Accurate and precise population estimates form the basis of conservation action but are lacking for many arboreal species due to the high costs and difficulty in surveying these species. Recently, researchers have started to use drones to obtain data on animal distribution and density. In this study, we compared ground and drone counts for spider monkeys (Ateles geoffroyi) at their sleeping sites using a custom-built drone fitted with a thermal infrared (TIR) camera. We demonstrated that a drone with a TIR camera can be successfully employed to determine the presence and count the number of spider monkeys in a forested area. Using a concordance analysis, we found high agreement between ground and drone counts for small monkey subgroups (10 individuals), with drone counts being higher than the corresponding ground counts in 83% of surveys. We could identify additional individuals from TIR drone footage due to a greater area covered compared to ground surveys. We recommend using TIR drones for surveys of spider monkey sleeping sites and discuss current challenges to implementation.

Search and rescue (SAR) is a vital line of defense against unnecessary loss of life. However, in a potentially hazardous environment, it is important to balance the risks associated with SAR action. Drones have the potential to help with the efficiency, success rate and safety of SAR operations as they can cover large or hard to access areas quickly. The addition of thermal cameras to the drones provides the potential for automated and reliable detection of people in need of rescue. We performed a pilot study with a thermal-equipped drone for SAR applications in Morecambe Bay. In a variety of realistic SAR scenarios, we found that we could detect humans who would be in need of rescue, both by the naked eye and by a simple automated method. We explore the current advantages and limitations of thermal drone systems, and outline the future path to a useful system for deployment in real-life SAR.

Underground peat fires are a major hazard to health and livelihoods in Indonesia, and are a major contributor to carbon emissions globally. Being subterranean, these fires can be difficult to detect and track, especially during periods of thick haze and in areas with limited accessibility. Thermal infrared detectors mounted on drones present a potential solution to detecting and managing underground fires, as they allow large areas to be surveyed quickly from above and can detect the heat transferred to the surface above a fire. We present a pilot study in which we show that underground peat fires can indeed be detected in this way. We also show that a simple temperature thresholding algorithm can be used to automatically detect them. We investigate how different thermal cameras and drone flying strategies may be used to reliably detect underground fires and survey fire-prone areas. We conclude that thermal equipped drones are potentially a very powerful tool for surveying for fires and firefighting. However, more investigation is still needed into their use in real-life fire detection and firefighting scenarios.

Situation awareness is the human function of perceiving, comprehending and projecting the state of the environment which is of critical importance to the safe operation of aircraft. A highly autonomous Unmanned Aerial System (UAS) must replicate this behaviour in order to maintain an acceptable level of safety verses a manned vehicle. Nowhere in the flight is situation awareness more critical than during operation in the terminal area. Of primary concern during this stage of flight is the awareness of other traffic heading for the same airfield. This paper presents of a novel method of spatial projection of traffic vehicles encountered by an autonomous UAS in the terminal stage of flight. This projection method relies on a cooperative means of traffic perception, such as Automated Dependant Surveillance - Broadcast (ADS-B) and assumes there is a predefined route which vehicles follow through the terminal region. Whilst this is the case at the majority of airfield, traffic vehicles will not follow this path perfectly. This uncertainty in path following accuracy is captured by utilising a curvilinear reference frame and dealing with discrete transitions (such as the initiation of a turn) separately. It is shown that whilst this technique increases the computational complexity of the problem it can offer significant performance benefit.