Estimating ground surface visibility on thermal images from drone wildlife surveys in forests

A thermal drone - an unmanned aerial vehicle equipped with a thermal camera - is a new tool in wildlife research. It enables effective surveys of animal species difficult to monitor with conventional methods. Over open terrain the detection of warm-blooded large animals with a thermal drone is nearly certain. However, in forest surveys, individuals hidden under the tree canopy may remain undetected which can lead to biased estimates of population size. Thus, it is important to estimate the percentage of surveyed area not obscured by tree canopy where animals can be easily detected. This figure may be used as a simple correction factor for animal counts from thermal images (thermograms) or as one of the covariates in more advanced statistical models of population density. We tested four methods of automatic image segmentation: 1) Otsu global thresholding, 2) local thresholding with Gaussian kernel, 3) ChanVese active contour model without edges, and 4) logistic regression and assessed each of their performances in differentiating between canopy and forest floor on a sample of 100 thermograms acquired with a drone during wildlife surveys in a range of forest habitats. We used segmentation results to calculate the estimated percentage of visible ground surface on images and compared it to the percentages assessed by manual classification. The Otsu global thresholding gave results that were in best agreement with the manual classification, had the fastest computing time and was superior to the other three segmentation methods. Visual inspection of the Otsu segmentation results showed that this method accurately separates tree canopy and ground surface on thermograms obtained across all forest types as well as in partially open areas. However, landscape features such as roads or water bodies that have a temperature similar to that of trees may cause segmentation errors and lead to underestimation of the ground visibility. While the use of Otsu segmentation to automatically assess percentage of visible ground surface on thermal images from drone surveys has certain limitations, the method is fast and can be readily applied by wildlife researchers. This approach can aid in correcting bias in estimates of animal population density and contribute to a better understanding of the use of thermal drones for wildlife surveys in forests.