Texture Classifying Neural Network Algorithm (TCNNA) days from Synthetic Aperture Radar (SAR) satellite polygons collected between April 2010 to August 2010 during the Deepwater Horizon oil spill response in the Northern Gulf of Mexico (NCEI Accession 0163820)

This Archival Information Package (AIP) contains Environmental Response Management Application (ERMA) GIS layers that are a compilation of all the individual Texture Classifying Neural Network Algorithm (TCNNA) days from Synthetic Aperture Radar (SAR) satellite polygons that were collected during th...

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Bibliographic Details
Format: Dataset
Language:unknown
Published: NOAA NCEI Environmental Data Archive 2017
Subjects:
SAR
DWH
Online Access:https://search.dataone.org/view/{EBE5E115-3C8F-453D-B4BB-F806C9734529}
Description
Summary:This Archival Information Package (AIP) contains Environmental Response Management Application (ERMA) GIS layers that are a compilation of all the individual Texture Classifying Neural Network Algorithm (TCNNA) days from Synthetic Aperture Radar (SAR) satellite polygons that were collected during the Deepwater Horizon oil spill in the Northern Gulf of Mexico. Daily polygons were converted to rasters with 50m cell size and added together using arcgis tools to output a cumulative days of oiling raster. These data were collected during the response to the Mississippi Canyon 252 Deepwater Horizon oil spill in the Northern Gulf of Mexico and used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP).