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 model (DEM) for wetlands throughout Collier county using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (15,223 points), NAIP-derived Normalized Difference Vegetation Index (2010), a 10 m lidar DEM from 2007, and a 10 m canopy surface model were used to generate a model of predicted bias across marsh, mangrove, and cypress habitats. The predicted bias was then subtracted from the original lidar DEM, masked by wetlands areas using polygons from the National Wetland Inventory dataset, and merged with the original lidar DEM. Only the area covered by the 2007 lidar was corrected; a piece of the inland area not covered by lidar was interpolated with the GPS survey data and merged with the corrected DEM. Lidar from 2017, which covers a narrow coastal strip, was also incorporated by first masking out the wetland areas and then mosaiking with the final DEM. Across all GPS points, mean initial lidar error was 34.3 cm (SD=28.2) and root-mean squared error (RMSE) was 44.5 cm. After correction with LEAN, mean error was 0 (SD=18.9) and RMSE was 18.9 cm, a 57.3 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.