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Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

  • Matthew J. Mccarthy
  • , Brita Jessen
  • , Michael J. Barry
  • , Marissa Figueroa
  • , Jessica McIntosh
  • , Tylar Murray
  • , Jill Schmid
  • , Frank E. Muller-Karger

Research output: Contribution to journalArticlepeer-review

Abstract

In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h−1. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km2). Approximately 35% (1.7 km2) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently.

Original languageAmerican English
JournalRemote Sensing
Volume12
DOIs
StatePublished - Jan 1 2020

Keywords

  • wetlands
  • WorldView-2
  • sunglint
  • supercomputing
  • Rookery Bay
  • National Estuarine Research Reserve (NERR)

Disciplines

  • Life Sciences

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