This work explores two areas of research. The first is the development of a video utility metric for use in aerial surveillance and reconnaissance tasks. To our knowledge, metrics that compute how useful aerial video is to a human in the context of performing tasks like detection, recognition, or identification (DRI) do not exist. However, the Targeting Task Performance (TTP) metric was previously developed to estimate the usefulness of still images for DRI tasks. We modify and extend the TTP metric to create a similar metric for video, called Video Targeting Task Performance (VTTP). The VTTP metric accounts for various things like the amount of lighting, motion blur, human vision, and the size of an object in the image. VTTP can also be predictively calculated to estimate the utility that a proposed flight path will yield. This allows it to be used to help automate path planning so that operators are able to devote more of their attention to DRI. We have used the metric to plan and fly actual paths. We also carried out a small user study that verified that VTTP correlates with subjective human assessment of video. The second area of research explores a new method of detecting GPS spoofing on an unmanned aerial system (UAS) equipped with a camera and a terrain elevation map. Spoofing allows an attacker to remotely tamper with the position, time, and velocity readings output by a GPS receiver. This tampering can throw off the UAS's state estimates, but the optical flow through the camera still depends on the actual movement of the UAS. We develop a method of detecting spoofing by calculating the expected optical flow based on the state estimates and comparing it against the actual optical flow. If the UAS is successfully spoofed to a different location, then the detector can also be triggered by differences in the terrain between where the UAS actually is and where it thinks it is. We tested the spoofing detector in simulation, and found that it works well in some scenarios.