Height above ground from 3DEP lidar point clouds along the Gulf of America and Atlantic Coast, 2013 to 2022

To enhance hurricane coastal impact models, we estimated height above ground (HAG) in millimeters for vegetation and other features using lidar point clouds associated with the U.S. Geological Survey (USGS) 3DEP 1-m digital elevation models (DEMs). Specifically, the mean, maximum, and standard deviation of the HAG was calculated with a spatial resolution of 3-m. For this dataset, we assumed a reasonable upper bound for vegetation height was the mean HAG plus two times the standard deviation of HAG. This data release includes three files: 1) the upper HAG estimate (2*SD + mean); 2) the median of the upper HAG estimate (2*SD + mean) summarized by land cover type; and 3) the interquartile range of the upper HAG estimate (2*SD + mean) summarized by land cover type. The latter two files were created by integrating the first HAG output with 1-m land cover maps from Simons et al. (2025). The purpose of the median and interquartile range files was to fill NoData gaps in the original lidar datasets with values summarized by land cover type. We used lidar point clouds without any additional clean-up steps. Consequently, there may be some artifacts in portions of the datasets (e.g., evidence of striping in parts of Massachusetts). Users are encouraged to clip the data to their area of interest and inspect the data to identify and address any artifacts or anomalies as needed. Users should also be aware that some areas may include elevated values (for example, HAG values greater than 1,000 mm) that may result from noise in lidar data rather than real height values. Additionally, the accuracy of height measurements below 500 millimeters may vary depending on the lidar quality. For example, in some areas or lidar acquisitions herbaceous-dominated wetland areas may have values of 0 mm for HAG (that is, no HAG), whereas others may have a HAG value greater than 0 mm. Due to these uncertainties, users are encouraged to evaluate the data quality within their specific area of interest to ensure these data are reasonable and appropriate for their intended use.