The first step in limiting uncertainty in this methodology is the use of trigonometry equations that help to define the size of beam footprints and swath size. For river reaches that have been previously surveyed a general idea of the topographic complexities contained within the reach are known and mapped. Using this map as a guideline trigonometry can be used to determine tracklines that will minimize the beam footprint over areas that have high topographic complexities. This will limit the amount of uncertainty as a reduced beam footprint over areas of topographic complexities will help to limit the range of elevations that are covered by the beam footprint.
The creation of DEM is straightforward process of converting to TINs and then interpolating a raster of 2 ft. resolution from these TINs. The file size of point clouds produced from MBES surveys (300 MB to 2 GB) can be cumbersome for most GIS applications to process. For this reason geoprocessing workflows using Python are under development which decrease processing times and give the user batch processing capabilities to quickly convert point clouds to DEM. All raster processing should adhere to the principles of orthogonallity and concurrency; custom geoprocessing tools follow these principles and GCD flags rasters not following these principles and can automatically correct these issues for users.
Surface roughness and slope have been identified as the spatial parameters most influencing survey uncertainty and representations of them are created in ToPCAT and GCD respectively. ToPCAT is a point cloud decimation algorithm that decimates point clouds to a user defined resolution and calculates a grid centered outputs of: minimum elevation, maximum elevation, mean elevation, elevation range, standard deviation of elevation, detreneded for local slope standard deviation, detrended mean elevation, and point count. For the purposes of representing surface roughness the locally detrended standard deviation output is used to create a raster surface. A representation of slope is produced in GCD which utilizes a maximum decent algorithm to calculate slope in degrees.
To relate surface roughness and slope to one another and their influence on survey and surface uncertainty a FIS has been constructed. The FIS calculations are performed in GCD to create an uncertainty value for each cell in the analysis extent. A DoD is then produced in GCD by differencing surveys from different years with the inclusion of an uncertainty raster for each survey. In addition to an uncertainty model a spatial cohesion filter can be utilized to update probabilities based on the values of neighboring cells in the DoD.
The remaining sections of this website clarify and expand upon these recommendations for the main steps of the workflow, field data acquisition, data processing, and post processing data analysis, and detail small steps not included in this basic description. For the purpose of brevity many supporting details are not included in this website but are contained in the supplementary resources and literature.
