Can Vegetation Breakpoints in Eastern Mongolia grassland be detected using Sentinel-1 coherence time series data?

Mongolian society and food production depends heavily on livestock farming, which is usually practiced with nomadic systems. Consequently, movement patterns of herders are crucial in respect of finding sufficient forage and sustainable use of pastures. In this study, a combination of InSAR, optical and weather time series data has been explored as a tool for spatio-temporal grazing monitoring. To detect movement patterns, a machine learning (ML) based method to detect breakpoints in vegetation condition has been developed and compared to the widely-used Breaks For Additive Season and Trend (BFAST) algorithm. The results have been validated using test sites spread across the entire eastern Mongolian steppe ecosystem, covering different grassland use intensities. The results indicate that (1) ML method performed superior compared to BFAST, detecting 41.5% of breakpoints. (2) Breakpoints in summer pastures mainly occurred from April to June, while on winter pastures, they emerged in October, November, and the following February and March. (3) Regarding spatial prediction, the model developed in this study predicts breakpoints in areas distinguish between summer and winter camps, However, there is insufficient data to conclusively attribute the occurrence of pasture breakpoints to herder movements.