Spotting East African Mammals in Open Savannah from Space

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.

Automatic Acquisition and Sustainable Use of Political-Ecological Data

“…Due to their size, the abundance of a megafauna species may be estimated with object recognition algorithms applied to satellite images (see Yang et al (2014)). Consider a region, R that is to be monitored.…”

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

“…Lower spatial resolution satellite images have proven inadequate to detect and count individual animals [12], but the availability of commercial satellite images with a spatial resolution of one meter or less (e.g., IKONOS, Remote Sens. 2017, 9, 878 2 of 16 QuickBird, GeoEye and WorldView) has made such an undertaking more feasible [13]. As a result, studies have been undertaken utilizing satellite remote sensing data to detect animals.…”

“…Although their findings demonstrated the potential of remote sensing applications for wildlife detection and monitoring, they also revealed the need for more automated detection processes to expedite analysis. Yang et al [16] explored mammal detection in open savanna country from VHR (0.5-2 m) GeoEye-1 satellite images, using a hybrid image classification approach. Through a two-step process of pixel-based and object-based image classification, they were able to demonstrate the feasibility of automated detection and counting of large wild animals in vast open spaces.…”

“…Descamps et al [46] counted large birds by fitting suspected objects (birds) into bright ellipses surrounded by a darker background. Expert knowledge can also play a critical role in image classification [16,47,48]. For example, Yang et al [16] developed a specific rule set using expert knowledge to remove misclassified objects generated by object-based analysis.…”

Good Practices for Object-Based Accuracy Assessment

“…Entity detection: GEOBIA is used to inventory well-defined geo-objects (such as cars [28], buildings [29], single trees [30] or animals [31]). These entities can be sparsely distributed on a background (e.g., cars on a road) or agglutinated (e.g., trees in a dense forest).…”