monitoring lichen distribution is of increasing concern as mapping is critical in characterizing habitat for woodland caribou. aerial photography collected using drones, the method used for this study, is a common method of lichen detection and is usually paired with field data collection. it employs cameras that provide images with red green blue (rgb) and near-infrared (nir) bands. low accuracies obtained from aerial drone imagery have been attributed to stand and site features restricting accurate readings. thus, before lichen mapping can be utilized on a broad scale, it is important to identify the amount of canopy closure under which classification accuracy is negatively affected. this was determined by classifying seven sites with varying canopy closure, resulting in classification accuracies corresponding to each plot. this report provides a current analysis of lichen detection under varying canopy closure. the objective of this study is to determine the crown closure percentage, as calculated by the winscanopy program, and its effect on lichen detection using drone imagery. the study was conducted in dryden, ontario, where the correlation between canopy closure and lichen detection was made. it was found that below 88% canopy closure, lichen classification accuracy significantly decreases and below 77% canopy closure, overall image classification is affected. the findings in this study support the hypothesis that canopy closure is directly correlated to the classification efficiency of uav imagery, however further investigation into improving classification is required. further investigation into the effects of bare ground and rock outcrop misclassification should also be conducted, as this played a significant role in lichen classification.