Environ Earth Sci (2010) 61:689–697 DOI 10.1007/s12665-009-0381-1
ORIGINAL ARTICLE
Wind erosion hazard assessment of the Mongolian Plateau using FCM and GIS techniques Huading Shi • Qingxian Gao • Yongqing Qi Jiyuan Liu • Yunfeng Hu
•
Received: 30 March 2009 / Accepted: 17 November 2009 / Published online: 5 December 2009 Springer-Verlag 2009
Abstract The most serious environmental problems of the Mongolian Plateau are land degradation and sand storms caused by wind erosion, but the evaluation of wind erosion at regional scales has been a difficult process in wind erosion research. In this study, fuzzy c-means clustering (FCM) was used to assess the spatial pattern of wind erosion hazard on the Mongolian Plateau. By fuzzy clustering four main wind erosion factors (vegetation cover, average degree of land surface relief, degree of soil dryness and intensity of wind energy), wind erosion hazard was classified into six grades. Results show that FCM can effectively integrate related information between wind erosion and environmental factors, which provides the basis for predictive mapping of wind erosion hazard. Spatial patterns of wind erosion hazard indicate a gradual trend of increasing hazard in the Mongolian Plateau from east to west. Similar patterns were also found in NDVI and soil dryness, indicating that soil moisture and vegetation are the most important factors in the formation of wind erosion hazard. In addition, the distribution of different levels of wind erosion hazard is basically consistent with the regional distribution of landscape vegetation types in the Mongolian Plateau. Keywords Mongolian Plateau Wind erosion hazard Fuzzy c-means clustering
H. Shi (&) Q. Gao Chinese Research Academy of Environmental Science, 8 Dayangfang, BeiYuan Road, 100012 Beijing, China e-mail:
[email protected] Y. Qi J. Liu Y. Hu Institute of Geographical Science and Natural Resource Research, Beijing, China
Introduction Soil wind erosion is the main cause of land degradation and desertification in arid and semi-arid areas, and is one of the most serious global environmental problems (Callot et al. 2000; Lal 1994; Prospero et al. 2002). With continued advances in observation methods at regional scales, such as remote sensing and the rise of global change research in the last 20 years, increasing attention has been given to research on changing ecological and land-cover patterns in arid and semi-arid areas. Research on wind erosion at regional scales has also gradually increased (Gomes et al. 2003). The Mongolian Plateau is a geomorphic unit in northeast Asia that is located in the transitional climatic zone between the Arctic and Pacific oceans. Most of the plateau belongs to Mongolia and the Inner Mongolia Autonomous Region, China, and it is located far inland. Annual rainfall in this region is \400 mm except in a few areas in its eastern and southern regions. In some desert areas of the plateau, annual rainfall is \50 mm. Thus, this region exhibits a typical arid and semi-arid continental climate with long, cold winters and dry, windy springs. Severely eroded soils in this region are the main source of dust storms that affect eastern Asia (Zhang et al. 2005). In this paper, GIS and remote sensing were used to establish an environmental factor database for soil wind erosion for the Mongolian Plateau. The natural clusterings of different combinations of environmental factors were obtained using the fuzzy c-means clustering (FCM) method, and the relations between wind erosion hazard and environmental factors were deduced based on previous research and expert knowledge. Based on these results, soil wind erosion hazard for the Mongolian Plateau was evaluated, and characteristics of spatial patterns of wind erosion were analyzed.
