1. Introduction
As a producer of the terrestrial ecosystem, vegetation is closely related to natural environmental elements such as climate, soil, topography, and water resources [
1]. It plays a crucial role in water conservation [
2], soil erosion prevention [
3], wind and sand control [
4], and ecosystem stability [
5]. Therefore, studying vegetation cover in different areas is crucial for ecological conservation in the region. Previous studies on the ecological aspects of western China have focused on large areas such as the Tibetan Plateau and the Taklamakan Desert. Still, to further study the characteristics of the local ecological environment in these areas, targeted studies are needed in Tibet and Qinghai.
Because of the advantages of remote sensing technology tools, such as extensive range, the ability to analyze long time series, and high resolution, they have become essential for studying the ecological environment. Among them, the normalized difference vegetation index (NDVI) can accurately respond to the status of surface vegetation cover and is thus widely used to evaluate vegetation growth and development and ecological environment changes [
6]. Previous studies on NDVI changes and drivers using trend analysis, correlation analysis, or partial correlation analysis have been conducted in different regions, including globally, across Europe, in Asia, in China, in the Yangtze River Basin, in the Yellow River Basin, and in the Qinghai–Tibet Plateau [
7,
8,
9,
10,
11,
12]. These methods have achieved good results; however, they still need to meet the needs of the current research stage. The Geodetector model proposed by Wang [
13] et al. bridges the gap between the correlation analysis methods used in previous studies. The technique has a greater advantage over other methods in quantifying the relationship between the NDVI and related drivers [
14]. For example, Gao [
15] et al. found that the Geodetector model could better reflect the spatial heterogeneity of vegetation cover and quantify the drivers of vegetation change and the interactions between individual factors in their study of vegetation cover in the Sanjiangyuan region. Wu (2022) et al. [
16] used Geodetector to examine the spatial and temporal variability in the NDVI and its dual response to climate change and human activities in three northeastern provinces of China.
Vegetation restoration is one of the most effective ways to improve the ecological environment and control soil erosion. Therefore, monitoring and predicting vegetation cover is significant to regional ecological restoration and environmental management. However, in the past, scholars mostly used Markov chains [
17] or empirical function models [
18] to predict vegetation cover, and although their results are scientific, they are less generalizable. The Hurst index is different from the traditional prediction models as it is an important indicator that describes the long period of non-function. It can detect the existence of ultra-long periodicity in a time series; thus, the Hurst index can be used to predict the future growth of vegetation. For example, Han [
19] used the Hurst index to predict the future based on the current vegetation growth trend in the region, and Zhang [
20] predicted future vegetation growth in the Qinba Mountains.
To study the characteristics of the local ecological environment of the Tibetan Plateau in western China and refine the driving mechanisms behind the local ecological environment, the TAR was selected for this paper. The TAR is located in the southwestern part of the Tibetan Plateau, a region with complex soil types, diverse land use types, and rich vegetation types, making studying vegetation conservation in the TAR extremely important. From a review of previous studies, we found that earlier studies on the characteristics of vegetation cover change in the TAR have used short time series or lower-resolution datasets. Thus, their results have varied [
21,
22,
23]. Meanwhile, previous studies on the factors influencing the NDVI in the TAR have focused on air temperature and precipitation. These studies needed to consider essential factors affecting vegetation growth, such as soil and land use types. They needed more research on anthropogenic factors and the interaction between anthropogenic and natural factors [
24]. In addition, studies on predicting future growth trends in the TAR have often used traditional mathematical and physical models [
25,
26,
27,
28,
29]. Therefore, integrated trend analysis, Geodetector, and Hurst indices can better provide a more detailed analysis of the local ecological environment.
