LEAN-Corrected Chesapeake Bay Digital Elevation Models, 2019
Dates
Publication Date
2019-08-02
Start Date
2010-06-01
End Date
2017-08-01
Citation
Buffington, K.J., Thorne, K.M., Carr, J.A., and Guntenspergen, G.R., 2019, LEAN-Corrected Chesapeake Bay Digital Elevation Models, 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9NQZXU3.
Summary
Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for Chesapeake Bay using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (3699 points, collected across four tidal marsh sites (Eastern Neck, Bishops Head, Martin, and Blackwater) in 2010 and 2017, Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (2013), a 1 m lidar DEM and a 1 m canopy surface model were used to generate models of predicted [...]
Summary
Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for Chesapeake Bay using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (3699 points, collected across four tidal marsh sites (Eastern Neck, Bishops Head, Martin, and Blackwater) in 2010 and 2017, Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (2013), a 1 m lidar DEM and a 1 m canopy surface model were used to generate models of predicted bias across the study domain. The modeled predicted bias for each cover type was then subtracted from the original lidar DEM to generate a new DEM. Across all GPS points, mean initial lidar error was -1.0 centimeters (SD=12.8) and root-mean squared error (RMSE) was 12.8 centimeters. After correction with LEAN, mean error was 0 cm (SD=6.4) and RMSE was 6.4 cm, a 50 percent improvement in accuracy.
References:
Buffington, K.J., Dugger, B.D., Thorne, K.M. and Takekawa, J.Y., 2016. Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. Remote Sensing of Environment, 186, pp.616-625.
The lean-corrected DEM is appropriate for use in vegetated baylands.
Rights
The authors of these data require that data users contact them regarding intended use and to assist with understanding limitations and interpretation. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
