Evaluating TIFFS (Toolbox for LiDAR Data Filtering and Forest Studies) in Deriving Forest Measurements from LiDAR Data
Schlagwörter:
Lidar, remote sensing, forestryAbstract
Recent advances in LiDAR (Light Detection and Ranging) technology have allowed for the remote sensing of important forest characteristics to be more reliable and commercially available. Studies have shown that this technology can adequately estimate forest characteristics such as individual tree locations, tree heights, and crown diameters. These values are then used to estimate biophysical properties of forests, such as basal area and timber volume. This study assessed the capability of a commercially available program, Tiffs (Toolbox for LiDAR Data Filtering and Forest Studies), to accurately estimate forest characteristics, as compared to data collected at the plot level using traditional timber sampling methods. We found a high, positive correlation coefficient (r) of 0.8223 for tree heights, between the LiDAR-derived measurements and the field measurements, which is somewhat promising. However, we found low correlations in tree count per plot (r = 0.1777) and tree crown radius (r = 0.1517), between the LiDAR-derived measurements and the field measurements, results which are far from satisfactory.
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