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Study area The Mongolian Plateau is an East Asian inland plateau and is the extension of the Eurasian Plateau in central Asia. The Mongolian Plateau is located far inland and is surrounded by high-altitude mid-mountains, which form a closed inland plateau typical of the Eurasian Plateau. Mountains are high in the west, low in the east, and the significant geographic boundaries of this region. Daxinganling lies in the east, while a basin, Sayan Ridge and the Altai Mountains, which were formed by tectonic faults, are located in the west. The Sayan, Kent and Yablonovy mountain ranges are in the north, and the Yinshan Mountain ranges are in the south. The territory includes all of Mongolia, southern Russia and the Inner Mongolia Autonomous Region of northern China. The span of longitude is 34360 , from east longitude 87400 to east longitude 122150 . The span of latitude is 15220 , from north latitude 53080 to south latitude 37460 (Fig. 1). The climate of the Mongolian Plateau is influenced by a unique land and sea distribution and the atmospheric circulation, which induces an extreme continental monsoon climate. Total solar radiation increases from north to south and from east to west. The Pacific monsoon brings moist air into the Mongolian Plateau from the east, while the western regions have less moisture. Vegetation and soil types in the area are distributed regularly, with temperate and boreal zones in the south and north, respectively. Semi-humid, semi-arid and arid areas are found from east to west. Various temperature and rainfall conditions create a variety of vegetation types at the landscape scale. Vegetation and soils consist of forest, meadow and steppe in the mountain semi-humid area; a combination of steppe-Chernozem and chestnut soil occurs
Fig. 1 Altitude of the Mongolian Plateau
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in the vast semi-arid region; desert-grassland vegetation and brown soil are found in arid areas; and desert-grassland vegetation and gray–brown desert soil are located in extreme arid areas. Administrative regions of the Mongolian Plateau mainly include Outer Mongolia and Inner Mongolia.
Methods and materials Soil wind erosion is caused by many factors, and relationships between wind erosion hazard and environmental factors are mostly nonlinear. Interactions also exist among environmental factors, but there are no distinct borders separating them. Traditional wind erosion assessment methods are limited to qualitative analyses or Boolean logic, so results of practical applications are often inaccurate. Fuzzy clustering is an automatic classification method that considers the distances of phenomena classified in multi-attribute space. It can accurately express gradual spatial changes and transitional characteristics of physical geographic phenomena with continuity character by using a continuously divided membership model. Fuzzy clustering of environmental factors can produce natural combinations of each environmental factor in multiple dimensions. In this paper, the FCM method was applied in clustering analysis to assess soil wind erosion hazard of the Mongolian Plateau. Applying FCM can produce a rational classification by optimally dividing the dataset based on theories and methods of fuzzy mathematics. In recent years, FCM has been successfully applied in such fields as terrain, soil, air quality, water quality, loss of water and
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soil, land use, etc., which indicates that FCM can be utilized to efficiently represent continuous distribution of physical geographic phenomena in spatial dimensions (Lark 1999; Odeh et al. 1992; Wu et al. 2004; Yu and Chen 2005; Yuan et al. 2001). A concept of vector is introduced into the FCM method, i.e. when a sample dataset Y is classified into c classes there will be a corresponding classified matrix U. The definition of vector V = [v1, v2, …, vc]T and membership is defined as follows: , n n X X m ð^ uik Þ yk ð^ uik Þm ; 1 i c ð1Þ v^i ¼ k¼1
u^ik ¼
k¼1
c X
!1 ðd^ik =d^jk Þ
2=ðm1Þ
;
1 k N;
1ic
j¼1
ð2Þ To obtain a rational and credible clustering center, FCM calculates it by establishing the equation J(U, v) as follows to ensure the minimized fuzzy clustering error (Bezdek et al. 1984): n X c X Jm ðU; vÞ ¼ ðuik Þm dik2 ð3Þ k¼1 i¼1
dik2
¼ kyk vi k2A ¼ ðyk vi Þs Aðyk vi Þ
ð4Þ
where y is a dataset, c is the number of classes, m is weight, u is one fuzzy clustering c of dataset y, v is a vector of the clustering center, dik is the weighted distance from point yk to the center of point vi, A is a matrix of the weighted distance, n is the number of data points in the dataset y, uik is the membership function in which data point k belongs to class I, Jm is the error of fuzzy clustering, which could be measured by the weighted square distance between data points and the clustering center. With an improvement in clustering results, Jm will decrease, i.e. each data point will be closer to its own clustering center on the whole. The weight of the clustering error of equation mentioned above is (uik)m; where yk is the m power of membership in class i. When m nears 1, the clustering result will incline to a crisp; when m ? ?, it will incline to faintness. The algorithm of FCM is separated from each m. The value not only affects the shape of protrusion and concavity of the objective function, it also controls the degree of fuzzy clustering and the superposed degree between each fuzzy class (Hanesch et al. 2001). Generally speaking, the virtual value of m varies from 1 to 30, but in practice, it is often restricted between 1 and 3 (Gao 2004; Yu 2003). To obtain the optimal classified structure, partition coefficient (F) and entropy (H) (Bezdek et al. 1984; Li et al. 2005) are introduced to ascertain the optimal clustering number:
Fc ð^ uÞ ¼
n X c X
ð^ uik Þ2 =n
ð5Þ
k¼1 i¼1
uÞ ¼ Hc ð^
n X c X
ð^ uik loga ð^ uik ÞÞ=n
ð6Þ
k¼1 i¼1
The value of the partition coefficient (F) is restricted between 1/c and 1, and the value of entropy (H) is between 0 and loga(c). F is used to measure the superposed degree between each class, which takes on an inverse proportion to the average superposed degree among different classes in the whole fuzzy dataset. H is the measurement of membership degree of fuzzy division U. In fact, the optimal clustering structure is obtained when F attains a maximum and H achieves a minimum. However, the class that draws near the clustering H or F that has the largest changes usually gets the optimal division.
