# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 1 Introduction
Therefore, the Hurst index estimated by the R/S method was introduced to predict the future trend of vegetation in order to understand whether the vegetation trend has long-range memory. The R/S method provides specific information on correlation and persistence and is an effective index for studying complex processes such as vegetation time series [28; 29]. Combining R/S and trend methods for future trend studies can provide insights into the continuity and future direction of vegetation change trends. Multiple researchers have introduced STM, Sen's slope, the Mann-Kendall test, and the Hurst index for studying changes in NDVI [30; 31; 32]. However, for the Inner Mongolia region, human activities such as national ecological projects, changes in grazing practices, and acceleratedurbanization, together with the impact of climate change on vegetation cover, are all non-negligible factors for the spatial heterogeneity of NDVI [33; 34; 35]. Therefore, it would be too simple to explore only the spatial and temporal variations in NDVI. In recent years, the ecological problems in Inner Mongolia have become increasingly severe [36; 37]. As an important ecological barrier in the northern region of China, the ecological condition in Inner Mongolia has an important impact in North China, and exploring the driving mechanism of vegetation change is an important prerequisite for solving ecological problems. Climate change and human activities are the main mechanisms studied in vegetation research [38; 39]. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change states that many aspects of vegetation phenology, composition, and productivity have been affected by global warming over the past 30 years [40; 41; 42]. Many studies have attempted to quantify the relationship between vegetation indices and meteorological factors, but most focused on linear relationships and only on two factors, temperature and precipitation, leading to inadequate conclusions and failing to quantify the contribution and interactions of the drivers of vegetation change [43; 44]. Additionally, the impact of human activities on vegetation cannot be ignored. Attempts have been made to study this using residual analysis, but this method is crude, allowing the quantification of the overall impact of human activities, but being unable to identify the human activity factor that is specifically responsible. In addition to the methods mentioned above, multiple linear regression methods [43], structural equation models [45], random forest models [46], and other methods have been introduced to study the effects of human activities and meteorological elements on NDVI. However, the influence mechanisms of vegetation are complex and diverse, and it is not sufficient to study complex multivariate nonlinear relationships using simple linear correlations. In order to obtain more objective and accurate evaluation results, we detected NDVI spatial differentiation and related drivers from a multivariate spatial perspective, using a Geodetector approach that can avoid the interference of subjective factors [47; 48]. We also explored the potential and interactive effects of anthropogenic and natural factors on vegetation growth. The Inner Mongolia region is located in the northern frontier of China, with a vast territory, high terrain, and complex and diverse landscapes, and is one of the most sensitive regions in the world to external environmental changes due to its arid and semi-arid climate [49]. Over the past 40 years, temperatures in the region have risen much faster than the global average, and the rapid rise in temperature and dramatic reduction in precipitation have exacerbated the degradation of vegetation [50; 51]. In addition, since 2000, the region has entered a phase of rapid socioeconomic development, and climate change and human activities are rapidly restructuring the structure and function of vegetation systems at all levels and simultaneously affecting the ecology and future sustainability of the region [52].