Salt marshes are valuable ecosystems that are susceptible to habitat loss due to changes in sea level and coastal flooding, and there is growing interest in obtaining accurate habitat and elevation maps for these areas. Remote sensing techniques such as Light Detection and Ranging (LIDAR) can produce digital elevation models (DEMs), but the accuracy of LIDAR in salt marshes is limited by a combination of sensor resolution, instrument errors, and poor laser penetration in dense vegetation. I assessed the accuracy of a LIDAR-derived DEM for the salt marshes surrounding Sapelo Island, GA using real time kinematic (RTK) GPS. These observations were used to develop and validate species-specific correction factors for ten marsh cover classes, which ranged from 0.03 to 0.25 m. In order to apply these corrections to the 13 km2 study site, I classified hyperspectral imagery by cover class and combined this information with elevation in a decision tree. This produced both an accurate habitat classification (nine salt marsh habitat classes were mapped with a 90% overall accuracy) and a corrected DEM (overall mean error was reduced from 0.10 0.12 (SD) to -0.003 0.10 m (SD) and root mean squared error at the 68 % confidence level decreased from 0.15 to 0.10 m) when validated with ground truth data. Finally, I evaluated the use of remote sensing-derived variables (DEM elevation, slope, distance metrics) versus field collected edaphic variables (soil organic matter, water content, salinity, redox) to develop predictive models of plant distributions with both linear discriminant analysis (LDA) and classification and regression trees (CART). Models that used remote sensing variables had accuracies of 0.78 and 0.79, whereas those for edaphic models were 0.63 and 0.72 for LDA and CART, respectively. Accuracies improved only slightly in the best models which combined remote sensing variables and soil organic matter (to 0.82 and 0.83 for LDA and CART, respectively), suggesting that remote sensing-derived variables alone can be effective predictors of marsh vegetation. Taken together, these findings show the potential for appropriately analyzed remote sensing data for evaluating elevation and habitat in marshes.