License Information

Use of this function requires a license for Whitebox Workflows for Python Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Description

This tool can smooth the roughness due to residual vegetation cover in LiDAR digital elevation models (DEMs). Sometimes when LiDAR data are collected under heavy forest cover, particularly conifer species, the DEM will contain substantial roughness, even if it is interpolated using last-return points only. This tool can be used to reduce the roughness of the ground surface under these conditions. It works by identifying grid cells that possess deviation in mean elevation (DEV, DevFromMeanElev) values that are higher than a specified threshold value (dev_threshold) for tested scales less than a specified threshold (scale_threshold). DEV is measured for the input DEM (input) using filter radii from 1 to a user-specified maximum (max_scale). The identified grid cells are then masked out and their elevations are re-interpolated using the surrounding, non-masked values.

This method can work well under some conditions, and will further benefit from multiple passes of the tool, i.e. run the tool using one set of parameters and then use the output (output) as the input for the second pass. Alternative approaches include use of the remove_off_terrain_objects tool, using low-pass filters such as the feature_preserving_smoothing tool, or, if the point-cloud source data are available, classifying the ground points using lidar_ground_point_filter and excluding non-ground points from the interpolation.

The following image shows an image of a DEM that is badly impacted by heavy forest cover, with obvious vegetation residual roughness.

This image shows the impact of two-passes of the smooth_vegetation_residual tool.

See Also

remove_off_terrain_objects, feature_preserving_smoothing, lidar_ground_point_filter, DevFromMeanElev

Project Links

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