沙漠研究 22-1, 231 -234 (2012 )"ICAL 1 / DT X"

Journal of Arid Land Studies－Refereed Paper－

Characteristics of Spatial Distribution of Vegetation Coveragein Grassland of the Bayan Soum, Mongolia
Khishigsuren NYAMSAMBUU*1), Kunihiko YOSHINO2) , Yudi SETIAWAN1), and Mohamed KEFI3)
Abstract: The semi-arid steppe of Bayan, in the central eastern part of Mongolia, was chosen for a field investigation in this study.
In this area, the grassland occupies 96% of the whole area, and the economy strongly depends on livestock production. Therefore,
the land is mainly used for grazing, as it constitutes around half of the population’s livelihood. In recent years, unmanaged
pastureland use is causing many issues in the area.
In order to improve the problematic land overuse of grassland, it is important to understand the spatial distribution of vegetation
coverage. We studied the characteristics of the spatial distribution of the vegetation coverage in the Bayan soum area using thepercentages of grass coverage in the field sampling data. Land cover surveys were carried out at 200 sites in the ground survey areain the summer of 2010. Photos of ground covering 1 m2 were taken to compute the percentages of ground surface components suchas green grass, dried grass, bare soil and shadows.
A geostatistical analysis was conducted to examine the spatial correlation of the vegetation coverage. The results indicated that
remote sensing at high resolutions, such as spatial resolutions at less than 130 m, are necessary for pastureland use planning based on
the vegetation coverage, particularly for green grass cover. The semivariograms also illustrate that the spatial distribution ofvegetation coverage is related to differences in soil type and elevation.
Key Words: Geostatistics, Image classification, Semi-arid, Semivariogram analysis, Vegetation coverage

1. Introduction
Previous studies on the vegetation coverage in thegrasslands of Mongolia have been focused on identifying thecomposition of plant species, the yields of plants, andmonitoring the pastureland changes using remote sensing. In
our research, geostatistics was applied to analyze and describe
the characteristics of the spatial distribution of vegetation
coverage. The geostatistics are a well-understood andfrequently applied image processing technique (Jakomulskaand Clarke, 2001). The variogram is the corner stone ofgeostatistics (Richard and Margaret, 2007).
Using semivariogram to determine what the minimum
spatial resolution of remote sensing or ground data should befor a particular study is recommended (Curran, 1998). They
are commonly used in geostatistics to describe the spatial
autocorrelation as a function of the distance between thesample points (Isaaks and Srivastava, 1989). In remotesensing, the semivariogram is also useful for estimating theappropriate spatial resolution required for remotely sensedimages. To choose a satellite image resolution, information isrequired on the spatial characteristics of the surface being
investigated. Regarding the geostatistical measure of thesemivariogram range is an appropriate method to select theoptimal resolution of satellite imagery, instead of using
inappropriate resolution of satellite imagery.
Aftercharacterizing the spatial distribution of the vegetation
coverage, we can readily find an appropriate resolution ofsatellite imagery, not only using high spatial resolution satellitedata, but also moderate and low spatial resolution satellite data.
Thus, examining the resolution of satellite data using
semivariograms is helpful. After choosing the appropriate
spatial resolution satellite data, land managers and herders canuse the estimate from the spatial distribution of vegetation
cover in areas of different productivity to plan pastureland
utilization efficiently. As well, the pastureland use planningwith locations of from these designs along with the seasonal
campus and the moving distances of livestock are beneficial.
Moreover, as many authors have reported, different land covertypes usually have different variogram patterns, and also ourhypothesis was that the spatial distribution of the vegetation
coverage may have dependent on different environmentalconditions.
The objectives of this study are 1) to characterize the spatial
distribution of the vegetation coverage and 2) to determine theinfluences of environmental factors on the spatial distributionof vegetation coverage in grassland.

* Corresponding Author: khishigee_ns@yahoo.com
Tennodai 1-1-1, Tsukuba, Ibaraki 305-8577, Japan1) Graduate School of Life and Environmental Sciences, University of Tsukuba
2) Faculty of Engineering, Information and Systems, University of Tsukuba3) Graduate School of System and Information Engineering, University of Tsukuba, JapanFig. 1. Map of the ground survey area with sampling points.

