2. Materials and methods

#### 2.1. Study area

been treated as one of the most serious social-economic-environmental issues in our world, the

of dry land live in desertified area [3]. The long-term intensive use and consume of land and vegetation resources, such as overgrazing, overcutting, excessive reclamation, and rapid urbanization, together with the climate change, made desertification expanded greatly in the globe over the past century, especially for the Sahel in Central and Northern Africa, Mediterranean region, Central and Western Asia, and North China [4–6]. For example, an analysis of a time series of remote sensing images between 1981 and 2003 revealed a persistently declining productivity throughout this period on over 20% of the global land, which had impact on 1.5

As one of the most important ecosystems and land cover types in the planet, deserts and desertified land also provide some critical ecosystem services to support their inhabitants and economic-social development, including carbon fixation and oxygen release, hydrological regulation, soil conservation and sand fixation, biodiversity maintenance, and ecological tourism [8, 9], which create ecological and economic value [10, 11]. And, the desertification expansion would lead to the loss of ecosystem services and economy; research sponsored by the United Nations Environment Programme showed that the global economic losses caused by desertification and drought were as high as US \$4.2 1010 each year, which was equivalent to all official aid to Africa in 2009 [12]. The reduction of ecosystem services and land production induced by desertification would have a great impact on the sustainable livelihoods of people living in rural community [13]; especially under the background of global warming and urbanization, the risk of land desertification and its potential impact on rural people would become higher and higher [14]. So, effectively control of desertification requires long-term systematic efforts aimed at restoring the functions of desert ecosystem services to realize the securing of both ecological and economic benefits. This will not only require the investment of large amounts of money and new technologies but also need the identification and effort of

Over the past few decades, deserts and desertified land have changed greatly due to climate change and human activities, which had resulted in a significant alteration to these areas' global and regional ecosystem services [16–18]. In the process of desertification reversion, the dominant species, plant community structure, and landscape pattern change significantly; annuals gradually evolve into shrubs and perennial herbs, and the species richness, vegetation coverage, and landscape heterogeneity increase; and the soil sand content decreases, as well [19, 20]. All these changes might lead to the enhancement of ecosystem services. For example, a previous study in Yuyang District, Shaanxi Province, China, showed that the Project of Returning Farmland to Forest and other ecological measures had led to an increase in the regional sand stabilization function value of 5.64 106 yuan per year from 1988 to 2003 [21]. However, in the process of desertification expansion, vegetation is destroyed, and more soils are exposed to the air, which will increase the risk of wind erosion and make sand hill active, and then lead to the decrease of ecosystem services, especially for the sand fixation function and service. Research conducted by Ben Mariem and Chaieb had shown that the suitable habitat for alfa grass in Tunisia had increased greatly with the increase in greenhouse gas, which would lead the reduction of ecosystem services provided by dry lands [22]. So, scholars

, and about 1–6% of inhabitants

total area affected by desertification reaches 6–12 million km<sup>2</sup>

billion people [7].

12 Community and Global Ecology of Deserts

local people [15].

Inner Mongolia lies between 37�24'N–53�23'N and 97�12<sup>0</sup> E–126�04<sup>0</sup> E in North China and includes a total of 88 counties or banners (hereinafter referred to as counties; Figure 1).

study was the Global Inventory Modeling and Mapping Studies (NDVI3g) dataset, with these data obtained from the National Aeronautics and Space Administration. The time resolution of this dataset was half a month with a spatial resolution of 8 km and the time range of 1981– 2010. This dataset has been preprocessed by geometric correction and graphic enhancement to ensure data quality. The land use data in 1980s and 2010s with an accuracy of 1 km was derived from the Chinese Academy of Sciences Resource and Environmental Data Center, and the national county-level administrative maps used to generate the boundary data of the study area came from the National Geomatics Center of China. The Landsat TM/ETM images covering the study area came from the United States Geological Survey (USGS) and Google Earth. Auxiliary data, such as meteorological and statistical information, were also collected from the National Meteorological Information Center and the national and regional Statistical Yearbooks. To facilitate spatial analysis and comparison, all the grid and vector data used in

The Impact of Desertification Dynamics on Regional Ecosystem Services: A Case Study of Inner Mongolia (China)

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this study were resampled or converted into grid data with an 8 km resolution.

2.3.2. Assessing the impact of desertification dynamics on ecosystem services

In this study, the ESV was used as a proxy to measure the impact of desertification dynamics on regional ecosystem services. The ESV per unit area of each land category was compared

