CHAPTER
22
Recent glacier changes in the Mongolian
Altai Mountains: Case studies from
Munkh Khairkhan and Tavan Bogd
Brandon S. Krumwiede, Ulrich Kamp, Gregory J. Leonard, Jeffrey S. Kargel,
Avirmed Dashtseren, and Michael Walther
ABSTRACT
22.1
INTRODUCTION
The glaciers of the Mongolian Altai mountains play
a vital role in the regional ecosystem and have
created a unique physical landscape; however, there
are limited data regarding the current state of the
glaciers, the physical landscape, the climate, and
the responses of glaciers to climate change in this
region. The purpose of this study was to map recent
glacier changes in the Munkh Khairkhan and
Tavan Bogd ranges, to evaluate the methodologies
and classifications employed, and to develop an
understanding of how climate change influences
glaciers and geomorphology within the region.
Through the use of multitemporal ASTER and
Landsat imagery and geomorphometric analysis
of digital elevation models (DEMs), it was possible
to monitor the glaciers and landscape dynamics.
Supporting fieldwork in the Munkh Khairkhan
range was conducted in the summer of 2009. The
results from our analyses indicate that in the
Munkh Khairkhan range there has been a decrease
in glacier area of 30% and an increase in equilibrium line altitude (ELA) by 22 m between 1990
and 2006; in the Tavan Bogd range, home of the
highest altitudes in Mongolia, glacier extents
decreased by 4.2% between 1989 and 2009. This
decrease in glacier area will impact landscape development, meltwater contribution to regional hydrology, and the people who depend on these glaciers to
maintain their nomadic way of life.
Glaciers serve as key indicators of climate change.
Small alpine glaciers in areas with high precipitation and high ablation rates respond rapidly in
length, thickness, and volume—commonly in just
a decade—to these changes. Others, particularly
large glaciers in areas subject to a cold dry climate,
integrate climate change signals and respond over
many decades or up to a century or more (Jóhannesson et al. 1989, Beniston 2000, Raper and
Braithwaite 2009). Glaciers play an important role
in shaping the landscape around them by eroding
bedrock and depositing various sediments. They
also serve as a source of water input to regional
hydrology, impacting fluvial geomorphic processes,
and providing a source of water for both the ecosystem and for human consumption.
One region of particular interest is the Altai
Mountains of western Mongolia (Fig. 22.1). At
present, it is still uncertain how many glaciers there
are and how large the glacierized areas are in
Mongolia. In the literature, the estimates range
between 120 and 731 glaciers and glacierized areas
are given totals between 300 and as much as 659
km 2 (e.g., Selivanov 1972, Baast 1999, Klinge 2001,
Enkhtaivan 2006). Kadota et al. (2011) reported
577 glaciers covering an area of 425 km 2 in the
Mongolian Altai; this was based on a survey of
2000–2002 Landsat imagery. These glaciers contain
10% of Mongolia’s estimated volume of fresh water
482
Recent glacier changes in the Mongolian Altai Mountains
Figure 22.1. Location of the two study regions, the Munkh Khairkhan and Tavan Bogd ranges, within the
Mongolian Altai mountains.
(Davaa et al. 2007). Baast (1999) estimated that the
area occupied by glaciers in the Mongolian Altai
decreased by 6% between the 1960s and the end of
the 1990s.
While many studies on glaciers and glacial
variation have been conducted in the northern
and western parts of the Mongolian Altai such as
those undertaken on the Tavan Bogd range,
especially its Potanin Glacier (Enkhtaivan 2006;
Kadota and Davaa 2007; Khrutsky and Golubeva
2008; Tuvjargal et al. 2008; Kadota et al. 2011;
Konya et al. 2010; as well as the Tavan Bogd sections in this chapter), only very limited work has
been done in the Munkh Khairkhan range within
the central Mongolian Altai. Studies focused on
understanding the spatial significance of the range
in the overall Asian land mass, climate effects on
geomorphic processes, glacial chronology, and
landscape evolution of the region are areas that
need to be improved to understand the Mongolian
Altai and the surrounding region as a whole. This
work expands on the few previous studies carried
out for glacier research in the Munkh Khairkhan
and Tavan Bogd ranges. By applying and evaluating satellite and field-based techniques we have been
able to identify glacier changes and their association
with shifting climate.
