Impacts of Unoccupied Aerial Systems navigation and sensor technology on ultra-shallow bathymetry mapping

Unoccupied Aerial Systems (UAS) have emerged as powerful tools for ultra-shallow (<2 m) bathymetric mapping using Structure-from-Motion (SfM) or spectrally derived bathymetry (SDB) based methods. However, the impact of equipment selection on the accuracy of bathymetric maps created through these methods is not well characterized. This study investigates how different UAS navigation and imaging sensors impact bathymetric accuracy and whether this differs for SfM- and SDB-based methods. We assessed five common UAS across two coral reef habitats. real-time kinematics or post-processing kinematics based positioning was critical for accurate SfM-based bathymetry (RMSE = 0.14 ± 0.02 m). However, shallow water adaptations of Stumpf’s band ratio trained on as few as 10 ground truth samples consistently outperformed SfM, regardless of the UAS configuration (RMSE = 0.11 ± 0.003 m). Spectrally trained support vector machines and single-layer neural networks demonstrated the strongest performance (RMSE = 0.09 ± 0.005 m) but required 200 ground truth measurements to reach such accuracies. Multispectral sensors improved the accuracy of spectrally derived bathymetric maps, but broadband RGB cameras also performed well (RMSE difference ≤0.06 m). SfM-derived bathymetry could be combined with spectral models to improve bathymetry reconstruction in texture-poor sand habitats. However, the overall accuracy of such combined models remained constrained by the accuracy of the original SfM reconstruction. Ultimately, Stumpf’s band ratio using the red image band as the denominator provided the most practical and accurate method for UAS-based ultra-shallow bathymetry retrieval when ground truth data was sparse, while spectral machine learning models performed the best when ground truth data were abundant.