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GCE-LTER Data Set Summary

Accession: GIS-GCES-1401g Research Theme: Geospatial Analysis (Directed Study)
Contributors: Christine M. Hladik, John Schalles
Title: Maximum likelihood classification of 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Abstract: Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included four flight lines flown for the examination of vegetation for the Duplin River salt marshes. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. Imagery were classified using the maximum likelihood classifier (MLC) and a post-classification decision tree to achieve an overall classification accuracy of 90%. Classification training and validation data were obtained from the 2006 Hyperspectral ground survey. See Hladik (2012) and Hladik, Alber, and Schalles (2013) and Schalles, et. al. (2013) for additional details.
DOI: 10.6073/pasta/575c75149bdaa30a68507e46607ce784
Key Words: Altamaha River, Blackbeard Creek, Dean Creek, Duplin River, hyperspectral, remote sensing, salt marshes, vegetation
LTER Core Area: Primary Production
Research Themes: Geospatial Analysis, Plant Ecology
Study Period: 20-Jun-2006 to 21-Jun-2006
Study Sites:
GCE-DP -- Duplin River, Georgia, USA
» Download Geographic Coverage: Google Earth
Data References: GIS-GCES-1401 (RTK elevation survey data), GIS-GCES-1401a (RTK elevation survey data), GIS-GCES-1401b (LIDAR data), GIS-GCES-1401c (LIDAR data), GIS-GCES-1401d (Digital elevation model), GIS-GCES-1401e (Hyperspectral imagery), GIS-GCES-1401f (Hyperspectral imagery classifications), GIS-GCES-1401h (NDVI images from hyperspectral imagery classifications)

Hladik, C.M. and Alber, M. 2012. Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model. Remote Sensing of the Environment. 121:234-235. (DOI: 10.1016/j.rse.2012.01.018)

Hladik, C.M. 2012. Use of Remote Sensing Data for Evaluating Elevation and Plant Distribution in a Southeastern Salt Marsh. Ph.D. Dissertation. University of Georgia, Athens, GA. 205 pages.

Hladik, C.M., Schalles, J.F. and Alber, M. 2013. Salt marsh elevation and habitat mapping using hyperspectral and LIDAR data. Remote Sensing of the Environment. 139:318 - 330. (DOI: 10.1016/j.rse.2013.08.003)

Schalles, J.F., Hladik, C.M., Lynes, A.R. and Pennings, S.C. 2013. Landscape estimates of habitat types, plant biomass, and invertebrate densities in a Georgia salt marsh. Special Issue: Coastal Long Term Ecological Research. Oceanography. 26:88-97. (DOI: 10.5670/oceanog.2013.50)

Downloads: Information

GIS Raster Data: GIS-GCES-1401g_Duplin_MLC (Zip file containing the GIS shape file of image classification for the Duplin River vegetation)

Access: Public (released 01-Sep-2013)

Metadata: XML (Ecological Metadata Language)

Data Formats: Zip archive [5,494 KB]

Citation: Hladik, Christine M. 2013. Maximum likelihood classification of 2006 AISA hyperspectral imagery of the GCE domain for vegetation. Georgia Coastal Ecosystems LTER Project, University of Georgia, Long Term Ecological Research Network. http://dx.doi.org/10.6073/pasta/575c75149bdaa30a68507e46607ce784

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This material is based upon work supported by the National Science Foundation under grants OCE-9982133, OCE-0620959, OCE-1237140 and OCE-1832178. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.