Abstract:
Effective forest management and conservation require monitoring tree
attributes such as height, crown diameter, and tree count to assess growth in
reforested areas. This study employs deep learning techniques to estimate these
attributes using drone imagery and compares the results with ground truth data. An
orthophoto generated from drone images was processed using DeepForest to detect
tree crowns, enabling the extraction of crown diameters and tree counts. Tree heights
were estimated using Meta’s DinoV2 global canopy height model and compared to
manually collected ground truth data. Evaluation metrics, including mean absolute
error (MAE), root mean square error (RMSE), and mean absolute percentage error
(MAPE), were used to assess accuracy. The findings demonstrate the potential of
deep learning and remote sensing for effective forest monitoring, offering a costeffective
and scalable alternative to traditional methods. This research contributes to
advancing sustainable forest management practices in reforested stands.
