## 4 Discussion and Conclusions
### The Importance of a Meteorological Prediction System for Local Stakeholders  
The difference between 2018 and 2019 (Section 3.2.2) reveals the impact of unpredictable meteorological conditions. Although conservation programs and human efforts can make a positive difference in the grassland region, the sensitivity of vulnerable grassland systems to environmental factors still acts as the main driver. This also sets obstacles for a successful implementation of grassland conservation programs, balancing the capacity of grassland and livestock on a yearly basis. Under these circumstances, an earlier and up-to-date grassland growing prediction system is of great importance, not only for the equitable implementation of conservation projects but also for efficient grassland use strategies of herders. Local government could develop improved planning for implementation of grazing bans or forage-livestock balancing measures in the region; herders could decide whether natural grassland needs to be reserved or could rationally be used during a predictable good production year. A similar project has already been employed in Mongolia, where natural hazards are the main issues in grassland systems [55], through the use of near real-time satellite-based climate data, computer simulation and modelling. Mongolia's livestock early warning system, accessible to all people, monitors and forecasts livestock forage conditions to help pastoralists and decision makers make timely decisions while facing natural hazards. This system can be a good reference for IMAR and other pastoral areas in the world in need of balanced grassland management. Some issues still need to be addressed beyond this research. Firstly, in the binary pixel model for calculation of FVC, the impact of soils on vegetation signatures is an important factor which often causes errors. The parameters of NDVI\({}_{\text{s}}\) and NDVI\({}_{\text{v}}\) are always under discussion and the selection principles vary throughout the research, especially concerning the determination of NDVI\({}_{\text{s}}\). Basically, the NDVI\({}_{\text{s}}\) vary from \(-0.1\) to \(0.2\)[56]. The estimation study of FVC in Northeast China pointed out that NDVI\({}_{\text{s}}\) ranges between \(0.07\) and \(0.22\)[37]. Research on FVC estimation in arid and semi-arid environments recommended NDVI\({}_{\text{s}}\) and NDVI\({}_{\text{v}}\) in grassland are \(0.045\) and \(0.593\), respectively [57]. Estimation research of FVC in IMAR by MODIS data in 2013 obtained the result that NDVI\({}_{\text{s}}\) and NDVI\({}_{\text{v}}\) in grassland are \(0.118\) and \(0.806\), while with in situ measurement the results are \(0.203\) and \(0.891\)[34]. In this research, the mean value of NDVI\({}_{\text{s}}\) and NDVI\({}_{\text{v}}\) from 2011 to 2019 are \(0.07\) (CV = \(0.0\%\)) and \(0.89\) (CV = \(0.7\%\)). The value of NDVI\({}_{\text{s}}\) shows in the normal ranges as mentioned above, but is lower than values obtained through in situ data in 2013. The value of NDVI\({}_{\text{v}}\) is accordance with the MODIS and in situ research result in IMAR [34]. Secondly, although the study has qualitatively and quantitatively analyzed the change of grassland based on FVC and NPP indicators, grassland species succession during the process of grassland degradation may also contain the increase of mixed shrub types, which will also lead to an increase in both FVC and NPP. As such it will be important to develop a method to differentiate between shrubs and grassland. With the development of LiDAR, hyperspectral data and unmanned aerial vehicles, attempts are arising in discriminate specific grassland species [58]. Although limited to regional and short-term studies due to limited data access and high-cost data, this discrimination process should be incorporated into future research to provide more precise grassland change results and reliable support for local decision making. Besides, livestock always act as the basic conjunction between human society and environment.