Response of the Alpine Timberline to Residual Permafrost Degradation in Mount Wutai

Abstract

In cold regions, there is a close relationship between vegetation and the underlying permafrost. While the vegetation protects the permafrost, the permafrost also provides the necessary water, nutrients, and physical support for the vegetation. As the most sensitive area of alpine vegetation to environmental changes, alpine timberlines respond rapidly to permafrost degradation. Based on the data from meteorological stations and remote sensing in Mount Wutai, the distribution and change in surface frost numbers under the influence of vegetation and alpine timberlines in Mount Wutai from 2001 to 2021 were produced. The results show that from 2001 to 2021, along with the continuous degradation of permafrost, the alpine timberline showed an upward followed by a slight downward trend. From 2000 to 2014, the alpine timberline consistently moved upward, with the melting of permafrost, which produces water playing a positive role. In 2014–2021, the permafrost near the timberline in the study area disappeared, and the upward trend in the alpine timberline was blocked, even downward in some areas. Analysis of the above phenomena shows that in the process of permafrost degradation, the functions of supporting and fixing roots as well as water storage for overlying forest vegetation by permafrost will be lost sequentially, which will have an impact on the growth of the vegetation and make the upward trend in NDVI in the growing season blocked. The process of permafrost degradation is beneficial to vegetation growth but is unfavorable at the end of the degradation period, which is reflected in the phenomenon of the alpine timberline rising and then falling.

1. Introduction

In the past 50 years, global temperatures have shown a significant upward trend. The IPCC predicts that the global average surface temperature has increased by around 1.1 °C compared with the global average surface temperature from 1850 to 1900 [1]. Rising temperatures and human activities will have a series of knock-on effects on the Earth’s environment. In the background of global warming, the increase in temperature is more significant in alpine regions [2]. Large-scale degradation of alpine permafrost is deeply affecting alpine frost ecosystems [3].

There is a symbiotic relationship between alpine forests and the permafrost in the soil below them. Overlying vegetation has a significant cooling effect on the seasonal active layer of permafrost [4], thereby protecting the permafrost below [5,6]. In the summer, Arctic tundra vegetation still has a cooling effect of 3 °C on the soil below [7]. The protective effect of vegetation on permafrost makes it possible for permafrost to persist in some areas with positive mean annual surface temperature [8]. Permafrost provides water and the function of supporting and fixing the roots of overlying forest vegetation, allowing it to grow at elevations unfavorable to it [9]. In the early stage of permafrost degradation, the thawing of permafrost increases the water and nitrogen content of the soil [10], which is favorable to the growth of vegetation. During degradation, the bottom of the active layer becomes deeper, which will cause trees with shallow roots to topple [11], as well as wetlands and swamps to form in poorly drained areas [12]. At the end of permafrost degradation, the water barrier formed by permafrost will disappear and the water in the surface soil will infiltrate, which is unfavorable to the growth of vegetation [13] and even leads to the succession of vegetation communities towards the direction of deeper roots [10]. During the degradation of permafrost, a moderate increase in moisture contributes to the development of vegetation. Denser vegetation can block heat waves more effectively [14], thus slowing down the degradation of permafrost. The alpine timberline is an ecological transition zone between alpine forests and the meadows, tundra, and scrub on mountain tops [15]. In studies of alpine timberlines related to remote sensing techniques, the upper boundary of the timberline is often referred to simply as the timberline [16,17]. Alpine timberlines are located at the edge of forest tolerance to low temperatures [18] and are very sensitive to climate change [19]. There are many research directions for alpine forest lines, such as the response of timberline location to climate change [20], changes in tree whorls near the timberline [21], and the species that make up the timberline [22]. However, in many studies on the response mechanisms of alpine timberlines to climate change, the effects of the existence and degradation of permafrost on vegetation growth have been neglected, making the conclusions erroneous.

