29

Simultaneous Monitoring of Livestock Distribution andDesertification
Kensuke KAWAMURA1* & Tsuyoshi AKIYAMA2
1

Graduate School for International Development and Cooperation, Hiroshima University, Japan
1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529, Japan
2
River Basin Research Center, Gifu University, Japan*e-mail: kamuken@hiroshima-u.ac.jp

Abstract
In the arid and semi-arid regions of Northeast Asia, grassland degradation has become a major environmental and economic problem, so sustainable utilization of resources is crucial in terms of not only supporting
the local and animal production but also thinking about the global environment. Especially in Mongolia andNorth China, grassland degradation has mainly been induced by artificial factors such as overgrazing bylivestock, inadequate management of arable land and uncontrolled cutting of trees for fuel. In particular,
overgrazing is one of the main causes of grassland degradation, and conservation and sustainable use of thegrasslands can be achieved by proper grazing management to match carrying capacity estimates. How, though,can we achieve the ability to maintain valuable ecosystems and sustainable livestock farming at the sametime? Satellite remote sensing and the Geographic Information System (GIS) are two of the most powerfultools to assist in grazing management. There is an increasing number of development analysis tools that can
aid in collecting data on seasonal changes in grazing behavior over long-term periods. Quantitative
assessment methods for monitoring spatial and temporal distributions of grazing animals at a regional scale
are required in studies designed to determine the driving causes of desertification. This paper first reviews
previous studies monitoring desertification through the use of remote sensing and tools for assessing livestock
distribution, and then discusses current developments and future perspectives of remote sensing of grasslands
in Northeast Asia.
Key words: grazing pressure, phenology, qualitative and quantitative assessment, remote sensing

1. Introduction
It is currently widely accepted that desertification is
one of the most serious environmental issues in semi-arid
and arid areas. According to Lepers et al. (2005), Asia
currently has the greatest concentration of areas showing
rapid land cover change, particularly dryland degradation.
In Northeast Asia, desertification is extremely serious
due to poor climate conditions, such as drought, severewind erosion and long-term unsustainable humanactivities, including overgrazing, extensive cutting andover-reclamation (Wang et al., 2006). Especially in
Mongolia and North China, grazing pressure may play a
critical role in maintaining ecosystem productivity
through concentration of limited resources (Wang &Wang, 1999) as well as the meteorological effect (Li et al.,2000).
The environmental impact from overgrazing in
Mongolia and North China can be reduced by more efficient forage utilization, and grassland diversity enhanced
through improved understanding of grazing selection. In
Global Environmental Research
14/2010: 29-36
printed in Japan

addition, the physical and economic productivity ofgrazing systems is intrinsically linked to the efficiency of
single grazing units. To date, numerous grazing trials
have been done by scientists in Mongolia and China inorder to determine the effects of grazing on plant
production (Li et al., 1999), plant species composition(Han et al., 1999, Tsutsumi et al., 2003), soil organic
matter (He et al., 2008) and soil structures (Jia et al.,1999). However, when applying these results in freeranging areas, some difficulties arise when relying on
direct observation alone because some ground-based
methods are not practical for assessing both plant biomass and grazing distribution over extensive geographic
areas. Satellite remote sensing and the Geographic
Information System (GIS) are two of the most powerfultools for assisting in grassland resource inventories andintegration of data and as mechanisms for analysis,modeling and forecasting to support decision-making(Tueller, 1989). However, it is difficult to evaluate livestock intake from satellite data alone because remotelysensed signatures only can provide information on exist©2010 AIRIES

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K. KAWAMURA & T. AKIYAMA

ing surface properties, such as soil, vegetation and water
bodies. Quantitative assessment methods for monitoring
spatial and temporal distributions of grazing animals at aregional scale are required in studies designed to determine the driving causes of desertification. The Global
Positioning System (GPS), as one potential tool, is now
widely applied in monitoring the spatial distribution ofgrazing animals.
In this review paper, we introduce ways to examine
grassland desertification using remote sensing technology with a focus on Mongolia and North China. We include recent developments in sensor technology forremote sensing and tools for monitoring livestock
distribution, with future perspectives for integrated use of
these technologies to combat desertification issues.
The sections include an overview, remote sensing formonitoring grassland degradation, tools for monitoring
livestock grazing behavior and their spatial distribution,and future perspectives on efficient grassland management.

