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

1
Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South, Saint Petersburg, FL 33701, USA
2
Rookery Bay National Estuarine Research Reserve, Florida Department of Environmental Protection, 300 Tower Rd, Naples, FL 34113, USA
3
Tatenda, Inc., 5800 SW 188th Ave., Southwest Ranches, FL 33332, USA
4
Center for Coastal Oceans Research, Institute of Water and Environment, Florida International University, Miami, FL 33199, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1740; https://doi.org/10.3390/rs12111740
Received: 7 April 2020 / Revised: 26 May 2020 / Accepted: 27 May 2020 / Published: 28 May 2020
(This article belongs to the Special Issue Satellite and Ground Remote Sensing for Wetland Environments)
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. View Full-Text
Keywords: wetlands; WorldView-2; sunglint; supercomputing; Rookery Bay; National Estuarine Research Reserve (NERR) wetlands; WorldView-2; sunglint; supercomputing; Rookery Bay; National Estuarine Research Reserve (NERR)
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McCarthy, M.J.; Jessen, B.; Barry, M.J.; Figueroa, M.; McIntosh, J.; Murray, T.; Schmid, J.; Muller-Karger, F.E. Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida. Remote Sens. 2020, 12, 1740.

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