Introduction

Aeolian desertification is one of the most serious eco-environmental and socio-economic problems, especially in the arid, semi-arid, and dry sub-humid areas1, 2. It increases land degradation, disrupts the surface water balance, reduces food security, and affects the regional climate2, 3. In the past three decades, there has been an increasing appreciation for biological feedbacks4, relationships between geomorphic processes and vegetation communities5, biogeomorphology6, aeolian erosion hazard7, and biogeographical patterns8 of desertification in the global, especially in the southwestern U.S. (Chihuahuan and Mojave deserts). In China, the area of aeolian desertification amounts to 182.63 × 104 km2 by the end of 20149, which is mostly distributed in the northern and northwestern China. Recently, aeolian desertification has attracted the concern of many researchers. The major common outcomes of aeolian research have related desertification not only to its spatiotemporal changes or driving forces10,11,12,13,14,15, but also to its impacts on the environment16, 17.

North Shanxi lies on the east of Loess Plateau, and is an important part of farming-pastoral ecotone of northern China. Due to harsh natural conditions, north Shanxi has been suffering from severe aeolian desertification for a long time. North Shanxi is rich in coal and iron resources, which is also included in the Important Base of Energy and Chemical Industry in China. The central government listed Shanxi in the Three-North Shelterbelt Forest Project and the Beijing-Tianjin Sand Source Control Program. At the beginning of the 21st century, the study of aeolian desertification is a sensitive topic due to the conflict between combating aeolian desertification and resource exploration. Over recent decades, researchers have conducted investigations on aeolian desertification in north Shanxi with based on status18, distribution12, and harmfulness17, 19, 20, but such studies did not show the temporal changes of aeolian desertification. Moreover, the relative importance of each specific driving force had not been quantitatively determined in previous investigations.

In the present study, we selected nine counties located in the northern Shanxi as the study area and tried to explore the long-term temporal dynamics of aeolian desertification using multi-spectral satellite images from 1975 to 2015. We then quantitatively identified the causes of aeolian desertification from natural factors (e.g., air temperature, precipitation, and gale days) and human factors (e.g., population, livestock, and human activities). The results intend to initiate a better-informed decision and develop a more effective strategy in controlling and managing aeolian desertification in north Shanxi12. It is expected that results of this study will be beneficial not only to the local level especially in Shanxi Province, but also to the global level.

Results

Status of ADL in 2015

Figure 1 shows the aeolian desertified land (ADL) distribution pattern of north Shanxi in 2015. ADL was mainly located over the edge of the study area, where was the higher-elevation terrain, such as piedmonts, alluvial fans, and terraces. The middle of north Shanxi did not distribute the desertified land, its geomorphological domains was basin and river valley. This reveals a weak risk of hazard of wind erosion in low-lying areas or valley fioors7. Light ADL was mainly located over the northern regions, such as Tianzhen, Yanggao, and Xinrong, where the ADL area accounted for 68.50% of the total ADL. Moderate and severe ADL exhibited in the southern regions. However, moderate ADL demonstrated a very wide distribution in Shanyin, Yingxian, and Hunyuan. The severe ADL continuously occurred over the southwestern region (Shanyin). Extremely severe ADL was fragmented and was mainly scattered over the northeastern region (Tianzhen). The extent and severity of ADL in the northern region were greater than in the southern region. In total, there were 3752.74 km2 of ADL (27.19% of total area) in 2015, of which light, moderate, severe, and extremely severe ADL were 2917.70, 582.16, 147.51, and 105.37 km2, accounting for 77.75%, 15.51%, 3.93%, and 2.81%, respectively, of the ADL in the study area (Table 1). The light ADL was the most widely exhibited type.

Figure 1
figure 1

Map of the distribution of ADL in north Shanxi in 2015. The map was plotted using ArcGIS 10.2 (http://www.esri.com/). L-Light ADL, M-Moderate ADL, S-Severe ADL, ES-Extremely severe ADL. The white areas are the land of no desertification.

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Table 1 Changes in the area of ADL in north Shanxi from 1975 to 2015.
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Temporal Change of ADL

The characteristics of ADL temporal change can be used to explore the relationship between aeolian desertification development and its driving forces1. From 1975 to 1990, the area of ADL had been expanded by 1448.76 km2, representing a 96.58 km2/year increase in the total of ADL since 1975 (Table 2). Thus, this period can be defined as a stage of slower expansion15. Four types of ADL expanded in a varying degree. From 1990 to 2000, the area of ADL expanded continuously at a rate of 213.65 km2/year, which was faster compared to that during 1975–1990. Therefore, this period can be classified as a stage of rapid expansion15. The area of ADL increased in all severity classes. From 2000 to 2015 the area of ADL decreased by 3773.64 km2 with a linear decrease rate of 251.58 km2/year. This period can be summarized as a stage of reversion. The area in all grades of ADL decreased to a different degree.

