Unmanned Aerial Systems (UASs) offer a new era of local-scale environmental monitoring where access to invaluable aerial data no longer comes at a substantial cost. This provides the opportunity to vastly expand the ability to detect natural hazards impacts, observe environmental conditions, quantify restoration efforts, track species propagation, monitor land surface changes, cross-validate existing platforms, and identify hazardous situations. While UASs have the potential to accelerate understanding of natural processes, much of the research using UASs has applied current remote sensing image processing techniques without questioning the validity of these in UAS applications. With new scientific tools comes a need to affirm that previous techniques are still valid for the new systems. To this end, the objective of the current study is to provide an assessment regarding the use of current remote sensing image processing techniques in UAS applications. The research reported herein finds that atmospheric effects have a statistically significant impact on low altitude UAS imagery. Correcting for these external factors affecting the imagery was successful using an empirical line calibration (ELC) image correction technique and required little modification for use in a complex UAS application. Finally, it was found that classification performance of UAS imagery was reliant on training sample size more than classification technique, and that training sample size requirements are larger than previous remote sensing studies suggest.