Emerging sensor platforms allow for seagrass extent mapping in a turbid estuary and from the meadow to ecosystem scale

Seagrass meadows are globally important habitats,
protecting shorelines, providing nursery areas for fish, and sequestering
carbon. However, both anthropogenic and natural environmental stressors have
led to a worldwide reduction of seagrass habitats. For purposes of management
and restoration, it is essential to produce accurate maps of seagrass meadows
over a variety of spatial scales, resolutions, and at temporal frequencies
ranging from months to years. Satellite remote sensing has been successfully
employed to produce maps of seagrass in the past, but turbid waters and difficulty
in obtaining low-tide scenes pose persistent challenges. This study builds on
an increased
availability of affordable high temporal frequency imaging platforms, using seasonal
unmanned aerial vehicle (UAV) surveys of seagrass extent on the meadow scale,
to inform machine learning classifications of satellite imagery of a 40 km2
bay. We find that object-based image analysis is suitable to detect seasonal
trends in seagrass extent from UAV imagery and find that trends vary between
individual meadows at our study site Bahía de San Quintín, Baja California,
México, during our study period in 2019. We further suggest that compositing
multiple satellite imagery classifications into a seagrass probability map
allows for an estimation of seagrass extent in turbid waters and report that in
2019, seagrass covered 2324 ha of Bahía de San Quintín, indicating a recovery
from losses reported for previous decades.