Assessment of Airborne Light Detection and Ranging (LiDAR) for use in common forest engineering geomatic applications

Graduation date: 2012Airborne Light Detection and Ranging (LiDAR) has become a popular remote sensing technology to create digital terrain models and provide forest inventory information. However, little research has been done to investigate the accuracy of using airborne LiDAR to perform measurement tasks common to Forest Engineering. This thesis contains two manuscripts investigating different measurement scenarios.\ud The first manuscript examines the use of airborne LiDAR to measure existing forest roads in support of a road assessment under four different canopy conditions. It was found that along existing centerlines the LiDAR data had a vertical RMSE of 0.28 m and a horizontal RMSE of 1.21 m. Road grades were estimated to within 1% slope of the value measured in the field and horizontal\ud curve radii were estimated with an average absolute error of 3.17 m. The results suggest that airborne LiDAR is an acceptable method to measure forest road grade, but some caution should be used in measuring horizontal curve radii, particularly on sharp curves.\ud The second manuscript compares profile corridor measurements using airborne LiDAR-derived elevations across different forest canopy types and terrain slopes ranging from 37 to 49%. Both LiDAR-derived DEM and raw LiDAR point elevations were compared to field data. The DEM elevations had an average RMSE error of 0.43 m across all canopy types compared to the field data, while the nearest LiDAR point had an average RMSE of 0.49 m compared to the field data. A skyline payload analysis suggested that profiles based on the DEM outperformed profiles based on nearest point elevations by 5% on average when compared to the field measured profiles. Results suggest that a forest engineer should consider using the DEM value rather than the nearest LiDAR point elevation for terrain elevations at discrete locations, particularly when forest canopy occludes locations of interest