- Email: [email protected]
Quantifying the synergistic effect of the precipitation and land use on sandy desertification at county level: A case study in Naiman Banner, northern China
Journal of Environmental Management 123 (2013) 34e41
Contents lists available at SciVerse ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Quantifying the synergistic effect of the precipitation and land use on sandy desertification at county level: A case study in Naiman Banner, northern China Ge Xiaodong a, b, *, Ni Jinren a, Li Zhenshan a, Hu Ronggui b, Ming Xin b, Ye Qing b a b
Department of Environmental Engineering, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China Department of Environmental Sciences and Engineering, Huazhong Agricultural University, Wuhan 430070, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 April 2009 Received in revised form 5 February 2013 Accepted 17 February 2013 Available online 9 April 2013
Assessing the driving forces of sandy desertification is fundamental and important for its control. It has been widely accepted that both climatic conditions and land use have great impact on sandy desertification in northern China. However, the relative role and synergistic effect of each driving force of sandy desertification are still not clear. In this paper, an indicator named as SI was defined to represent the integrated probability of sandy desertification caused by land use. A quantitative method was developed for characterizing the relative roles of annual precipitation and land use to sandy desertification in both spatial and temporal dimensions at county level. Results showed that, at county level, land use was the main cause of sandy desertification for Naiman Banner since 1987e2009. In the case of spatial dimension, the different combination of land use types decided the distribution of sandy desertification probability and finally decided the spatial pattern of bared sand land. In the case of temporal dimension, the synergistic effect of land use and precipitation highly influenced the spatial distribution of sandy desertification. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Sandy desertification Land use Regression County level
1. Introduction Sandy desertification, the land degradation by wind erosion, mainly results from the excessive human activities and climate change in arid, semi-arid and part of sub-humid regions (UNCCD, 2004). It has been one of the most serious social-economicenvironmental problems in northern China in the past 50 years (Zhu and Chen, 1994; Wang, 2002, 2004; Wang et al., 2004). Accurate assessment on the causes of sandy desertification will be instrumental in developing global/regional action plans to prevent and eradicate the problems (Yang et al., 2002; Sun et al., 2005). However, there are still no widely accepted assessing methodologies, because of the controversy over the causes of sandy desertification (Zhu, 1985, 1998; Yang et al., 2002). It has been debated for many years whether the degradation is caused primarily by climatic change or human activities (Wang et al., 2005). In traditional opinions, climatic variables are the predominant drivers for sandy desertification in northern China (Zhao, 1981; Wang, 2002; Han, 2003; Runnström, 2003). For example, Wang X.
* Corresponding author. Department of Environmental Sciences and Engineering, Huazhong Agricultural University, Wuhan 430070, China. Tel.: þ86 027 87672332; fax: þ86 027 87285950. E-mail address: [email protected] (G. Xiaodong). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.02.033
(2008) claimed that desertification in China was consistently associated with specific trends in temperature, drought and wind regime. Among all climatic variables, the precipitation, particularly annual precipitation, has more influence on sandy desertification because it helps to improve the vegetation cover and decreases sand transport, consequently significantly contributes to ecosystem rehabilitation (Verburg et al., 1999). With low precipitation, the surface vegetation becomes sparse and the proportion of surface soil being turned into sandy soil becomes relatively large (An et al., 2002). Sandy desertification, hence, usually occurs in regions with annual precipitation less than 450 mm and spring precipitation less than 90 mm (Wang et al., 2008). Although this climate-driving hypothesis is supported by historical records and archaeological evidence in literature, improper land use has been widely recognized as the dominant factor leading to sandy desertification in recent years in arid and semi-arid areas of China (Wu, 2001). Based on the consensus it is accepted that land use is the most direct way which has the largest impact on biodiversity and land cover (Jia et al., 2004; Hao and Wu, 2006; Dong, 1992). The influence of each land use type on sandy desertification has been studied widely. For example, Zhao (2003) reported that reclamation would destruct the original ground vegetation and deteriorate the chemical or physical properties of soil. Bao et al. (2003, 2004) found that livestock’s crunching and trampling would destroy the vegetation coverage of grassland and destruct
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
35
the soil’s crust layer. These studies have attributed the rapid desertification in North China to over-grazing, over-reclamation and extensive cutting respectively (Zhu, 1994; Zhao et al., 2005). Because of the dispute, there are few successful methods for the quantitative analysis of relationship between two kinds of driving factors. Only recently people have made efforts to analyze the relative role of climate variables and human activities on sandy desertification (Luo et al., 2005; Sun et al., 2006; Li et al., 2008; Xu et al., 2011). Modeling approach is important in describing the process of sandy desertification and evaluating the respective impacts of the causes. Normally, regression models and multivariable analysis techniques, such as principle component analysis, are used in comparative studies (Xu et al., 2010). For example, Luo et al. (2005) used satellite images, meteorological and socioeconomic data to assess the landscape change from 1993 to 2002. Sun et al. (2006) developed an index (RI) for Minqin County in China by means of statistical modeling techniques to explore the variability of desertification risk. Sun et al. (2007) also made efforts to test whether a cost-distance connectivity index could act as an indicator for explaining the contribution of the causes to sandy desertification. However, according to the author’s knowledge, few researchers have made efforts to evaluate the relative role of climate and land use in both spatial and temporal scales. The research on the assessment method is still in urgent need. In this paper, we selected Naiman Banner in Inner Mongolia of China as the research region and attempted to develop a quantitative method for the relative effect of annual precipitation and land use on sandy desertification in both spatial and temporal scales at county level. 2. Study area Naiman Banner is a county in Inner Mongolia, which is located in northern China with a geo-location from 120190 E to 120 450 E and 42140 N to 43 320 N (Fig. 1). It covers an area of 8210 km2 and lies in the farming-pastoral ecotone where the ecosystem is vulnerable. The average annual precipitation varies from 343.3 mm to 451.0 mm with significant inter-annual variation. The average annual wind speed ranges from 3.5 m/s to 4.1 m/s. Under the natural conditions, the exposed soil is prone to erosion. Sandy desertification has been severe in Naiman Banner in history. Land use in Naiman Banner plays a vital role in the process of sandy desertification in the way of accelerating the soil exposure and the wind erosion. Naiman Banner consists of twenty one townships which vary in land use pattern and sandy desertification extent. 3. Materials and methods 3.1. Indicator selection and acquisition In this study, three kinds of indicators were acquired to reflect the changes of the climatic conditions, the land use and the extent of sandy desertification, respectively. The indicator of Total Precipitation (PC) was chosen to reveal the change of the climatic conditions. The area percentage of bare sand land (BI) was selected to reveal the extent of the sandy desertification. An indicator named as SI was defined with the area information of land use types to reveal the contribution of all land use types to sandy desertification in a specific region. (1) The indicator of PC Generally, the indicator of annual amount of precipitation is used to indicate the depth of water, such as rain, snow, or sleet, which condenses from the atmosphere in a year. This paper uses
Fig. 1. Geographical location of Naiman Banner.
the indicator of Total Precipitation (PC) to identify the difference of the climatic conditions between the townships in temporal and spatial dimension. Total Precipitation (m3) equals to the product of the annual amount of precipitation (mm) and the area (m2) of the given region. It means the whole volume of the water which condenses from the atmosphere in a specific region in one year. During the period from 1987 to 2009, there were only two meteorological stations in Naiman Banner. Annual precipitation data obtained from the statistical yearbooks were the average value from these two meteorological stations, illustrating the average amount of precipitation of the whole county. Total Precipitation was calculated based on the data of annual of precipitation and the area of the specific township. The coefficient of variation of the annual precipitation is about 25.1%. (2) The indicator of BI The area percentage of bare sand land (BI) was a traditional indicator for presenting the extent of sandy desertification. The greater BI is, the more severe sandy desertification is. So BI was selected to reveal the extent of the sandy desertification in this paper. BI was extracted from Landsat-5 TM images of 1987, 1992, 1996, 2000 and 2002. After radiometric calibration, geometric correction, cloud removal and geometrical corrections, maximum likelihood supervised classification was carried out to identify the bare sand land. Generally, the sand land was divided into two types, fixed sand dune and semi-fixed sand. Semi-fixed dune was the sand dune with less than 30% of the vegetation cover, while the fixed sand dune was the sand dune with more than 30% of the vegetation cover.
36
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
(3) The indicator of SI
Table 2 The relative possibility of sandy desertification between land use types.
