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Remote Sens. 2015, 7(5), 5098-5116; doi:10.3390/rs70505098

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

1
Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
2
Florida Fish and Wildlife Research Institute, St. Petersburg, FL 33701, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Alisa L. Gallant, Deepak R. Mishra and Prasad S. Thenkabail
Received: 2 February 2015 / Revised: 10 April 2015 / Accepted: 20 April 2015 / Published: 24 April 2015
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
View Full-Text   |   Download PDF [5868 KB, uploaded 24 April 2015]   |  

Abstract

We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images. View Full-Text
Keywords: benthic reflectance; supervised classification; Landsat; Florida Bay; seagrass landscapes; long-term monitoring benthic reflectance; supervised classification; Landsat; Florida Bay; seagrass landscapes; long-term monitoring
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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MDPI and ACS Style

Blakey, T.; Melesse, A.; Hall, M.O. Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series. Remote Sens. 2015, 7, 5098-5116.

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