We mapped 66 Ecological Mapping Systems (EMS) for eight coastal counties in south Texas, from Refugio and Aransas County south to the Mexican border. Land cover (LC), geophysical setting information, and woody vegetation height were all attributed to image objects derived from 10 m Sentinel-2 satellite imagery to model EMS type. A supervised process with training data collected from aerial photographs, aided by quantitative, species-specific, ground-collected virtual plot data, was used to classify LC in a RandomForest framework. Out of bag (OOB) error for LC was 15.24%. Recently collected LiDAR point cloud information was used to map height for woody vegetation, and the height was, in turn, used to distinguish between herbaceous, shrubland, and woodland/forest types via modification of LC results, and to define several canopy >10 m versions of forested EMS types. Geophysical settings were mapped based primarily on the distribution of soil Map Units (MUs) from the national digital soil survey (gSSURGO). Elevation and potential ponding information were derived from analysis of LiDAR-derived digital elevation models (DEMs) as an aid in mapping several EMS types. Heads-up modification of both LC and EMS modeling results using aerial photograph interpretation improved results. The agreement between EMS mapped type and field-collected data (most 10 years old or more) was >75%. The most abundant EMS types included Coastal and Sandsheet: Deep Sand Grassland (10.7% of the region), Native Invasive: Mesquite/Mixed Shrubland (5.0%), Gulf Coast: Coastal Prairie (4.6%), and South Texas: Sandy Mesquite Savanna Grassland (4.4%). The improved land cover, geophysical settings data, vegetation height data, and the use of finer-resolution image objects for modeling enabled mapping of all EMS types more accurately than previous datasets. The new EMS dataset will facilitate analysis and conservation of important habitats and modeling of species of concern that are tied to those habitats.