The response of plateau ecosystems to global climate change is obvious, but the fragile environment in which plateau vegetation grows is highly vulnerable to damage by natural and anthropogenic factors. Therefore, the purposes of this paper are as follows: to analyze the evolution of the NDVI in the TAR between 1998 and 2019 using one-dimensional linear regression and F-significance tests; to quantitatively study the significant effects of natural and anthropogenic factors on the NDVI and their interactions on the NDVI with the help of Geodetector; and to make reasonable predictions of future vegetation growth trends using the Hurst index. These studies are intended to provide ecological and environmental protection departments with a scientific basis and reference for decision making in vegetation conservation and the formulation of relevant policies.
4. Discussion
In the past few decades, under the influence of human activities and climate change, vegetation coverage in most regions of China has gradually increased [
10], especially in northern areas such as Inner Mongolia and Xinjiang [
31,
32]. In the experiment, we selected long time series NDVI data. Through the combination of multiple factors and a comprehensive comparison of various methods, the experimental results are consistent with the changing trend of NDVI in China. This result shows that vegetation in the TAR grew well from 1998 to 2019 and that our experimental results are accurate.
In this study, we used the F-significance test and the Hurst index to analyze the vegetation change trend in the TAR from 1998 to 2019 and used the Hurst index to predict the future NDVI change trend in the TAR. Finally, we used four models of geographical exploration to analyze the driving factors and the interaction of elements of the NDVI in the TAR. In the following chapters, the research findings of this paper are discussed in detail.
4.1. Trend and Prediction Analysis of NDVI Changes
The study in this paper found that the vegetation index of the TAR has significant spatial differences. The regions with higher vegetation coverage in the TAR are Linzhi, Changdu, and Shannan. The central, northern, and western areas of the TAR have sparse vegetation coverage, and the distribution of NDVI is similar to the spatial distribution of air temperature and precipitation. This is relatively consistent with the research results of Feng et al. [
17]. The results of the geographical detector also show that vegetation growth in the TAR is affected by both natural and human factors. Nevertheless, climate change is the main driving force of vegetation growth in the study area. The results of the F-significance test show that the changing trend of NDVI in the TAR from 1998 to 2019 shows an upward trend, with the increased part accounting for 72.56% of the total area of the autonomous region, which is consistent with the research results of Wu et al. [
16]. The results show that vegetation coverage in the TAR is gradually increasing, and the vegetation growth status is good. The results of the F-significance test also showed that NDVI values in snow mountains and permanent ice cover areas showed a downward trend. Combined with the results of geographical detectors, we can see that the soil types and vegetation cover types in the study area have a significant impact on the intensity and direction of the NDVI change trend. At the same time, the spatial correlation is high, and the characteristics of NDVI changes in terms of the change in soil type and vegetation cover type are consistent with the research results of Sun [
31] and Yang [
32].
The results of related studies also show that current vegetation cover in the TAR is poor [
33]. Therefore, with the help of the Hurst index, future vegetation growth can be predicted, and relative conservation measures can be made to develop vegetation. In this paper, the Hurst index showed that 72.06% of the area in the TAR showed an inverse trend in future vegetation growth, which is consistent with the results of Liu et al. [
34]. In some regions of the Himalayas and Ali, the vegetation growth trend will decrease. Therefore, emphasis should be placed on protecting areas with low vegetation cover and areas where the Hurst index predicts a decrease. At the same time, the supervision and protection of areas prone to desertification and sandstorms should be increased.