The techniques of remote sensing for the study of distributions of permafrost have become mature. For example, the frost number model [23], the TTOP model [24], the SiB2.5 model [25], the CLM4 model [26], and so on. According to a large amount of in situ monitoring data, the surface frost number model [27] was constructed with consideration of the influence of vegetation. This model is based on the frost number model and is used in this research. In this paper, we select Mount Wutai as the study area, which is located at the southern boundary of the alpine permafrost zone in North China [28]. We use remote sensing technology to study the response mechanism of the alpine timberline to permafrost degradation in Mount Wutai under the background of global warming.

2. Materials and Methods

2.1. Study Area

Mount Wutai is situated in Shanxi Province, China (38°55′–39°66 N, 113°29′–113°39″ E, Figure 1a), and belongs to the temperate semi-humid monsoon climate zone. Northern Terrace, with an elevation of 3061.1 m, is the highest peak in Mount Wutai and also the highest peak in North China. Central Terrace with an elevation of 2894 m, is the second-highest peak in Mount Wutai. In the study area, as the elevation rises, low mesa earth and rocky hills, high mesa earth and rocky hills, and subalpine gently sloping platforms are sequentially distributed [29], and the tops of the platforms develop typical continental ice-marginal geomorphic patterns under the influence of the cold climate [30]. The average temperature at the top of the platform is about −5 °C throughout the year, with temperatures ranging from a minimum of −39.5 °C to a maximum of 20 °C, and winters lasting up to 270 days. The average annual precipitation within the mountain is 828.5 mm, which is concentrated in the summer months and is strongly influenced by the topography, with more on the southern slopes than on the northern slopes [31]. Northern Terrace is distributed with a montane deciduous broad-leaved forest zone, cold-temperate coniferous forest zone, subalpine scrub zone, and alpine meadow zone from low to high altitude [32]. Vegetation grows better on shady slopes than on sunny slopes. In terms of the distribution of elevation, the upper limit of forests is higher on shady slopes than on sunny slopes [29,33] (Figure 1b). The timberline is mostly composed of Picea meyeri and Larix Principis-rupprechtii, with the latter accounting for the largest proportion [30,34] The forest near the timberline on the northern slopes is denser and less affected by humans, while the forest near the timberline on the southern slopes is sparser, with some areas close to human activity areas.

2.2. Research on Permafrost in the Study Area

The study area contains a high degree of topographic relief, and the temperature decreases by 0.5 °C to 0.8 °C for every 100 m increase in average elevation [32]. In October 1955, a meteorological station was constructed at Central Terrace at an altitude of 2895.8 m in the study area. In January 1988, the meteorological station was moved to a new site nearby at an altitude of 2208.3 m. According to the data from the meteorological station, in 1955–1980, the average annual temperature in the study area at an altitude of 2895.8 m was −4.5 °C, with the lowest temperature reaching −45 °C, and the average annual precipitation was 900 mm. Low temperatures and ample precipitation provide the conditions for permafrost to survive. Zhang discovered permafrost at the top of both North Terrace and Middle Terrace [35]. In 1976, during the construction of the meteorological station at Central Terrace, it was discovered that permafrost existed at a depth of 1 m. Zhu found that the upper limit of permafrost depth under the subalpine meadow at the top of the Central Terrace was 72 cm, the upper limit of permafrost depth under the coniferous forest belt on the northern slope of the North Terrace was 20 cm, and no permafrost was found under the alpine meadow up to the 120 cm layer of soil at an altitude of 3000 m on the southern slope of the North Terrace [36]. Yugo considered Mount Wutai located at the southern boundary of the alpine permafrost zone in North China [28]. The IPA map classifies the Mount Wutai area as an isolated permafrost zone [37]. JIN considers that the permafrost in Mount Wutai is distributed between the elevations of 2300 and 3058 m [38]. According to Cui, the lower boundary elevation of the island permafrost in the alpine meadow zone of Mount Wutai is 2800 m [39]. Saito showed that permafrost exists at high elevations in the Mount Wutai region on a “dimension–elevation” relationship map of LPM permafrost distribution constructed based on the PMIP2 model [12].

