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Seagrass meadows are undergoing significant decline locally and globally from human and climatic impacts. Seagrass decline also impacts seagrass-dependent macrofauna benthic activity, interrupts their vital linkage with adjacent habitats, and creates broader degradation through the ecosystem. Seagrass variability (gain and loss) is a driver of marine species diversity. Still, our understanding of macrofauna benthic activity distribution and their response to seagrass variability from remotely sensed drone imagery is limited. Hence, it is critical to develop fine-scale seasonal change detection techniques appropriate to the scale of variability that will apply to dynamic marine environments. Therefore, this research tested the performance of the VIS and VIS+NIR sensors from proximal low altitude remotely piloted aircraft system (RPAS) to detect fine-scale seasonal seagrass variability using spectral indices and a supervised machine learning classification technique. Furthermore, this research also attempted to identify and quantify macrofauna benthic activity from their feeding burrows and their response to seagrass variability. The results from VIS (visible spectrum) and VIS+NIR (visible and near-infrared spectrum) sensors produced a 90–98% classification accuracy. This accuracy established that the spectral indices were fundamental in this study to identify and classify seagrass density. The other important finding revealed that seagrass-associated macrofauna benthic activity showed increased or decreased abundance and distribution with seasonal seagrass variability from drone high spatial resolution orthomosaics. These results are important for seagrass conservation because managers can quickly detect fine-scale seasonal changes and take mitigation actions before the decline of this keystone species affects the entire ecosystem. Moreover, proximal low-altitude, remotely sensed time-series seasonal data provided valuable contributions for documenting spatial ecological seasonal change in this dynamic marine environment.

Detecting the Spatial Variability of Seagrass Meadows and Their Consequences on Associated Macrofauna Benthic Activity Using Novel Drone Technology