Dunes with a high relative topography can often be easily distinguished in high-resolution lidar-based digital elevation models (DEMs). Thus, researchers have begun using relative topography metrics, such as the topographic position index (TPI; Weiss, 2001), to identify ridges and upper slopes for extracting dunes from lidar-based DEMs (Wernette et al., 2016; Halls et al. 2018). DEMs are often used for automated delineations of intertidal and supratidal habitats in coastal applications despite issues related to vertical uncertainty. However, the level of vertical uncertainty from data collected with conventional aerial topographic lidar systems has been found to be as high as 60 cm in densely vegetated emergent wetlands throughout the United States (Medeiros et al., 2015; Buffington et al., 2016; Enwright et al., 2018). This uncertainty can also impact elevations in other habitats such as dunes due to vegetation cover and slope (Su and Bork, 2006). Another challenge when mapping geomorphology-based habitats (e.g., dune, beach, intertidal marsh, forest) on dynamic barrier islands is the need for standardized methods that are efficient and repeatable. In response, we developed an approach that builds on recent efforts using relative topography to identify ridges and upper slopes for dune delineation (Wernette et al. 2016; Halls et al. 2018) by also applying Monte Carlo simulations to treat elevation uncertainty in coastal settings when extracting elevation-dependent habitats from a DEM (Liu et al. 2007; Enwright et al. 2018) for a case study on Dauphin Island, Alabama. Beyond just the application of uncertainty, we refined ridges and upper slopes extracted from a DEM by removing small noisy polygons and using manual refinement. This data release contains each of these iterations to show the importance of uncertainty analyses and manual refinement when using automated extraction of elevation-dependent habitats from a DEM. This data release includes a TPI directory, which contains four polygon shapefiles that represent each step in the TPI-based dune delineation process, which includes: 1) step1_raw_ridges_upper_slopes.shp; 2) step2_refinement_extreme_water_level.shp; 3) step3_refinement_via_noise removal.shp; and 4) step4_final_refinement_from_visual_inspection.shp. Since this a step-wise process, each step includes the prior steps. A second component of this data release is a raster named “Prob_Abv_Storm” that estimates the probability of a pixel being above the extreme water level with a 10-percent annual exceedance probability for National Oceanic and Atmospheric Administration’s Dauphin Island tide gauge (station ID: 8735180).