2. Overview
China is one of the most severely desertified countries
in the world. Almost 60% of its population is living inareas at risk, covering approximately 3.3 million km2 inthirteen provinces and autonomous regions, mainly inNorth China (CNC-UNCCD, 1996). There are eight large
deserts and four large sandy lands (Zha & Gao, 1997)(Fig. 1). Most lie in regions above 35ºN and havean annual rainfall of <450 mm (Wang et al., 2008), or<400 mm according to Zha and Gao (1997). Historically,in China, ground surveys have been conducted to collectinformation on desertification, and this method has been
gradually replaced by aerial photographic interpretation
at small scales. Since the emergence of space-borne
remote sensing, satellite images have been utilized todelineate the extent of desertified areas (Zha & Gao,1997).
Mongolia is a landlocked country with a land area ofover 150 million ha, of which over 83% is rangelands(Angerer et al., 2008). Its climate is continental withextremely cold, dry winters and warm summers. Livestock production is its major agricultural industry,accounting for approximately 20% of the gross domestic
product. Mongolia’s livestock production depends muchon the productivity of natural grasslands. Wuyunna andOkamoto (2006) reported on changes in Mongolia’s
agriculture, stock raising, ecosystems and ecologicaldynamics.
How then can we get an overview of the present andfuture states of grasslands ? Grassland conditions will bedecided by the balance of grass production (GP) andherbage intake by grazing animals (HI) (Fig. 2). When HI
is greater than GP, grasslands face degradation. On theother hand, if GP is greater than HI, grasslands will beconserved and the land will recover. Akiyama andKawamura (2007) proposed a system for measuring GP
and HI to predict grassland conditions. GP is determinedby climate, soil, plant species and so on, whereas HI
depends on grazing intensity, including the stocking rate,
animal species, palatability and so on. This system isintended for real-time monitoring of GP and HI using
satellite remote sensing data, the GIS, GPS and mathematical models.

Fig. 1 Landsat mosaic image for China and Mongolia with locations of desert and sandy lands in China (Source: modified from
Zha & Gao, 1997), and boundary line (yellow line) at 400 mm annual precipitation.

Simultaneous Monitoring of Livestock Distribution and Desertification

Fig. 2 Concept of grassland condition balance between
grass production and herbage intake (Akiyama& Kawamura, 2007).

3. Remote Sensing for Monitoring Grassland
Degradation
3.1 Changes in land cover type
Desertification dynamics can be accurately monitored
and assessed via a combination of satellite observations
and in situ information (Collado et al., 2002). Satellite
remote sensing data can provide alternative data sources
for large areas, allowing classification of the land covertypes and estimations of above-ground biomass andprimary production. In general, changes in vegetation are
the most direct indicator of land degradation (Yang et al.,2005). Satellite-based remote sensing data have been
applied to the monitoring of desertification in Northeast
Asia, the mapping of land use and land cover (LULC)
and the ascertainment of desertification indicators.
Numerous studies of large-scale land cover mapping
have used data from the National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very High
Resolution Radiometer (AVHRR). AVHRR-derived normalized-difference vegetation index (NDVI) data may be
the most widely used data source and has providing somesuccess at assessing the condition of vegetation in arid
and semi-arid regions of North China (Akiyama &Kawamura, 2003, Kawamura et al., 2004, Lee et al.,2002) and Mongolia (Kogan et al., 2004). Vegetation
indices (VIs) are spectral transformations of two or morebands designed to enhance the contribution of vegetation
properties. Perhaps, NDVI is the most widely used VIs
because of their simplicity, which can be calculated by
red and near-infrared bands. The AVHRR sensors,
however, were originally designed for meteorologicalapplications and only cover two spectral bands (red andnear-infrared) that can be used to generate spectral
indices of vegetation greenness. A new generation of
optical sensors designed for terrestrial applications waslaunched in the 1990s. These include the VEGETATION(VGT) sensor onboard the SPOT-4 satellite andthe Moderate Resolution Imaging Spectroradiometer(MODIS) onboard the Terra and Aqua satellites.
In the Temperate East Asia (TEA) region, Boles et al.
(2004) applied the multi-temporal Land Surface Water
Index (LSWI) and Enhanced Vegetation Index (EVI) to31

