Coastal marsh habitat and its associated vegetation are strongly linked to substrate elevation and local drainage patterns. As such, accurate representations of both the vegetation height and the surface elevations are requisite components for systematic analysis and temporal monitoring of the habitat. Topographic Light Detection and Ranging (LIDAR) data can provide high-resolution, high-accuracy elevation measurements of features both aboveground and at the surface. However, because of poor penetration of the laser pulse through the marsh vegetation, bare-earth LIDAR elevations can be markedly less accurate when compared with adjacent upland habitats. Consequently, LIDAR ground-elevation errors (i.e., standard deviation [SD] and bias) can vary significantly from the standard upland land-cover classes quoted in a typical data provider's quality-assurance report. Custom digital elevation model (DEM) generation techniques and point classification processes can be used to improve estimates of ground elevations in coastal marshes. The simplest of these methods is minimum bin gridding, which extracts the lowest elevation value included within a user-specified search window and assigns that value to the appropriate DEM grid cell. More complex point-to-point classification can be accomplished by enforcing stricter slope limits and increasing the level of smoothing. Despite lowering the spatial resolution of the DEM, the application of these techniques significantly improves the vertical accuracy of the LIDAR-derived bare-earth surfaces. By employing the minimum bin technique to the bare-earth classified LIDAR data, the overall bias in the resultant surface was reduced by 12 cm, and the vertical accuracy was improved by 8 cm when compared with the “as-received” data.