Importantly, there is no systematic study about the permafrost in Mount Wutai yet. Mount Wutai is located in the front of the permafrost degradation zone. Within this area, changes in the distribution of the permafrost in terms of time and space will indicate its degradation status, while changes in timberlines will reveal the mechanisms by which the overlying vegetation responds to alterations in the permafrost.

2.3. Data Description and Data Sources

Digital Elevation Model (DEM) data were obtained from the SRTM dataset provided by the National Aeronautics and Space Administration (NASA) of the United States of America with a spatial resolution of 30 m. The downloaded data were imported into ArcGIS for preprocessing work such as splicing, cropping, and extracting relevant information such as slope, slope direction, and contour lines.

Landsat series images (data source: https://glovis.usgs.gov) were used to obtain the alpine timberline. The 30 m spatial resolution images with less than 10% cloud cover from April to September from 2001 to 2021 were selected, and then the downloaded images were pre-processed with radiometric calibration, geometric correction, and projection.

Normalized Difference Vegetation Index (NDVI) data were derived from NASA’s MOD13Q1 dataset (data source: https://ladsweb.modaps.eosdis.nasa.gov/), which has a raw spatial resolution of 250 m. Then, the data were resampled to a 1000 m resolution. We processed the data from 2001 to 2021 on the GEE platform to obtain the annual average NDVI for the vegetation growing season (June–September) for those years.

Net Primary Production (NPP) data were derived from the MOD17A3H dataset (data source: https://glovis.usgs.gov), and the data have a raw spatial resolution of 500 m. Then the data were resampled to 1000 m resolution.

Meteorological data were obtained from the China Meteorological Data Service Centre (http://data.cma.cn/). Temperature and precipitation data from Mount Wutai meteorological station were selected.

Land surface temperature (LST) were derived from the MOD11A1 and MYD11A1 datasets provided by NASA (data source: https://lpdaac.usgs.gov/), with a spatial resolution of 1000 m.

2.4. Analysis Methods

2.4.1. Extraction of Alpine Timberline

In this section, Landsat images were used to extract the alpine timberline (Figure 2). We selected 60 sample points for each of the land types, such as subalpine and alpine meadow, subalpine and alpine scrub, spruce forest, larch forest, bare ground, and man-made buildings, respectively, in ENVI v5.6 software. In this process, it was important to pay attention to ensuring it passed the separation test by adjusting the position of the sample points. Subsequently, the supervised classification results for each land type were obtained using the maximum likelihood method. The timberline was extracted from the results of the previous step in ArcGIS 10.8 software.

2.4.2. Formula for NDVI in Terms of Bands

NDVI can reduce most of the errors related to instrument calibration,, topography, cloud shading, and atmospheric conditions, and is therefore often used to research the growth status of vegetation [40]. The formula is:

$$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=\frac{{\mathrm{D}\mathrm{N}}_{\mathrm{N}\mathrm{I}\mathrm{R}}-{\mathrm{D}\mathrm{N}}_{\mathrm{R}}}{{\mathrm{D}\mathrm{N}}_{\mathrm{N}\mathrm{I}\mathrm{R}}+{\mathrm{D}\mathrm{N}}_{\mathrm{R}}}$$

(1)

where NDVI shows the status of vegetation on the ground surface, ranging from −1 to 1. $\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}<0$ means there are clouds above the ground, or the ground surface is covered with water or clouds; $\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=0$ means the ground is bare soil or rock; and $\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}>0$ means there is vegetation on the ground surface, and the larger $\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}$ is, the more abundant the vegetation is. ${\mathrm{D}\mathrm{N}}_{\mathrm{N}\mathrm{I}\mathrm{R}}$ means the reflectance value in the near-infrared band. ${\mathrm{D}\mathrm{N}}_{\mathrm{R}}$ means the reflectance value in the red band.