22.2
REGIONAL BACKGROUND
22.2.1 Quaternary history of glaciers in
the Mongolian Altai
During the Pleistocene it is believed that glaciers
up to 500 m thick covered 20,700 km 2 in the Mongolian Altai (Klinge 2001, Lehmkuhl et al. 2004,
Enkhtaivan 2006). However, the extent and history
of Pleistocene glaciations in the Mongolian Altai is
not completely understood and still under debate
(Lehmkuhl et al. 2004, 2011, Rudaya et al. 2009).
The largest glacial advances of the Quaternary are
Regional background 483
believed to have occurred between 70,000 and
15,000 years ago during the MIS 2 (Sartan Glaciation) and MIS 4 (Early Zyrianka Glaciation) of the
Wisconsin (Würm) glacial period (Lehmkuhl et al.
2004; MIS stands for Marine Isotope Stage). Typically, glacial extent was controlled by topography,
underlying lithology, and climate (Klinge 2001,
Lehmkuhl et al. 2004, Khrutsky and Golubeva
2008). To date, no glacial deposits older than the
local Last Glacial Maximum (LGM) have been
found within Mongolia (Klinge 2001, Lehmkuhl
et al. 2004, Bader et al. 2008). There is a close
connection between equilibrium line altitude
(ELA) and local climate (Benn and Lehmkuhl
2000, Lehmkuhl et al. 2004); since the LGM, ELAs
in some parts of western Mongolia have increased
in elevation by 500 m (Bader et al. 2008); in Tavan
Bogd, the ELA rise since the LGM was 320 m
(Lehmkuhl et al. 2011). ELA rises correspond to a
warmer climate (Batima et al. 2005).
During the Holocene and Recent there has been
an oscillation between cooler drier climates and
warmer moister climates, which has affected glacier
and lake levels in the Mongolian Altai (Starkel
1998). Rudaya et al. (2009) used pollen and diatoms
collected from lake sediment cores from Hoton
Nuur in western Mongolia to reconstruct the
climate between 11,000 years ago up to the present. This demonstrated that prior to 10,000 years
ago the climate in the Mongolian Altai was probably cool and dry. Between 10,000 and 5,000 years
ago, the climate became warmer and more humid,
allowing expansion of the tree line in the mountains. From 5,000 years ago the climate again
shifted towards drier and cooler conditions with a
scattering of brief periods of increased warmth and
humidity from 3,000 to 2,000 years ago (Starkel
1998, Rudaya et al. 2009).
22.2.2 Recent history of glaciers in the
Mongolian Altai
Some recent studies have used tree ring analysis of
Siberian larch (Larix sibirica) to reconstruct the
climate from 1215 to 2004 (Davi et al. 2009). Their
results suggested that Mongolia is currently undergoing significant climatic change, with an increasing
moisture trend starting in the 20th century and
continuing into the 21st century. Over the last century the climate has steadily warmed in the Mongolian Altai by 3–4 C, including a 1.66 C warming
over the last 60 years; the 20th century was the
warmest in the last 1,000 years (Batima et al.
2005). This general trend of temperature increase
in Mongolia is slightly above the overall trend in the
Northern Hemisphere (Jacoby et al. 1996, 1999).
Almost all of this warming is confined to the winter
months with little to no change during summer
(WWF Russia, 2001). Spatially, the warming is
more pronounced in mountains and associated
valleys, and least in the Gobi Desert (Batima et
al. 2005). Instrumental records from the 1940s to
2001 also indicate that there is both a seasonal and
spatial change in temperature and precipitation
(Batima et al. 2005). Batima et al. (2005) have documented pronounced warming in mountainous areas
and their associated valleys from 1971 to 2001. In
the Altai Mountains this warming is concurrent
with an increase in precipitation by 2–60 mm and
a change in precipitation occurrence: typically, most
precipitation falls during late spring and early summer; however, over the last 30 years, snowfall,
which accounts for 20% of annual precipitation
in Mongolia, has tended to occur earlier in autumn
and last later into the spring (Batima et al. 2005).
