Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Abstract

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

Vegetation Responses to Climate Change and Anthropogenic Activity in China, 1982 to 2018

“…The Theil–Sen median method (capable of reflecting trend changes) and the Mann–Kendall (M–K) method (which assesses the significance of trend changes) were used for vegetation trend analysis over long time series [ 16 , 34 ]. Compared with the common linear regression method for assessing interannual variations in vegetation changes, the methods proposed here have a stronger ability to avoid data error and reduce the influence of outliers while increasing the accuracy and reliability of the analytical test results.…”

“…Gao et al found that precipitation was the most important factor affecting the variation of NDVI in the Three-River Headwater Region [ 15 ]. Yao et al found that annual precipitation was the first dominant factor affecting vegetation growth in Inner Mongolia [ 16 ]. Chen et al studied the impact of climate change and human activities on grasslands in Central Asia, and found that precipitation was the main climatic factor affecting grassland dynamics in Central Asia, and overgrazing accelerated grassland degradation [ 17 ].…”

Quantitative Analysis of Natural and Anthropogenic Factors Influencing Vegetation NDVI Changes in Temperate Drylands from a Spatial Stratified Heterogeneity Perspective: A Case Study of Inner Mongolia Grasslands, China

“…Combining the process with the Sentinel dataset or other vegetation indexes (e.g., EVI, SAVI) may help to obtain more precise estimates of vegetation dynamics. Last but not least, if breakpoints, which indicate a shift in the mechanism of influence on the time series under certain circumstances, are neglected, the results of the trend analysis may lead to a misjudging of the factors that influence vegetation changes [40]. In future studies, the times at which breakpoints occurred should be first identified, noting points at which the time series was split into sub-series.…”

“…emote Sens. 2022, 14, 3320 19 of 2 neglected, the results of the trend analysis may lead to a misjudging of the factors tha influence vegetation changes [40]. In future studies, the times at which breakpoints oc curred should be first identified, noting points at which the time series was split into sub series.…”

“…The GeoDetector method does not involve complex parameter settings, nor does it follow the restrictive assumptions of traditional statistical methods. This technique has been used to evaluate the influences of factors in the eco-environmental and social science fields [15,[36][37][38][39][40]. The GeoDetector method can be a promising tool for exploring the associations between various impact factors and vegetation changes in drylands.…”