Authors - Akbar Kushanoor, Sanjay K. Sahay Abstract - Traditional tree classification methods are inefficient, requiring tremendous effort, time, and labor. To address this, the primary objective of this research was to develop and implement a machine learning model that utilizes 3D Light Detection and Ranging (LiDAR) data, acquired via an unmanned aerial vehicle (UAV), for the accurate classification of tree species in the Philippines. Then, the collected data was pre-processed in preparation for the next portions of the methodology. Once completed, the features used in preparation for machine learning were extracted for the creation and training of the model. Ground truth data, validated by two licensed foresters, were used to ensure species accuracy, focusing on the five most abundant tree species in the dataset. Several machine learning algorithms were evaluated, with the XGBoost model achieving the best performance, reaching an overall accuracy of 85.63%, a mean class accuracy of 84.98%, and a Kappa accuracy of 81.57%. All producers’ accuracy exceeded 70%, indicating robust model reliability. Additionally, a user interface was developed to visualize the LiDAR data, tree attributes, and classification results. The findings demonstrate that LiDAR data obtained from UAVs can effectively be used for tree species classification in the Philippines, supporting forest inventory initiatives and reforestation efforts. Future work may include expanding the dataset, incorporating more species, and testing additional machine learning algorithms to further enhance classification accuracy.
Open Zoom