|SPAETE, L - Idaho State University
|GLENN, N - Idaho State University
|DERRYBERRY, D - Idaho State University
|SANKEY, T - Idaho State University
Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/10/2010
Publication Date: 12/1/2011
Citation: Spaete, L.P., Glenn, N.F., Derryberry, D.R., Sankey, T.T., Hardegree, S.P. 2011. Vegetation and slope effects on accuracy of a LiDAR-derived DEM in the sagebrush steppe. International Journal of Remote Sensing. 2(4):317-326.
Interpretive Summary: LiDAR is a remote sensing tool for measuring topography, but it can also be used to estimate vegetation-canopy characteristics. Most vegetation applications, however, have been for relatively tall, forested vegetation. Shorter vegetation, of the type typically found in semi-arid rangelands, is sometimes difficult to distinguish from the underlying ground. In this study, LiDAR measurements were compared to detailed ground measurements to determine whether low-lying vegetation causes errors in estimates of ground topography, and whether these errors are affected by slope. Topographic errors were found to be relatively greater for shorter vegetation, and in areas with relatively low slope. At this time, detection of low-stature semi-arid shrubs is very near the detection threshold of LiDAR technology. Improvements in LiDAR-detection systems will greatly enhance our ability to understand and characterize these extensive semi-arid plant communities.
Technical Abstract: This study analyzed the errors associated with vegetation cover type and slope on a LiDAR derived DEM in a semiarid environment in southwestern Idaho, USA. Reference data were collected over a range of vegetation cover types and slopes. Reference data were compared to ground raster values and Root Mean Square Error (RMSE) was used to quantify errors. Results indicate that vegetation cover type and slope have statistically significant effects on the accuracy of a LiDAR-derived bare earth DEM. RMSE ranged from 0.072 to 0.220 m with the largest RMSE associated with low sagebrush and low slope environments. While the RMSEs in this study were lower than those reported by others, this study suggests that elevation errors in semiarid rangeland studies may equate to a significant portion of the vegetation height. In summary, further refinement of the accuracy of LiDAR systems may be needed for vegetation studies in rangeland environments.