Since any type of land use practices, such as deforestation, grazing and agriculture, can affect the ecosystem structure and function, different type of land use has different possibility to cause land degradation. Different area of any land use type with its specific possibility of sandy desertification may contribute to a different extent of sandy desertification in a region. In order to qualify the integrated possibility of sandy desertification resulting from all land use types, an indicator named SI was defined as Formula (1). SI was the sum of sandy desertification possibilities of all land use types in a specific region.
SI ¼
X ðpi $Ai Þ
(1)
where pi was the sandy desertification probability of land use type i, and Ai was the area of land use type i in a given region. Ai was extracted with remote sensing data. pi was calculated according to the local experience. In detail, Ai was extracted using Landsat-5 TM remote sensing data at 30 30 m spatial resolution for 1987, 1992, 1996, 2000 and 2002. All spectral data were collected in summer and autumn, mainly in September. Radiometric calibration, geometric correction and cloud removal were carried out from all of these images, which were geo-referenced to the WGS_1984 UTM Projected Coordinate System with a geometric precision of 0.5 pixels. And then, the geometrical corrections and classifications were carried out. Seven land use types were identified including irrigated cultivated land, dry farmland, forestry, grassland, resident land, surface water and unused land. The sandy desertification possibility of land use type i (pi) was calculated by a method based on the Analytic Hierarchy Process (AHP) (Satty, 1977, 1985). A reciprocal matrix (Table 1) was built up with the Cij values that were collected through the questionnaires. A consistency ratio (CR) was defined to measure the consistency level of the reciprocal matrix (Formula 2). If the CR value was less than 0.1, the reciprocal matrix was acceptable for further calculation.
CR ¼ CI=RI
(2)
CI ¼ ðlmax nÞ=ðn 1Þ
(3)
In Formula 3, lmax was the maximum eigenvalue of the reciprocal matrix, and n was the power of the matrix. RI was determined by n (Table 3). To obtain the probability (pi), the eigenvector and the maximum eigenvalue of the reciprocal matrix were calculated as Formula 4.
Scale
Meanings
1 3
The same possibility between two land use types Land use type i is a little easier to be desertified than Land use type j Land use type i is obviously easier to be desertified than Land use type j Land use type i is strongly easier to be desertified than Land use type j cij is the relative possibility between land use type i and element j is, while cji is the relative possibility between land use type j and element i
5 7 Reciprocal
3.2. Questionnaires 64 questionnaires were prepared to collect the data for calculating the possibilities of each land use type in July, 2004. In 2004 and 2008, 22 more questionnaires were sent to collect more data in different townships. Like this, total eighty six questionnaires were collected, ten of which were from local government, fourteen were from experts and the rest were from local residents. In these questionnaires, the ease of sandy desertification between any two land-use types was compared by local people was recorded. Based on the surveys, the prevalent opinion about the possibility of sandy desertification between any two land use types was collected and a reciprocal matrix was constructed. 3.3. Assessment method The mathematical formalization of the relationship between the area of bare sand land, the annual precipitation and the land use was built up as Formula 5.
log BI ¼ A þ a$ln PC þ b$ln SI
(5)
where A > 0, a > 0 and b > 0. BI was the area percentage of bare sand land. PC was the annual precipitation. A was the coefficient that reflected the contribution of all other factors besides the precipitation and land use to sandy desertification. Multiple regression analysis was used to evaluate a and b. Two kinds of relationships were analyzed, including (1) the relationship between BI and PC, BI and SI, respectively; and (2) that between BI and PC relatively. The adjust coefficient of determination (R2) was used as a measure of the amount of variation explained, while the standardized a and b indicated the relative importance of individual variables in Formula 5. 4. Results and analysis 4.1. The sandy desertification probabilities of each land use type
R ¼ lmax $½p1 ; /; pi ; /; pn 0
(4)
where R was the reciprocal matrix, lmax was the maximum eigenvalue, [p1, ., pi, ., pn]0 was the eigenvector. pi was the sandy desertification possibility of land use type i. Table 1 Reciprocal matrix. SI
Type 1
Type j
.
Type n
Type 1 . Type i . Type n
C11 . Ci1 . Cn1
C1j . Cij
. . . . .
C1n . Cin . Cnn
.
Cnj
Cij: relative possibility of sandy desertification between land use type i and land use type j. The marks of Cij were given in Table 2.
Based on ninety-seven questionnaires collected in 2004, 2008 and 2010 in Naiman Banner, the relative importance between any two land use types was determined and the reciprocal matrix were constructed (Table 4). Among the questionnaires, ten were taken from local government, twenty-five were from local researchers and the rest were taken from local residents. The maximum eigenvalue of the matrix was 6.03. So, the CI, RI and CR were 0.02, 1.26 and 0.005 respectively. The reciprocal matrix Table 3 RI value for the reciprocal matrix with 1e15 power. Power RI Power RI
1 0 9 1.46
2 0 10 1.49
3 0.52 11 1.52
4 0.89 12 1.54
5 1.12 13 1.56
6 1.26 14 1.58
7 1.36 15 1.59
8 1.41
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41 Table 4 The reciprocal matrix based on surveys. Land use type
Irrigated land
Dry farmland
Forestry
Grassland
Resident land
Unused land
Irrigated land Dry farmland Forestry Grassland Resident land Unused land
1 3 1/3 1 1 3
1/3 1 1/5 1/3 1/3 1
3 5 1 3 2 7
1 3 1/5 1 1 3
1 3 1/5 1 1 3
1/3 1 1/7 1/3 1/3 1
37
defined as BI, was calculated for each township. For the whole county, BI increased from 1987 to 1996, reflecting the expanding of sandy desertification. And after 1996, BI decreased obviously reflecting the recovery of the ecosystem. The area percentage of bare sand land varied in different townships. The spatial distribution of BI values showed that the bared sand land was mainly concentrated in the western part of Naiman Banner, and also in the central part. The BI values in central Naiman changed more frequently than that in other part of the county through the years. 4.4. Regression models
was tested to be consistent and the corresponding eigenvector of the matrix was accepted. The probabilities of different land use types were shown in Table 5. The probabilities revealed the relative contribution to sandy desertification between land use types. Among all, the forestry was found to be important for soil and water conservation. It was hardest to be degraded in all land use types. Cultivation and grazing were the main anthropogenic activities in Naiman Banner. According to local opinions, although the cultivation and the grazing affected sandy desertification in different ways, the irrigated land and grassland had always the closed extent of contribution to sandy desertification. Dry farmland was difficult to be irrigated with surface water or ground water. It had less soil moisture and microorganisms and, hence, was easier to be sandy desertified. Generally, unused land was under high sandy desertification risk, because the original vegetation was destroyed and the land was liable to be eroded by wind force, like abandoned farmland or sand dunes. 4.2. Results of SI The SI values were calculated for each township in Naiman Banner (Fig. 2). From 1987 to 1992, SI slightly decreased. From 1992 to 2002, SI increased obviously, reflecting the strengthening of the contribution of land use to sandy desertification. The amount of the townships, whose SI value was below 0.3, was 10, 14, 4 and 0 in 1987, 1992, 1996 and 2000 respectively. The amount of the townships, whose SI value was above 0.4, was 3, 1, 6 and 9, respectively. From 2000 to 2009, SI values decreased again in the whole county. The amount of the townships, whose SI value was below 0.3, was 0 in 2002 and 10 in 2009. The amount of the townships, whose SI value was above 0.4, was 4 in 2002 and 2 in 2009. The SI values varied greatly in different townships, reflecting the different contribution of land use to sandy desertification in spatial dimension. For Naiman Banner, the townships in the west had the highest SI, while the ones in the north and in the south had the lowest SI. The SI values in the central of Naiman Banner ranged from 0.3 to 0.5. The spatial distribution of SI showed that the townships had different combination of land use types and influenced the sandy desertification differently. 4.3. The results of BI The bare sand land was compelled with TM images and was shown in Fig. 3. The area percentage of bare sand land, which was
Table 5 The relative probabilities of land use types. Land use type
Irrigated land
Dry farmland
Forestry
Grassland
Resident land
Unused land
Relative probability
0.23
0.63
0.07
0.23
0.21
0.66
Two types of regression models were constructed. Regression models of the first type were built up to test the spatial correlation of the precipitation, land use and sandy desertification in a specific year. Any model of the first type was built up with the data of all townships of a specific year. Results were shown in Table 6. The adjusted R2 of any model was satisfactory, so that all the regression models were acceptable. The standardized coefficient of SI varied between 0.741 and 0.837 with the significance below 0.01, while the standardized coefficient of PC varied between 0.029 and 0.041 with significance above 0.05. The standardized coefficients of SI were much greater than that of PC. With all regression models of the first type, SI was positively related to BI, while PC was sometimes negatively related to BI. The regression models of the second type were built up to test the temporal correlation of the precipitation, land use and sandy desertification within a specific township. Any model of this type was built up with the time series data of a specific township. As shown in Table 7, twelve of the regression models were accepted with satisfactory adjusted R2. The standardized coefficient of SI varied between 0.44 and 2.07. The standardized coefficient of PC varied between 0.32 and 1.33. The significance of most models within the twelve townships was at the 0.05 level. As for the other nine townships, the adjusted R2 was not satisfactory. 5. Discussions 5.1. The precipitation and sandy desertification Sandy desertification is one of the main types of desertification which is driven by wind erosion. In many arid and semi-arid areas, once soil was exposed, wind erosion occurs immediately. So the extent of soil exposure greatly influenced the extent of sandy desertification. The precipitation was the most important climatic factor that would affect the ground vegetation. When the annual precipitation decreased, especially with less spring precipitation, there would be less vegetation. The soil exposure would be accelerated. The drought resulting from less precipitation also hastened the wind erosion. However, when the annual precipitation increased, there would be more vegetation. Soil exposure would be prevented. At the same time, more precipitation helped to increase soil moisture, biomass and biodiversity, which would keep soil stable and difficult to be eroded. The variation of the precipitation in the agro-pastoral transition zone of northern China was studied extensively. In Horqin Sand land, slightly decreasing annual precipitation was observed over the past several years. The vegetative succession and the dominant species with largest distribution in this area were affected by the precipitation changes. The tendency of being desertified would be strengthened while some vegetable species was seriously degraded and then the bare land was eroded. Most scientists had theoretically attributed the precipitation as important backdrop for sandy desertification in Horqin Sand Land.
