Use of Remote Sensing Data for Evaluating Elevation and Plant Distribution in a Southeastern Salt Marsh. Hladik, C. M. Ph.D. Thesis, University of Georgia, Athens, GA, 2012.
abstract   bibtex   
Salt marshes are valuable ecosystems that are susceptible to habitat loss due to changes in sea level and coastal flooding, and there is growing interest in obtaining accurate habitat and elevation maps for these areas. Remote sensing techniques such as Light Detection and Ranging (LIDAR) can produce digital elevation models (DEMs), but the accuracy of LIDAR in salt marshes is limited by a combination of sensor resolution, instrument errors, and poor laser penetration in dense vegetation. I assessed the accuracy of a LIDAR-derived DEM for the salt marshes surrounding Sapelo Island, GA using real time kinematic (RTK) GPS. These observations were used to develop and validate species-specific correction factors for ten marsh cover classes, which ranged from 0.03 to 0.25 m. In order to apply these corrections to the 13 km2 study site, I classified hyperspectral imagery by cover class and combined this information with elevation in a decision tree. This produced both an accurate habitat classification (nine salt marsh habitat classes were mapped with a 90% overall accuracy) and a corrected DEM (overall mean error was reduced from 0.10
@phdthesis{hladik_use_2012,
	address = {Athens, GA},
	title = {Use of {Remote} {Sensing} {Data} for {Evaluating} {Elevation} and {Plant} {Distribution} in a {Southeastern} {Salt} {Marsh}},
	abstract = {Salt marshes are valuable ecosystems that are susceptible to habitat loss due to changes in sea level and coastal flooding, and there is growing interest in obtaining accurate habitat and elevation maps for these areas.  Remote sensing techniques such as Light Detection and Ranging (LIDAR) can produce digital elevation models (DEMs), but the accuracy of LIDAR in salt marshes is limited by a combination of sensor resolution, instrument errors, and poor laser penetration in dense vegetation.  I assessed the accuracy of a LIDAR-derived DEM for the salt marshes surrounding Sapelo Island, GA using real time kinematic (RTK) GPS.  These observations were used to develop and validate species-specific correction factors for ten marsh cover classes, which ranged from 0.03 to 0.25 m.  In order to apply these corrections to the 13 km2 study site, I classified hyperspectral imagery by cover class and combined this information with elevation in a decision tree.  This produced both an accurate habitat classification (nine salt marsh habitat classes were mapped with a 90\% overall accuracy) and a corrected DEM (overall mean error was reduced from 0.10},
	school = {University of Georgia},
	author = {Hladik, Christine M.},
	year = {2012},
	keywords = {GCE, LTER, classification and regression trees (CART), digital elevation model (DEM), habitat mapping, hyperspectral imagery, lidar, linear discriminant analysis (LDA), remote sensing, salt marsh, sapelo island}
}

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