2.4.3. Trend Analysis of NDVI

Ordinary Least Squares [41] was used to analyze the trends in vegetation NDVI, temperature, and precipitation over the last 21 years in the study area. The formula is:

$$\mathrm{S}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{e}=\frac{\mathrm{n}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{i}\times (\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{g}}{)}_{\mathrm{i}}-({\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{i}\left)\right({\sum }_{\mathrm{i}=1}^{\mathrm{n}}(\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{g}}{)}_{\mathrm{i}})}{\mathrm{n}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{i}}^2-({\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{i}{)}^2}$$

(2)

where $\mathrm{S}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{e}$ is the regression slope of the mean value of growing season NDVI from 2001 to 2021. $\mathrm{S}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{e}>0$ represents an improving trend in vegetation, and vice versa. n is the monitoring year, which varies from 1 to 21. $\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{g}}$ means growing season NDVI.

2.4.4. Vegetation Coverage

In this paper, vegetation coverage was obtained based on NDVI data using the dimidiate pixel model [42]. The formula is:

$${\mathrm{F}}_{\mathrm{v}\mathrm{c}}=\frac{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}-\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$$

(3)

where ${\mathrm{F}}_{\mathrm{vc}}$ is vegetation coverage. $\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ is the NDVI value of completely bare land, taking an NDVI value with a cumulative probability of 5%; $\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ is the NDVI value of completely vegetated land, taking an NDVI value with a cumulative probability of 95%.

2.4.5. Surface Frost Number Model

We used a surface frost number model [27,43] that takes into account the influence of vegetation to characterize the development of permafrost. The formula is:

$${\mathrm{E}}_{\mathrm{t}}={\mathrm{F}}_{\mathrm{v}\mathrm{c}}\times \left(\right(\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{g}}{)}_i+1)$$

(4)

$${\mathrm{F}}_{\mathrm{n}}=\frac{\sqrt{\mathrm{D}\mathrm{D}\mathrm{F}}}{\sqrt{\mathrm{D}\mathrm{D}\mathrm{F}}+\sqrt{\mathrm{D}\mathrm{D}\mathrm{T}}}$$

(5)

$$\mathrm{D}\mathrm{D}\mathrm{T}={\sum }_{\mathrm{m}=1}^{12}(\overline{\mathrm{L}\mathrm{S}{\mathrm{T}}_{\mathrm{m}}}\times \mathrm{N})\hspace{1em}({\overline{\mathrm{L}\mathrm{S}\mathrm{T}}}_{\mathrm{m}}>0 ℃)$$

(6)

$$\mathrm{D}\mathrm{D}\mathrm{F}={\sum }_{\mathrm{i}=7}^{12}\left|\right({\overline{\mathrm{L}\mathrm{S}\mathrm{T}}}_{\mathrm{m}}{)}_{\mathrm{i}}\times \mathrm{N}|+{\sum }_{\mathrm{i}=1}^6\left|\right({\overline{\mathrm{L}\mathrm{S}\mathrm{T}}}_{\mathrm{m}}{)}_{\mathrm{i}+1}\times \mathrm{N}|\hspace{1em}({\overline{\mathrm{L}\mathrm{S}\mathrm{T}}}_{\mathrm{m}}<0 ℃)$$

(7)

$${\mathrm{F}}_{\mathrm{n}\mathrm{c}}={\mathrm{E}}_{\mathrm{t}}\times {\mathrm{F}}_{\mathrm{n}}$$

(8)

where ${\mathrm{E}}_{\mathrm{t}}$ is the vegetation factor in the model, reflecting the effect of NDVI and vegetation coverage on permafrost. $\mathrm{i}$ is a number from 1 to 21, representing the years 2001 to 2021. ${\mathrm{F}}_{\mathrm{n}}$ is the surface frost number without considering the effect of vegetation; $\mathrm{D}\mathrm{D}\mathrm{T}$ is the ground melting index; and $\mathrm{D}\mathrm{D}\mathrm{F}$ is the ground freezing index. $\overline{\mathrm{L}\mathrm{S}{\mathrm{T}}_{\mathrm{m}}}$ is the mean value of the 8-day LST data from MODIS by month; m represents the month as numbers from 1 to 12; and N is the number of days in the corresponding month. ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ is the surface frost number model that takes into account the influence of vegetation. When ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ > 0.5, it is judged that there is permafrost in the area, and the larger the value of ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$, the more stable the permafrost.