4.2. Impact of Natural and Human Factors on NDVI
Climate change has a specific contribution to vegetation recovery [
35]. Among the influencing factors selected for this experiment, the influence of precipitation on the NDVI in the TAR reached about 0.45, indicating that precipitation is one of the main factors influencing changes in vegetation growth in the TAR; NDVI values increased first and then started decreasing as average annual precipitation changes rose [
10]. Additionally, intense precipitation can significantly affect changes in the spatial and temporal dimensions of the NDVI [
36]. Yang et al. [
37] suggested that air temperature was the main factor affecting the NDVI in the TAR compared to the effect of precipitation on the NDVI. This result may be due to the different time scales of the study and the various resolutions of the data used. From 1998 to 2019, the TAR experienced higher air temperature changes than China [
38] and lower precipitation than China [
39]. The rapid increase in air temperature [
40] and the relatively stable precipitation [
41] in the TAR have led to regional warming and drought [
42]. Some studies have shown that alpine meadows and grasslands strongly respond to precipitation [
18]. In 2006, there was an extreme drought in the TAR that damaged pasture growth and destroyed grassland ecosystems, decreasing the NDVI [
17]. As the NDVI has a lagging effect on precipitation [
43], extreme weather in 2018 led to a sharp decrease in NDVI values in the autonomous region in 2019. Extreme precipitation events had a more pronounced effect on NDVI values than extreme air temperature events in the Tibetan Plateau region, indicating that vegetation is more sensitive to changes in precipitation. This study showed that the impact of precipitation on vegetation growth conditions was higher than that of air temperature on the NDVI, which is consistent with the findings of Ichii et al. [
35]. This result will provide an essential basis for the study of vegetation cover in the TAR.
Elevation has an important influence on vegetation growth and human activities [
44]. In the TAR, the topography is complex. The mountains are crisscrossed, with snow-capped mountains and ancient ice caps in some areas, which have apparent constraints on vegetation growth. The results of this experiment show that the influence of elevation on the NDVI is about 0.25, and the ecological environment is suitable in areas below 4000 m above sea level. The trend in vegetation cover with elevation changes first fluctuates upwards, then plateaus, and finally sharply decreases. This is similar to the findings of Li et al. [
45].
Soil type is also an essential factor in determining the spatial variation of the NDVI [
46]. Soil type not only affects the growth of plants but also limits vegetation’s spatial distribution. In this experiment, soil type always appeared as the most influential factor in the results of each investigation. This is consistent with the findings of Xin et al. [
47].
In this experiment, human factors had a weak influence on the NDVI, which is consistent with the results of Huang et al. [
26]. This results from the fact that the TAR is sparsely populated [
48] and that the water and heat conditions in most areas are not sufficient to supply vegetation growth and human life [
49].
5. Summary and Conclusions
Using the NDVI, this study investigated the dynamic changes in vegetation in the TAR from 1998 to 2019. It analyzed the correlation between the NDVI and soil type, vegetation cover type, terrain factors (altitude, slope, and aspect), climate factors (air temperature and precipitation), and human activity factors (population density, gross regional product, land use type) using spatial trend analysis, F-significance tests, the Hurst index, and a geographic detector. The main conclusions are as follows:
(1) The areas with good vegetation cover in the TAR are the Linzhi, Lhasa, Shannan, and Changdu areas, and vegetation cover in Ali is the worst; the annual mean NDVI values of the TAR from 1998 to 2019 show an overall increasing trend, and the linear incremental rate for the mean NDVI value is 0.002/per year. The areas with severely degraded vegetation cover account for 1.95% of the total area, and areas with significant improvement account for 20.78%.
(2) The impact factors for NDVI in the TAR are ranked as follows: soil type > average annual precipitation > DEM > vegetation cover type > land use type > average annual air temperature > regional gross domestic product > slope > population density > aspect. The main driving factors are soil type, average annual precipitation, and DEM, with respective influences of about 0.58, 0.5, and 0.45.
(3) The influence factors for NDVI in the TAR in order of influence are as follows: soil type > average annual precipitation > DEM > vegetation cover type > land use type > average annual air temperature > gross regional product > slope > population density > aspect. The main driving factors are soil type, average annual precipitation, and DEM, with respective influences of about 0.58, 0.5, and 0.45.
This study not only presents the trends of NDVI mean values in the TAR for 22 years but also restores and predicts the past and future trends of the NDVI in space; meanwhile, it quantitatively describes the strength of each factor in explaining the spatial variation of the NDVI and provides a research direction for future vegetation protection in the TAR.