Factor selections for wind erosion hazard assessment and database construction Factor selection for wind erosion hazard assessment The main environmental factors that determine changes in the spatial pattern of soil wind erosion include vegetation, terrain, soil, air temperature, rainfall and wind intensity. In this research, the four main indexes listed below were chosen as FCM parameters to evaluate the soil wind erosion hazard of the Mongolian Plateau. 1.
2.
3.
Vegetation cover (%). MODIS data (1-km resolution) from January to May 2000 covering the region of the Mongolian Plateau were used. The average maximum NDVI was used to describe the background value of vegetation cover. A larger value of NDVI indicated higher vegetation cover. Average degree of land surface relief. With the help of the GRID module in ArcGIS, window analysis was used to extract the average degree of land surface relief. The target grid was appointed as the center, the FOCALRANGE function was adopted and a rectangular window with a 5 9 5 radius was cut out. The difference of the height in this window was then calculated and regarded as the degree of land surface relief of the target grid. Finally, each grid point on the DEM was selected as the target grid, and the window was used to calculate the difference of height grid by grid to obtain the digital grid matrix of the degree of land surface relief. Degree of soil dryness. A revised Selianinov model was used to calculate the degree of soil dryness (Natural Regionalization Working Committee 1959):
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D ¼ 0:16
4.
X
T [ 10 C =P
ð7Þ
where D represents the degree of soil dryness, P is annual precipitation and T[10 is the annually cumulated temperature that is higher than 10C. Intensity of wind energy. In winter, the Mongolian Plateau is confronted by strong Mongolian high pressure, which results in frequent strong winds (Tang and Gao 1996). In this study, the equation used to determine soil wind erosion (RWEQ), created by the American Agricultural Ministry, was used to calculate the intensity of wind energy (Fryrear et al. 1998): n n X W¼ U ðU Uc Þ2 ð8Þ 500 i¼1 where W represents the wind energy intensity factor in m3/s3, U represents the wind velocity 2 m above the ground surface, Uc represents the critical wind velocity 2 m above the ground surface and is always computed as 5 m/s.
Construction of environmental factor database Various environmental indicators were obtained for the year 2000. NDVI data was downloaded from LAADS (http://ladsweb.nascom.nasa.gov). The DEM data adopt the radar elevation data collected by NASA space shuttle, the resolution of which is 90 m. Air temperature, rainfall and wind speed data, which were used to calculate wind energy intensity, were acquired from the Federal Climate Complex Global Surface Summary of Day Data manufactured by the American National Climatic Data Center (NCDC). ArcGIS was used to convert these data into GRID format with a grid size of 1 km 9 1 km, and each index was unified with the following formula: xi xmin x0i ¼ ð9Þ xmax xmin The spatial distribution of the unified data is shown in Fig. 2.
