2015Modern Applied ScienceOpen access

LiDAR Elevation and DEM Errors in Forested Settings

Curtis B. Edson, Michael G. Wing

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Abstract

igital elevation models (DEMs) form the basis of LiDAR derived tree height measurements and other topographic modeling needs within natural resource applications. We compared 2873 digital total station elevations to the closest discrete LiDAR elevation point and DEM raster cell across several forest and topographic settings. We also examined limiting comparisons to points within 0.5 m and within one meter. Using all nearest LiDAR points, average total station plot elevation differences ranged from -0.06 m (SD 0.40) to -0.59 m (SD 0.23) indicating that LiDAR elevations are higher than actual elevations. LiDAR DEM differences ranged from -0.09 (SD 0.41) to -0.56 m (SD 0.70). We also compared mapping-grade GPS receiver measurements to LiDAR point elevation and DEMs. Average plot GPS elevation differences ranged from 0.24 (SD 1.55) to 2.82 m (SD 4.58) for the nearest LiDAR point, and from 0.27 (SD 2.33) to 2.69 m (SD 5.06) for LiDAR DEMs. We believe that our efforts represent one of the most robust studies of LiDAR measurement errors available in published literature. The relatively small measurement differences that we found between LiDAR elevations and our most reliable field-based method of elevations, the digital total station, demonstrate the potential for LiDAR in forestry and natural resource applications.

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What this paper is about

igital elevation models (DEMs) form the basis of LiDAR derived tree height measurements and other topographic modeling needs within natural resource applications. We compared 2873 digital total station elevations to the closest discrete LiDAR elevation point and DEM raster cell across several forest and topographic settings. We also examined limiting comparisons to points within 0.5 m and within one meter. Using all nearest LiDAR points, average total station plot elevation differences ranged from -0.06 m (SD 0.40) to -0.59 m (SD 0.23) indicating that LiDAR elevations are higher than actual elevations. LiDAR DEM differences ranged from -0.09 (SD 0.41) to -0.56 m (SD 0.70). We also compared mapping-grade GPS receiver measurements to LiDAR point elevation and DEMs. Average plot GPS elevation differences ranged from 0.24 (SD 1.55) to 2.82 m (SD 4.58) for the nearest LiDAR point, and from 0.27 (SD 2.33) to 2.69 m (SD 5.06) for LiDAR DEMs. We believe that our efforts represent one of the most robust studies of LiDAR measurement errors available in published literature. The relatively small measurement differences that we found between LiDAR elevations and our most reliable field-based method of elevations, the digital total station, demonstrate the potential for LiDAR in forestry and natural resource applications.

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Available abstract

igital elevation models (DEMs) form the basis of LiDAR derived tree height measurements and other topographic modeling needs within natural resource applications. We compared 2873 digital total station elevations to the closest discrete LiDAR elevation point and DEM raster cell across several forest and topographic settings. We also examined limiting comparisons to points within 0.5 m and within one meter. Using all nearest LiDAR points, average total station plot elevation differences ranged from -0.06 m (SD 0.40) to -0.59 m (SD 0.23) indicating that LiDAR elevations are higher than actual elevations. LiDAR DEM differences ranged from -0.09 (SD 0.41) to -0.56 m (SD 0.70). We also compared mapping-grade GPS receiver measurements to LiDAR point elevation and DEMs. Average plot GPS elevation differences ranged from 0.24 (SD 1.55) to 2.82 m (SD 4.58) for the nearest LiDAR point, and from 0.27 (SD 2.33) to 2.69 m (SD 5.06) for LiDAR DEMs. We believe that our efforts represent one of the most robust studies of LiDAR measurement errors available in published literature. The relatively small measurement differences that we found between LiDAR elevations and our most reliable field-based method of elevations, the digital total station, demonstrate the potential for LiDAR in forestry and natural resource applications.

Key concepts: Lidar, Digital elevation model, Elevation (ballistics), Remote sensing, Environmental science, Raster graphics, Geology, Computer science

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