Performance of automated geoprocessing methods for culvert detection in remote Forest environments

Greater availability of digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) has made it possible to map precisely hydrographic features such as streams over large watersheds. Road embankments are precisely detected, given that DEMs are especially accurate over open areas, while culverts are not. Consequently, mapped stream positions are often erroneous along and through these anthropogenic structures. The position of actual culverts is often imprecise, incomplete or unavailable for large territories; thus, there is a need to develop and evaluate automated methods to locate culvert positions by remote sensing. Six geoprocessing methods were tested and compared to field-based culvert positioning data gathered in forested areas. These methods rely on preprocessing of depressions, manipulation of road embankment elevation, or both. When exact locations of culverts were unknown, the 'Breach Depressions' algorithm (WhiteBox GAT) was most accurate in reducing omission and commission errors. Depending upon the expected stream flow regime, it was possible to reduce cumulative error from 10% to 30% by using this method compared to less effective methods. When exact locations of culverts were known, it was possible to reduce cumulative error from 20% to 45% by burning them into the DEM. Comparisons of two different methods revealed that no automated geoprocessing allowed accurate detection of poorly located culverts, i.e. where small streams deviated into road-side ditches. Despite automated geoprocessing methods that are available, a database geolocating all culverts within a territory is the best way to create exact hydrographic networks without road embankment influence.