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Sensors 2018, 18(3), 829; https://doi.org/10.3390/s18030829

Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park

1
The Everglades Foundation, 18001 Old Cutler Road, Palmetto Bay, FL 33157, USA
2
Department of Biological Sciences, Florida International University, 11200 S.W. 8th Street, Miami, FL 33199, USA
3
Geographic Information Systems and Remote Sensing Center, Florida International University, 11200 S.W. 8th Street, Miami, FL 33199, USA
*
Author to whom correspondence should be addressed.
Received: 1 December 2017 / Revised: 22 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Remote Sensing of Mangrove Ecosystems)
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Abstract

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata. Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species’ habitats. Our results also offer a method to monitor vegetation change in other threatened habitats. View Full-Text
Keywords: mangrove; species spectral separability; random forest classifier; rare species; habitat monitoring; sea level rise; climate change; conservation mangrove; species spectral separability; random forest classifier; rare species; habitat monitoring; sea level rise; climate change; conservation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wendelberger, K.S.; Gann, D.; Richards, J.H. Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park. Sensors 2018, 18, 829.

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