map land cover types and investigate the seasonal
dynamics of various land cover types, distinguishing fiveclasses of barren or sparsely vegetated land. Later, in
TEA region, Huang and Siegert (2006) investigated thecharacteristics and usefulness of low resolution SPOTVGT satellite imagery for assessment and monitoring ofdesertification in North China. They found that moresparsely vegetated land can be detected better by
optimized VGT-NDVI data than by Global Land Cover2000 or MODIS Vegetation Continuous Field data. The
areas at risk of desertification were modeled, and theresults suggested 1.60 million km2 were at risk of desertification.
Various high-resolution satellite data such as Landsat
TM and Terra ASTER, were also used to investigate
desertification processes (Collado et al., 2002). Imagawa
et al. (1997) attempted to detect desertified areas using
multi-temporal images, and developed a monitoring
method in the Horqin desert area. Using three indicesobtained using a Landsat TM-band combination, they
determined the yearly changes in desertified areas. Basedon a GIS approach, Zhang et al. (2003) investigateddesertification of transitional zones of the Desert Loess in
the Yulin region using two Landsat TM images between
1987 and 1999. Their results showed that desertificationis still severe and that the desertified land accounts for
67.7% of the total land area.
3.2 Yearly changes in net primary production
At the regional to global scale, there have been
numerous attempts to predict terrestrial net primary production (NPP) or above-ground NPP (ANPP) from space
using NOAA AVHRR (Paruelo et al., 1997; Wylie et al.,
1991; Tucker & Sellers, 1986), Spot VGT (Sabbe &Veroustraete, 2000), and Terra MODIS (Running et al.,2004). For instance, Fig. 3 shows the improved MODIS
NPP product (MOD17A3) data of 2000 and 2006 forMongolia and China (Zhao et al., 2005). The original
MODIS NPP/GPP algorithm is described in Running
et al. (2004) and Heinsch et al. (2003). The difference inimages in Fig. 3 shows that NPP had decreased in
Mongolia and North China between 2000 and 2006.
According to Angerer et al. (2008), recent studies in
Mongolia have indicated that NPP will be reduced by
5%~30% in the forest steppe and steppe zones by 2080.
High mountain areas and desert steppe zones, however,are expected to have higher NPP (25%~75%) due to rising temperatures in the high mountain zone and increasing rainfall in the desert steppe zones.
3.3 Monitoring aboveground biomass and forage
quality
In Inner Mongolia, Lei and Yokoyama (1999) estimated mean above-ground biomass using AVHRRNDVI on a regional scale in 1996. A similar study in
Kawamura et al. (2003) using AVHRR-NDVI found thatthe mean aboveground biomass of the entire Xilingol
steppe in the summer of 2001 was 1,189 kg/ha, which
was 40% lower than the amount that Xiao et al. (1997)

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K. KAWAMURA & T. AKIYAMA

Fig. 3 NPP maps of China and Mongolia in 2000 (a), 2006 (b) and the difference between 2000 and 2006 (c),using the improved MODIS primary production product (MOD17) (Zhao et al., 2005).

Fig. 4 Seasonal changes in the MODIS-EVI estimated live biomass ( ), standing CP (■) and CP
concentration (○) in (a) NG: non-grazed grassland, (b) LG: lightly grazed grassland, (c) MG:
intermediately grazed grassland, and (d) HG: heavily grazed grassland in the Xilingol steppe(Kawamura et al., 2005a) with pictures showing respective NG, LG, MG and HG sites.

obtained by analyzing Landsat TM data in 1987. It
demonstrated the decrease in steppe biomass over thosefourteen years. In the same region, Kawamura et al.
(2005b) compared the capability of spectral VIs derived
from AVHRR and MODIS in estimating biomass,

standing crude protein (CP) and CP concentrations inleaves. A regression analysis revealed that MODIS-VI
achieved a good coefficient of determination (R2 =0.77 – 0.83) with regard to estimations of total and live
biomass. Furthermore, MODIS-EVI, which uses a blue

Simultaneous Monitoring of Livestock Distribution and Desertification

wavelength, was a good predictor of standing CP(R2 = 0.74) compared to AVHRR (R2 = 0.53). These
results suggest that MODIS-VI can reliably detect thephenology and pasture quantity and quality of steppe
grassland area. The NDVI exhibits scaling problems,
asymptotic (saturated) signals under high biomass conditions and is very sensitive to canopy backgroundvariations; NDVI degradation is particularly strong withhigher canopy background brightness (Huete, 1988). The
EVI on the other hand, was developed to optimize thevegetation signal with improved sensitivity in highbiomass regions and improved vegetation monitoring
through a decoupling of the canopy background signal
and a reduction in atmosphere influences. Kawamura
et al. (2005a, 2005b) also conducted a study to determine
the potential suitability of Terra MODIS for monitoring
short-term phenological changes in pasture conditions ina semiarid region. Applying the regression model, the
EVI accounted for 80% of the variation in live biomass,
77% of that in the total biomass, 11% in the CP concentration and 74% in the standing CP. Applying theseresults, seasonal changes in live biomass and the standing
CP could be described at the four selected sites withdifferent degrees of grazing intensity (Fig. 4).

