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.