To analyze the impacts of desertification on the regional ecosystem services, the land use and land cover of the study area were classified into two categories: deserts and desertified land and non-desertified land. According to the land use type, non-desertified land was further divided into farmland, forest, undegraded grassland, built-up land, and water areas. The deserts and desertified land were further divided into regions of low, medium, high, and severe desertification based on the degree of desertification. The NDVI data, land use map, and high-resolution images were used to classify the land use and land cover types. Our procedure was as follows: (1) We extracted non-desertified lands. A 1:100,000 land use map was used to identify farmland, forest, built-up land, and water areas. By combining highresolution remote sensing maps from Google Earth and our field investigation, 100 sample points for each subregion were selected for undegraded grassland; the NDVI threshold value system was then used to extract the undegraded grassland. (2) We extracted deserts and desertified land with different grades. The method of undegraded grassland extraction involved the selection of 100 sample points for each subregion to identify the desert regions. Then, the NDVI threshold desertification value system for the lands (low, medium, high, and severe) was established by equal interval dividing the NDVI value between the deserts and undegraded grassland. To avoid classification errors caused by short-term climate fluctuations, the average NDVI values for nearly 3 years were used to extract the information about deserts and desertified land in 1981 and 2010. In this study, a visual inspection was conducted to correct the classification errors and adjust the threshold values. Fifty checkpoints were randomly selected in every subregion to verify the results, and the overall classification accu-

2.3.1. Land classification and desertification monitoring

2.3. Methods

racy was more than 90%.

Figure 1. The area of study.

The study area covered approximately 1.18 106 km<sup>2</sup> or 12.3% of the country's total area. The temperate zone continental monsoon climate has an average temperature of 0–8C and an annual total precipitation of 50–450 mm that progressively decreased from east to west. The region receives an average annual of 2850 h of sunshine and stands at an average elevation of 1000 m. The soils are mostly brown desert, chestnut, and sandy soils. Gales occur on 10–40 days annually, mainly in spring, and 5–20 days with sandstorms. Influenced by climatic factors (such as sandstorms, temperature, precipitation, etc.) and unsustainable human activities (such as grassland reclamation, abandonment of cultivated land, etc.), desertification become a serious problem in this region. To facilitate statistical and comparative analyses, the study area was divided into 10 subregions according to the climate characteristics and natural geography [40], including the Hulun Buir grassland (hlbr), Horqin grassland (horq), Hunshandake sandy land (hsdk), Chahar grassland (char), Bashang area (bash), Wumeng Qianshan and Tumote plain (wmt), Houshan region in Inner Mongolia (nmhs), Hetao plain (htpy), Erdos grassland (erdos), and Alashan plateau (alsh).

#### 2.2. Data collection and process

The data used in this study included NDVI (normalized difference vegetation index) data, land use data, high-resolution remote sensing images, and other auxiliary data. NDVI used in this study was the Global Inventory Modeling and Mapping Studies (NDVI3g) dataset, with these data obtained from the National Aeronautics and Space Administration. The time resolution of this dataset was half a month with a spatial resolution of 8 km and the time range of 1981– 2010. This dataset has been preprocessed by geometric correction and graphic enhancement to ensure data quality. The land use data in 1980s and 2010s with an accuracy of 1 km was derived from the Chinese Academy of Sciences Resource and Environmental Data Center, and the national county-level administrative maps used to generate the boundary data of the study area came from the National Geomatics Center of China. The Landsat TM/ETM images covering the study area came from the United States Geological Survey (USGS) and Google Earth. Auxiliary data, such as meteorological and statistical information, were also collected from the National Meteorological Information Center and the national and regional Statistical Yearbooks. To facilitate spatial analysis and comparison, all the grid and vector data used in this study were resampled or converted into grid data with an 8 km resolution.

#### 2.3. Methods

The study area covered approximately 1.18 106 km<sup>2</sup> or 12.3% of the country's total area. The temperate zone continental monsoon climate has an average temperature of 0–8C and an annual total precipitation of 50–450 mm that progressively decreased from east to west. The region receives an average annual of 2850 h of sunshine and stands at an average elevation of 1000 m. The soils are mostly brown desert, chestnut, and sandy soils. Gales occur on 10–40 days annually, mainly in spring, and 5–20 days with sandstorms. Influenced by climatic factors (such as sandstorms, temperature, precipitation, etc.) and unsustainable human activities (such as grassland reclamation, abandonment of cultivated land, etc.), desertification become a serious problem in this region. To facilitate statistical and comparative analyses, the study area was divided into 10 subregions according to the climate characteristics and natural geography [40], including the Hulun Buir grassland (hlbr), Horqin grassland (horq), Hunshandake sandy land (hsdk), Chahar grassland (char), Bashang area (bash), Wumeng Qianshan and Tumote plain (wmt), Houshan region in Inner Mongolia (nmhs), Hetao plain

The data used in this study included NDVI (normalized difference vegetation index) data, land use data, high-resolution remote sensing images, and other auxiliary data. NDVI used in this

(htpy), Erdos grassland (erdos), and Alashan plateau (alsh).

2.2. Data collection and process

Figure 1. The area of study.