Implementation of FCM fuzzy clustering for wind erosion assessment The FCM algorithm used in this research was generated with the C?? programming language. A flowchart demonstrating creation of the FCM algorithm is shown in Fig. 3. The main body of the program included the following four parts. First, the ToFcm module was used both to convert the loaded environmental factors into suitable formats needed in the FCM model and then to resample the
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environmental factors. Second, the resampled environmental data were input into the module of the FCM cluster to obtain the clustered center. Third, the FCM Assignment module calculated and assigned the membership value of each cell to each class. Finally, the Col2Img module produced the grid image file of the result. Determination of fuzzy index m and optimal number of categories c Determination of the fuzzy index m and optimal number of categories c is an important step during the process of FCM clustering. This step requires the combination of mathematical verification and input of expert knowledge (Yu 2003). The designation of fuzzy weight m is a balance between fuzzy degree and distinct clustering structure, which means that it not only fully reflects changes in the type of fuzzy degrees in extent, but it also achieves a perfect clustered structure. According to this rule, the value of m was defined as 2.0. Table 1 shows the main outputs of the program when m equaled 2.0. As the clustering number increased, the trend in entropy change was opposite that of the division coefficient, i.e. decreasing division coefficient was associated with increasing entropy, and the fuzzy cluster error decreased (Table 1). When the division coefficient attained a maximum value, entropy was reduced to a minimum value. When it reached a certain class number, entropy change was higher than the previous and latter entropy change values (i.e. the apices of entropy change curve), the increasing scope of entropy became comparatively small; meanwhile, change in the division coefficient was lower than the previous and latter changes (i.e. the valley of the division coefficient change curve), and the decreasing scope of the division coefficient became small, all of them indicate that this cluster number is the optimal clustering number (Li et al. 2005). Figure 4 shows that the change of entropy and division coefficient began to stabilize after the sixth class. Therefore, m was ultimately defined as 2.0, and c was 6 in this study. Environmental factor values of the cluster center are shown in Table 2. Figure 5 shows the membership distribution of each class. Assessment of wind erosion hazard of the Mongolian Plateau The sequence of environmental combinations of spatial distribution was acquired by clustering the environmental data, in which every class represented different environmental combinations. Classification of wind erosion hazard in this research corresponds with the national industrial standard titled ‘Classification Standard of Soil Erosion’
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Fig. 2 Spatial distribution of environmental components of soil wind erosion
The
data
layer
of
environmental factors
TO FCM The data layer of all samples The data layer of resampling
Clustering results of environment portfolio
FCM Assignment
All documents of
FCMCluster
membership grades
Image files
Col2Img
Fig. 3 Flowchart of the FCM program
(Ministry of Water Resources of the People’s Republic of China 1997), in which erosion hazard is classified as no hazard, slight hazard, moderate hazard, intense hazard and severe hazard. We determined the wind erosion hazard for each class of environmental combinations by using expert knowledge and previous research (Table 3) (Hu et al. 2004; Zhang et al. 2001; Zhao et al. 1999).