4. Remote Sensing in Conjunction with GIS
and GPS
4.1 Tools for monitoring grazing behavior and its
spatial distribution
Monitoring changes in spatial distribution and foraging behavior of grazing animals provides knowledge that
can help farmers understand the relationship between

33

their internal state (i.e., nutritional requirements, health,etc.) and their environment (i.e., sward state, climate,etc.) (Penning & Rutter 2004). This knowledge would
help also land managers provide optimum resource
management and forecasting to support decision making.
To date, many investigations in behavior analysis
have been conducted at various scales ranging fromplant-part to landscape (Bailey et al., 1996; Senft, 1989;
Hirata & Ogura, 2003). At a landscape level, GPS technology has been increasingly applied in animal science tomonitor spatial distribution and track routes (Barbari
et al., 2006; Ganskopp, 2001; Ganskopp et al., 2000),
and is often combined with equipment for monitoring
animal activity (Champion et al., 1997). For example,
Penning (1983) developed a jaw movement recording
system for sheep. Combined with a microcomputer therecording system could analyze the movements to determine periods of eating and ruminating and the total
numbers of jaw movements, using “Graze” software(Rutter, 2000).
More recently, a simple neckband bite-counter (BC)was developed by Umemura et al. (2009) for cattle, thatrecords jaw movements only when the cattle are foraging.
Using BC, Kawamura (2005) monitored the daily patternof grazing times for sheep in the Xilingol steppe (Fig. 5),
where the herd in study grazed freely in the area between
sunrise and sunset, and then were kept in fenced
enclosures near the farmhouses overnight. The results
elucidated the diurnal patterns of grazing activities in
Mongolian sheep throughout the grazing season. Higher
rates of grazing were observed in the morning andevening, while lower rates tended to occur as temperatures increased around 15:00 in the afternoon. The

Fig. 5 Changes in daily patterns of grazing time with air temperature
between June and September in 2004 (Kawamura, 2005).

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K. KAWAMURA & T. AKIYAMA

results confirmed previous findings from animal behavior observations that daily patterns of grazing behavior
and total times spent grazing were influenced by hotsummer environments (Kurosaki et al., 1982; Matsui,1996).
4.2 Quantifying grazing intensities using integrated
RS, GIS and GPS
As a powerful information technology system which
stores, analyzes and displays both spatial and non-spatial
data, the GIS is expanding rapidly with the growthof computer technology. Kawamura et al. (2005c)
developed a methodology, using Terra MODIS-derived
NDVI data combined with GPS data and GIS technology
for quantifying the effects of grazing intensities (GI) on
plant productivity in the Xilingol steppe, Inner Mongolia.
To quantify the grazing pressure, a GI map of three herdsof sheep was created using a grid cell method, capturing
the tracking data by GPS. The relationship between GI
and estimated plant biomass showed a poor negative
correlation. When the plant biomass data were separated
into two different vegetation types, marshy meadow andtypical steppe, a stronger negative correlation was
obtained between GI and biomass (R2 = 0.887) for the
typical steppe data. The higher values of GI were showedaround the farmer’s house and near the Xilin riverside
area. In addition, the grazing area was used to extended
area in June and to narrow area in July (Kawamura,2005). It may be explained because of that; in early
spring and autumn season, sheep walk long distance withlooking for grass, while in late spring to summer season,
there are enough grass to eat.
Using remote sensing integrated with GPS and GIS,
the effects of grazing on forage quantity and quality canbe determined in a free ranging area. However, considered the other way around, animal performance and their
spatial distribution are themselves affected by forage
quality and quantity (Ganskopp & Bohnert, 2009). Until
now several factors have been identified in observations
as having an effect on animal behavior, including air
temperature (Kurosaki et al., 1982; Matsui, 1996), degreeof slope (Ganskopp & Vavra, 1987). These factors areinterrelated and influence both grass production andgrazing distribution. In future studies, it will be important
to determine the interactions between grass production
and animal performance in order to promote sustainablegrassland management.