38
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
Fig. 2. Distribution of SI values in Naiman County.
The results in Table 7 supported some previous studies to some extent. Some previous studies suggested that the main climatic factors responsible for desertification were variations in precipitation. The results proved that the impact of the precipitation on the ecosystem turned to be considerable, especially in temporal dimension. In Table 7, most of the coefficients of PC were negative with statistical significance (P 0.05). It proved that, the increasing of the precipitation would help prevent sandy desertification. In the year with more precipitation, the extent of sandy desertification might be lightly reversed. Nevertheless, as shown in Table 6, the role of the precipitation on sandy desertification was not significant in the spatial dimension. The value of the standardized coefficient of PC of any regression model was very close to zero. And the relationship between BI
and PC was insignificant. The results in Table 6 implied that the precipitation had very limited influence on the spatial difference of sandy desertification. Actually, as shown in Fig. 3, the occurrence of the bare sand land was obviously related to the spatial location. The bared sand land was mainly concentrated in the western part and the central part of Naiman Banner. The change of the bared sand land mainly occurred in central Naiman changed. However, the precipitation had quite limited ability to explain the spatial difference of sandy desertification. 5.2. Land use and sandy desertification Many studies suggested that desertification in semi-arid North China was the result of human activities during this period,
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
39
Fig. 3. The distribution of bared sand land.
and government policies had also accelerated desertification to some extent. In northern china, most of the human activities and government policies were related to land use, which provided agricultural products to satisfy local demand of economic Table 6 Regression models for each year (n ¼ 21). Year
Standardized coefficient of PC
Standardized coefficient of SI
Adjusted R2
1987 1992 1996 2000 2002 2009
0.04 0.03 0.08 0.04 0.18 0.16
0.84** 0.89** 0.82** 0.81** 0.74** 0.80**
0.714* 0.803* 0.652* 0.675* 0.658* 0.701*
Significance levels: *<0.05, **<0.01.
development. The need of agricultural products kept increasing pressure to land cover changes. Land use helped to accelerate soil exposure. Since different land use types had different ways of soil exposure, the combination of all land use types had integrated effect on sandy desertification. To give assessment to the effect of land use pattern on sandy desertification, an indicator SI was defined as the integrated possibility of sandy desertification under the condition of a specific land use pattern. The results showed that SI varied in both spatial and temporal dimensions. Furthermore, results in Figs. 2 and 3, Tables 6 and 7 proved that there were strong correlation between land use and sandy desertification. As shown in Table 6, the standardized coefficient of SI was positive with significance 0.01 in any regression model of the first type. The greater SI was, the greater BI was. It proved that land use
40
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
Table 7 The regression models for each township (n ¼ 6). Township
Standardized coefficient of PC
Standardized coefficient of SI
Adjusted R2
Ping’an Dongmin Zhi’an Desheng Baxiantong Weiliansu Nailin Baiyintala Guribanhuan Bagaborihe Daqintala Xinzhen Yilongyong Huanghuatala Sharihaolai Baiyinchang Tuchengzi Qinghe Minren Zhanggutai Qinglongshan
0.32 0.81* 1.33* 0.67* 0.68* 0.75* 0.85* 0.48 0.35* 0.45* 0.73* 1.17** 0.48 0.37 0.38 0.27 0.22 0.35 0.50 0.12 0.40
0.85* 0.79* 2.07* 0.86* 0.70* 0.76* 1.23* 0.79* 1.00** 0.92* 0.44* 1.34** 0.48 0.56 0.38 0.40 0.37 0.10 0.12 0.66 0.40
0.957 0.966 0.811 0.643 0.714 0.975 0.646 0.800 0.883 0.759 0.965 0.979 0.468 0.121 0.284 0.153 0.218 0.172 0.019 0.464 0.085
Significance levels: *<0.05; **<0.01.
in Naiman Banner contributed to the expansion of sandy desertification. When the combination of land use types of a township had higher sandy desertification probability, the area percentage of bared sand land was higher and sandy desertification was more severe. The difference of land use pattern between the townships explained the spatial difference of sandy desertification. As shown in Figs. 2 and 3, the townships in the east had the highest SI, and they faced the most severe sandy desertification. The townships in the north and in the south had the lowest SI, while the area percentage of bare sand land in the north and in the south were the lowest. SI in central Naiman changed more frequently than that in other part of Naiman Banner, while BI in central Naiman changed the most frequently through the years. The spatial correlation between SI and BI illustrated that land use was the key factor to cause the spatial difference of sandy desertification. According to the definition, the value of SI was decided by the areas of different land use types. The townships in the west part of Naiman Banner had more unused land or dry farmland, which were the easiest to be eroded by wind, and then faced more severe sandy desertification. As for the townships in the central part of Naiman Banner, where most grassland and arable land were located, the soil was more difficult to be eroded by wind than unused land. Nevertheless, the household alternative between crop-planting and livestock-grazing led to the frequent change of land use types, which accelerated the soil exposure and resulted in the frequent change of sandy desertification. In temporal dimension, as shown in Table 7, the twelve regression models with acceptable adjust R2 also explained the contribution of land use to sandy desertification. The standard coefficients of SI were positive. It implied that land use helped to accelerate soil exposure and increase the integrated possibility of being desertified. In Table 7, not all townships had a regression model with satisfactory adjust R2. It implied that some factor besides land use and the precipitation impacted the sandy desertification in those townships during this period.
climate changes over the past half century, the debates about key role of the driving forces of sandy desertification lasted for a long time. Some researches claimed the human activities as the main cause of sandy desertification, while others regarded the climatic factors played the key role in local environmental changes. The regression models showed that the precipitation had significant influence on the temporal changes of sandy desertification, but it had trivial impact on the spatial difference of sandy desertification. Land use had impact on the extent of sandy desertification both in spatial and temporal dimensions. Furthermore, the results in this paper implied that the causes of sandy desertification were not independent. They played different roles in different dimensions, and they couldn’t be simply separated from each other. Land use and the precipitation synergistically influenced the trend of sandy desertification. The synergistic roles of land use and the precipitation should be paid more attention to. In Table 7, in the regression models with acceptable significance, the abstract of the standardized coefficient of PC and that of SI were very close, reflecting that land use had relatively equivalent role in sandy desertification as the precipitation in temporal dimension. The standardized coefficient of PC was negative, while that of SI was positive. The increase of the precipitation would lead to the recovery of the ecosystem, while the increase of the tension of land use might result in sand land expansion. As the annual precipitation varied greatly between years, the combination of the precipitation and land use made these townships vulnerable and uncertain in the process of sandy desertification. The geographic location of these townships affirmed this point of view that the synergistic effect of the precipitation and land use decided the spatial distribution of sandy desertification (Fig. 4). The townships with acceptable regression models were located in the western and central parts of Naiman Banner, where sandy desertification were more severe and changed more frequently. The change of the precipitation and the change of land use synergistically greatly impacted the land cover in this region, in different directions respectively. The integrated roles of the precipitation and land use hastened the uncertainty of land cover changes. In details, as shown in Table 7, the abstract of standardized coefficients of PC and that of SI were compared for the regression models with acceptable significance. Among the twelve townships,
5.3. The synergistic effect of land use and precipitation on sandy desertification Although there was consensus that the vulnerability to sandy desertification was a function of both anthropogenic pressures and
Fig. 4. The geographically distribution of acceptable regression models.
