspectral imagery were acquired simultaneously in September 2014 from a Cessna Caravan, with the LiDAR data used to map topography of dry land and the imagery used to map water depth in the wetted channel. Topo-bathymetric DEMs of channels and floodplains with 1-m resolution were constructed for the study reaches by using remotely sensed hyperspectral image data to estimate water depths within the below-water portion of the channel and using remotely-sensed LiDAR for the above-water portion of the channel. Water depths were subtracted from water-surface elevations measured by the LiDAR to obtain bed elevations within the wetted channel. The digital elevation model above the water surface was created using the LiDAR data. We used a Leica Airborne Laser Scanner ALS50, with mean point densities >12 points/m2 and reported horizontal and vertical accuracies of 2 cm and 7 cm, respectively. The raw LiDAR point cloud was processed into bare-earth DEMs with 1 m grid cells. The digital elevation model for areas below the water surface was created using the hyperspectral imagery. Hyperspectral imagery was collected using a Compact Airborne Spectographic Imager (CASI) 1500 (ITRES 2014), producing imagery with 48 spectral bands (wavelengths 380 to 1050 nm). Raw image flight strips were geometrically and radiometrically corrected with ITRES software, then atmospherically corrected using ATCOR4 (ReSe 2014). The final images were in units of reflectance for each band, with a spatial resolution of 0.5 m. Water depths were estimated from the imagery using the Optimal Band Ratio Analysis (OBRA) depth retrieval algorithm, a calibration technique that relates field-measured water depths (d) to an image-derived quantity defined as the natural logarithm of the ratio of two spectral bands (Legleiter et al. 2009).
Author(s) | Carl J. Legleiter, Lee R. Harrison, Ryan R. Richardson, Colin Nicol |
Publication Date | 2021-09-23 |
Beginning Date of Data | 2014-09-01 |
Ending Date of Data | 2014-09-30 |
Data Contact | |
DOI | https://doi.org/10.5066/10.5066/P9MUPT5X |
Citation | Legleiter, C.J., Harrison, L.R., Richardson, R.R., and Nicol, C., 2021, Topo-bathymetric digital elevation models of the upper Merced and Tuolumne Rivers in California derived from hyperspectral image data and near-infrared LiDAR acquired in 2014: U.S. Geological Survey data release, https://doi.org/10.5066/10.5066/P9MUPT5X. |
Metadata Contact | |
Metadata Date | 2021-09-23 |
Related Publication | There was no related primary publication associated with this data release. |
Citations of these data | No citations of these data are known at this time. |
Access | public |
License | http://www.usa.gov/publicdomain/label/1.0/ |
Harvest Date: 2024-07-30T04:03:30.725Z