2. Materials and Methods
2.1. Study area
Bayan soum (N 46°50’- 47°20’, E107°10’ - 108°20’) is
located in the Tuv province of Mongolia. The primary land
use is grazing land. Poor management of pastureland iscausing many issues in the area. Bayan soum has an annual
precipitation of about 190 mm. Average annual temperatureis -1.5-2°C and the temperature difference between summerand winter the year is 43°C. The average temperature has
increased by 1.5°C during the last 40 years (Institute ofMeteorology and Hydrology, 2009). For collecting fieldsamples, a 3 km × 3 km ground survey area was selected in thecentral part of Bayan soum (Fig. 1). The altitude of theground survey area ranged from 1380-1460 m above sea level.
According to the soil map of the study area, the ground survey
area has three different kinds of soil types: 1) sandy loam withgravel, 2) sandy clay loam with gravel and 3) sandy clay loamwith saline. According to our field study, the dominant plant
species are Stipa krylovii and Artemesia adamsii, followed byCleistogenes songorica, Scorzonera divaricata and Elymus
chinenses.
2.2 Data collection and pre-processing
The field survey was conducted on July 23rd, 2010, and
200 ground data points were sampled and ground surface
conditions were recorded with a digital camera along fourtransects. The ground cover data were derived from photosof a 1 m2 grid area in the ground survey. These photos wereanalyzed using Paint Shop Pro 6 for image rectification andMultiSpec for an unsupervised classification of digital photos
to determine the percentage of four ground cover components;
green grass, dried grass, bare soil and shadows. A 1:100 000
soil map of Bayan soum from the Institute of Geoecology inMongolia and a Digital Elevation Model were used tocharacterize the spatial distribution of the vegetation coveragedepending on the different soil type and elevation.
The percentage of vegetation cover is one of the mostimportant indicators in quantifying grassland productivity andit has also been linked to soil protection (Avaadorj andBaasandorj, 2007). We measured the green grass, dried grass,
and the total grass cover which was the sum of green and driedgrass cover. It should be noted that livestock eats both green
grass and dried grass during different times of the year since
the grass only grows during the short summer in Mongolia.
Therefore, the condition of the pastureland vegetation depends
on both natural factors and human activities (Erdenetuya andBolortsetseg, 2004). From the agro-meteorological point ofview, the main environmental factors for plant growth are heat,moisture, light and nutrients in soil (Erdenetsetseg, 2004).
The nutrients themselves depend on water in the soil. For thisreason, the characteristics of the spatial distribution ofvegetation coverage have been considered in the three regionswith different soil types or in the two ranges of altitudes (higherarea or lower area) in the ground survey area in this study.
2.3. Statistical methods
The spatial correlation of the vegetation coverage data werestudied using semivariograms defined as follows: Thesemivariance function γ(h) is equal to half of the expectedsquared difference between the values at locations separated bya given lag, h. It is used to express the spatial variations(Journel and Huijbregts, 1978). The spatial analysis used theempirical with a semivariance estimated by Eq.1 :γ ( h) =[1 N (h)∑ Z ( xi ) − Z ( xi + h) 2
2 N (h) i =1](Eq.1)

Where γ(h) is the sample semivariance; N(h) is the number ofsample pairs at each distance interval h, and Z(xi) and Z(xi + h)
are values of the vegetation coverage at locations separated bydistances of vectors h.
Surfer 7.0 software was used to produce appropriate
models of the semivariograms which have sill and range
parameters. Models of semivariogram have been fitted
without nugget effect, so the nugget equals 0. The range is
the important parameter in this study and it is the separation
distance where the semivariogram levels off, indicating theaffected scope of the regional variable. The range provides ameasure of the distance around at point in which the spatial
interpolation or processing is valid (Zawadzki et al., 2005) andit defines the distance beyond which the ground resolutionelements are independent of each other (no autocorrelation)(Curran, 1988). The sill is the value of semivariogram at thepoint at which it levels off. We obtained the exact value of
actual lag distance after calculating distances between the fourTable 1. Ranges of the all fitted semivariograms.
Semiariograms
Green grass
Dried grass
Total grass
Green grass in 1380-1420 m elevation
Dried grass in 1380-1420 m elevation
Total grass in 1380-1420 m elevation
Green grass in 1420-1480 m elevation
Dried grass in 1420-1480 m elevation
Total grass in 1420-1480 m elevation
Green grass in sandy loam gravelly soil
Green grass in sandy clay loam gravelly soil
Green grass in sandy clay loam saline soil
Dried grass in sandy loam gravelly soils
Dried grass in sandy clay loam gravelly soil
Dried grass in sandy clay loam saline soil
Total grass in sandy loam gravelly soil
Total grass in sandy clay loam gravelly soil
Total grass in sandy clay loam saline soil

Ranges13017011025030090125
150
8090
No range defined
50160
No range defined3080
No range defined
50

lines with points in the ground survey area using Arc GIS 9.3
software. We used both a maximum lag interval of 500 m,
which is 2/3 of the actual lag distance and also a lag interval of1000 m. In these semivariograms, the Gaussian model wasgenerally the best fitting model, followed by the spherical andexponential model. In each case, in order to determine therange, only the best-fitting model of semivariogram waschosen.

