Application of a technology coupling satellite remote sensing and the internet of things for dynamic land and bio-resource management

Nature reserves are complex systems that include land and ancillary resources, wildlife, livestock, and human society. The coupling of internet of things technology with satellite telemetry can significantly improve management efficiency. In this study, the relationship between animal husbandry and grassland resources is used as a model scenario. NDVI (normalized difference vegetation index) analysis of Sentinel-2 satellite images is used to obtain vegetation coverage. Positions and behavior of yaks are obtained through wearable smart terminals. The minimum convex polygon (MCP) and overall dynamic body acceleration (ODBA) methods were adopted to calculate home ranges and activity status. NDVI decreased significantly in autumn, but did not change in winter. Average NDVI reached a maximum (0.65) in September 2019, then decreased sharply. Pasture fences and feeding demand imposed limiting boundaries on activity capacity of experimental organisms, but there was no obvious difference in home range size between yaks with calves and yaks without calves. Among the factors that affected the scope of an animal’s home range, human factors were based on subjective characteristics, and their influence was far greater than that of other factors. Physical isolation factors exerted greater impact than seasonal and other environmental factors. The yak’s home range was smallest in February 2020. Due to limited feeding resources after winter, pastures are often supplemented with grain feeding, and additional fences are used to control the range of yak activity to prevent excessive energy consumption. Driven by human intervention and reduced feeding resources, experimental organisms tend to adopt conservative energy consumption strategies. At the same time, organisms without calves have a lighter burden, less activity and energy consumption, and have better survival capabilities in harsh environments. This research provides new ideas for analytical methods including methods for data acquisition, data coupling, analysis, and decision-making systems. Subsequent research embeds existing experimental process methods into a relatively infinite space with a low-interference experimental environment, and completes a closed feedback loop of data collection-coupling analysis-reverse decision intervention verification.