Mapping Garry Oak (Quercus garryana) Using LiDAR and Multispectral Imagery in Greater Victoria

This study addressed this knowledge gap by developing an automated workflow to identify and map individual Garry oak crowns across the Greater Victoria region using a fusion of airborne laser scanning (ALS) and four-band multispectral imagery.

This imagery was used to extract structural and spectral metrics to train a Random Forest classifier. Results indicated high model performance with an overall accuracy of 89.7% and a specificity of 96.2%. A sensitivity of 61.0% reflected the challenges of discriminating interlocking crowns within high density forested patches and the spectral diversity of non-native species in an urban environment.