Application of Deep Learning in Remote Sensing Monitoring of Large Herbivores- A Case Study in Qinghai Tibet Plateau
Application of Deep Learning in Remote Sensing Monitoring of Large Herbivores- A Case Study in Qinghai Tibet Plateau
Wei Luo 1,2,3, Yongtao Jin 1,2,3, Xuqing Li 1,2,3 and Ke Liu 1,2,3*
ABSTRACT
In this study, we monitored large herbivores in Maduo County of Qinghai Province by means of Unmanned aerial vehicles (UAV) remote sensing.The monitoring objects include three kinds of domestic herbivores: Tibetan sheep, yaks, and horses, and three kinds of wild herbivores: Tibetan antelopes, Tibetan wild assess, and blue sheep.All the kinds of large herbivores in the aerial images are detected and located using deep learning model of the MASK R-convolutional neural network (CNN), and the average recall, correct, and leakage are 89%, 98.4%, and 10.8%, respectively. Furthermore, the contour vector of the herbivores is obtained by extracting the mask generated in the detection of the MASK R-CNN, following which the information of both the population number and the distribution of all kinds of large herbivores can be estimated. According to the data of domestic herbivores on hand provided by the General grassland station of Qinghai province, the difference percentage of Tibetan sheep, yak, and horse is 7.5%, 8.1%, and 18.7%, respectively.
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February 2022
Vol. 54, Iss. 1, Pages 1-501
