Data fusion of hyperspectral and LIDAR imagery for salt marsh mapping

Data fusion of hyperspectral and LIDAR imagery for salt marsh mapping. Hladik, C. M., Alber, M., & Schalles, J. F. Remote Sensing of Environment, 2012.
abstract bibtex

Accurate mapping of both elevation and habitat distribution in salt marshes is important for management and conservation goals. Although Light Detection and Ranging (LIDAR) is effective at measuring surface elevations, laser penetration is limited in dense salt marsh vegetation. In a previous study, we found that LIDAR-derived DEM error varied with vegetation cover and quantified cover class-specific correction factors to reduce DEM errors. As part of this, we found that the three height classes of the dominant macrophyte in Southeastern salt marshes, Spartina alterniflora, required different correction factors. In order to apply these corrections to a LIDAR-derived DEM, it is necessary to have information on the distribution of cover classes at the study site. Hyperspectral imagery (HSI) has been shown to be suitable for the separation of marsh vegetation species by spectral signatures, but there is persistent confusion between the Spartina height classes and mud, which results in what we term the

@article{hladik_data_2012,
	title = {Data fusion of hyperspectral and {LIDAR} imagery for salt marsh mapping},
	abstract = {Accurate mapping of both elevation and habitat distribution in salt marshes is important for management and conservation goals.  Although Light Detection and Ranging (LIDAR) is effective at measuring surface elevations, laser penetration is limited in dense salt marsh vegetation.  In a previous study, we found that LIDAR-derived DEM error varied with vegetation cover and quantified cover class-specific correction factors to reduce DEM errors.  As part of this, we found that the three height classes of the dominant macrophyte in Southeastern salt marshes, Spartina alterniflora, required different correction factors.  In order to apply these corrections to a LIDAR-derived DEM, it is necessary to have information on the distribution of cover classes at the study site.  Hyperspectral imagery (HSI) has been shown to be suitable for the separation of marsh vegetation species by spectral signatures, but there is persistent confusion between the Spartina height classes and mud, which results in what we term the},
	journal = {Remote Sensing of Environment},
	author = {Hladik, Christine M. and Alber, Merryl. and Schalles, John F.},
	year = {2012},
	keywords = {GCE, remote sensing, salt marsh, lidar, dem correction, hyperspectral}
}

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