Table 2 Changes in the area of ADL in various intensity grades.
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During the 40 years between 1975 and 2015, the total area of ADL dwindled by 188.42 km2 and light ADL also shrank by 376.34 km2. The other three types of ADL had enlarged by 124.92 km2 for moderate, 33.25 km2 for severe, and 29.75 km2 for extremely severe (Table 2). Therefore, the trend of aeolian desertification in north Shanxi was identified as “an overall reversal but partial deterioration” within the past 40 years2.

Discussion

Aeolian desertification is a complicated process at the level of regional landscape due to the variety of natural factors and anthropogenic factors12, 16, 21, 22. In the current project, climate change and human activities were analyzed and quantified with their relative contributions to the dynamics of aeolian desertification23.

Climate factor

Temperature, precipitation, and gale days (wind velocity ≥17 m/sec) are three important climate factors affecting aeolian desertification process in north Shanxi. By collecting and analyzing changes of these three variables in this research during the study period (Fig. 2), we found that the average temperature increased markedly with a mean of 0.042 °C/year in our study area, which was greater than the overall rate for China since 1965 (0.022 °C/year) and the global mean rate of increase (0.003–0.006 °C/year) during 1975–201515, 24, 25. The average precipitation showed a larger inter-annual fluctuation with a maximum value of 470.6 mm in 1978 and a minimum value of 350.6 mm in 2011. A significant decreasing rate of 0.716 mm/year was documented from 1975 to 2015. Eventually, the combination of rising temperature and decreasing precipitation resulted in a more arid condition20, which intensified vegetation deterioration, wind erosion, and aeolian desertification exacerbation26. Meanwhile, the data in Fig. 2 demonstrates that the average gale days fluctuated slightly with a slowly lessening tendency (about 0.014 day/year). However, the gale days were greater from 1975 to 2000 (25.185 day/year) than from 2001 to 2015 (24.680 day/year). There is a cubic relationship between the wind’s erosive power and wind velocity and frequency existed27. It is noted that the influence of wind on aeolian desertification was decreasing throughout the study area during 1975–2015 period23.

Figure 2
figure 2

Variations in climate factors in north Shanxi Province from 1975 to 2015.

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Human factors

Aside from the above climate driving factors, anthropogenic activities such as population, livestock, vegetation, and arable land were critical important driving factors affecting aeolian desertification dynamics16. In north Shanxi Province, the human population increased from 175.6 × 104 in 1975 to 284.6 × 104 in 2000, then decreased steadily to 244.5 × 104 in 2015 (Fig. 3) due to the change of people’s views of family planning. The population density had been 127–206 people/km2, which is much higher than the population carrying capacity of 7–22 people/km2 in arid and semi-arid regions17. The huge population number and excessive population growth rate increased the necessary requirement of land resources and living space, which led to more land for over-cultivation. During the past 40 years, the area of arable land had undergone a tremendous change with an expansion from 36.96 × 104 hm2 to 56.61 × 104 hm2 during 1975–2001 and reduction from 56.61 × 104 hm2 to 43.89 × 104 hm2 during 2001–2015, representing a 53.17% increase and 22.47% decrease, respectively. Due to the expansion of arable land and the activation of topsoil, the soil either became eroded or the reclaimed land was subsequently invaded and covered by windblown sand12. Although the overall livestock number increased by 2.11 times from 179.6 × 104 to 378.9 × 104 within the past 40 years, it decreased by 8.89% from 415.9 × 104 in 2002 to 378.9 × 104 in 2015. According to our investigation, the actual number of livestock grazing far exceeded the local bearing capacity of grassland (216.9 × 104)20, especially in the winter times. Overgrazing and trampling would result in a serious damage to the natural vegetation and the soil structure, thereby accelerating the aeolian desertification processes. Under the influence of the above three proxies of human factors, the vegetation area had an average decrease of 0.31 × 104 hm2/year during 1975–2000 and increase of 0.43 × 104 hm2/year during 2000–2015. Many people within the study area were living in a poverty-stricken area where unreasonable human activities such as over-cultivation, over-grazing, and over-cutting were very common that led to the vegetation cover decrease. Without the adequate surface protection, the soil was exposed to wind erosion and the fixed sandy land was re-activated into mobile sandy land.

Figure 3
figure 3

Changes of anthropogenic factors in north Shanxi Province during 1975–2015.

