Aerial drone systems are now widely used to survey wildlife, but validation in the detectability of individuals is rarely assessed. This knowledge gap is critical, given the influence of local environments on wildlife detectability from the air. In this study, we integrated Animal Biotelemetry technology with aerial drones to evaluate the temporal and environmental factors influencing animal detection probability and subsequent population estimates. Wild-caught feral pigs (Sus scrofa) were fitted with GPS tracking collars and releasing them into a large natural habitat enclosure in northern Australia. Utilizing a fixed-wing drone equipped with a dual camera (thermal infrared and RGB), we conducted multiple flights over the study area during both wet and dry seasons, from sunrise to sunset. The study found that the probability that a pig was visible in aerial imagery was highly variable depending on the timing of the aerial survey. Detection probability was at its lowest during mid-afternoon (5 to 20%), while the early evening yielded the highest detection probability (50 to 75%). We observed seasonal differences, with detection probabilities exceeding 50% in the mornings of the wet season, in contrast to less than 30% during the dry season. Temporal trends in detection probability were similar in both thermal infrared and RGB imagery. The GPS location data enabled us to assess how localized factors (canopy cover, land cover, ambient temperature) altered animal detection probability. This information facilitated the identification of survey times to maximize feral pig detection and the development of a correction factor to account for non-detected individuals at specific times and locations. The study demonstrates the value of integrating Animal Biotelemetry technology and aerial drones to account for variations in detection probability when undertaking wildlife aerial surveys. Insights gained from this approach have implications for enhancing the accuracy of population assessments and contributing to more effective wildlife management and conservation.