沙漠研究 19-1, 29- 32 (2009 )Journal of Arid Land Studies“Desert Technology IX ”
Refereed Paper

GIS Analysis of Grazing Selection by Goats in Tov Province, Mongolia
Sawahiko SHIMADA1, Hideki YOKOYAMA1, Hiromichi TOYODA1, Ayako SEKIYAMA1,
Buhe AOSIER2, Michinari YOKOHAMA3
Abstract: In Mongolia, the rapid grassland degradation induced by overgrazing in these years
is assumed to worsen the disaster called dzud. In this study, we attempted to monitor thegrazing behavior of flock of goats in connection with the vegetative and topographic
environments in Mongolian grassland. From the results of the GIS analysis, goats’ movementin the grazing situation is found out to be less than the velocity of 4 km h-1 and the mode value
to be ca. 0.4 km h-1. Slight correlation (r=0.67) of the grazing velocity with NDVI value wasfound.
Keywords: Goat, GPS, Grazing selection, Mongolia, NDVI

1. Introduction
Mongolian grasslands experienced severe livestock losses (more than 5 million livestock) due toharsh winter disasters called dzud in 2000 and 2001. The rapid grassland degradation induced byovergrazing in these years (Kawamura et al., 2005) is assumed to worsen the disasters (Shimada et al.,2007). Within the six years of between 1993 and 1999, the total number of livestock had increased morethan 8 million in Mongolia. Moreover, the change in the livestock composition, especially the increase ingoat breeding is obvious in Mongolia. The total number of goat livestock has surpassed that of sheep
since 2003 (ca. 13 million goats in 2006) owing to the cashmere’s industry establishment as a major export
product. However, the goats have negative impact on grassland due to their natural grazing behavior
which involves uprooting the vegetation. In this study, we attempted to monitor the grazing behavior offlock of goats in connection with the vegetative and topographic environments in Mongolian grassland.
2. Materials and Methods
Portable GPS receivers (Garmin eTrexand Geko201) were set on the goats’ back(Fig. 1) during the daytime for eachobservation in order to track their grazing
movement. We collected 9 GPS tracking
datasets taken in summer time during the yearof 2003 – 2007, mostly from Tov Province inMongolia (Fig. 2). The tracking intervals
were set to be 30 second (except Plot S1 andS2), but they varied dependent on theposition of GPS sensor. The tracking latitude,longitude and time were recorded in the GPSlogger. Total tracking distance, duration andspeed were calculated from the tracking
records. The tracking points were, then using
GIS, overlaid onto satellite imageries (Fig. 3)
and topographic data in order to trace goats’
grazing selection from the vegetation andgeomorphologic information, respectively(Table 1). Average moving velocity value ofgoats for each plot was calculated by dividing
1GPSFig. 1. GPS receiver attached Goat.
ºEº
S1, S2

U1, U2JKºXTov

M
GDundgovi

KhentiiºNOPPlot NameFig. 2. Location of the GPS tracking plots in Mongolia.

Faculty of Regional Environment Science , Tokyo University of Agriculture, Setagaya, Tokyo 156-8502, Japan
Faculty of Environment Systems, Rakuno Gakuen University, Ebetsu, Hokkaido, 069-8501, Japan
3
Faculty of Bio-industry, Tokyo University of Agriculture, Abashiri 099-2493, Japan230

Cloud09AUG2003
14AUG2004

NDVI

13AUG2003

115AUG2004

0
Plot-M

Plot-OP

Plot-U1, U2

AST-15AUG2003

AST-08SEP2003

ETM-16AUG2004

16AUG2006
15AUG200619AUG2005

Plot-JK

12AUG2006

13AUG2007

Plot-G

Plot-S1, S2

TM-12AUG2006

TM-12AUG2006

Plot-X

Fig. 3. Satellite imagery (NDVI) used for each plot analysis (except Plot-JK and X) superimposed by the GPS
tracking points.