3. Results

3.1. Climate and Vegetation Change

The vegetation growing season is a period in which vegetation grows significantly, and temperature and precipitation together determine its duration [44]. The average temperature and precipitation for each month in the study area from 2001 to 2021 (Figure 3a) shows that the temperature and precipitation reached high values simultaneously from June to September. Therefore, June to September each year was confirmed as the vegetation growing season in the study area. Analyzing the tendency of NDVI, temperature, and precipitation (Figure 3b,c) during the growing season of vegetation in the study area from 2001 to 2021, it was found that the mean NDVI of vegetation during the growing season increased at the rate of 0.00359/year, the mean temperature increased at the rate of 0.01815 °C/year, and the mean precipitation increased at the rate of 18.412 mm/year. The trend in NDVI and temperature during the growing season was generally consistent, reflecting the limiting effect of temperature on vegetation growth in the alpine region. The melting of permafrost will result in increased utilization of water by vegetation [45], which deepens the effect of temperature on vegetation in the study area. Precipitation had a significant effect on vegetation growth in the study area during the same period [40], and the increase in precipitation contributed to the increase in NDVI values. Vegetation NPP (Figure 3d) also fluctuated upwards in the area under the combined effect of temperature and precipitation.

3.2. Permafrost and Overlying Vegetation

Based on Formulas (3)–(8), the distribution of permafrost in the study area from 2001 to 2021 can be plotted (Figure 4a). Here, ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ represents the thermal state of the surface, and a larger value means that the surface takes less heat from the atmospheric space. When ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ > 0.5, it is determined that permafrost is present in the area [46]. Comparing the permafrost distribution maps over the years, we find that the permafrost in the study area was in a phase of drastic change. The permafrost area reached its lowest point in 2004 and its highest point in 2011, with the other years fluctuating within this range. The permafrost in the study area was in a highly unstable state. We calculated the number of times each image element was identified as permafrost in the years 2001 to 2021 (Figure 4b) as a method to characterize the permafrost status, referred to as the frequency of permafrost (Fop). In other words, Fop means a frequency of ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ > 0.5, ranging from 0 to 21. Fitting Fop and the average ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ value in the same region (Figure 4e), the correlation coefficient was 0.93 (p < 0.5). In conclusion, Fop can replace ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ to represent the permafrost status of the study area.

Comparing ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ and the mean vegetation growing season NDVI in the permafrost region of the study area from 2001 to 2021 (Figure 4d), it can be found that ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$ decreased at a rate of 0.0006/year while the NDVI increased. The permafrost in the study area was generally degrading but at a small rate. Analyzing the changes in temperature and precipitation (Figure 2b,c), the permafrost in the study area gradually degraded with the increase in temperature, but the growth of vegetation and the increase in precipitation have an inhibiting effect on the degradation of permafrost [47]. The permafrost in the study area was in a state of slow degradation under the combined effect of temperature, precipitation, and vegetation.

The distribution of permafrost in the study area can be described by analyzing Figure 4b,c,e. The elevation of permafrost in the study area ranges from 2300 m to 3060 m, which is consistent with the results of Jin [38] on the distribution range of permafrost in Mount Wutai. The area of permafrost in the study area is 98.5 km², and most of these areas are located in the forested areas of the shaded slopes, while a small portion is distributed in the high elevations of the sunny slopes and under the alpine meadow. The deep seasonal frozen soil covers an area of 89 km², which is distributed along the edge of the permafrost. Based on Wei’s criteria for classifying the stability of permafrost [27], combined with the linear relationship between Fop and ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$, the permafrost in the study area was classified (

Table 1. Percentage and distribution of surface thermal states of the zone (Fop > 0) in Mount Wutai.

