UAV-acquired imagery with photogrammetry provides accurate measures of mudflat elevation gradients and microtopography for investigating microphytobenthos patterning

Intertidal mudflats are highly productive ecosystems where elevation gradients and complex microtopography drive the growth of benthic microalgae (microphytobenthos; MPB) that form the basis of estuarine foodwebs and are crucial for nutrient cycling, shoreline stabilization, and the persistence of marine and coastal species. Mudflat ecosystems are threatened by human activity and natural stressors and thus need to be mapped and monitored. Unoccupied aerial vehicle (UAV) technologies and digital aerial photogrammetry (DAP) have been successfully implemented to study mudflat environments. However, standardizing the UAV flight parameters needed for optimal DAP performance on mudflats remains outstanding. Here, we systematically determined the optimal flight parameters for collection and photogrammetric processing of UAV-acquired data on mudflats by (1) testing across-track overlap (50, 70, and 90%) and flight elevation (73 m and 110 m) parameters, assessing the accuracy of DAP results against reference data from a mobile laser scanner (MLS), and (2) comparing semi-variograms of digital surface models (DSMs) from two UAV flight elevations. We found that all combinations of UAV flight parameters yielded accurate DAP products; flight elevation had a marginal effect on image alignment and had no effect on accuracy, while across-track overlap had no effect on image alignment of DSM of difference (DoD) values. All UAV and MLS point clouds were aligned with and accuracy of < 0.016 m and absolute values of mean DoDs were all sub-millimeter, ranging from 0.0001 ± 0.0322 to 0.0083 ± 0.0270 m. We conclude that conducting UAV surveys at 110 m elevation with 50% across-track image overlap is sufficient for high-accuracy DAP in mudflats. Finally, we tested the utility of such fine-scale topographic data for ecological applications by comparing elevation and topographic position indices (TPI) of DAP-derived DSMs to MPB abundance, measured as chlorophyll a (chl-a), calculated from UAV-acquired NDVI data. We found that elevation and TPI account for 1.6–17% of the variation in chl-a concentration, and that these relationships depend on distance from shore and mudflat morphology. Our findings contribute to standardizing the application of UAV technologies in mudflats and demonstrate the potential of UAV-acquired data for modeling the relationship between microtopography and MPB on ecologically important mudflats.