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
most of them had greater abstract of the coefficient of SI. Only two of them had greater abstract of the coefficient of PC. It implied that, the temporal change of sandy desertification in the western and central part of Naiman Banner was mainly caused by land use changes. As for the townships with unsatisfied regression models, which were located in the northern and southern Naiman Banner, neither the precipitation nor land use significantly impacted the change of the bare sand land. The synergistic effect of the precipitation and land use made these regions more stable. The north and the south of Naiman Banner faced the lightest extent of sandy desertification. And the extent of sandy desertification changed more slowly. 6. Conclusion In this paper, linear regression model was used to quantify the contribution of land use and the precipitation to sandy desertification, respectively. Results showed that, the precipitation and land use contributed to sandy desertification independently, however, their synergistic effect was more important. In details, at county level, land use acted as the main cause of sandy desertification in spatial dimension, the different combination of land use types determined the geographically distribution of sandy desertification. In temporal dimension, the coactions of land use and the precipitation impacted sandy desertification greatly. Relatively, land use contributed more to the temporal change of sandy desertification than precipitation during this study period. For the case study area, the western and central parts of the county were affected mostly by land use. The frequently change in land use made the fluctuation of the extent of sandy desertification in the central through the years. The northern and southern parts of the county were impacted by land use in spatial dimension but also by other factors in temporal dimension. The land use pattern in the north and south were relatively stable, so that there were lower extent of sandy desertification in this region than in other parts of Naiman Banner. The precipitation was important to the temporal change of sandy desertification, especially in few townships in the central Naiman. The synergistic effect of the precipitation and land use greatly influenced the spatial distribution of sandy desertification. Acknowledgments This research was supported by the National Nature Science Foundation of China under the grant the grant NO. 40801235. It was also funded by the Start-up Foundation from Huazhong Agricultural University, Wuhan, China. The authors are pleased to acknowledge the reviewers for their good suggestions. References An, G., Sun, I., Lian, Y., 2002. The climatic change analysis of Oianan in the last 40 years. Climatic and Environmental Research 7, 370e376. Bao, H.J., Yao, Y.F., Zhang, X.L., Li, Z.S., 2003. Studies on changes of landscape patterns in Keerqin Sandy Land. Journal of Arid Land Resources and Environment 17 (2), 83e88. Bao, H.J., Bao, G.Q., Tian, L., Guan, J.S., 2004. A study on desertification dynamics of Keerqin sandy land. Acta Scientiarum Naturalium Universitatis NeiMongol 35 (2), 172e176.
41
Dong, Y., 1992. Roles of human factors in desertification. Journal of Desert Research 12 (1), 16e26. Han, Z., 2003. The evolution of the Mao wusu Desert and the reclamation in the adjacent areas in the Ming Dynasty. China Social Sciences 5, 191e204. Hao, C., Wu, S., 2006. The effects of land-use types and conversions on desertification in Mu Us Sandy Land of China. Journal of Geographical Sciences 16, 57e67. Jia, B.Q., Zhang, Z.Q., Ci, L.J., 2004. Oasis land use dynamics and its influence on the oasis environment in Xinjiang, China. Journal of Arid Environments 56, 11e26. Li, F., Zhang, B., Su, W., He, Y., Wang, Z., Song, K., Liu, D., Liu, Z., 2008. Sandy desertification change and its driving forces in western Jilin Province, North China. Environmental Monitoring and Assessment 136, 379e390. Luo, F., Qi, S.Z., Xiao, H.L., 2005. Landscape change and sandy desertification in arid areas: a case study in the Zhangye Region of Gansu Province, China. Environmental geology 49, 90e97. Runnström, M.C., 2003. Rangeland development of the Mu Us sandy land in semiarid China: an analysis using Landsat and NOAA remote sensing data. Land Degradation and Development 14, 189e202. Satty, T.L., 1977. A scaling method for priorities in Hierarchical structures. Journal of Mathematical Psychology 15, 59e62. Satty, T.L., Kearns, K.P., 1985. Analytical planning. In: The Organization of Systems. Pergamon Press, Oxford. Sun, D.F., Dawson, R., Li, B., 2006. Agricultural causes of desertification risk in Minqin, China. Journal of Environmental Management 79, 348e356. Sun, D.F., Dawson, R., Li, H., Li, B., 2005. Modeling desertification change in Minqin County, China. Environmental Monitoring and Assessment 108, 169e188. Sun, D.F., Richard, D., Li, H., Rong, W., Li, B., 2007. A landscape connectivity index for desertification assessment: a case study of Minqin county, China. Landscape Ecology 22, 531e543. UNCCD, 2004. Preserving Our Common Ground. UNCCD 10 Years on. United Nations Convention to Combat Desertification, Bonn, Germany. Verburg, P.H., Veldkamp, A., Fresco, L.O., 1999. Simulation of changes in the spatial pattern of land use in China. Applied Geography 19, 211e233. Wang, W., 2002. When Maowusu became a desert? View through new archaeological finds. Archaeology and Cultural Relics 5, 80e85. Wang, T., 2004. Progress in sandy desertification research of China. Journal of Geographical Sciences 14 (4), 387e400. Wang, T., Wu, W., Xuan, X., Sun, Q., Zhang, W.M., Han, Z.W., 2004. Spatial and temporal change of desertified soil in North China for last 50 years. Acta Geographica Sinica 59 (2), 203e211. Wang, X., Chen, F.H., Dong, Z., Xia, D., 2005. Evolution of the southern Mu Us Desert in North China over the past 50 years: an analysis using proxies of human activity and climate parameters. Land Degradation and Development 16, 33e48. Wang, X.M., Cheng, F.H., Hasi, E., Li, J.C., 2008. Desertification in China: an assessment. Earth-Science Reviews 88, 188e206. Wu, W., 2001. Study on processes of desertification in Mu Us Sandy Land for last 50 years, China. Chinese Science Bulletin 43, 2437e2440. Xu, D.Y., Kang, X.W., Zhuang, D.F., Pan, J.J., 2010. Multi-scale quantitative assessment of the relative roles of climate change and human activities in desertification e a case study of the Ordos Plateau, China. Journal of Arid Environments 74, 498e507. Xu, D.Y., Li, C., Zhuang, D., Pan, J.J., 2011. Assessment of the relative role of climate change and human activities in desertification: a review. Journal of Geographical Sciences 21 (5), 926e936. Yang, X., Zhang, K., Jia, B., Ci, L., 2002. Desertification assessment in China: an overview. Journal of Arid Environments 63, 517e531. Zhao, Y., 1981. Mu Us Desert evolution in historical periods. Historical Geography 1, 34e47. Zhao, H.L., 2003. Desertification Process and Restoration Function of Horqin Desert. Navy Press, China. Zhao, W.Z., Xiao, H.L., Liu, Z.M., Li, J., 2005. Soil degradation and restoration as affected by land use change in the semiarid Bashang area, northern China. CATENA 59, 173e186. Zhu, Z., 1985. The principles and methods for compiling the map of desertification of China. Journal of Desert Research 4 (1), 3e15. Zhu, 1994. A review and outlook of soil desertification. Geographical Research 13 (1), 104e113. Zhu, Z., 1998. Concept, cause and control of desertification in China. Quaternary Science 2, 145e155. Zhu, Z., Chen, G., 1994. Sandy Desertification in China. Sciences Press, Beijing.