Fop Value	Surface Thermal State	Percentage	Distribution
16~21	Stabilized surface thermal state	20.1%	High elevation on shady slopes
11~15 6~10	Sub-stabilized surface thermal state	8.81%	Middle elevations of shaded slopes, Hilltop Terrace
	Unstable surface thermal state	13.75%	Low-elevation ridges, high elevation forest edges
1~5	Deep seasonal frozen soil (max. freezing depth > 1.8 m)	57.34%	Lower elevation, high elevation on sunny slopes

). Only 37.6 km² of the permafrost in the study area is in a stable state, which is distributed in the forests of the upper part of the shaded slopes. The remaining portion of permafrost is in various stages of degradation and is centered on stabilized permafrost in a ring. Permafrost is most unstable in areas near the edge of permafrost.

Figure 4f shows the relationship between permafrost stability and vegetation growth conditions. In the calculation of ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$, NDVI is involved in the formulas and is positively correlated with ${\mathrm{F}}_{\mathrm{n}\mathrm{c}}$. However, Figure 4f shows a general downward trend in NDVI as Fop rises, with only a small initial increase. On the one hand, it shows that the temperature factor plays a greater role than the vegetation factor in the frost number model with vegetation influence. On the other hand, it also indicates that, as permafrost begins to develop, upper soil moisture starts to accumulate, which is favorable for plant growth. But as permafrost continues to develop, the soil’s upper layer gradually becomes cold and hypoxic [48], and the nutrient level decreases [49], which is rather unfavorable for plant growth.

In the study of the relationship between permafrost degradation and NDVI slope (Figure 4f), it is found that the NDVI Slope shows a fluctuating upward trend as Fop rises from 1 to 21. Taking Fop = 12 as the cut-off point, the NDVI Slope can be divided into two change ranges, reaching the valley when Fop = 5 and 13, respectively. There are a few studies about the relationship between permafrost degradation and NDVI Slope. Guo [13] found a significant decrease in NDVI Slope in an island permafrost region compared with a continuous permafrost region, leading to the conclusion that the vegetation growth trend will be weakened as permafrost disappears. It is found that in the background of permafrost degradation, the trend in the NDVI Slope of overlying vegetation and the decline in the stability of permafrost can be divided into two phases. The value of Fop decreases from 21 to 1, indicating permafrost degradation until it disappears. In the first stage, permafrost progresses from a stabilized state to a sub-stabilized state, and the NDVI Slope has a downward trend. This process may be related to the fall of trees. The melting of permafrost alters the mechanical structure of the soil, making it inadequate to support and anchor the roots of the trees above it, resulting in collapse [11,50,51]. At the end of the first stage, vegetation is gradually adapting to the environment, as shown by the rebound in the NDVI Slope. In the second stage, the permafrost is degraded from sub-stabilized to completely disappeared, the water-insulating effect of the permafrost gradually disappears [9], and the moisture content of the surface soil decreases [52]; hence, vegetation growth is inhibited once again. After the permafrost has been completely degraded, the vegetation gradually adapts to the new environment and the NDVI Slope rebounds. Throughout the degradation of permafrost, the interdependent vegetation will become unstable [5] and even a secondary succession of vegetation populations will occur [10].

3.3. Alpine Timberline Changes in Mount Wutai

Picea meyeri and Larix Principis-rupprechtii make up the alpine timberline of Mount Wutai. Large areas of subalpine and alpine meadows are distributed above the timberline. Based on the maximum likelihood method, timberlines were extracted using Landsat series of remote sensing images for the years 2001, 2007, 2014, and 2021 (Figure 5a). Landsat RGB true-color imagery of the study area shows sparser vegetation on sunny slopes and denser vegetation on shady slopes. This is because temperatures on sunny slopes are higher than on shady slopes [53] and the sunshine duration is longer, resulting in vegetation on sunny slopes being lack of water.

