The variability of vegetation in rangelands can be over generalized in spatial representation and vegetation types mapped by moderate resolution vegetation maps. A high resolution (<1 meter), site specific, vegetation map may better represent the diversity and spatial complexity of rangelands – a necessity for analyzing pre-fire conditions. We pansharpened two 8-band VNIR Worldview 2 scenes to map pre-fire vegetation in Johnston Draw (1.8 square kilometers) in the Reynolds Creek Experimental Watershed in Southwest Idaho. The two Worldview 2 scenes represent peak greenness (June 14, 2023) and pre-fire (September 23, 2023) conditions with spatial resolutions of 50 centimeters and 42 centimeters, respectively. A prescribed fire burned the area on October 6, 2023. We trained a pixel based random forest classifier to map 10 site specific vegetation classes at a 50-centimeter spatial resolution. We applied a majority filter to remove speckling. Map accuracy was 83.3% when validated using a test set of 54, 30-meter diameter, plots selected to represent the following dominant vegetation types: deciduous/riparian (classes were collapsed into a single class for validation), living juniper, dead juniper, sagebrush, mixed low sage and bunchgrass, bitterbrush, and annual grasses. Barren and water classes were not validated. Training data was developed through a combination of site visit based knowledge and a training set of 30-meter diameter dominant vegetation class plots. The 1.5-billion-dollar cost of fire prevention, suppression, and restoration is stretched thin over the vast area of wildfire occurrence, where site-specific high-resolution vegetation maps are essential to mitigate fire potential and address post fire recovery. In addition to the pre-fire vegetation map a post fire burn product will also be submitted to Ag Data Commons and the related materials will be updated to reflect these complimentary submissions.