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Small unmanned aerial systems (sUAS) have proven useful to identify and map vegetation species. Scientists and rangeland managers have access to a range of sUAS vehicles and sensors for quantifying rangeland environments. �This purpose of this study was to compare two sUAS, the DJI Phantom 4 with a Sentera Single Sensor NDVI camera and the 3DR Solo Quadcopter with Sony QX-1 RGB and NIR cameras, to determine which provides the most accurate data used in creating orthomosaic images. These images can then be processed to discriminate vegetation life forms (grasses, forbs, shrubs), individual plant species, or surface attributes (density, cover, percent bare-ground, litter). Image data were acquired over basin and range sites just west of Elko, Nevada. Flight missions were flown at 100ft above ground level using automated flight paths, and individual images were processed into an orthomosaic using Pix4D software. Processing the Phantom 4 data was more automated than processing 3DR Solo Data, reducing the number of steps and overall time required to complete the process. However, preliminary analysis suggests that there may be more spectral detail in the data acquired by the SONY QX-1 cameras flown on the 3DR Solo sUAS. E-cognition object-based image analysis was conducted on images from both sensors, and compared with spectral classification techniques. Preliminary results suggests the both platforms are effective in acquiring large datasets that can be processed into orthomosaics preparatory to additional image classification. With well-designed geolocation abilities, data collection and initial processing is simplified for images collected by the DJI Phantom 4. Therefore, while the DJI Phantom 4 provides easy and rapid image processing and analysis, the 3DR Solo provides high quality images in relation to spectral analysis. This information can help managers determine which drone will provide the most useful platform and products considering the type of monitoring or range assessment desired.