The distribution of timberlines in the study area is between 2246.55 (2021) and 3026.84 (2021) m elevation and concentrated around 2650 m. The mean value of the elevation of the alpine timberline shows an upward and then downward trend (Figure 5i). The rate of upward movement of the timberline from 2001 to 2021 is 0.89 m/year, with the fastest rate of timberline rise in the southwest aspect (Figure 5d). In this paper, timberline extraction was carried out through remote sensing imagery, which covers a wider study area, instead of determining the timberline location through ground surveys with a limited scope [45] and semi-empirical methods such as the 10 °C isotherm of the hottest month [54].

The existence of permafrost has a significant impact on the distribution of timberlines in the study area. Figure 5b shows the relationship between permafrost distribution and timberlines. The timberlines on the shaded slopes are distributed on permafrost zones and are mostly in the region of degraded permafrost. Figure 5c shows that the elevation of the timberline in the study area is strongly influenced by topography. The timberline on shady slopes is at the edge of the summit platform, while those on sunny slopes are on the slopes. Combining Figure 5c,d, it can be found that the elevation of the timberline in the study area shows higher distribution on shady slopes than on sunny slopes, which is consistent with the findings of Liu Hongyan et al. [30]. Timberlines are distributed at significantly higher elevations on shaded slopes than on sunny slopes. This phenomenon is partly caused by higher temperatures in sunny slopes, causing more evaporation of soil moisture. On the other hand, it is also related to the presence of large areas of permafrost on shaded slopes. The existence of permafrost prevents the infiltration of upper soil water, making soil nutrients accumulate at the bottom of the active layer [10], which in turn allows trees to grow at unfavorable elevations [9].

The degradation of permafrost has had a significant impact on vegetation near timberlines in the study area (Figure 5f,h,i). The distribution of massive heaving domes on Central Terrace, Mount Wutai (Figure 5g) suggests that permafrost is present in the region, supporting our calculations. The permafrost in the region is currently at the frontier of degradation. During the process of permafrost degradation, ice in permafrost melts, and in some places, thaw lakes form (Figure 5h). Meanwhile, trees can fall because of the decrease in support and anchoring of their roots. During surveys in the study area, we found numerous forests of fallen “bent trees” on shady slopes (Figure 5f), which is the landmark of permafrost degradation zones. Permafrost degradation also has a significant impact on alpine meadows above the timberline. In some studies, it has been pointed out that with the degradation of permafrost, soil moisture begins to decline, and alpine meadows gradually succeed to alpine desert steppes [10,55].

4. Discussion

4.1. The State of Permafrost in Mount Wutai

Permafrost in Mount Wutai belongs to the type of alpine permafrost under the protection of vegetation, which is in a degraded state. Most of the permafrost is distributed on shady slopes, with other portions on sunny slopes and hilltop terraces at high elevations. From 2001 to 2021, both temperature and precipitation in the study area showed an upward trend. The temperature rise caused the permafrost to degrade, and the increase in precipitation slowed the speed of degradation. As the climate warmed, permafrost at high elevations on sunny slopes and low elevations degraded first. Subsequently, the permafrost on the hilltop terrace and the lower and middle parts of the shaded slopes began to degrade. The permafrost at the high part of the shaded slopes was in a relatively stable state. The NDVI in the study area was also increasing year by year, and the dense vegetation had a cooling effect on the ground and slowed down the degradation of permafrost.