Climatic change may also be causing an increase
in extreme weather events. Much of this may be due
to a weakening of the Siberian High that has dominated over recent decades (Gong and Ho 2002).
Weakening of the Siberian High results in warmer
temperatures and increased precipitation during the
winter. These warmer winter events cause snowmelt
at a time when the ground below remains frozen. As
the meltwater cannot penetrate into the frozen
ground, it refreezes at the surface either on top of
shallow snow cover or on vegetation, causing large
losses of livestock as a result of not being able to
forage. These harsh winters are called zud or dzud
(‘‘white death’’) and can have a devastating impact
on local livestock and their dependent herder populations. Moreover, from 1999 to 2002 Mongolia
experienced some of the worst drought conditions
on record, when 3,000 water sources dried up
(Batima et al. 2005). These unforgiving conditions
during both the winter and summer had a severe
impact on the people of Mongolia, whose primary
income is largely dependent on livestock and agriculture, causing economic hardship for individual
nomadic families and for Mongolia as a whole.
A basic characterization of glaciers in the Mongolian Altai relative to other glaciers worldwide is
still lacking (Lehmkuhl et al. 2004, Khrutsky and
Golubeva 2008). Most of the current glaciers in the
Mongolian Altai fall into one of three types: cirque,
484
Recent glacier changes in the Mongolian Altai Mountains
valley, and plateau; the latter dominate the region
(Lehmkuhl et al. 2004, Kadota and Davaa 2007).
Many recent glacial studies were primarily focused
on understanding glacier retreat (Kadota and
Davaa 2007, Bader et al. 2008, Khrutsky and
Golubeva 2008, Kamp et al. 2013) and mass balance (Tuvjargal et al. 2008). Like most glaciers
around the world, those in Mongolia are generally
retreating. There are also reports of an increase in
the rate of glacier shrinkage within the Mongolian
Altai (Bader et al. 2008, Khrutsky and Golubeva
2008). In the Tavan Bogd region and the Turgen,
Kharkhiraa, and Tsambagarav massifs, mean
glacier length retreat was 2.5–5.7 m yr1 with a
corresponding reduction in area of 10–30% from
1970 to 2000 (Kadota and Davaa 2007, Khrutsky
and Golubeva 2008). During the 1960s, 1970s, and
late 1990s in these four study areas, warmer
temperatures did not seem to dramatically affect
glacial area; however, thinning of the glaciers may
have occurred (Kadota and Davaa 2007). During
1987, 1988, and 1993 some glaciers began to
advance in the Turgen–Kharkhiraa region
(Khrutsky and Golubeva 2008) while others
remained stationary.
Glacial margins and thickness fluctuations in the
Mongolian Altai have been mapped using a combination of satellite imagery, aerial photos, topographic maps, and fieldwork (Kadota and Davaa
2007, Khrutsky and Golubeva 2008, Kadota et al.
2011, Kamp et al. 2013). Unfortunately, satellite
imagery and aerial photos covering the Mongolian
Altai are in short supply due to poor data quality
associated with cloud and snow cover. Kamp et al.
(2013) described glacial recession from 1910 to 2010
in the Turgen range based on repeat photography
and satellite imagery. Kadota and Davaa (2007)
were only able to compare glacial area from
1:100,000-scale topographic maps from 1970/1971
and Landsat imagery from 2000; more recent
fieldwork at Potanin Glacier, undertaken by joint
Japanese/Mongolian-sponsored teams, has focused
on determination of ice thickness and velocity
(Kadota et al. 2011), and meteorological and ablation studies (Konya et al. 2010). Khrutsky and
Golubeva (2008) also used Landsat imagery from
1992 and 2002 to determine the dynamics of
glaciers in the Turgen–Karkhiraa range. Only one
study has been performed in the Mongolian Altai
using Advanced Land Observing Satellite/Phased
Array L-band Synthetic Aperture Radar (ALOS/
PALSAR) data to calculate glacier and snow depth
(Tuvjargal et al. 2008).