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Identification of driving force

In this study, the method of standardization and principal component analysis (PCA) were used to analyze the correlation between ADL and both natural and anthropogenic factors. The dynamics of ADL were more consistent with arable land (x 2) and population (x 1), and less consistent with precipitation (x 6) and gale days (x 7) (Fig. 4). The correlation coefficients (R) were 0.964, 0.786, 0.352, and 0.245, respectively (Table 3). The trend of ADL change was inconsistent with proxies of vegetation (x 4), livestock (x 3), and temperature (x 5), as correlation coefficients (R) were of −0.926, −0.871, and −0.489, respectively.

Figure 4
figure 4

Comparison of ADL in 1975–2015 relative to the proxies of climate and human factors in north Shanxi. All data are normalized.

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Table 3 Correlation matrix of variable driving changes in ADL.
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The PCA had reduced the total variance of 7 variables to 2 uncorrelated principal components28 such as first principal component and second principal component, the former has the largest possible variance (that is, accounts for as much of the variability in the data as possible). The eigenvalue of the first principal component (4.380) and second principal component (1.257) were both above 1, with co-cumulative reaching 88.528% (å 85%) in north Shanxi (Table 4). This could explain the mechanisms responsible for the ADL dynamics during 1975–201529, 30. Although the variables on the first component showed either as positively or negatively correlated effects, most of them loaded a high absolute value without the large difference. This reflects the versatility and comprehensiveness of the component affecting ADL. The absolute value of factor score coefficients of population (x 1), livestock (x 3), vegetation (x 4), and arable land (x 2) (0.953, 0.937, 0.929, and 0.902, respectively) was more than 0.90. Thus, the first principal component had a close relation with the factors reflecting anthropogenic activity, we may refer to it as a ‘human’ factor, and believe the larger influences of population (x 1) and livestock (x 3) on ADL. Continuous increases of population and livestock exerted a tremendous pressure on the land and water resources16, over-cultivation, over-grazing, and over-cutting were very common, which would destroy the surface layer of the soil that protect against wind erosion. For the second principal component, gale days (x 7) and temperature (x 5) had a higher component loading, with the absolute value of factor score coefficients reaching 0.806 and 0.584, respectively. Therefore, the second component had a close relation with climate variation, we may refer to it as ‘natural’ factor, where the gale days (x 7) play a leading role. Windy weathers directly results in the process of removal and transport of soil particles31, reactivation of part fixed/semi-fixed aeolian land. Factors affecting erosion rates including soil physical and chemical properties, roughness elements32, and surfaces crusts33, but wind erosion results in loss of soil thickness and fertility and is a precursor to desertification31. The first component and the second component explained 66.572% and 21.956% of the variance in the original 7 variables, respectively. However, the first component was more important. The results confirm the notion that human activity impacts on ADL development had been strengthened in recent decades28.

Table 4 Eigenvalues and cumulative of components matrix of driving factors in ADLa.
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Methods

Study Area

The study area is located at longitudes 112°25′ E to 114°32′ E, latitudes 39°11′ N to 40°43′ N (Fig. 5). It covers about 1.38 × 104 km2 and includes nine counties (Tianzhen, Yanggao, Xinrong, Nanjiao, Datong, Hunyuan, Huairen, Yingxian, and Shanyin). The terrain of the region is mostly within Datong basin. It is surrounded by the Hongtao Mountains to the west, the Taihang Mountains to the east, the Hengshan Mountains to the south, and the Great Wall to the north. Elevations vary between 1200 and 2400 m above sea level. The region is characterized by a temperate continental monsoon climate, i.e., with dry and cold winter, arid and windy spring17. The mean annual air temperature ranges from 4.5 to 8.0 °C. The average annual precipitation varies from 350 to 460 mm, in which nearly 75% to 80% of the rainfall occurs during June-September. The annual potential evaporation is between 1780 and 1950 mm, which is 4.2 to 5.1 times that of the average annual precipitation. The mean annual wind velocity was more than 4.2 m/sec with prevailing NW/NNW winds in winter and spring, SE winds in summer and autumn. The mean number of gale days is more than 25 days (mainly in the period from March to May). The study area is a transition zone between warm temperate deciduous forest and temperate grassland17. The dominant species are Hippophae rhamnoides Linn., Ostryopsis davidiana Decne., Caragana korshinskii Kom., Bothriochloa ischaemum (L.) Keng, Rosa xanthina LindlI, and Artemisia argyi Levl. Across the region, zonal soil mainly includes Calci-Ustic Isohumosols, Hapli-Ustic Argosols, Haply-Udic Cambosols, and Loessi-Orthic Primosols12, 34, the mechanical composition of these soil is predominantly loose and sandy material, the clay fraction (<0.002 mm) was under 20%, which to be extremely easy to cause wind erosion.

