Automated Tree Counting Using Airborne LiDAR and Multispectral imagery: A Case of Entoto Forest Reserve, Ethiopia

Accurate and efficient tree counting is essential for forest inventory, biodiversity conservation, and sustainable forest management. This study presents an automated tree counting in the study area, by integrating airborne Light Detection and Ranging (LiDAR) and multispectral imagery. LiDAR provides high-resolution three-dimensional forest structure data, while multispectral imagery offers critical spectral information for vegetation classification and health assessment. Primary data, including LiDAR point clouds and aerial photographs, were obtained from the Space Science and Geospatial Institute (SSGI) during a flight on March 11, 2021, conducted at 4,556 meters above mean sea level, with a point density of 4 points/m² and an image resolution between 0.07 and 0.1 meters. Data were preprocessed using HxMap and Terasolid software, enabling geo-referencing, radiometric correction, and classification of ground and non-ground points. Advanced segmentation algorithms, machine learning models, and data fusion techniques were applied to delineate individual trees, resulting in the detection of 36,531 trees with heights ranging from 4 to 52 meters and trunk diameters (DBH) between 2 and 89 meters. Total estimated biomass in the study area reached 59,874,692 units. Validation against ground survey and high-resolution imagery collected in 2025 showed that 92 out of 100 randomly selected trees were correctly detected, with precision, recall, and F1-score values all exceeding 95%, demonstrating the reliability of the method. Variations in height and diameter were attributed to temporal differences between field and airborne data collection. The study concludes that integrating high-density LiDAR with multispectral imagery provides a scalable, accurate, and cost-effective solution for automated tree detection and forest assessment in complex environments. It is recommended that similar integrated methodologies be adopted in other forest regions of Ethiopia to support afforestation planning, carbon stock estimation, biodiversity management, and long-term forest monitoring, while also calling for expanded availability of high-resolution airborne data and improved field validation efforts.