After achieving the membership distribution of each type of wind erosion hazard, the classified map of wind erosion hazard was acquired by hardening the wind erosion membership vector of each pixel. The process of hardening involves assigning each pixel to the specific grade represented by the maximum membership value of each pixel. The wind erosion map in a grid format can then be converted to a conventional map based on polygons, which enables the map to be analyzed and compared with traditional wind erosion maps. The hardened map of wind erosion hazard for the Mongolian Plateau is shown in Fig. 6. Air temperature, rainfall, intensity of wind energy and wind direction in the Mongolian Plateau are closely related to the extreme continental monsoon circumfluence (Fig. 6). The degree of wind erosion hazard tends to intensify gradually from the northeast to the southwest. The zone of severe soil wind erosion hazard, with an area of 16 9 104 km2, is mainly distributed in the Alashan Desert, which is located in Altai Mountain and southwest of Gobi Altai Mountain. This area is located in western Mongolian Plateau and is the most arid area in this region. It has low vegetation cover, and the land surface is mostly desert. The
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Table 1 Summary of FCM results when m = 2.0 Fuzzy cluster error Jm
Loop times J
138.116
20
78.975
23
-0.129 -0.193
60.054 48.988
40 42
1.071
-0.070
35.330
50
1.188
-0.117
30.202
50
0.030
1.289
-0.101
26.285
50
0.376
0.031
1.393
-0.103
22.983
50
10
0.350
0.026
1.484
-0.092
20.054
50
11
0.328
0.021
1.569
-0.085
18.323
50
12
0.308
0.020
1.649
-0.079
16.845
50
13
0.292
0.017
1.720
-0.071
15.605
50
14
0.276
0.015
1.786
-0.066
14.116
50
15
0.270
0.006
1.832
-0.046
13.325
50
Class number c
Division coefficient F
Changes of division coefficient F(c) - F(c ? 1)
Entropy H
Changes of entropy H(c) - H(c ? 1)
2
0.749
3
0.608
0.141
0.679
-0.276
4 5
0.569 0.485
0.039 0.084
0.808 1.001
6
0.481
0.004
7
0.437
0.044
8
0.407
9
0.402
The curve of entropy change [H(c) - H(c ? 1)] and division coefficient change [F(c) - F(c ? 1)] with changing clustering numbers is shown in Fig. 4
Partition coefficient change
0.15 0.1 0.05
Partition
Entropy change
Entropy change
0 -0.05 -0.1 -0.15 -0.2 -0.25 -0.3
0
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Category ID
Fig. 4 Change in entropy and change in the partition coefficient when m = 2.0 Table 2 Environmental factor values of cluster centroids when cluster number was 6 and m = 2.0 Class
Average relief degree of land surface
The degree of soil dryness
The intensity of wind energy
NDVI
Class1
0.044
0.257
0.374
0.126
Class2
0.154
0.117
0.373
0.346
Class3
0.035
0.712
0.309
0.077
Class4
0.055
0.162
0.363
0.208
Class5
0.044
0.417
0.374
0.095
Class6
0.259
0.124
0.421
0.169
potential for wind erosion is high. With an area of 47 9 104 km2, the desert landscape is widely distributed around the periphery of the severe hazard zone located both on the east and west side of Altai Mountain and south of Gobi Altai Mountain. This desert mainly includes the South Gobi, East Gobi and Fore Khangai Provinces in
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Mongolia and the southern area of the Alashan League in Inner Mongolia Autonomous Region, China. The climate is extremely dry, and the vegetation cover is sparse, thereby creating an intense wind erosion hazard zone. Areas of moderate hazard are distributed east and north of the intense hazard zone. With an area of 56 9 104 km2, the moderate hazard zone has an altitude of approximately 500–1,500 m and belongs to the desert-grassland transition zone in which the vegetation cover is higher than that of the desert area. However, soils of the flat landform in this area are easily eroded by wind. In the area of Kente Mountain, the south side of Khangai Mountain and the Daxing’anling region in northern Mongolian Plateau, forest-grassland vegetation cover is high and has an area of 37 9 104 km2. The climate is relatively humid, wind intensity is weak and the average degree of land surface relief is comparatively high. These factors result in very little wind erosion in this area. The zone of slight wind erosion hazard in the Mongolian Plateau is the largest with an area of 113 9 104 km2. Various grasslands are widely distributed throughout the east, north and northwest Mongolian Plateau. In general, the spatial pattern of the degree of hardened soil wind erosion hazard closely resembles that of the NDVI and the degree of soil dryness of the Mongolian Plateau. This indicates that water and vegetation conditions are the primary factors determining wind erosion hazard on the Mongolian Plateau. In addition, partition results of different degrees of wind erosion hazard nearly coincide with the distribution pattern of the typical landscape and vegetation types of the Mongolian Plateau.
