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High-resolution image sequence from the Salcha River, Alaska, August 31, 2018, for Particle Image Velocimetry
This data release consists of a sequence of high spatial resolution optical images used to derive remotely sensed estimates of surface flow velocity via particle image velocimetry (PIV). These data were acquired from the Salcha River in Alaska on August 31, 2018, along with field measurements of flow velocity used to assess the accuracy of image-derived velocity estimates. The images were obtained using a Hasselblad A6D-100C 100 megapixel digital mapping camera deployed within a pod mounted on the landing gear of a Robinson R44 helicopter. Image sequences were acquired at a frame rate of 1 per second (1 Hz) while the helicopter hovered in a fixed location approximately 200 m above the river. Also within the pod was an ATLANS GPS/Inertial Motion Unit (IMU) that recorded the position and orientation of the platform during the flight. This information was used to geo-reference the images by performing aerial triangulation and bundle adjustment within the SimActive Correlator3D software package. The bundle adjustment phase of this process also incorporated surveyed ground control targets that were placed in the field and were visible in the images. The resulting orthorectified images had a spatial resolution (pixel size) of 0.018 m and effectively stabilized the image sequence prior to PIV analysis. The images were converted to grayscale and saved as TIF files with corresponding world files (*.tfw) that contain the spatial referencing information for each image; the projected coordinate system is UTM Zone 6N, NAD83. The sequence consists of 68 individual images representing over one minute of data collection. The entire sequnce of TIF images and worldfiles is contained within a zip archive.
Complete Metadata
| @id | http://datainventory.doi.gov/id/dataset/9dd4d4505a48d808487674810ac6003e |
|---|---|
| bureauCode |
[ "010:12" ] |
| identifier | USGS:5e39bd6ce4b0a79317e15d63 |
| spatial | -147.07397460356,64.437262439701,-146.76086425201,64.524823167324 |
| theme |
[ "geospatial" ] |