USDA Aerial LiDAR of 16 Dams

GRW performed LiDAR mapping, field surveys and produced DTM elevation data of sixteen dams (including LiDAR at nine individual dam sites on lakes and farmland in Kentucky located between Jackson County and Carlisle County) as part of a USDA Natural Resources Conservation Service contract.

Data was collected at an altitude of 1,000 meters above ground and a pulse rate of 70,000 points per second. Each site was flight planned to acquire a final point spacing of 0.78 meters. Field survey efforts were planned for two different purposes. The first layer of field surveys was designed to provide calibration check points, used in the initial processing of data. The second layer of field surveys was intended to satisfy QC efforts within differing types of vegetation, as specified by FEMA.

Following the acquisition of LiDAR and field surveys data, LiDAR data was calibrated to remove systematic errors (such as roll, pitch, and heading) and to remove any bias when compared to surveyed calibration check points within the project. Once an acceptable calibration was reached, each site was manually edited to complete the process of generating a bare earth solution. Part of this process included the collection of hydrographic breaklines for major features (bodies of water and streams over 12’ in width) to be included in the generation of the final Digital Terrain Model (DTM) of each site.

Field validation points were withheld from processing of the project data until a final DTM was generated. Once the DTM was in hand, GRW utilized field validation points to provide a QC of the accuracy of the LiDAR data in various vegetation types, such as grass, bare ground, brush, and forest. For each of the nine survey sites, GRW reported the accuracy of the LiDAR within each of the classes separately and as an overall accuracy.

Final deliverables for this project included DTM in GeoDatabase format, ESRI TIN, and ESRI grid (1m spacing), as well as a comprehensive report detailing both the process and the accuracy of the each dataset.