Effects of Viewing Geometry on Multispectral Lidar-Based Needle-Leaved Tree Species Identification

Identifying tree species with remote sensing techniques, such as lidar, can improve forest management decision-making, but differences in scan angle may influence classification accuracy. The multispectral Titan lidar (Teledyne Optech Inc., Vaughan, ON, Canada) has three integrated lasers with different wavelengths (1550, 1064 and 532 nm), and with different scan angle planes (respectively tilted at 3.5 degrees, 0 degrees and 7 degrees relative to a vertical plane). The use of multispectral lidar improved tree species separation, compared to mono-spectral lidar, by providing classification features that were computed from intensities in each channel, or from pairs of channels as ratios and normalized indices (NDVIs). The objective of the present study was to evaluate whether scan angle (up to 20 degrees) influences 3D and intensity feature values and if this influence affected species classification accuracy. In Ontario (Canada), six needle-leaf species were sampled to train classifiers with different feature selection. We found the correlation between feature values and scan angle to be poor (mainly below |+/- 0.2|), which led to changes in tree species classification accuracy of 1% (all features) and 8% (3D features only). Intensity normalization for range improved accuracies by 8% for classifications using only single-channel intensities, and 2-4% when features that were unaffected by normalization were added, such as 3D features or NDVIs.

Automated summary

Identifying tree species using multispectral lidar can improve forest management decision-making, but the influence of scan angle on classification accuracy needs to be evaluated. This study found that the correlation between feature values and scan angle was poor, with minimal impact on species classification accuracy.