This portion of the data release presents a bathymetric point cloud from an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The point cloud has been corrected for the effects of refraction using the techniques described in Dietrich (2017a). The point cloud 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 gain an accurate measurement of the 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 refraction-corrected point cloud showed a substantial improvement in accuracy over 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 point cloud was 0.241 meters with a mean residual of -0.010 meters, whereas the vertical RMSE for the uncorrected point cloud was 0.426 meters with a mean residual of -0.334 meters (see the Vertical Positional Accuracy Report in the metadata for a complete description of the accuracy analysis). For this data release, the final refraction-corrected point cloud is presented in the LAZ format (LAS 1.2 specification). The point cloud has 35,083,205 points with an average point spacing of 0.07 meters. Each point in the point cloud contains an explicit horizontal and vertical coordinate and red, green, and blue (RGB) color values. 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.