Emerging Sensor Platforms Allow for Seagrass Extent Mapping in a Turbid Estuary and from the Meadow to Ecosystem Scale

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 18; p. 3681
• Main Authors: Krause, Johannes R., Hinojosa-Corona, Alejandro, Gray, Andrew B., Burke Watson, Elizabeth
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2021
• Subjects: Aerial surveys; Anthropogenic factors; Carbon; Carbon sequestration; Environmental stress; Estuaries; Estuarine ecosystems; Image analysis; Image processing; Learning algorithms; Machine learning; Mapping; object-based image analysis; planet; Platforms; Remote sensing; Satellite imagery; Satellites; seagrass; Sensors; Sequestering; Shore protection; Shoreline protection; Shorelines; Trends; turbid; unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13183681

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 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 at 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.