Remote sensing from small unmanned aircraft systems (sUAS) has potential in agriculture because low flight altitudes allow image acquisition at very-high spatial resolution. Crop damage caused by Colorado potato beetles (CPB) may occur rapidly, so early detection would benefit precision pest management. However, the best methods for determining early crop damage are not known. In 2014, we conducted an experiment in plots of ‘Ranger Russet’ potatoes that had variable CPB damage from 0 to 29%.  We flew a DJI Spreading Wings S800 hexacopter with a 6-band Tetracam Mini Multispectral Camera Array, which had one band used for an up-looking sensor to measure incident radiation.  Plot-scale spectral indices, such as the normalized difference vegetation index and the soil adjusted vegetation index, were not sensitive to the damage level.  Object based image analysis using Excelis ENVI Feature Extraction was accurate when the two scaling parameters were adjusted to get the best fit, but this method may not be feasible for large multi-temporal datasets. Three dimensional point clouds were determined using Agisoft Photoscan Pro, and relative vegetation height was strongly correlated to damage. However, 3-D point clouds require large amounts of computer processing time and ground control points to correct image artifacts. Traditional methods of remote sensing developed for high-altitude aircraft and satellites not downscale well for remote sensing from sUAS. However, methods based on computer vision need more research to be less subjective.