Contents lists available at SciVerse ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Quantifying the synergistic effect of the precipitation and land use on sandy desertification at county level: A case study in Naiman Banner, northern China Ge Xiaodong a, b, *, Ni Jinren a, Li Zhenshan a, Hu Ronggui b, Ming Xin b, Ye Qing b a b
Department of Environmental Engineering, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China Department of Environmental Sciences and Engineering, Huazhong Agricultural University, Wuhan 430070, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 April 2009 Received in revised form 5 February 2013 Accepted 17 February 2013 Available online 9 April 2013
Assessing the driving forces of sandy desertification is fundamental and important for its control. It has been widely accepted that both climatic conditions and land use have great impact on sandy desertification in northern China. However, the relative role and synergistic effect of each driving force of sandy desertification are still not clear. In this paper, an indicator named as SI was defined to represent the integrated probability of sandy desertification caused by land use. A quantitative method was developed for characterizing the relative roles of annual precipitation and land use to sandy desertification in both spatial and temporal dimensions at county level. Results showed that, at county level, land use was the main cause of sandy desertification for Naiman Banner since 1987e2009. In the case of spatial dimension, the different combination of land use types decided the distribution of sandy desertification probability and finally decided the spatial pattern of bared sand land. In the case of temporal dimension, the synergistic effect of land use and precipitation highly influenced the spatial distribution of sandy desertification. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Sandy desertification Land use Regression County level
1. Introduction Sandy desertification, the land degradation by wind erosion, mainly results from the excessive human activities and climate change in arid, semi-arid and part of sub-humid regions (UNCCD, 2004). It has been one of the most serious social-economicenvironmental problems in northern China in the past 50 years (Zhu and Chen, 1994; Wang, 2002, 2004; Wang et al., 2004). Accurate assessment on the causes of sandy desertification will be instrumental in developing global/regional action plans to prevent and eradicate the problems (Yang et al., 2002; Sun et al., 2005). However, there are still no widely accepted assessing methodologies, because of the controversy over the causes of sandy desertification (Zhu, 1985, 1998; Yang et al., 2002). It has been debated for many years whether the degradation is caused primarily by climatic change or human activities (Wang et al., 2005). In traditional opinions, climatic variables are the predominant drivers for sandy desertification in northern China (Zhao, 1981; Wang, 2002; Han, 2003; Runnström, 2003). For example, Wang X.
* Corresponding author. Department of Environmental Sciences and Engineering, Huazhong Agricultural University, Wuhan 430070, China. Tel.: þ86 027 87672332; fax: þ86 027 87285950. E-mail address: [email protected] (G. Xiaodong). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.02.033
(2008) claimed that desertification in China was consistently associated with specific trends in temperature, drought and wind regime. Among all climatic variables, the precipitation, particularly annual precipitation, has more influence on sandy desertification because it helps to improve the vegetation cover and decreases sand transport, consequently significantly contributes to ecosystem rehabilitation (Verburg et al., 1999). With low precipitation, the surface vegetation becomes sparse and the proportion of surface soil being turned into sandy soil becomes relatively large (An et al., 2002). Sandy desertification, hence, usually occurs in regions with annual precipitation less than 450 mm and spring precipitation less than 90 mm (Wang et al., 2008). Although this climate-driving hypothesis is supported by historical records and archaeological evidence in literature, improper land use has been widely recognized as the dominant factor leading to sandy desertification in recent years in arid and semi-arid areas of China (Wu, 2001). Based on the consensus it is accepted that land use is the most direct way which has the largest impact on biodiversity and land cover (Jia et al., 2004; Hao and Wu, 2006; Dong, 1992). The influence of each land use type on sandy desertification has been studied widely. For example, Zhao (2003) reported that reclamation would destruct the original ground vegetation and deteriorate the chemical or physical properties of soil. Bao et al. (2003, 2004) found that livestock’s crunching and trampling would destroy the vegetation coverage of grassland and destruct
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
35
the soil’s crust layer. These studies have attributed the rapid desertification in North China to over-grazing, over-reclamation and extensive cutting respectively (Zhu, 1994; Zhao et al., 2005). Because of the dispute, there are few successful methods for the quantitative analysis of relationship between two kinds of driving factors. Only recently people have made efforts to analyze the relative role of climate variables and human activities on sandy desertification (Luo et al., 2005; Sun et al., 2006; Li et al., 2008; Xu et al., 2011). Modeling approach is important in describing the process of sandy desertification and evaluating the respective impacts of the causes. Normally, regression models and multivariable analysis techniques, such as principle component analysis, are used in comparative studies (Xu et al., 2010). For example, Luo et al. (2005) used satellite images, meteorological and socioeconomic data to assess the landscape change from 1993 to 2002. Sun et al. (2006) developed an index (RI) for Minqin County in China by means of statistical modeling techniques to explore the variability of desertification risk. Sun et al. (2007) also made efforts to test whether a cost-distance connectivity index could act as an indicator for explaining the contribution of the causes to sandy desertification. However, according to the author’s knowledge, few researchers have made efforts to evaluate the relative role of climate and land use in both spatial and temporal scales. The research on the assessment method is still in urgent need. In this paper, we selected Naiman Banner in Inner Mongolia of China as the research region and attempted to develop a quantitative method for the relative effect of annual precipitation and land use on sandy desertification in both spatial and temporal scales at county level. 2. Study area Naiman Banner is a county in Inner Mongolia, which is located in northern China with a geo-location from 120190 E to 120 450 E and 42140 N to 43 320 N (Fig. 1). It covers an area of 8210 km2 and lies in the farming-pastoral ecotone where the ecosystem is vulnerable. The average annual precipitation varies from 343.3 mm to 451.0 mm with significant inter-annual variation. The average annual wind speed ranges from 3.5 m/s to 4.1 m/s. Under the natural conditions, the exposed soil is prone to erosion. Sandy desertification has been severe in Naiman Banner in history. Land use in Naiman Banner plays a vital role in the process of sandy desertification in the way of accelerating the soil exposure and the wind erosion. Naiman Banner consists of twenty one townships which vary in land use pattern and sandy desertification extent. 3. Materials and methods 3.1. Indicator selection and acquisition In this study, three kinds of indicators were acquired to reflect the changes of the climatic conditions, the land use and the extent of sandy desertification, respectively. The indicator of Total Precipitation (PC) was chosen to reveal the change of the climatic conditions. The area percentage of bare sand land (BI) was selected to reveal the extent of the sandy desertification. An indicator named as SI was defined with the area information of land use types to reveal the contribution of all land use types to sandy desertification in a specific region. (1) The indicator of PC Generally, the indicator of annual amount of precipitation is used to indicate the depth of water, such as rain, snow, or sleet, which condenses from the atmosphere in a year. This paper uses
Fig. 1. Geographical location of Naiman Banner.
the indicator of Total Precipitation (PC) to identify the difference of the climatic conditions between the townships in temporal and spatial dimension. Total Precipitation (m3) equals to the product of the annual amount of precipitation (mm) and the area (m2) of the given region. It means the whole volume of the water which condenses from the atmosphere in a specific region in one year. During the period from 1987 to 2009, there were only two meteorological stations in Naiman Banner. Annual precipitation data obtained from the statistical yearbooks were the average value from these two meteorological stations, illustrating the average amount of precipitation of the whole county. Total Precipitation was calculated based on the data of annual of precipitation and the area of the specific township. The coefficient of variation of the annual precipitation is about 25.1%. (2) The indicator of BI The area percentage of bare sand land (BI) was a traditional indicator for presenting the extent of sandy desertification. The greater BI is, the more severe sandy desertification is. So BI was selected to reveal the extent of the sandy desertification in this paper. BI was extracted from Landsat-5 TM images of 1987, 1992, 1996, 2000 and 2002. After radiometric calibration, geometric correction, cloud removal and geometrical corrections, maximum likelihood supervised classification was carried out to identify the bare sand land. Generally, the sand land was divided into two types, fixed sand dune and semi-fixed sand. Semi-fixed dune was the sand dune with less than 30% of the vegetation cover, while the fixed sand dune was the sand dune with more than 30% of the vegetation cover.
36
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
(3) The indicator of SI
Table 2 The relative possibility of sandy desertification between land use types.
