Abstract
Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques.
Highlights
Knowledge of population dynamics is essential for managing and conserving wildlife [1,2,3]
The classification result was explicitly presented in all validation areas with commission and omission errors (Fig. 6)
From the accuracy results we found that the omission error of each pilot study area was very close to each other; the commission error of the samples in Pilot A was around 5% higher than Pilot B
Summary
Knowledge of population dynamics is essential for managing and conserving wildlife [1,2,3]. Traditional methods of counting wild animals such as aerial survey or ground counts have many challenges [4]. Most animals are very sensitive to human disturbance and to low-flying airplanes due to the engine noise [5, 6], which may affect the accuracy of survey results. Traditional counting methods are exceedingly time consuming and labour-intensive [7]. Though some current census methods can achieve relatively high accuracy, balancing the need for accurate estimates of wildlife populations with survey costs is a great challenge [8]. The results of traditional survey methods can be unreliable due to the observational bias [9] with a large standard error of survey results [10, 11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