4.2. Influence of Permafrost Degradation on Vegetation in Mount Wutai

At different stages of degradation, permafrost affects vegetation differently in Mount Wutai. The frequency with which an image element is recognized as having permafrost is referred to as “Fop”, which is used to indicate the steady state of permafrost. The relationship (Figure 4f) between Fop and the trend in NDVI (NDVI Slope) shows that there are two fluctuating changes in NDVI Slope with the gradual decrease in permafrost stability, which decreases first and then rebounds. Rising temperatures lifted the low-temperature limitation of high-elevation vegetation, and the degradation of permafrost provided moisture for vegetation growth. Therefore, the NDVI Slope is positive throughout the degradation of permafrost. Two significant phases of the decline in the NDVI Slope occurred during the degradation of permafrost from a steady state to a complete disappearance. It is realized that this phenomenon is due to the loss of the two effects of permafrost on the overlying forest vegetation in turn, including the function of supporting and fixing roots and the function of water storage. Evidence of this is the collapse of trees (Figure 5f), the formation of thaw lakes (Figure 5h), and the reduction in the number of wet plants [46]. The process is accompanied by the adaptation of vegetation species to the new environment, and the corresponding NDVI Slope also rebounded. After the permafrost is completely degraded, the entire ecosystem will evolve in a drier direction [10].

4.3. Response of Alpine Timberlines to Permafrost Degradation in the Mount Wutai

The degradation of permafrost provides water for upward movement of the timberline, but continual degradation will have negative impacts (Figure 5d,e). The average elevation of Mount Wutai’s alpine timberlines is on an upward trend, with a decrease in recent years. The majority of Mount Wutai permafrost is distributed on shady slopes. Permafrost prevents the continued infiltration of water, saving sufficient moisture for the growth of overlying vegetation. As a result, timberline elevations on shady slopes are significantly higher than those on sunny slopes. In the process of permafrost degradation, the melting of permafrost provides water and soil nutrients to vegetation, which contributes to the upward movement of the timberline. With the disappearance of permafrost, the effect of supporting and fixing roots [44] and the effect of water storage [46] on the overlying vegetation of permafrost will be lost in turn. The upward movement of the timberline will be impeded or even moved downward because of the complete disappearance of the permafrost. The temperature and precipitation in the study area showed an increasing trend from 2001 to 2021, which is favorable for the continuous growth of vegetation. Dai [45] once investigated the development of vegetation near the timberline in Mount Wutai between 1989 and 2004. Dai found that the thawing of permafrost promoted the growth of trees by studying tree annual rings. In Dai’s study, it was found that the lower limit of the alpine meadows in Mount Wutai rose by about 50–60 m during the 15-year period. There is also a subalpine scrub zone between the alpine meadows and the timberline, and the change in the timberline in Mount Wutai between 1989 and 2004 can only be estimated based on the rising distance of the alpine meadows. In the study in this paper, the average elevation of the timberline from 2001 to 2014 increased by about 30 m. The mean elevation of the timberline continued to increase from 2001 to 2014, consistent with the pattern of timberline in response to climate change. The average elevation of the 2021 timberline was slightly lower than the average elevation of the 2014 timberline. Combined with the rise in temperature (Figure 3b), bent trees found in the alpine meadow (Figure 5f), and the hot thaw lake found on the upper shaded slopes (Figure 5h), we conclude that this phenomenon is a result of permafrost degradation over the years.

5. Conclusions

The mechanisms of vegetation response to permafrost degradation in Mount Wutai were analyzed from a set of perspectives, including the elevation change in the alpine timberline as well as the trend in NDVI of the vegetation near the timberline. The result shows the following:

(1)

The permafrost in the Mount Wutai area is mainly located in the forests on shady slopes at high elevation and to a lesser extent on the terrace at the top of the mountain. The dense vegetation protects the permafrost and reduces the rate of its degradation.

(2)

The disappearance of permafrost can negatively affect the development of forest vegetation.

(3)

The melting of the permafrost in Mount Wutai will release water, which will facilitate the upward movement of the alpine timberline. When the permafrost disappears, the alpine timberline will move downward.

In studies on alpine timberlines, the degradation of alpine permafrost is a factor that cannot be ignored. We hope that this research will contribute to studies on the relationship between timberline and climate change. The degradation of permafrost will result in environmental changes that are detrimental to species diversity. Permafrost degradation has important implications for the ecology of cold regions and even the global carbon cycle. We hope the results of this study will provide a new research idea for two hot directions in the field of environmental science, which are permafrost degradation as well as alpine timberline change. At the same time, we also hope that our results will contribute theoretical support to the response to global climate change.