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Fig. 5 FCM cluster membership maps when cluster number was 6 and m = 2.0 Table 3 Environmental element distribution relating to each combination class Distribution of environmental factors
Hazard degree
Class 1
Low vegetation coverage, low relief degree of land surface, low degree of soil dryness, middle intensity of wind energy
Moderate
Class 2
high vegetation coverage, high relief degree of land surface, low degree of soil dryness, low intensity of wind energy
No
Class 3
comparatively low vegetation coverage, comparatively low relief degree of land surface, comparatively high degree of soil dryness, comparatively high intensity of wind energy
Severe
Class 4
middle vegetation coverage, comparatively low relief degree of land surface, low degree of soil dryness, low intensity of wind energy low vegetation coverage, low relief degree of land surface, middle degree of soil dryness, middle intensity of wind energy
Slight
middle vegetation coverage, comparatively high degree of land surface, comparatively low degree of soil dryness, comparatively high intensity of wind energy
Slight
Class 5 Class 6
Verification of results To date, no research has quantified soil wind erosion hazard for the Mongolian Plateau at the regional level, so graded results of wind erosion hazard produced in this study were qualitatively verified by comparing them with previously obtained soil wind erosion data for Inner Mongolia. Remote sensing data for soil wind erosion included maps of wind erosion produced by integration of expert knowledge and computer-generated results. This method determines soil wind erosion intensity based on TM images, land-use data, soil texture data, DEM data, field investigation data and related materials. Wind erosion intensity acquired from remote sensing mainly reflects actual soil wind erosion, whereas wind erosion hazard
Intense
assessment emphasizes the estimation of potential wind erosion. Both are conducted by quantitatively evaluating the influence of each environmental factor on the occurrence and development of soil wind erosion. Similarities between the methods enable a certain amount of comparison between them. Results of the investigation on wind erosion intensity and degree of wind erosion hazard in Inner Mongolia are shown in Fig. 7. Both wind erosion intensity and hazard were highest in the west and gradually declined moving eastward. This pattern follows variations in climate and vegetation types. Soil wind erosion hazard and wind erosion intensity are indexes of actual and developing erosion. Therefore, the degree of hazard in the eastern areas of Inner Mongolia where no wind erosion has occurred is defined as non- and slight hazard types. Moving eastward away from
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Fig. 6 Hardened map of soil wind erosion hazard for the Mongolian Plateau
Fig. 7 Maps of wind erosion intensity (a) and hazard (b) in Inner Mongolia
areas with high soil wind erosion intensity, the degree of wind erosion hazard changes from severe to intense. In central Inner Mongolia, wind erosion intensity varied from intense to weak. Likewise, the soil wind erosion hazard also varied from intense to slight. The high consistency between these two results shows the reliability of determining the occurrence and development of wind erosion hazard in the Mongolian Plateau by the fuzzy clustering of environmental factors coupled with expert knowledge.
Conclusions In this study, a map of wind erosion hazard was acquired through the fuzzy clustering of environmental factors related to wind erosion, combined with expert knowledge. This allowed a certain wind erosion hazard to be assigned to each combination of environmental factors. The study shows that the FCM method can be effectively applied to identify relations between wind erosion and environmental variables, thus providing the basis for cartography forecasting
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of the degree of wind erosion hazard. Some conclusions can be drawn from this research. First, an effective method was found that quickly acquires understanding of the relationships between soil wind erosion and environmental factors at the regional scale. The membership distribution of each environmental combination was acquired using the fuzzy clustering method (FCM). Graded maps of the degree of wind erosion hazard were then obtained using expert knowledge to associate each combination of environmental factor with a specific degree of soil wind erosion hazard. Second, the spatial pattern of wind erosion hazard of the Mongolian Plateau was analyzed. Wind erosion hazard gradually intensified from east to west. With an area of 16 9 104 km2; the severe hazard zone was mainly located in northwestern Alashan Desert Plateau. The intense hazard type zone was located near the severe hazard zone on both sides of Altai Mountain and south of Gobi Altai Mountain and had an area of 47 9 104 km2. The moderate hazard zone had an area of 56 9 104 km2 and was distributed east of the intense hazard zone and on northern parts of southfacing slopes of Khangai Mountain. Kente Mountain, the
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north slope of Khangai Mountain and the Daxing’anling forest had very little wind erosion and included an area of 37 9 104 km2. The slight hazard zone had the largest area with 113 9 104 km2 and was widely distributed in the open tableland in western and northwestern Mongolian Plateau and the south slope of Khangai and Kente mountains. Vegetation cover in this area is mostly typical grassland. Water condition and the amount of vegetation cover are the primary factors that determine the wind erosion hazard of the Mongolian Plateau. The distribution of different wind erosion hazard zones closely aligns with the distribution pattern of dominant landscape vegetation types of the Mongolian Plateau. Acknowledgments This study was sponsored by the Natural Science Foundation of China (Grant No. 40801105) and Special Funding for basic R&D projects of public research institutes with central level, CRAES (Grant No. 2007KYYW42).
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