Remote Sensing (Oct 2022)

Multi-Sensor Remote Sensing of Intertidal Flat Habitats for Migratory Shorebird Conservation

  • Richard G. Lathrop,
  • Daniel Merchant,
  • Larry Niles,
  • Danielle Paludo,
  • Carlos David Santos,
  • Carmen Espoz Larrain,
  • Stephanie Feigin,
  • Joseph Smith,
  • Amanda Dey

DOI
https://doi.org/10.3390/rs14195016
Journal volume & issue
Vol. 14, no. 19
p. 5016

Abstract

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Many species of shorebirds migrate long distances from their overwintering grounds in the southern hemisphere to breeding grounds in the northern hemisphere. The coastal intertidal zone, consisting of sand and mud flats exposed at low tide and covered at high tide, is heavily used as a migratory stopover or overwintering habitat. Understanding the spatial distribution of sediment types at these stopover sites is a critical step for understanding habitat use by shorebird species. Due to their importance as overwintering and stopover habitat for the imperiled western Atlantic subpopulation of the shorebird, the red knot (Calidris canutus rufa), as well as other migratory shorebirds, the northern coast of Brazil between Pará and Maranhão, and Bahía Lomas in northern Tierra del Fuego, Chile, were selected for further investigation as to the applicability of remotely sensed characterization of the intertidal flat habitats. Examination of the Landsat 8 multispectral reflectance and Sentinel-1 SAR backscatter reveals that sand and mud represent endmembers at opposite ends of a continuous gradient in feature space. While remotely sensed data can be used to discriminate between mud and sand intertidal types, the spectral relationships varied between the two very different geographic locations. The inclusion of both multispectral and radar sensing imagery can lead to important insights about the physical properties of the sediment that would be omitted by using one data source alone. Spectral unmixing techniques in Google Earth Engine were used to map the intertidal zone into general sediment classes spanning the gradient (i.e., mud, sandy mud, muddy sand, and sand). Comparison of the mapped outputs with field reference data suggests that mapping of mud- vs. sand-dominated areas can be accomplished with reasonable accuracy (overall accuracy of 75%).

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