total tracking distance by the tracking duration. Mode value of the velocity was derived by observing thefrequency chart for each plot.
Five satellite imagery scenes (viz. Terra-ASTER, Landsat-TM and –ETM+) that cover tracking plots
at corresponding date to the observations were obtained (Table 1). ASTER and Landsat data wereobtained from ERSDAC (http://www.ersdac.or.jp/) and USGS EarthExplorer (http://edcsns17.cr.usgs.gov/),
respectively. NDVI values from each image were calculated by the following equation:
NDVI = (DNred – DNNIR) / (DNred + DNNIR),
where DNred: digital number of red band, DNNIR: digital number of near-infrared band.
SRTM-DEM data were used to clarify the topographic characteristics of the grazing plots.
SRTM-DEM data were acquired via USGS EarthExplorer. The topographic information on the goats’
tracking points for each plot, such as altitude values with the variety in altitude values (Table 1), slope andaspect were extracted from the SRTM-DEM.
Correlation analyses were conducted both between goats’ moving velocity and vegetative parameter(i.e., mean NDVI) among 7 available plots and between goats’ moving velocity and topographic
Table 1. Tracking information, location, and corresponding satellite image of the 9 tracking plots.
Plot Number ofCenter of the plot
Altitude (m)Total tracking Total tracking
Datename GPS pointdistance (km)duration (h) Latitude (N) Longitude (E) Mean ±SD
M51409AUG2003
8.02
6.27
47º 43’ 54” 106º 21’ 33” 1210 ±41.9
OP516
13AUG2003
6.06
6.55
47º 40’ 16” 108º 27’ 50” 1311 ±22.0
U1947
14AUG2004
6.64
6.80
48º 13’ 14” 108º 37’ 40”
1400 ±8.8U2144915AUG2004
13.80
12.00
48º 13’ 28” 108º 37’ 03”
1415 ±9.6
JK1017
19AUG2005
9.50
10.94
48º 13’ 20” 104º 38’ 23” 1410 ±85.2
G1063
12AUG2006
9.408.80
46º 35’ 59” 105º 49’ 56” 1646 ±23.8
S133*15AUG2006
5.50
4.05
48º 28’ 32” 106º 47’ 17” 1098 ±27.9
S229*16AUG2006
6.26
5.12
48º 28’ 14” 106º 46’ 42” 1115 ±30.6
X1237
13AUG2007
12.88
13.89
48º 04’ 53” 109º 21’ 10” 1367 ±34.8* GPS track saving interval was set to be 10 minutes for Plot-S1 and S2.

Corresponding
Satellite image
AST, 15AUG
AST, 08SEP
ETM, 16AUG
ETM, 16AUG
No data
TM, 12AUG
TM, 12AUG
TM, 12AUG
No data

31

1800
1700

Plot-G (2006)

Altitude (m.a.s.l.)

1600
1500

Plot-U2 (2004)

1400
1300

Plot-JK (2005)

Plot-OP (2003)

Plot-X (2007)

1200Plot-M (2003)

1100

Plot-S1 (2006)

1000
0Plot-S2 (2006)
51015

Cumulative Distance (km)Fig. 4. Comparison of altitude versus distance chart of goats’ movement between each tracking plot.

