What Should a Bare Earth Digital Terrain Model (DTM) Portray?

Professor Peter L. Guth Department of Oceanography United States Naval Academy Annapolis MD, USA [email protected] DEM—General Term • Unfortunately no agreement on definitions • This paper not the place to debate terminology • Whatever term you want to use, this issue exists • DTM=bare earth

4 DEMs from a lidar point cloud DTM/DSM Uses

• Landscape visualization • Hydrology • Geomorphometric statistics • Others…….

Denmark Does one size fit all? National Mapping Program Lidar

• Coverage varies from complete nation to priority regions • All allow free download of point clouds (LAS/LAZ) and grids (Geotiff) • List probably incomplete, also OpenTopography, NOAA Digital Coast Assumptions

• Interested in geomorphometry from DTMs • Modern lidar easily supports 1 m grids—”noisy”? • Geomorphometry might only need 2-5 m grids • No interpolation between points, but single value to represent many ground points (faster, simpler) • While we want grids, point clouds might supplement • Can we afford to just do geomorphometry in pristine landscapes? Water issues

• Inconsistent LAS classification • Many voids in water • Often only close to nadir

• Should we • Interpolate across water? • Artificially flatten waves? • Leave voids along? • Add bathymetry (ideal, not yet practical)? • Create mask to skip points for statistics? Building Issues

• Leave in (NVS instead of DTM; much easier to create) • Interpolate across? • Leave as voids? • Artificial, unnatural flat surfaces • Create mask to skip for statistics Partial building remnants

Building scars Road Issues

• Currently not handled, except for bridges and overpasses (which leave scars) • Flat road surface • Scarps on edges • Do we want to? • Leave in • Filter out • Mask as void DTM Creation from Point Cloud

• OpenTopography has options for its data, but not for the much more numerous data from national mapping agencies. These options work with any point clouds USGS DTM (they call it DEM) • Hydroflattened • Clear standards • QA/QC in production • Nice, consistent product • Contractor methods proprietary

• But do we want their decisions on roads, buildings, water? • Do those decisions affect statistics and “truth”? • Use ground classified points • Issues with water, buildings, left as voids here • USGS 1 m DTM for comparison Compare Surface Generation From Ground Classified Points

• TIN removes buildings, water • Others leave building, water voids DTM with different ground point classifications

• No attempt to fill in water or buildings • Compare to USGS DTM— small differences on steep slopes 1 m DTMs Noisy

• Do we want every small pothole or boulder removed • Ground classification removes some, but not all • Will landscape studies be better with larger grid? Drop in the bucket, 5 m grid Need one elevation each cell • Max • Mean • Min • Nearest

All fast—one pass through point cloud Resulting 5 m DTM grids

• Mean obviously smooths, others still have small scale “noise” Elevation Moments--Effect of grid cell elevation selection algorithm

• Very little difference Slope Moments--Effect of grid cell elevation selection algorithm

• Very subtle differences Profiles across the DTMs

• Max and min always extremes • Other two intermediate • Differences only noticeable on slopes Effect of water and buildings in DTM NED • Lower elevation • Lower slope

• Low, flat water • Buildings

• NED masked very similar to DTM from point cloud DTM Generalization

• Independent DTMs created from point cloud at multiple resolutions • 1 m DTM taking as starting point of downsampling and upsampling • Behrens, T., Schmidt, K., MacMillan, R.A., Viscarra Rossel, R.A., 2018, “Multiscale contextual spatial modelling with the Gaussian scale space”. Geoderma, 310:128-137. DTM Generalization Options

• Buildings & water voids not filled • Voids fill with filtering and larger cell sizes Slope maps Statistics Comparisons

Elevation mean and std dev, very small changes Slope mean and slope standard deviation: • ↓ decreases as resolution ↑ (table columns) • ↓ decreases as method ↓ (table rows) Conclusions

• National mapping agencies should produce DTMs and DSMs • Need more complete LAS classification of point clouds from national mapping agencies (ground, buildings, vegetation, water) • If DTMs voids filled, need water and building masks from improved LAS classification • Need more complete open point cloud classification algorithms and software • Simple drop in the bucket algorithms for lower resolution DTMS sufficient