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. Müller-Karger

REMOTE SENSING(2020)

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摘要
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 (similar to 5 km(2)). Approximately 35% (1.7 km(2)) 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.
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关键词
wetlands,WorldView-2,sunglint,supercomputing,Rookery Bay,National Estuarine Research Reserve (NERR)
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