# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 4 Discussion
Meanwhile, the eolian sandy soil in the northwest gray desert zone has low soil fertility and poor soil texture, making it susceptible to changes in external conditions. Therefore, this is the main area where sudden changes in vegetation occur and the vegetation is less stable [10]. Inner Mongolia is one of the regions currently experiencing significant environmental changes as a result of tremendous economic development, including changes and intensification of grazing systems and increased intensity of external anthropogenic activities, such as agricultural production. In this study, we highlighted the contribution of anthropogenic factors to vegetation growth, and found that land use types strongly influence vegetation in Inner Mongolia. The impact of human activities is more spatially concentrated, with human activities leading to changes in land-use types and shifts in land-use types affecting the growth of vegetation. This leads to the spatial differentiation of vegetation, as evidenced by the effects of policy implementation, such as returning farmland to forest and grazing bans. Since the late 1990s, the Chinese central and local governments have implemented and introduced large-scale ecological restoration projects, including the \"Returning Farmland to Forests Project,\" the \"Returning Grassland to Grass Ecological Project in Northern China,\" and the \"National Ecological Protection.\" These programs have had a positive impact on vegetation growth in Inner Mongolia owing to the implementation of specific measures to protect grasslands and vegetation through rotational grazing, grazing bans, and converting pasturelands to croplands. NDVI is the most widely used observation method for vegetation. NDVI is a good proxy for vegetation density parameters such as leaf area index (LAI), vegetation cover (FVC), and absorbed photosynthetically active radiation (fAPAR). However, NDVI has two major limitations in characterizing biomass and productivity. First, the relationship between NDVI and green biomass is non-linear and can be saturated in areas with high vegetation cover. The second limitation is that NDVI mainly reflects vegetation greenness rather than photosynthesis itself. However, total primary productivity (GPP) can decline without any reduction in LAI or chlorophyll. Combining the shortcomings of NDVI, a new vegetation index, kNDVI, was proposed by applying the theory of nuclear methods to NDVI, using machine learning. The kNDVI correlated with GPP similarly or better than other indices globally. kNDVI correlated with SIF better than other indices in general and in all biomes, especially in deciduous broadleaf forests and for herbaceous and cultivated crops. The correlations of kNDVI were higher in nearly all cases (e.g., Spearman correlation, distance correlation), thus confirming the advantage of kNDVI over other indices. Using the kNDVI index in future studies on vegetation will increase the significance of geo-monitoring and terrestrial biosphere studies [85]. The use of MODIS NDVI data as the only input in this study may reduce the accuracy of the vegetation breakpoint results. This limitation can be addressed in future studies by incorporating data sources with higher temporal resolution and finer spatial resolution. For example, the data provided by the GF-1 and Sentinel-2 satellite multispectral scanners could be combined into more comprehensive driving force data. The spatial pattern driving mechanism of vegetation change should be further explored in conjunction with climate change and ecological protection policies, to provide more scientific data support for improving the ecological environment in Inner Mongolia.