Since any type of land use practices, such as deforestation, grazing and agriculture, can affect the ecosystem structure and function, different type of land use has different possibility to cause land degradation. Different area of any land use type with its specific possibility of sandy desertification may contribute to a different extent of sandy desertification in a region. In order to qualify the integrated possibility of sandy desertification resulting from all land use types, an indicator named SI was defined as Formula (1). SI was the sum of sandy desertification possibilities of all land use types in a specific region.
SI ¼
X ðpi $Ai Þ
(1)
where pi was the sandy desertification probability of land use type i, and Ai was the area of land use type i in a given region. Ai was extracted with remote sensing data. pi was calculated according to the local experience. In detail, Ai was extracted using Landsat-5 TM remote sensing data at 30 30 m spatial resolution for 1987, 1992, 1996, 2000 and 2002. All spectral data were collected in summer and autumn, mainly in September. Radiometric calibration, geometric correction and cloud removal were carried out from all of these images, which were geo-referenced to the WGS_1984 UTM Projected Coordinate System with a geometric precision of 0.5 pixels. And then, the geometrical corrections and classifications were carried out. Seven land use types were identified including irrigated cultivated land, dry farmland, forestry, grassland, resident land, surface water and unused land. The sandy desertification possibility of land use type i (pi) was calculated by a method based on the Analytic Hierarchy Process (AHP) (Satty, 1977, 1985). A reciprocal matrix (Table 1) was built up with the Cij values that were collected through the questionnaires. A consistency ratio (CR) was defined to measure the consistency level of the reciprocal matrix (Formula 2). If the CR value was less than 0.1, the reciprocal matrix was acceptable for further calculation.
CR ¼ CI=RI
(2)
CI ¼ ðlmax nÞ=ðn 1Þ
(3)
In Formula 3, lmax was the maximum eigenvalue of the reciprocal matrix, and n was the power of the matrix. RI was determined by n (Table 3). To obtain the probability (pi), the eigenvector and the maximum eigenvalue of the reciprocal matrix were calculated as Formula 4.
Scale
Meanings
1 3
The same possibility between two land use types Land use type i is a little easier to be desertified than Land use type j Land use type i is obviously easier to be desertified than Land use type j Land use type i is strongly easier to be desertified than Land use type j cij is the relative possibility between land use type i and element j is, while cji is the relative possibility between land use type j and element i
5 7 Reciprocal
3.2. Questionnaires 64 questionnaires were prepared to collect the data for calculating the possibilities of each land use type in July, 2004. In 2004 and 2008, 22 more questionnaires were sent to collect more data in different townships. Like this, total eighty six questionnaires were collected, ten of which were from local government, fourteen were from experts and the rest were from local residents. In these questionnaires, the ease of sandy desertification between any two land-use types was compared by local people was recorded. Based on the surveys, the prevalent opinion about the possibility of sandy desertification between any two land use types was collected and a reciprocal matrix was constructed. 3.3. Assessment method The mathematical formalization of the relationship between the area of bare sand land, the annual precipitation and the land use was built up as Formula 5.
log BI ¼ A þ a$ln PC þ b$ln SI
(5)
where A > 0, a > 0 and b > 0. BI was the area percentage of bare sand land. PC was the annual precipitation. A was the coefficient that reflected the contribution of all other factors besides the precipitation and land use to sandy desertification. Multiple regression analysis was used to evaluate a and b. Two kinds of relationships were analyzed, including (1) the relationship between BI and PC, BI and SI, respectively; and (2) that between BI and PC relatively. The adjust coefficient of determination (R2) was used as a measure of the amount of variation explained, while the standardized a and b indicated the relative importance of individual variables in Formula 5. 4. Results and analysis 4.1. The sandy desertification probabilities of each land use type
R ¼ lmax $½p1 ; /; pi ; /; pn 0
(4)
where R was the reciprocal matrix, lmax was the maximum eigenvalue, [p1, ., pi, ., pn]0 was the eigenvector. pi was the sandy desertification possibility of land use type i. Table 1 Reciprocal matrix. SI
Type 1
Type j
.
Type n
Type 1 . Type i . Type n
C11 . Ci1 . Cn1
C1j . Cij
. . . . .
C1n . Cin . Cnn
.
Cnj
Cij: relative possibility of sandy desertification between land use type i and land use type j. The marks of Cij were given in Table 2.
Based on ninety-seven questionnaires collected in 2004, 2008 and 2010 in Naiman Banner, the relative importance between any two land use types was determined and the reciprocal matrix were constructed (Table 4). Among the questionnaires, ten were taken from local government, twenty-five were from local researchers and the rest were taken from local residents. The maximum eigenvalue of the matrix was 6.03. So, the CI, RI and CR were 0.02, 1.26 and 0.005 respectively. The reciprocal matrix Table 3 RI value for the reciprocal matrix with 1e15 power. Power RI Power RI
1 0 9 1.46
2 0 10 1.49
3 0.52 11 1.52
4 0.89 12 1.54
5 1.12 13 1.56
6 1.26 14 1.58
7 1.36 15 1.59
8 1.41
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41 Table 4 The reciprocal matrix based on surveys. Land use type
Irrigated land
Dry farmland
Forestry
Grassland
Resident land
Unused land
Irrigated land Dry farmland Forestry Grassland Resident land Unused land
1 3 1/3 1 1 3
1/3 1 1/5 1/3 1/3 1
3 5 1 3 2 7
1 3 1/5 1 1 3
1 3 1/5 1 1 3
1/3 1 1/7 1/3 1/3 1
37
defined as BI, was calculated for each township. For the whole county, BI increased from 1987 to 1996, reflecting the expanding of sandy desertification. And after 1996, BI decreased obviously reflecting the recovery of the ecosystem. The area percentage of bare sand land varied in different townships. The spatial distribution of BI values showed that the bared sand land was mainly concentrated in the western part of Naiman Banner, and also in the central part. The BI values in central Naiman changed more frequently than that in other part of the county through the years. 4.4. Regression models
was tested to be consistent and the corresponding eigenvector of the matrix was accepted. The probabilities of different land use types were shown in Table 5. The probabilities revealed the relative contribution to sandy desertification between land use types. Among all, the forestry was found to be important for soil and water conservation. It was hardest to be degraded in all land use types. Cultivation and grazing were the main anthropogenic activities in Naiman Banner. According to local opinions, although the cultivation and the grazing affected sandy desertification in different ways, the irrigated land and grassland had always the closed extent of contribution to sandy desertification. Dry farmland was difficult to be irrigated with surface water or ground water. It had less soil moisture and microorganisms and, hence, was easier to be sandy desertified. Generally, unused land was under high sandy desertification risk, because the original vegetation was destroyed and the land was liable to be eroded by wind force, like abandoned farmland or sand dunes. 4.2. Results of SI The SI values were calculated for each township in Naiman Banner (Fig. 2). From 1987 to 1992, SI slightly decreased. From 1992 to 2002, SI increased obviously, reflecting the strengthening of the contribution of land use to sandy desertification. The amount of the townships, whose SI value was below 0.3, was 10, 14, 4 and 0 in 1987, 1992, 1996 and 2000 respectively. The amount of the townships, whose SI value was above 0.4, was 3, 1, 6 and 9, respectively. From 2000 to 2009, SI values decreased again in the whole county. The amount of the townships, whose SI value was below 0.3, was 0 in 2002 and 10 in 2009. The amount of the townships, whose SI value was above 0.4, was 4 in 2002 and 2 in 2009. The SI values varied greatly in different townships, reflecting the different contribution of land use to sandy desertification in spatial dimension. For Naiman Banner, the townships in the west had the highest SI, while the ones in the north and in the south had the lowest SI. The SI values in the central of Naiman Banner ranged from 0.3 to 0.5. The spatial distribution of SI showed that the townships had different combination of land use types and influenced the sandy desertification differently. 4.3. The results of BI The bare sand land was compelled with TM images and was shown in Fig. 3. The area percentage of bare sand land, which was
Table 5 The relative probabilities of land use types. Land use type
Irrigated land
Dry farmland
Forestry
Grassland
Resident land
Unused land
Relative probability
0.23
0.63
0.07
0.23
0.21
0.66
Two types of regression models were constructed. Regression models of the first type were built up to test the spatial correlation of the precipitation, land use and sandy desertification in a specific year. Any model of the first type was built up with the data of all townships of a specific year. Results were shown in Table 6. The adjusted R2 of any model was satisfactory, so that all the regression models were acceptable. The standardized coefficient of SI varied between 0.741 and 0.837 with the significance below 0.01, while the standardized coefficient of PC varied between 0.029 and 0.041 with significance above 0.05. The standardized coefficients of SI were much greater than that of PC. With all regression models of the first type, SI was positively related to BI, while PC was sometimes negatively related to BI. The regression models of the second type were built up to test the temporal correlation of the precipitation, land use and sandy desertification within a specific township. Any model of this type was built up with the time series data of a specific township. As shown in Table 7, twelve of the regression models were accepted with satisfactory adjusted R2. The standardized coefficient of SI varied between 0.44 and 2.07. The standardized coefficient of PC varied between 0.32 and 1.33. The significance of most models within the twelve townships was at the 0.05 level. As for the other nine townships, the adjusted R2 was not satisfactory. 5. Discussions 5.1. The precipitation and sandy desertification Sandy desertification is one of the main types of desertification which is driven by wind erosion. In many arid and semi-arid areas, once soil was exposed, wind erosion occurs immediately. So the extent of soil exposure greatly influenced the extent of sandy desertification. The precipitation was the most important climatic factor that would affect the ground vegetation. When the annual precipitation decreased, especially with less spring precipitation, there would be less vegetation. The soil exposure would be accelerated. The drought resulting from less precipitation also hastened the wind erosion. However, when the annual precipitation increased, there would be more vegetation. Soil exposure would be prevented. At the same time, more precipitation helped to increase soil moisture, biomass and biodiversity, which would keep soil stable and difficult to be eroded. The variation of the precipitation in the agro-pastoral transition zone of northern China was studied extensively. In Horqin Sand land, slightly decreasing annual precipitation was observed over the past several years. The vegetative succession and the dominant species with largest distribution in this area were affected by the precipitation changes. The tendency of being desertified would be strengthened while some vegetable species was seriously degraded and then the bare land was eroded. Most scientists had theoretically attributed the precipitation as important backdrop for sandy desertification in Horqin Sand Land.
