Counting wildlife is vital for management but is costly and surprisingly tough. Fortunately, a new enhanced technique is on the horizon. Save the Elephants has trialed a completely new method of counting wildlife over large areas with an innovative technique known as the Oblique Camera Count, in partnership with survey professional Dr. Richard Lamprey (who created the technique), the Kenya Wildlife Service and also the Department of Resource Surveys and Remote Sensing.
Oblique Camera Count (OCC) – using an automated high-definition oblique camera system elevates multi-species, aerial counts, with better accuracy and efficiency. It might also mean the end of traditional, expensive strategies using human observers. A document on the results has been issued.
What is an Oblique Camera Count (OCC)?
It embraces new technologies in improving multi-species aerial counts. An aerial count is a crucial tool for providing fundamental data for wild-life management.
Existing standard techniques for counting wild-life over large areas work with light aircraft flying very low over surfaces in transects while rear-seat observers count animals. The 2 principal variants are Sample Counts (where amounts of wild-life observed in a sample strip are counted, and the overall estimated with statistical techniques) along with Total Counts (where pretty much all observed animals are counted). Kenya traditionally conducts a total number of counts. The latest Great Elephant Census, a pan African count, utilized sample counts.
Working with Kenya Wildlife Service along with the Department of Resource Surveys & Remote Sensing, Save the Elephants commissioned a trial of revolutionary OCC process for Tsavo, Kenya, to evaluate an automated high definition oblique camera system to standard techniques. Approximately 180,000 images had been taken during the exercise, which had been analyzed by photo interpreters to be able to create by far the most accurate count yet conducted.
What are the advantages of OCC?
In aerial wildlife surveys, human observers are fallible. Fixed-wing aircraft need to fly above a specific speed to stay aloft, and in areas where a lot of individuals occur, or where a number of species come together, human observers are unable to make an exact count at the moment when the animals are in view. On hot days, animals try to get shade and might be very difficult to notice with naked eyes that adjusted for bright sunlight. And sitting for long hours in hot, small, turbulent cockpits are able to leave human observers susceptible to fatigue and at times airsickness too.
Modern high-definition camera systems effectively provide a freeze-frame of the landscape at the time in which the survey aircraft passed, enabling the survey zone to analyzed slowly and a lot more accurately. The survey can additionally be repeated if needed, simply by re-analyzing the photos. This enables a more accurate estimation of just how many animals are present in the landscape.
With this trial, all images were analyzed by human teams of interpreters, but it was an expensive and time-consuming process: it had taken a group of 12 individuals 9 months to complete. Artificial Intelligence promises savings that are significant in both time and costs in the processing stage. Incoming it’s probable that the digital camera systems might be installed on drones, allowing additional savings.
What are the results of OCC?
Human observers in hot cockpits tend to miss a lot of things, results show.
When compared to the analyzed OCC photo dataset, rear-seat observers in the same aircraft, the cameras missed 60% of giraffe, 48% of zebra, 66% of large antelopes and 14% of elephants. The human observers or the rear-seat observers in the total count conducted for two weeks missed 57% of giraffe, 33% of buffalo, 27% of elephants and 85% of elephant carcasses.
The results imply that rear-seat observer-based techniques conducted in East Africa during the last 60 years significantly underestimated a few wildlife populations. This was presumably as a result of the fact that human observers generally fail to identify animals as a result of the reality they were just overlooked or unavailable for detection as a result of dense vegetation cover.