5. Future Perspectives on Efficient Grassland
Management
A great deal of management knowledge has been
accumulated on grazing in artificial grasslands. It has
become possible to estimate the short- and long-term
production of each pasture grass under given climate
conditions. At the same time, grazing pressure from animals can be calculated from the number of animals, theirgrazing period, the pasture size and plant production.
However, grassland management is more difficult for

native grasslands under free-ranging conditions where
the area of grazing grassland cannot be defined. It may bestrongly affected by unrestricted animal behavior,biodiversity of plant species and unstable climate
conditions. Therefore, a strong need has arisen for accurate monitoring of free ranging grasslands from theviewpoint of both sustainable animal production andconservation of fragile grassland ecosystems.
To meet such demands, we need to be able to determine the distribution of plant biomass, its nutritive value
and the proportion of unpalatable grass as real-time
grassland information. In addition, information about
behavior, diet and herbage intake by animals should becomprehended so as to prevent land degradation due toexcessive grazing pressure. Up against such challenges,
we have been able to get accurate information throughprecise ecological experiments and field studies inhomogeneous artificial grasslands (we call them “ecological process studies”), but this cannot be applied tovast native grasslands under free ranging conditions.
Remote sensing is a technology which has been suitable for approximate surveys of vast areas extending over
several hundred square kilometers, but is not good atevaluating functions or providing accurate analyses. In
recent years, though, the domains of remote sensing andprocess ecology have moved closer and have started
partially overlapping each other (Fig. 2). As a result,scientists can investigate the same object by independent
methods from different viewpoints, thanks to improvements in onboard satellite sensors. Entering the 21st
century, the spatial, spectral and temporal resolution ofsatellite sensors has greatly improved (Akiyama et al.,2004), making functional analysis of grassland ecosystems possible for us. At the same time, ground-based
instruments for ecosystem study have also improved. For
example, the GPS has become an irreplaceable tool forfield surveys. Bite counters (BC) are another effective
tool for investigating the intake of grazing animals. Manysmall but useful devices have been developed to record
multiple facets of environmental factors, expanding thedimensions of ecological process studies in grassland
science.
In the “precision grazing” proposed by Kawamura
et al. (2010), a daily grassland biomass map of 250 m ×
250 m resolution may be drawn using Terra MODIS
images. The botanical composition and nutrient condition of grasslands can be distinguished using hyperspectral images, which will improve information on grassland
management. As for grazing animals, GPS technology
can record the daily behavior of grazing animals, and BC
data can show the times and numbers of bites in thegrassland. The GIS is a powerful tool for building grassland management models. System models for grassland
management will improve the short- and long-term
production of grasslands and livestock. At the same time,sustainable grassland utilization can be initiated.

Simultaneous Monitoring of Livestock Distribution and Desertification

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Kensuke KAWAMURA
Kensuke Kawamura was born in Yamaguchi on 11
April 1977, graduated from Gifu University andreceived his Ph.D. in March 2005 under Professor
Tsuyoshi Akiyama’s supervision, majoring in
Grassland Ecology and Remote Sensing. His
doctoral thesis was entitled “Application of satellite remote sensing and GIS/GPS for sustainable use of Inner Mongolia
grassland.” Since then, his major interest has been remote sensing ofgrassland ecosystems. From April 2004 to March 2006, he was a JSPS
post-doctoral research fellow (DC2) at the River Basin Research Center,
Gifu University. From April 2006 to June 2008, he received another JSPS
research fellowship (PD) at the National Institute for Agro- Environmental
Sciences, Japan. As a JSPS research fellow, he had opportunities twice tovisit the AgResearch Grassland Research Centre in New Zealand as avisiting post-doctoral researcher in July 2005–March 2006 and October
2007–September 2008. During his stay at the AgResearch, he learned about
New Zealand’s advanced grazing management system and studied pasture
mass and quality assessment using hyperspectral remote sensing techniques. Since July 2008, he has been an Associate Professor at the Graduate
School for International Development and Cooperation, Hiroshima
University. His current interest is developing environment-friendly
precision farming systems applying hyperspectral remote sensing acquired
through multi-stage platforms (field, airborne and satellite) for precise
assessment of pasture mass and quality, and GPS for monitoring grazing
animals’ behavior.

Tsuyoshi AKIYAMA
Tsuyoshi Akiyama got his doctorate at theGraduate School of Agriculture, Kyushu
University in 1974. He worked in the Ecology
Division of the National Institute of Grassland
Science, Ministry of Agriculture, Forestry and
Fisheries of Japan from 1973 to 1983. There heengaged in the study of grassland ecosystems. During that time, he went
abroad to study at the Laboratory for Applications of Remote Sensing,
Purdue University, USA, during 1978 and 1979. After returning, he waspromoted to Chief of the Laboratory of Remote Sensing, National Instituteof Agro-Environmental Sciences, to apply remote sensing technology tocrops and the agro-environment. In 1996, he transferred to the River Basin
Research Center at Gifu University. His work there was analysis of thespatial-temporal dynamics of regional environments, such as propagationof abandoned bamboo forests in the western part of Japan and grassland
degradation in Inner Mongolia, China. He is now Professor Emeritus at
Gifu University since his retirement in March, 2009.

(Received 27 April 2010, Accepted 13 May 2010)