Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTree species identification forms an integral part of biodiversity monitoring. Locating at-risk species and predicting their distribution is equally as important as tracing invasive alien plant species distributions. The high prevalence of the latter and their destructive impact on the environment is the focus for this thesis. In areas of the world where technology limitations are restrictive, an approach using low-cost, available RGB drone imagery is proposed to train advanced deep learning models to distinguish individual tree species; three dominant species (Pinus elliotti, Eucalyptus grandis and Syzygium cordatum) providing the bulk of sampling data, of which the first two are highly invasive in the region. This study explored the efficacy of utilizing Mask RCNN, an instance segmentation deep neural network, in identifying multiple classes of trees within the same image. In line with the low-cost approach, Google Colaboratory was utilized which drastically lowers the training time necessary and alleviates the need for high GPU systems. The model was trained on imagery from three study areas which were representative of three distinct landscapes: very dense forest, moderately dense forest with overlapping canopies, and open forest. The results indicate decent performance in open forest landscapes where overlapping tree crowns is infrequent with mean Average Precision of 0.71. On the contrary, in a dense forest landscape with many interlocking tree crowns, a mean Average Precision of 0.43 is highly indicative of the model’s poor performance in such environments. The trained network was also observed to have higher confidence scores of detected objects within the open forest study areas as opposed to dense forest
