| Description |
This portion of the data release presents a bathymetric digital surface model (DSM) from an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The DSM has a horizontal resolution of 10 centimeters per pixel and has been corrected for the effects of refraction using the techniques described in Dietrich (2017a). The DSM was created from structure-from-motion (SfM) processing of aerial imagery collected using a UAS with a Ricoh GR II digital camera fitted with a circular polarizing filter. During the survey, a pressure sensor was deployed in the survey area to derive an accurate measurement of the mean water surface elevation (WSE). After a preliminary dense point cloud was derived from SfM processing, the WSE was used to calculate apparent water depths. These apparent depths along with the estimated camera positions and orientations were used as inputs for the multi-view refraction correction python script (py_sfm_depth.py) described in Dietrich (2017b). The resulting point cloud was then rasterized to create the DSM. The refraction-corrected DSM showed a substantial improvement in accuracy over a DSM created using the uncorrected point cloud. When compared to the 2013 U.S. Army Corps of Engineers Topobathy Lidar for the area in the central portion of the data set the vertical RMSE for the refraction-corrected DSM was 0.233 meters with a mean residual of -0.013 meters, whereas the vertical RMSE for the uncorrected DSM was 0.428 meters with a mean residual of -0.340 meters (see the Vertical Positional Accuracy Report in the metadata for a complete description of the accuracy analysis). The refraction-corrected bathymetric DSM is presented in a cloud-optimized GeoTIFF format with internal overviews and masks to facilitate cloud-based queries and display. References Cited: Deitrich, J.R., 2017a, Bathymetric Structure-from-Motion: extracting shallow stream bathymetry from multi-view stereo photogrammetry: Earth Surface Processes and Landforms, https://doi.org/10.1002/esp.4060. Deitrich, J.R., 2017b, py_sfm_depth: Github online repository, https://github.com/geojames/py_sfm_depth. [More]
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