14 Community and Global Ecology of Deserts

## 2.3.1. Land classification and desertification monitoring

To analyze the impacts of desertification on the regional ecosystem services, the land use and land cover of the study area were classified into two categories: deserts and desertified land and non-desertified land. According to the land use type, non-desertified land was further divided into farmland, forest, undegraded grassland, built-up land, and water areas. The deserts and desertified land were further divided into regions of low, medium, high, and severe desertification based on the degree of desertification. The NDVI data, land use map, and high-resolution images were used to classify the land use and land cover types. Our procedure was as follows: (1) We extracted non-desertified lands. A 1:100,000 land use map was used to identify farmland, forest, built-up land, and water areas. By combining highresolution remote sensing maps from Google Earth and our field investigation, 100 sample points for each subregion were selected for undegraded grassland; the NDVI threshold value system was then used to extract the undegraded grassland. (2) We extracted deserts and desertified land with different grades. The method of undegraded grassland extraction involved the selection of 100 sample points for each subregion to identify the desert regions. Then, the NDVI threshold desertification value system for the lands (low, medium, high, and severe) was established by equal interval dividing the NDVI value between the deserts and undegraded grassland. To avoid classification errors caused by short-term climate fluctuations, the average NDVI values for nearly 3 years were used to extract the information about deserts and desertified land in 1981 and 2010. In this study, a visual inspection was conducted to correct the classification errors and adjust the threshold values. Fifty checkpoints were randomly selected in every subregion to verify the results, and the overall classification accuracy was more than 90%.

#### 2.3.2. Assessing the impact of desertification dynamics on ecosystem services

In this study, the ESV was used as a proxy to measure the impact of desertification dynamics on regional ecosystem services. The ESV per unit area of each land category was compared with different biomes and assigned based on the results derived by Xie et al. [31], who had estimated the equivalent weight factors and modified the ESV coefficient per hectare of terrestrial ecosystems in China. To quantitatively assess the impact of land with different desertification degrees on the regional ESV, the ESV for the low, medium, high, and severely desertified lands was assigned by the use of the equal interval grading weight factor between the ESV of the deserts and undegraded grassland:

$$ESV = \sum\_{i=1}^{n} P\_{i\bar{\jmath}} \times A\_i \tag{1}$$

3. Results

170,900 km<sup>2</sup>

desertification expansion.

3.1. The desertification dynamic in Inner Mongolia from 1981 to 2010

, with an expansion area of 204,300 km<sup>2</sup> (Figure 2).

From 1981 to 2010, the area of deserts and desertified land in Inner Mongolia expanded from 555.3 km2 in 1981 to 624.5 km<sup>2</sup> in 2010. Except for a decrease of 28,000 km<sup>2</sup> in deserts, the area of land with low, medium, high, and severe desertification all showed different increases. The largest increment is caused by medium desertification, which increased 48.77% over 1981. Reversion and expansion between lands with different desertification degrees and between desertified and non-desertified lands are equally significant, which showed a significant spatial heterogeneity. In the 30 years, the area of desertification reversion in Inner Mongolia was

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The area of desertification reversion was mainly distributed in erdos, alsh, bash, and other areas in southwest Inner Mongolia. Among them, the area of desertification reversion in erdos accounted for 8.71% of the desertified land area of Inner Mongolia. The area of desertification reversion in alsh and bash also reached 8.33 and 5.1% of the total desertification area, respectively. The reversal area among different degrees of desertification accounted for 17.98% of the total desertification area. The reversal area from deserts and desertified land to non-desertified

Figure 2. Land use and desertification change in Inner Mongolia autonomous region from 1980s to 2010s. (a) Land use and desertification map in 1981, (b) land use and desertification map in 2010, (c) desertification reversion and (d)

where ESV is the total ecosystem service value of one region (10,000 yuan�km�<sup>2</sup> �a�<sup>1</sup> ), Pij is the adjusted ESV per unit area of land use and land cover type <sup>i</sup> (10,000 yuan�km�<sup>2</sup> �a�<sup>1</sup> ), and Aij is the area of land use and land cover type i (km<sup>2</sup> ).

We took into consideration the differences in the ESV provided by the same type of land use and land cover in different regions and introduced an adjusting factor in this study by biomass because the ESV always has a robust positive relationship with the biomass [41]:

$$P\_{i\dagger} = \left(b\_{\dagger}/B\right)P\_i \tag{2}$$

where Bj is the biomass of the land use and land cover type i, which was replaced by the NDVI in this study. B is the average biomass of the land use and land cover type i in China, which was also calculated by NDVI; and Pi is basically the ESV per unit area of land use and land cover type <sup>i</sup> in China (10,000 yuan�km�<sup>2</sup> �a�<sup>1</sup> ), which is shown in Table 1.

#### 2.3.3. Sensitivity analysis

In this study, the sensitivity coefficient was conducted to assess the changing degrees of the regional ESV that was caused by desertification. The sensitivity coefficient (SAF) measured the sensitivity degree by comparing the changes in the ESV of the whole region to the deserts and desertified land from 1981 to 2010:

$$SAF = \frac{(VC'-VC)/VC}{(S'-S)/S} \tag{3}$$

where SAF is the sensitivity coefficient and higher values of ∣SAF∣ indicate greater changes in the regional ESV caused by desertification; VC<sup>0</sup> is the regional ESV in 2010; VC is the regional ESV in 1981; S<sup>0</sup> is the ESV of the deserts and desertified lands in 2010; and S is the ESV of the deserts and desertified lands in 1981.


Table 1. The ESV per unit area of different land types in Inner Mongolia (million yuan/km<sup>2</sup> /a).
