Drones (Sep 2024)
Detecting Canopy Gaps in Uneven-Aged Mixed Forests through the Combined Use of Unmanned Aerial Vehicle Imagery and Deep Learning
Abstract
Canopy gaps and their associated processes play an important role in shaping forest structure and dynamics. Understanding the information about canopy gaps allows forest managers to assess the potential for regeneration and plan interventions to enhance regeneration success. Traditional field surveys for canopy gaps are time consuming and often inaccurate. In this study, canopy gaps were detected using unmanned aerial vehicle (UAV) imagery of two sub-compartments of an uneven-aged mixed forest in northern Japan. We compared the performance of U-Net and ResU-Net (U-Net combined with ResNet101) deep learning models using RGB, canopy height model (CHM), and fused RGB-CHM data from UAV imagery. Our results showed that the ResU-Net model, particularly when pre-trained on ImageNet (ResU-Net_2), achieved the highest F1-scores—0.77 in Sub-compartment 42B and 0.79 in Sub-compartment 16AB—outperforming the U-Net model (0.52 and 0.63) and the non-pre-trained ResU-Net model (ResU-Net_1) (0.70 and 0.72). ResU-Net_2 also achieved superior overall accuracy values of 0.96 and 0.97, outperforming previous methods that used UAV datasets with varying methodologies for canopy gap detection. These findings underscore the effectiveness of the ResU-Net_2 model in detecting canopy gaps in uneven-aged mixed forests. Furthermore, when these trained models were applied as transfer models to detect gaps specifically caused by selection harvesting using pre- and post-UAV imagery, they showed considerable potential, achieving moderate F1-scores of 0.54 and 0.56, even with a limited training dataset. Overall, our study demonstrates that combining UAV imagery with deep learning techniques, particularly pre-trained models, significantly improves canopy gap detection accuracy and provides valuable insights for forest management and future research.
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