38
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
Fig. 2. Distribution of SI values in Naiman County.
The results in Table 7 supported some previous studies to some extent. Some previous studies suggested that the main climatic factors responsible for desertification were variations in precipitation. The results proved that the impact of the precipitation on the ecosystem turned to be considerable, especially in temporal dimension. In Table 7, most of the coefficients of PC were negative with statistical significance (P 0.05). It proved that, the increasing of the precipitation would help prevent sandy desertification. In the year with more precipitation, the extent of sandy desertification might be lightly reversed. Nevertheless, as shown in Table 6, the role of the precipitation on sandy desertification was not significant in the spatial dimension. The value of the standardized coefficient of PC of any regression model was very close to zero. And the relationship between BI
and PC was insignificant. The results in Table 6 implied that the precipitation had very limited influence on the spatial difference of sandy desertification. Actually, as shown in Fig. 3, the occurrence of the bare sand land was obviously related to the spatial location. The bared sand land was mainly concentrated in the western part and the central part of Naiman Banner. The change of the bared sand land mainly occurred in central Naiman changed. However, the precipitation had quite limited ability to explain the spatial difference of sandy desertification. 5.2. Land use and sandy desertification Many studies suggested that desertification in semi-arid North China was the result of human activities during this period,
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
39
Fig. 3. The distribution of bared sand land.
and government policies had also accelerated desertification to some extent. In northern china, most of the human activities and government policies were related to land use, which provided agricultural products to satisfy local demand of economic Table 6 Regression models for each year (n ¼ 21). Year
Standardized coefficient of PC
Standardized coefficient of SI
Adjusted R2
1987 1992 1996 2000 2002 2009
0.04 0.03 0.08 0.04 0.18 0.16
0.84** 0.89** 0.82** 0.81** 0.74** 0.80**
0.714* 0.803* 0.652* 0.675* 0.658* 0.701*
Significance levels: *<0.05, **<0.01.
development. The need of agricultural products kept increasing pressure to land cover changes. Land use helped to accelerate soil exposure. Since different land use types had different ways of soil exposure, the combination of all land use types had integrated effect on sandy desertification. To give assessment to the effect of land use pattern on sandy desertification, an indicator SI was defined as the integrated possibility of sandy desertification under the condition of a specific land use pattern. The results showed that SI varied in both spatial and temporal dimensions. Furthermore, results in Figs. 2 and 3, Tables 6 and 7 proved that there were strong correlation between land use and sandy desertification. As shown in Table 6, the standardized coefficient of SI was positive with significance 0.01 in any regression model of the first type. The greater SI was, the greater BI was. It proved that land use
40
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
Table 7 The regression models for each township (n ¼ 6). Township
Standardized coefficient of PC
Standardized coefficient of SI
Adjusted R2
Ping’an Dongmin Zhi’an Desheng Baxiantong Weiliansu Nailin Baiyintala Guribanhuan Bagaborihe Daqintala Xinzhen Yilongyong Huanghuatala Sharihaolai Baiyinchang Tuchengzi Qinghe Minren Zhanggutai Qinglongshan
0.32 0.81* 1.33* 0.67* 0.68* 0.75* 0.85* 0.48 0.35* 0.45* 0.73* 1.17** 0.48 0.37 0.38 0.27 0.22 0.35 0.50 0.12 0.40
0.85* 0.79* 2.07* 0.86* 0.70* 0.76* 1.23* 0.79* 1.00** 0.92* 0.44* 1.34** 0.48 0.56 0.38 0.40 0.37 0.10 0.12 0.66 0.40
0.957 0.966 0.811 0.643 0.714 0.975 0.646 0.800 0.883 0.759 0.965 0.979 0.468 0.121 0.284 0.153 0.218 0.172 0.019 0.464 0.085
Significance levels: *<0.05; **<0.01.
in Naiman Banner contributed to the expansion of sandy desertification. When the combination of land use types of a township had higher sandy desertification probability, the area percentage of bared sand land was higher and sandy desertification was more severe. The difference of land use pattern between the townships explained the spatial difference of sandy desertification. As shown in Figs. 2 and 3, the townships in the east had the highest SI, and they faced the most severe sandy desertification. The townships in the north and in the south had the lowest SI, while the area percentage of bare sand land in the north and in the south were the lowest. SI in central Naiman changed more frequently than that in other part of Naiman Banner, while BI in central Naiman changed the most frequently through the years. The spatial correlation between SI and BI illustrated that land use was the key factor to cause the spatial difference of sandy desertification. According to the definition, the value of SI was decided by the areas of different land use types. The townships in the west part of Naiman Banner had more unused land or dry farmland, which were the easiest to be eroded by wind, and then faced more severe sandy desertification. As for the townships in the central part of Naiman Banner, where most grassland and arable land were located, the soil was more difficult to be eroded by wind than unused land. Nevertheless, the household alternative between crop-planting and livestock-grazing led to the frequent change of land use types, which accelerated the soil exposure and resulted in the frequent change of sandy desertification. In temporal dimension, as shown in Table 7, the twelve regression models with acceptable adjust R2 also explained the contribution of land use to sandy desertification. The standard coefficients of SI were positive. It implied that land use helped to accelerate soil exposure and increase the integrated possibility of being desertified. In Table 7, not all townships had a regression model with satisfactory adjust R2. It implied that some factor besides land use and the precipitation impacted the sandy desertification in those townships during this period.
climate changes over the past half century, the debates about key role of the driving forces of sandy desertification lasted for a long time. Some researches claimed the human activities as the main cause of sandy desertification, while others regarded the climatic factors played the key role in local environmental changes. The regression models showed that the precipitation had significant influence on the temporal changes of sandy desertification, but it had trivial impact on the spatial difference of sandy desertification. Land use had impact on the extent of sandy desertification both in spatial and temporal dimensions. Furthermore, the results in this paper implied that the causes of sandy desertification were not independent. They played different roles in different dimensions, and they couldn’t be simply separated from each other. Land use and the precipitation synergistically influenced the trend of sandy desertification. The synergistic roles of land use and the precipitation should be paid more attention to. In Table 7, in the regression models with acceptable significance, the abstract of the standardized coefficient of PC and that of SI were very close, reflecting that land use had relatively equivalent role in sandy desertification as the precipitation in temporal dimension. The standardized coefficient of PC was negative, while that of SI was positive. The increase of the precipitation would lead to the recovery of the ecosystem, while the increase of the tension of land use might result in sand land expansion. As the annual precipitation varied greatly between years, the combination of the precipitation and land use made these townships vulnerable and uncertain in the process of sandy desertification. The geographic location of these townships affirmed this point of view that the synergistic effect of the precipitation and land use decided the spatial distribution of sandy desertification (Fig. 4). The townships with acceptable regression models were located in the western and central parts of Naiman Banner, where sandy desertification were more severe and changed more frequently. The change of the precipitation and the change of land use synergistically greatly impacted the land cover in this region, in different directions respectively. The integrated roles of the precipitation and land use hastened the uncertainty of land cover changes. In details, as shown in Table 7, the abstract of standardized coefficients of PC and that of SI were compared for the regression models with acceptable significance. Among the twelve townships,
5.3. The synergistic effect of land use and precipitation on sandy desertification Although there was consensus that the vulnerability to sandy desertification was a function of both anthropogenic pressures and
Fig. 4. The geographically distribution of acceptable regression models.