parameters (i.e., mean altitude, mean slope) among 9 plots.
3. Results and Discussion
According to the analysis results of the GPS tracking data from each plot, total distance of goats’
movements varied from ca. 6 – 14 km (Table 1). The mode values of moving velocity ranged between
0.1 – 0.8 km h-1, and the values of total tracking distance per duration ranged between 0.87 –1.36 km h-1(Table 2). Fig. 4 shows the altitude versus distance chart for each tracking plot. This chart indicatesthat the flock of goats likely to select to climb on hills up to relative height of ca. 100 m. This tendency
can be seen at most of the plots (except Plot-U1, U2 and G). The cumulative proportion of frequency inthe degree of moving velocity from 9 tracking plots is shown in Fig. 5. This result shows that goats’
movement in the grazing situation is mostly less than the velocity of 4 km h-1 (occupies 88% of the total)
and the mode value is ca. 0.4 km h-1 (moving velocity of the range between 0.3 – 0.4 km h-1 occupies 4%of the total). Goats’ moving velocity of the range between 0 – 0.5 km h-1 occupies 20% of the total.
Slight correlations were found between average moving velocity and mean NDVI value (r=0.57), andbetween mode moving velocity and mean NDVI (r=0.67) (Fig. 6 (A)). However the both correlations
were not statistically significant (P>0.05), above results indicates that goats tend to move relatively rapid
when the grazing environment is rich (i.e., mean NDVI of over 0.3), while relatively moderate when poor
(i.e., mean NDVI of lower than ca. 0.2). Slight negative correlations were found in average moving
velocity versus mean slope (r=-0.60) (Fig. 6 (B)) and in average moving velocity versus mean slope
Table 2. Goats’ behavior, topographic and NDVI characteristics from the 9 tracking plots.
Mode moving
Mean Slope
Average moving
velocity (km h-1)
(degree)velocity (km h-1)
M
1.28
0.4
3.14
OP
0.92
0.1
4.35
U1
0.98
0.5
1.17
U2
1.15
0.4
1.22
JK
0.87
0.3
10.19
G
1.07
0.2
4.45
S1
1.36
0.4*
3.37
S2
1.22
0.8*
3.94
X
0.93
0.3
5.02
* GPS track saving interval was set to be 10 minutes for Plot-S1 and S2.
Plot nameDistribution of Aspect (%)
NorthSouth
Others
25.4
43.0
31.6
42.0
13.2
44.9
42.3
24.3
33.4
16.7
50.2
33.1
38.2
19.4
42.4
14.3
74.5
11.1
55.9
0.0
44.1
63.3
20.0
16.7
29.7
60.0
10.3NDVI
Mean ±SD0.13 ±0.09
0.12 ±0.08
0.28 ±0.10
0.30 ±003
0.20 ±0.06
0.49 ±0.05
0.45 ±0.09-32Frequency (GPS point)300

Plot250X
S2S1
GJK
U2U1OP
M

200
150
100
50
000.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6Moving velocity (km h-1)

Fig. 5. Cumulative proportion of frequency in the degree of moving velocity from 9 tracking plots.

Meanr = 0.57

0.4
0.3
0.2

Mean Altitude (m)Moder = 0.67

0.5

Mean NDVI

1800

(A)

12(B) ModeMeanr = -0.60

r = -0.55

1600

1400

12000.1(C)10

Mean Slope (degree)

0.6Moder = -0.29

8Meanr = -0.48

6420

1000
0.0

0.5

1.0

Moving Velocity (km h-1 )

1.5

0

0.0

0.5

1.0

Moving Velocity (km h-1 )

1.5

0.0

0.5

1.0

1.5

Moving Velocity (km h-1 )Fig. 6. Relations of (A) NDVI versus moving velocities, (B) altitude versus moving velocities (C) slope versus
moving velocities among tracking plots.

(r=-0.48) (Fig. 6 (C)). However, the correlations were not significant (P>0.05) and there seems noeffective explanation for the relation between grazing selection and topographic environment.
4. Conclusions
In this study, we found out that the vegetative environment affects goats’ grazing behavior but could
not found out good correlation between goats’ behavior and topographic environment. Our findings areas below:(1) The grazing movement velocity of the flock of goats is ranged between 0.87 – 1.36 km h-1 in averageand mostly in the velocity of between 0 – 0.5 km h-1.
(2) Rich vegetative environment (i.e., high mean NDVI) tends to accelerate the velocity of grazing
movement of the flock of goats. The reason for this may be that goats don’t have to take time toselect for grazing under the rich vegetative condition.
We still need to analyze how do multiple factors of vegetative and morphological environment affectgoats’ grazing selection in order to predict their behavior and the effect of over grazing.
References
Kawamura K., Akiyama T., Yokota H., Tsutsumi M., Yasuda T., Watanabe O., Wang S. (2005): Quantifying grazing intensities usinggeographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia. China Agriculture,Ecosystems & Environment, 107(1, 2): 83-93.
Shimada S., Sekiyama A., Yokohama M., Aosier B., Tamaki K. (2007): Developing the prediction method of soil moisture andvegetation cover ratio in Mongolian grassland using remote sensing images. J. Arid Land Studies, 17(2): 77-80 (in Japanese).