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.
The upsurge in the development of RPAS technology for low altitude remote sensing and miniaturized sensors for enhanced imaging, have led to an increase in marine ecological applications. However, the ubiquity of RPAS with sensors in the visible electromagnetic spectrum may be limiting the applications of fine-scale mapping, monitoring, and identification of biogenic marine habitats along temperate intertidal rocky reefs. Here we used a low-cost RPAS coupled with a multispectral sensor (MicaSense® RedEdge™) and object-based image analysis (OBIA) workflow to produce very high-resolution maps of biogenic oyster reefs in Waitemata Harbour, Auckland, New Zealand. The results show that having spectral bands beyond the visible electromagnetic spectrum gradually enhances feature detection on the imagery and increases the potential to delineate targeted features within a heterogeneous marine ecosystem. Using a rule-based classification technique to extract target features, based on their spectral characteristics following segmentation, yielded an overall accuracy of 83.9% and a kappa coefficient of 69.8%.Spectral resolution improved for habitat mapping of oyster reefs with additional spectral bands. Highspatial scale monitoring and mapping of turbid exposed intertidal rocky reefs presents unique challenges, but these challenges can be mitigated by targeting flights during ideal meteorological and oceanographic conditions with RPAS.
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