Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data

This paper evaluates the ability of small footprint, multiple return and pulsed airborne scanner data to classify tree genera hierarchically using stepwise cluster analysis. Leaf-on and leaf-off airborne scanner datasets obtained in the Washington Park Arboretum, Seattle, Washington, USA were used for tree genera classification. Parameters derived from structure and intensity data from the leaf-on and leaf-off laser scanning datasets were compared to ground truth data. Relative height percentiles and simple crown shapes using the ratio of a crown length to width were computed for the structure variables. Selected structure variables from the leaf-on dataset had higher classification rate (74.9%) than those from the leaf-off dataset (50.2%) for distinguishing deciduous from coniferous genera using linear discriminant functions. rvised stepwise cluster analysis was conducted to find groupings of similar genera at consecutive steps using k-medoid algorithm. The three stepwise cluster analyses using different seasonal laser scanning datasets resulted in different outcomes, which imply that genera might be grouped differently depending on the timing of the data collection. When combining leaf-on and leaf-off LIDAR datasets, the cluster analysis could separate the deciduous genera from evergreen coniferous genera and could make further separations between evergreen coniferous genera. When using the leaf-on LIDAR dataset only, the cluster analysis did not separate deciduous from evergreen genera. The overall results indicate the importance of the timing of laser scanner data acquisition for tree genera separation and suggest that the potential of combining two LIDAR datasets for improved classification.