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China is a large country of grassland and animal husbandry. Overloading and overgrazing is one of the main causes of grassland degradation in China. To protect the grassland, it is necessary to precisely monitoring livestock carrying capacity, which is the key to evaluation and control grass-livestock balance. However, traditional method of livestock carrying capacity such as hierarchical statistics, sampling field survey and online camera monitoring is whether time-consuming, labor-intensive, costly or poor quality. So it is very urgent to find a kind of efficient and precise monitoring method of livestock carrying capacity in grassland. To achieve this goal, this research proposes an efficient and precise monitoring method of the livestock in grassland by using of sub-meter resolution satellite image. The method not only fuses multi-scale features of livestock in sub-meter resolution satellite image such as "blob feature"、"flock feature"、"moving feature" , but also integrates deep learning technology and object oriented recognition technology. Firstly, considering that the livestock in satellite image is a kind of small (tiny) target, it uses kinds of image enhancement method such as bi-lateral filtering and Laplace of Gaussian (LoG) operator to enhance the weak livestock signal successfully. Secondly, in consideration of "flock feature" of livestock flock, it use a kind of "livestock flock detection model" based on deep learning technology to get the rough distribution area of livestock flocks. Thirdly, in consideration of "blob feature" and "moving feature" of livestock, it use a kind of "livestock blob detection method" based on LoG Gradient Difference and object oriented recognition technology to get the possible livestock blobs. Finally, by integrating the detecting result of both livestock flocks and livestock blobs, it uses the livestock blobs result to enhance and verify the livestock flocks result, and the enhanced and verified livestock flocks and the livestock blobs within them is finally get by using some simple manually revising work. Through an experiment in Xilingol grassland, it is found that the approach has good effect on livestock flock detection: with positive detection rate about 0.802 and false detection rate about 0.244, especially as to big livestock flock, the positive detection rate is up to 0.937, the false detection rate is low to 0.072. It is very helpful for the monitoring and supervision of livestock flock in grassland, and can also provide reference for remote sensing monitoring of other "small (tiny) targets". It is of great significance both in terms of technological innovation and business application. It makes a litter effort in promoting the livestock satellite remote sensing monitoring into an intuitive and fine monitoring era - "Number-Counting Era".