# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 1 Introduction  
Vegetation is an important component of ecosystems and plays a key role in the carbon cycle [1; 2; 3]. Distinctive interannual and seasonal variations have been observed while studying vegetation trends [2; 4; 5]. Vegetation indices can provide substantial information on terrestrial vegetation; they are often used as an important parameter in characterizing surface vegetation for environmental quality assessments. Furthermore, vegetation indices are important for studying the hydrology, ecology, and regional changes [6; 7]. Numerous studies have shown that the normalized difference vegetation index (NDVI) correlates well with the biomass and leaf area indices, allowing a good representation of the surface vegetation coverage. This can be used to effectively characterize vegetation activity and productivity and is suitable for representing changes in surface vegetation cover [1; 8; 9; 10]. With recent advancements in remote sensing technology, moderate resolution imaging spectroradiometer (MODIS) NDVI has become the primary data source for large-scale vegetation cover research because of its advantageous characteristics, such as high quality, wide areal coverage, high temporal resolution, and accessibility [11; 12; 13]. Currently, researchers mostly focus on the study of the overall trend of NDVI. Sen's slope is often used for quantifying the overall trend of the NDVI time series, as it is a non-parametric method that is less sensitive to outliers and skewed distributions [14]. However, this method can only be used to estimate the magnitude of NDVI changes and cannot determine the significance of the slope, so the results lack statistical significance. Therefore, researchers are increasingly applying the Mann-Kendall test to assess the significance of monotonic trends (linear or nonlinear) using a combination of Mann-Kendall test and Sen's slope algorithm for time series with outliers, to simultaneously assess and quantify monotonic interannual trends. This strategy is more robust than using parametric statistics, such as ordinary least squares. Therefore, the Mann-Kendall method is well suited to examine trends in vegetation [15; 16]. However, only studying the interannual variations in NDVI can ignore the changes in the details of NDVI time series, and for a detailed description of NDVI changes, the ephemeral disturbances that occur in a time series need to be assessed [17]. Several methods have been proposed to detect sudden changes in vegetation, including the Landsat-based detection of trends in disturbance and recovery [18], breaks for additive season and trend (BFAST) [19], vegetation change tracker [20], and the detection of breakpoints and estimation of segments in trend methods. Due to the diversity and uncertainty of vegetation change, only high-frequency time series can describe the entire process of vegetation change in a short time interval [21; 22]. Therefore, only change detection with dense satellite time series data can meet the requirements for the dynamic monitoring of vegetation cover changes [7]. A trend estimation method based on seasonal trend model (STM), originating from BFAST, considers the seasonality and noise of the NDVI time series. Therefore, it allows changes in the phenological cycle, as well as long-term NDVI trends, to be detected, distinguished, and quantified [23; 24; 25]. Therefore, it has received wide attention in the analysis of mutations that occur in a time series, and researchers are increasingly using this method for trend and breakpoint analysis of NDVI time series, time series smoothing and interpolation, and surface phenology analysis [26; 27; 25]. In this study, trends in the NDVI time series were explored using a combination of methods, analyzing both overall and mutational details. Vegetation trends need to be fully characterized to provide information for decision making.