UAV RGB Imagery and Lightweight Deep Learning Map Semi-Mangrove Shrubs on Jeju Island

Semi-mangrove shrubs are important indicators of change in temperate–subtropical coastal ecotones and provide conservation-relevant habitats in shoreline transition zones. On Jeju Island, South Korea, the distribution of two key semi-mangrove species (Hibiscus hamabo and Paliurus ramosissimus) remains incompletely documented despite their monitoring value. Because these shrubs occur as narrow, fragmented patches that are difficult to delineate in satellite imagery, they may be omitted from coarse-resolution inventories. Here, we produced high-resolution semi-mangrove maps from 1 cm UAV RGB orthomosaics using a lightweight Tiny U-Net semantic segmentation model trained on field-confirmed, expert-digitized polygons from nine coastal sites. Model performance was evaluated using a site-wise training, validation, and test split. The final model achieved a pooled semi-mangrove IoU of 0.677, balanced accuracy of 0.921, precision of 0.771, recall of 0.848, and a false-positive rate of 0.007, despite the low semi-mangrove prevalence of 2.59%. On the independent test site, Tiny U-Net also outperformed standard U-Net with fewer parameters and shorter training time (IoU = 0.873 vs. 0.568; 1.9 M vs. 31.4 M parameters; 37 vs. 123 min). Probability outputs also highlighted high-confidence candidate patches outside of the labeled polygons, supporting targeted field verification and iterative inventory refinement. This UAV–deep learning workflow provides a practical baseline for fine-scale habitat assessment and repeat monitoring of vegetation dynamics along Jeju’s temperate–subtropical coast.