22.3
REGIONAL CONTEXT AND STUDY
AREAS
22.3.1 Geography and climate
Within Mongolia’s far northwestern corner, the
Mongolian Altai Mountains emerge from the
Mongolia–Russia–China trinational border area
and extend for 1,300 km southeast along the
Mongolian–Chinese border before bending east
and terminating within the Gobi Desert (Fig.
22.1). The Altai is centrally located in the Eurasian
land mass in a transition zone between the boreal
taiga forest biomes of Siberia to the north and the
Gurbantunggut, Taklamakan, and Gobi Desert
biomes to the south and east. Although detailed
climatological information is sparse, these mountains appear to impose an abrupt climatic boundary
along western Mongolia, highlighting the importance of understanding climatic variation in this
region.
The Mongolian Altai is in an area well known for
its extreme continental climate due to its central
location on the Eurasian land mass. The winters
are dominated by a strong Asian anticyclone (the
Siberian High), while the summers in the mountains
are short and cool. Modern climate information has
been systematically recorded in Mongolia ever since
the early 1940s (Batima et al. 2005). However, the
national climate station network is sparse and
climate data for the Mongolian Altai are limited
to only six meteorological stations that are situated
in the valleys between the mountain ranges, thus
making it difficult to estimate the climate and
weather at higher elevations.
In the valleys of the Mongolian Altai, mean temperature is 30 to 34 C in January and less than
15 C in July (Batima et al. 2005). For Ulaangom
(939 m asl, 49 55 0 N, 92 03 0 E; Fig. 22.1) located in a
basin northeast of the Turgen–Kharkhiraa Mountains in the northern Mongolian Altai, measured
mean annual temperature is 4 C, and mean
monthly temperature ranges between 32 C in January and 19 C in July (Jansen 2010). In Ulaamgom,
measured mean annual precipitation is 136 mm and
the rainy season extends from June to September
(Jansen 2010). In general, winter is dominated by
the Mongolian anticyclone and dry cold weather,
infrequent precipitation, and drying winds, so that
fallen snow sublimates rapidly (Khrutsky and
Golubeva 2008). In the mountains above 2,500 m
winter precipitation from snow amounts to 400–500
mm, but maximum annual precipitation generally
occurs in July (Khrutsky and Golubeva 2008).
Regional context and study areas
There is a paucity of long-term climate data for
the Tavan Bogd region, although recent efforts
have seen the emplacement of automated weather
stations on the glaciers there (Konya et al. 2010).
Specifically, Konya et al. (2010) observed meteorological conditions within the ablation zone of
Potanin Glacier during the 2007/2008 seasons and
reported a mean annual air temperature on the
glacier of 8.8 C (at 3,040 m asl). Minimum and
maximum air temperatures for this year were
29.6 C (January 18, 2008) and 7.6 C (July 21,
2007), respectively. Measured only over the summer
months (June 12 to September 8, 2007), precipitation was 191.6 mm rain, with much of this (37%)
falling over three wet days in late July. This
seasonal total, however, was 3.5 times higher than
precipitation that fell in Olgii, the aimag (provincial) capital located about 150 km to the east (and
lower at 1,710 m asl); this is likely due to the
occurrence of orographic precipitation in the mountainous Tavan Bogd.
