Description: Contains the 2011 Indian River Lagoon Seagrass GeoDatabase which is a map showing the location of seagrass within the project boundary. The map includes the area between Ponce Inlet south to Jupiter Inlet. Seagrass was photo interpreted from digital aerial photography captured on August 18-23, 2011 and October 1 & 2, 2011. The aerial photography was acquired using a Vexcel UltracamX. Features were classified according to a modified Florida Land Use Cover and Forms Classification System (FLUCCS). The 2011 seagrass mapping for the Indian River Lagoon Project was accomplished in stereo using SOCET SET softcopy photogrammetric workstations. Field work was conducted throughout the duration of the project for signature identification and ground truthing.
Description: The Indian river Lagoon (IRL) Seagrass Mapping Project covers 1951 square kilometers and includes Mosquito Lagoon north to Ponce de Leon Inlet, Banana River Including Newfound Harbour, Indian River Proper including Turnbull Creek up to US 1, Banana Creek up to Route 3, St. Lucie River up to the Roosevelt Bridge, and the lagoonal system from St. Lucie Inlet south to Jupiter Inlet including Hobe and Jupiter Sounds. Portions of the project area occur in Volusia, Brevard, Indian River, St. Lucie, Martin and Palm Beach Counties in Florida. Dewberry produced Ortho Mosaics for 1053 tiles covering this area. The orthos were created with a 0.3 meter cell size.
Description: The Indian river Lagoon (IRL) Seagrass Mapping Project covers 1951 square kilometers and includes Mosquito Lagoon north to Ponce de Leon Inlet, Banana River Including Newfound Harbour, Indian River Proper including Turnbull Creek up to US 1, Banana Creek up to Route 3, St. Lucie River up to the Roosevelt Bridge, and the lagoonal system from St. Lucie Inlet south to Jupiter Inlet including Hobe and Jupiter Sounds. Portions of the project area occur in Volusia, Brevard, Indian River, St. Lucie, Martin and Palm Beach Counties in Florida. Dewberry produced Ortho Mosaics for 1053 tiles covering this area. The orthos were created with a 0.3 meter cell size.
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
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
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
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
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
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
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
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
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
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
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
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
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
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