Surveying large wild herbivores ( > 0.6 m) and livestock is the basis for the protection of wild animals, the development of modern animal husbandry and grassland ecological civilization construction. This research systematically reviews the main survey methods of large wild herbivores and livestock, including terrestrial surveys, spaceborne surveys, manned aerial surveys, unmanned aircraft system (UAS) surveys, and focuses on the devices used, data type, data resolution, coverage, and surveyed species. Further, the advantages and disadvantages of different identification algorithms and regional population estimation methods for large herbivores are analyzed. Finally, some problems related to the survey methods, identification algorithms and regional population estimation methods for wild herbivores and livestock surveys are discussed, and some future research directions are suggested. The research finds that submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large wild animals at large spatial and temporal scales, but has difficulty discerning small-sized ( < 0.6 m) animals at the species level, although very-high-resolution commercial satellites, such as WorldView-3 and -4, have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode. UAS surveys are seen as a safe, convenient and less expensive alternative to ground-based and conventional manned aerial surveys for detecting animals and their body features, but most UASs can cover only small areas occasionally. This situation will not change unless the endurance is greatly improved and UAS with docks are widely applied in the future. Docks allow UASs to land, recharge, take off, and execute missions by remote control, thus the data can be acquired with the higher frequencies compared with conventional UASs. The data fusion of multi-platforms and multi-sensors is helpful for producing large-scale and long-time animal data sets. It is necessary to develop high-precision models for detecting dense and small herbivores, estimate population density at a regional scale based on machine learning, and reveal the complex correlation between regional population density and environmental factors such as meteorology and terrain. To synchronously obtain multi-source and multi-scale animal data and verify remote sensing products, a national-scale UAS remote sensing observation network needs to be built to fill the scale gaps between satellites and ground quadrats. Real-time satellite and UAS connectivity software and hardware modules should be developed for quickly acquiring and processing animal data so as to build a smooth channel to connect data and users, and this will fully leverage the value of satellite and airborne data in future for biodiversity monitoring.