Figure 5
figure 5

Location of the study area. The map was plotted using ArcGIS 10.2 (http://www.esri.com/).

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The major economy for the local residents is dominated by rain-fed agriculture and mining industry. During the past few decades, the growth of rural population and urban expansion have deteriorated the vegetation and exposed the soil35. These factors, together with adversely natural conditions (e.g. arid climate, windy weather, and sandy soil), made aeolian desertification being a serious environmental problem in the northern Shanxi.

ADL Classification System

Some major factors (i.e., dispersed, patchy vegetation cover, and sand sheets) were considered as the visual indicators of environmental changes and the main landscape characteristics of aeolian desertification12, 36. Referring to the classification criteria proposed in previous studies1, 10, 28, 37,38,39,40, field investigations, and analysis of Landsat images, the proportions of the total area covered by mobile sand, wind-eroded areas, vegetation cover, surface temperature retrieving, and texture analysis were selected to describe the severity of aeolian desertification in the current project16. The aeolian desertification of the study area was identified in four intensity categories: light, moderate, severe, and extremely severe, according to the criteria shown in Table 5 and Fig. 6  12.

Table 5 Classification of ADL in the northern Shanxi.
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Figure 6
figure 6

Main types of ADL in the northern Shanxi. (Images were taken by Z.J. Xue). (A–D) correspond to the types of ADL in Table 5, A-Light ADL, B-Moderate ADL, C-Severe ADL, D-Extremely severe ADL.

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Data Acquisition and Processing

Remote sensing (RS) and geographic information system (GIS) combined techniques have played an important role in the exploration of temporal-spatial variability of aeolian desertification14, 41, 42. RS data used in this study included 8 scenes (2 scenes for each period) of four sets of Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM) images in 1975, 1990, 2000, and 2015. The identification of RS images and the data collection were to apply the multi-spectral data combination technology and image mosaic technology in v9.3 of the Erdas Imagine software (http://www.hexagongeospatial.com). We acquired MSS images for the 1975 data, TM images for the 1990 and 2015 data, and ETM images for the 2005 data, with a spatial resolution of 80-m (MSS) and 30-m (TM/ETM). The MSS image was obtained from Shanxi Center of Geographical Information, and the TM/ETM images were downloaded from the site http://glovis.usgs.gov. All cloud-free/little cloud (less than 5%) images were obtained from July to September because vegetation typically reaches its maximum growth. The control points were selected in the topographic map. Landsat images were corrected by the quadratic polynomial resample method30 to ensure the correction precision within 0.5 pixels, and rectified by the cubic convolution resample method30 to set up the Unified Albers Coordinate Projection System, with a superposition analogy of RS images to analyze and extract ADL information. The GIS techniques allow the use of information available in digital format that is compatible to and implementable in different databases (ArcGIS 10.2 (http://www.esri.com/))43. Under the support of Erdas Imagine v9.3 and ArcGIS 10.2, the multi-spectral directed classification and field investigations were used to obtain ADL data by the visual interpretation of RS images according to Table 5. The interpretation accuracy was over 95% according to in-situ field survey results44.

Local meteorological and socio-economic data were collected and analyzed to explore the mechanism and processes of aeolian desertification. Meteorological data, including mean annual precipitation, mean annual temperature, and mean annual gale days from 1975 to 2015, were obtained from 7 meteorological stations in and near the study area. As the proxies of socio-economic data between 1975 and 2015, we chose population, livestock numbers, arable land area, and forest coverage, which were collected from Datong Bureau of Statistics of Shanxi, Shuozhou Bureau of Statistics of Shanxi, and www.stats-sx.gov.cn. Thematic maps, including DEM maps, soil maps, and land use maps, were also used as the supplementary data sources.

Analytical Methods

In other regions in the northern China, the temporal changes of ADL are expressed in the following equation14.

$$V=({S}_{1}-{S}_{2})/T$$
(1)

where V is the temporal dynamic degree (dimensionless) of ADL, S 1 is the area (km2) for the year, S 2 is the ADL area (km2) for the previous year, and T is the time interval (year) between S 1 and S 2.

The method of standardization and PCA were used to identify the correlation between ADL and the factors affecting desertification. The standardization formula has been described by Shen et al.28.

$$z=(x-\mu )/\sigma $$
(2)

where z is the standardized data in dimensionless, x is the original data in units of above 7 variables, μ is the arithmetic mean of original data in units of above 7 variables, and σ is the standard deviation in units of above 7 variables. Then, we conducted PCA to determine the contribution of the natural and human factors to ADL development, using the Social Sciences software (SPSS 19.0).