The collection of field-based animal data is laborious, risky and costly in some areas, such as various nature reserves. Although multiple studies have used satellite imagery, aerial imagery, and field data individually for some animal species surveys, several technical issues still need to be addressed before full standardization of remote sensing methods for modeling animal population dynamics over large areas. This study is the first to model the population dynamics of livestock in the Longbao Wetland National Nature Reserve, China by utilizing yak estimations from Worldview-2 satellite imagery (0.5 m) collected in 2010 and yaks counted in a ground-based survey conducted in 2011 in combination with the animal population structure precisely extracted from UAS imagery captured in 2016. As a consequence, 5501, 5357, and 5510 yaks were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. In total, 1092, 1062 and 1092 sheep were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. The uncertainty of the presented method is also discussed. Primary experiments show that both the satellite imagery and UAS imagery are promising for use in yak censuses, but no sheep were observed in the satellite imagery because of the low resolution. Compared to the ground-based survey conducted in 2011, the UAS image estimate and satellite imagery count deviated in yak quantity by 2.69% and 2.86%, respectively. UASs are a reliable and low-budget alternative to animal surveys. No discernable changes in animal behaviors and animal distributions were observed as the UAS passed at a height of 700 m, and the accuracy of UAS imagery counts were not significantly affected by the short-distance animal movement and image mosaicking errors. The experimental results illustrate the advantages of the combination of satellite and UAS imagery in modeling animal population dynamics.
Keywords: Animal population estimation; Longbao Wetland; Satellite imagery; Unmanned aircraft system (UAS).
Copyright © 2020. Published by Elsevier B.V.