G. Xiaodong et al. / Journal of Environmental Management 123 (2013) 34e41
most of them had greater abstract of the coefficient of SI. Only two of them had greater abstract of the coefficient of PC. It implied that, the temporal change of sandy desertification in the western and central part of Naiman Banner was mainly caused by land use changes. As for the townships with unsatisfied regression models, which were located in the northern and southern Naiman Banner, neither the precipitation nor land use significantly impacted the change of the bare sand land. The synergistic effect of the precipitation and land use made these regions more stable. The north and the south of Naiman Banner faced the lightest extent of sandy desertification. And the extent of sandy desertification changed more slowly. 6. Conclusion In this paper, linear regression model was used to quantify the contribution of land use and the precipitation to sandy desertification, respectively. Results showed that, the precipitation and land use contributed to sandy desertification independently, however, their synergistic effect was more important. In details, at county level, land use acted as the main cause of sandy desertification in spatial dimension, the different combination of land use types determined the geographically distribution of sandy desertification. In temporal dimension, the coactions of land use and the precipitation impacted sandy desertification greatly. Relatively, land use contributed more to the temporal change of sandy desertification than precipitation during this study period. For the case study area, the western and central parts of the county were affected mostly by land use. The frequently change in land use made the fluctuation of the extent of sandy desertification in the central through the years. The northern and southern parts of the county were impacted by land use in spatial dimension but also by other factors in temporal dimension. The land use pattern in the north and south were relatively stable, so that there were lower extent of sandy desertification in this region than in other parts of Naiman Banner. The precipitation was important to the temporal change of sandy desertification, especially in few townships in the central Naiman. The synergistic effect of the precipitation and land use greatly influenced the spatial distribution of sandy desertification. Acknowledgments This research was supported by the National Nature Science Foundation of China under the grant the grant NO. 40801235. It was also funded by the Start-up Foundation from Huazhong Agricultural University, Wuhan, China. The authors are pleased to acknowledge the reviewers for their good suggestions. References An, G., Sun, I., Lian, Y., 2002. The climatic change analysis of Oianan in the last 40 years. Climatic and Environmental Research 7, 370e376. Bao, H.J., Yao, Y.F., Zhang, X.L., Li, Z.S., 2003. Studies on changes of landscape patterns in Keerqin Sandy Land. Journal of Arid Land Resources and Environment 17 (2), 83e88. Bao, H.J., Bao, G.Q., Tian, L., Guan, J.S., 2004. A study on desertification dynamics of Keerqin sandy land. Acta Scientiarum Naturalium Universitatis NeiMongol 35 (2), 172e176.
41
Dong, Y., 1992. Roles of human factors in desertification. Journal of Desert Research 12 (1), 16e26. Han, Z., 2003. The evolution of the Mao wusu Desert and the reclamation in the adjacent areas in the Ming Dynasty. China Social Sciences 5, 191e204. Hao, C., Wu, S., 2006. The effects of land-use types and conversions on desertification in Mu Us Sandy Land of China. Journal of Geographical Sciences 16, 57e67. Jia, B.Q., Zhang, Z.Q., Ci, L.J., 2004. Oasis land use dynamics and its influence on the oasis environment in Xinjiang, China. Journal of Arid Environments 56, 11e26. Li, F., Zhang, B., Su, W., He, Y., Wang, Z., Song, K., Liu, D., Liu, Z., 2008. Sandy desertification change and its driving forces in western Jilin Province, North China. Environmental Monitoring and Assessment 136, 379e390. Luo, F., Qi, S.Z., Xiao, H.L., 2005. Landscape change and sandy desertification in arid areas: a case study in the Zhangye Region of Gansu Province, China. Environmental geology 49, 90e97. Runnström, M.C., 2003. Rangeland development of the Mu Us sandy land in semiarid China: an analysis using Landsat and NOAA remote sensing data. Land Degradation and Development 14, 189e202. Satty, T.L., 1977. A scaling method for priorities in Hierarchical structures. Journal of Mathematical Psychology 15, 59e62. Satty, T.L., Kearns, K.P., 1985. Analytical planning. In: The Organization of Systems. Pergamon Press, Oxford. Sun, D.F., Dawson, R., Li, B., 2006. Agricultural causes of desertification risk in Minqin, China. Journal of Environmental Management 79, 348e356. Sun, D.F., Dawson, R., Li, H., Li, B., 2005. Modeling desertification change in Minqin County, China. Environmental Monitoring and Assessment 108, 169e188. Sun, D.F., Richard, D., Li, H., Rong, W., Li, B., 2007. A landscape connectivity index for desertification assessment: a case study of Minqin county, China. Landscape Ecology 22, 531e543. UNCCD, 2004. Preserving Our Common Ground. UNCCD 10 Years on. United Nations Convention to Combat Desertification, Bonn, Germany. Verburg, P.H., Veldkamp, A., Fresco, L.O., 1999. Simulation of changes in the spatial pattern of land use in China. Applied Geography 19, 211e233. Wang, W., 2002. When Maowusu became a desert? View through new archaeological finds. Archaeology and Cultural Relics 5, 80e85. Wang, T., 2004. Progress in sandy desertification research of China. Journal of Geographical Sciences 14 (4), 387e400. Wang, T., Wu, W., Xuan, X., Sun, Q., Zhang, W.M., Han, Z.W., 2004. Spatial and temporal change of desertified soil in North China for last 50 years. Acta Geographica Sinica 59 (2), 203e211. Wang, X., Chen, F.H., Dong, Z., Xia, D., 2005. Evolution of the southern Mu Us Desert in North China over the past 50 years: an analysis using proxies of human activity and climate parameters. Land Degradation and Development 16, 33e48. Wang, X.M., Cheng, F.H., Hasi, E., Li, J.C., 2008. Desertification in China: an assessment. Earth-Science Reviews 88, 188e206. Wu, W., 2001. Study on processes of desertification in Mu Us Sandy Land for last 50 years, China. Chinese Science Bulletin 43, 2437e2440. Xu, D.Y., Kang, X.W., Zhuang, D.F., Pan, J.J., 2010. Multi-scale quantitative assessment of the relative roles of climate change and human activities in desertification e a case study of the Ordos Plateau, China. Journal of Arid Environments 74, 498e507. Xu, D.Y., Li, C., Zhuang, D., Pan, J.J., 2011. Assessment of the relative role of climate change and human activities in desertification: a review. Journal of Geographical Sciences 21 (5), 926e936. Yang, X., Zhang, K., Jia, B., Ci, L., 2002. Desertification assessment in China: an overview. Journal of Arid Environments 63, 517e531. Zhao, Y., 1981. Mu Us Desert evolution in historical periods. Historical Geography 1, 34e47. Zhao, H.L., 2003. Desertification Process and Restoration Function of Horqin Desert. Navy Press, China. Zhao, W.Z., Xiao, H.L., Liu, Z.M., Li, J., 2005. Soil degradation and restoration as affected by land use change in the semiarid Bashang area, northern China. CATENA 59, 173e186. Zhu, Z., 1985. The principles and methods for compiling the map of desertification of China. Journal of Desert Research 4 (1), 3e15. Zhu, 1994. A review and outlook of soil desertification. Geographical Research 13 (1), 104e113. Zhu, Z., 1998. Concept, cause and control of desertification in China. Quaternary Science 2, 145e155. Zhu, Z., Chen, G., 1994. Sandy Desertification in China. Sciences Press, Beijing.