Name: Seagrass Classification Results - Jul-2012

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Description: Seagrass cover was mapped using automated classification methods from high resolution, archived satellite imagery acquired between 2011 and 2016, where spectral and environmental conditions permit (e.g., appropriate cloud cover, glint, and turbidity). Satellite imagery consisted of archived WorldView (WV-2 and WV-3) and GeoEye-1 images which were obtained from the US Geological Survey. Image scenes were selected based on water clarity conditions, radiometric image quality, and availability of concurrent in situ observations. Prior to classification satellite imagery were mosaicked and corrected for radiometric inconsistencies, and geometric errors. A Maximum Likelihood (ML) classification method was performed to classify seagrass percent cover from depth-corrected images using ArcGIS Spatial Analyst software. The ML method classified pixel values by relating percent cover observations to depth-corrected spectral values within a two-meter radius of each georeferenced quadrat. The bathymetric grid was also used as a ML classification variable. Upon multiple classification iterations, percent cover was binned into ordinal percent cover categories to achieve optimum classification. Training data and the resulting ML output were classified into five percent cover categories (<5%, 5-25%, 25-50%, 50-75%, 75-100%). An additional 200 training points were added in deeper water (>2.8m) to reduce classification errors outside of the typical optical zone for seagrass in the IRL.

Copyright Text: Florida Fish and Wildlife Conservation Commission

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