Detecting mangrove seedlings from UAV imagery using deep learning for restoration monitoring

Mangrove ecosystems are the focus of extensive conservation and restoration efforts due to their critical roles in coastal protection, carbon sequestration, and climate change mitigation. Detecting mangrove seedlings in remotely sensed imagery is essential for evaluating restoration success and prioritizing conservation areas. While seedling detection research has predominantly been conducted in agricultural settings, studies in natural environments remain limited, and no dedicated methods have been proposed for mangrove ecosystems. To address this gap, this study develops a model for detecting mangrove seedlings in ultra-high-resolution UAV imagery (0.85 cm) across 22 seeding sites in the Emirate of Abu Dhabi, United Arab Emirates. Building on the success of deep learning in crowd counting tasks, the seedling detection model was developed through a two-stage process. In the first stage, Gaussian blurring was applied to seedling locations to generate a density map, which was then predicted from UAV images using an encoder-decoder MaxViT-UNet architecture. In the second stage, the predicted density map was further thresholded using a Difference of Gaussians method to accurately localize individual seedlings. The model achieved variable performance, with a peak F1-score of 0.70 on the validation dataset (precision: 0.65, recall: 0.76). The developed model was also benchmarked against ResNet-DETR, a state-of-the-art object detection framework, and achieved 9% improvement in F1-score. While the developed model shows promising performance in identifying mangrove seedlings, challenges remain, such as labelling inaccuracies, potential inconsistencies in UAV imagery over time, and inherent limitations associated with deep learning methods. Nevertheless, this study highlights the potential of UAV-based deep learning models for accurately detecting mangrove seedlings at scale, providing a powerful tool to support restoration monitoring and inform more effective conservation strategies in mangrove ecosystems.