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 tidal marsh areas around San Francisco Bay using the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). Survey-grade GPS survey data (6614 points), NAIP-derived Normalized Difference Vegetation Index, and original 1 m lidar DEM from 2010 were used to generate a model of predicted bias across tidal marsh areas. The predicted bias was then subtracted from the original lidar DEM and merged with the NOAA Sea-Level Rise Viewer DEM to create a new seamless DEM for the San Francisco Bay. Across all GPS points, mean initial lidar error was 22.8 cm (SD=12.0) and root-mean squared error (RMSE) was 25.8 cm. After correction with LEAN, mean error was 0 (SD=0.07) and RMSE was 7.4 cm. 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.

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 tidal marsh areas around San Francisco Bay using the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). Survey-grade GPS survey data (6614 points), NAIP-derived Normalized Difference Vegetation Index, and original 1 m lidar DEM from 2010 were used to generate a model of predicted bias across tidal marsh areas. The predicted bias was then subtracted from the original lidar DEM and merged with the NOAA Sea-Level Rise Viewer DEM to create a new seamless DEM for the San Francisco Bay. Across all GPS points, mean initial lidar error was 22.8 cm (SD=12.0) and root-mean squared error (RMSE) was 25.8 cm. After correction with LEAN, mean error was 0 (SD=0.07) and RMSE was 7.4 cm. 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.