3. Results
Each of the three biomass types (green grass, dried grass
and total grass) showed differences in their spatial correlationin the all the ground survey data and area, as determined bytheir semivariances. They show clear differences when areas
with different environmental factors were consideredseparately. Ranges of the all the fitted semivariograms wereplotted in Table 1.
3.1. Spatial distribution of vegetation coverage in thewhole ground survey area
The fitted semivariogram model reveals that the range ofthe green grass is 130 m, but the range of the dried grass waslonger, reaching 170 m. The range of the spatial correlation
for the total grass was just 110 m; smaller than those of green
grass and dried grass. These semivariograms indicate thatpoints less than 130 m apart have a more similar percentage ofgreen grass cover, and the points less than 170 m and 110 m
apart have a more similar percentage of the dried grass andtotal grass cover, respectively.
As was introduced in the methodology, the range of thesemivariogram will be used to determine an optimal resolution

with remote sensing imagery for vegetation monitoring study,
with consideration of area having different environmentalconditions such as different elevations and the different soil
types.
3.2. Spatial distribution of vegetation coverage in 2 areas
with different elevations
The results suggest that the spatial correlation of green
grass cover at low altitudes is higher than that of the higher
altitude. More specifically, at lower altitudes, green grasscover is spatially correlated up to a distance of 250 m
compared with 125 m for higher altitudes. For example,
green grass in low elevation area shows the largest range andthe average vegetation percentage had more density that in the
low elevation area.
Dried grass had relatively a larger spatial correlation thanthe other vegetation coverage, as compared with the previous
semivariograms. Dried grass at different elevations also has
different spatial correlations and again the lower elevations hadlarger range parameters. At both low and high elevations, thespatial distributions of total grasses are similar with ranges
around 80 to 90 m.
3.3. Spatial distribution of vegetation coverage in threeareas of different soil types
In this study, three soil types were considered. The fitted
range for green grass was 90 m in sandy loam gravelly (SLgravelly), and 50 m sandy clay loam saline (SCL saline) soil.
A very large range was found for dried grass coverage with SL
gravelly soil. By contrast, its range was very small in SCL
saline soils. Thus, it seems that dried grass cover is spatiallycorrelated up to distances of around 160 m for SL gravelly
soils, compared to that of only around 30 m for SCL salinesoils. In addition, large variations from point to point of driedgrass cover were observed in these semivariograms.
When the spatial distribution of total grass coverage wasconsidered, the SCL saline soils once again had a lower range
parameter than those of the SL gravel soils. Overall, theempirical semivariograms, estimated in terms of the soil types
for vegetation percentage groups, were often rather variable,
particularly for SCL gravelly soils.

4. Discussions
For all biomass types, the empirical semivariogram of theSCL gravel soils was showed a slight relationship. This may
be due to an insufficient sample size when the area is divided
into 3 groups according to soil types. It may also reflect therather small size of the study area. Or, perhaps, other factors(such as the distribution of water in the landscape and itsinteraction with soil type) are affecting the results.
To determine the spatial resolution for each pixel in the
remote sensing data, ground sampling points in that area wereobtained from up to 130 m for green grass, 170 m for driedgrass, and 110 m for the total grass on a per pixel basis. The
optimal resolution satellite data would be at maximum 130 m,according to the semivariogram patterns of green grasscoverage, because the range is reaches to 130 m of lag distance.
For this reason, our results suggest that more than 130 m
resolution data can provide only summary information on
grassland through whole study area, whereas with highresolution data with less than 130 m resolution would make itpossible to study the fundamental spatial distribution ofvegetation coverage, namely the individual zones withdifferent vegetation productivity. Spatial resolution remotesensing imagery with a pixel size of 130 or less, allowsvegetation productivity mapping which would be useful for
pastureland use planning in different utilization zones.
Although clearly the optimal spatial resolution is dependent on
the pixel size of vegetation area in the image, typically a pixel
size of 130 m or smaller has been found necessary for
individual-vegetation remote sensing in most study regions.
If the correlation of the ground data is small, we need more
precise resolution of satellite data. For instance, a vegetation
monitoring study with a percentage of green grass in sandy
clay loam saline soil requires a spatial resolution of remotelysensed data which should not be higher than the spatial
characteristics of the vegetation percentage for this area.
Thus, a similar percentage of data points recorded closely
together at 90 and 50 m, the high spatial resolution satellite
data with 90 and 50 m of pixel size are preferred in an areawith green grass in sandy loam gravelly soil and green grass insandy clay loam saline soil, respectively.

5. Conclusions
The followings are concluded from this research:1) Remote sensing at a high resolution, such as spatialresolutions of less than 130 m (e.g. ALOS AVNIR-2,ASTER), is necessary for proper land use planning ofpastureland based on a spatial distribution of the vegetation
coverage, particularly green grass cover.
2) Remote sensing images with geostatistical analysis would
allow pastureland managers to plan more efficiently, andthen to help herders move to areas by means of efficientand sustainable grazing.
3) These semivariograms also show that vegetation coverageis strongly dependent on soil types and elevations. Spatial

distributions of vegetation coverage in the study site aredifferent depending on the different environmental
conditions such as soil type, and elevation.
4) The water flow and water content in the landscape mayinteract with soil type and affect plant growth. Sample
points in SCL saline soils generally had higher green grasscover than points in SCL gravelly soils. However, SL
gravelly soils in the catchments benefit plant growth.
Although there were only small numbers of samples, thearea of SL gravelly soils showed an unexpectedly higher
percentage of green grass cover. This could result fromenough water flow due to landscape characteristics such as
shallow valleys in this area.
5) The regions with different characteristics of spatial
distribution of vegetation coverage make it difficult todistinguish an optimal resolution of satellite imagery forvegetation monitoring in this area.

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