22.3.2 Munkh Khairkhan range
The Munkh Khairkhan range is located in the central Mongolian Altai (Fig. 22.1). Munkh Khairkhan
485
Uul (Eternal Sacred Mountain Peak) is the second
highest peak in Mongolia with an elevation of 4,208
m asl and is capped by ice and snow with glaciers
extending into valleys on both the north and south
sides of the range (Fig. 22.2). The remoteness of this
location makes it a pristine study area free from
severe anthropogenic impacts, providing an ideal
opportunity to understand local glacier and landscape sensitivity to climate influences. Our study
area covers 685 km 2 , of which 308 km 2 are
located within the Munkh Khairkhan National
Park, founded in 2006. Specifically, the study area
is focused on three valleys located in the northern
part of the range: Shuurkhai, Doloo Nuur, and
Khokh Nuur (Fig. 22.2). Shuurkhai (Stormy Looking) is situated on the north side of the Munkh
Khairkhan range and extends 28 km to the east;
it contains Shuurkhai Glacier, Khar Tsünkh (Black
Bag) Glacier, and Arts (Juniper) Glacieret (Fig.
22.2). Joining Shuurkhai Valley from the south is
Khokh Nuur (Blue Lake) Valley, which is 10 km
long and contains another glacier, called Arts Glacier. Doloo Nuur (Seven Lake) Valley contains
Doloo Nuur Glacier and extends to the north for
15 km. Munkh Khairkhan soum (village) is the
closest settlement to the national park and has a
Figure 22.2. Munkh Khairkhan range, central Mongolian Altai showing study area (red area) and Munkh
Khairkhan National Park boundaries (white line).
486
Recent glacier changes in the Mongolian Altai Mountains
population of 800 people. Meltwater originating
from the glaciers in Munkh Khairkhan National
Park is the source of drinking water for the village
and livestock within the region via the Dund Tsenkher Gol (Middle Blue River). The Munkh Khairkhan region is geographically and ecologically
significant in that it sits in the transition zone
between deserts and steppe to the south and east,
and the boreal taiga forest to the north. Plant species common to the study area include edelweiss
(Leontopodium alpinum), short grasses, wild onions,
and saxaul bushes (Haloxylon spp.). At higher elevations, juniper (Juniperus) can be found intermixed among boulder fields. Several varieties of
mosses and lichens are also present in areas next
to streams, lakes, and periglacial features.
22.3.3 Tavan Bogd range
The Tavan Bogd (Five Holy Peaks) range is located
in the extreme northwestern corner of Mongolia
(Fig. 22.3) and is largely contained within the Altai
Tavan Bogd National Park. This compact, northwest to southeast-trending range occupies 2,400
km 2 and contains Khuiten Uul (Cold Mountain),
which at 4,374 m asl is the highest mountain in
Mongolia, and four other major peaks: Burged
(Eagle, 4,310 m asl), Naran (Sunny, 4,280 m asl),
Olgii (Motherland, 4,100 m asl), and Malchin
(Herdsman, 4,050 m asl).
Figure 22.3. Tavan Bogd range, far northwest Mongolia showing study area (dashed yellow line) and
approximate international boundaries (red line) (map
base: ASTER GDEM V2 with local SRTM infill).
Figure 22.4. Aerial views of Potanin and Alexandra
Glaciers, Tavan Bogd National Park. (Top) Potanin
and Alexandra Glaciers and principal peaks of Tavan
Bogd. Khuiten Uul (4,374 m asl) the highest peak in
Mongolia is shown at top center of photo. (Bottom)
Terminus area of Potanin and Alexandra Glaciers. Note
dead ice zone and proglacial lake development (photos
courtesy of M. Saandar, August 29, 2011). Figure can
also be viewed in higher resolution as Online Supplement 22.1.
The study area comprises roughly 1,500 km 2 ,
merely a subset of the complete Tavan Bogd range,
and includes Potanin and Alexandra Glaciers,
which at 10 and 8.5 km in length, respectively
(excluding a 2 km proglacial zone of small lakes
and dead ice, Fig. 22.4) are the largest glaciers in
Mongolia. Potanin Glacier emanates just below the
summit of Mt. Khuiten Uul, flows eastward and
terminates in a valley at an elevation of 2,870 m
asl. Alexandra Glacier starts south-southeast of
Khuiten Uul and joins the terminus area of Potanin
Glacier in the lower valley. These glaciers, and the
numerous other valley and mountain (cirque-like)
glaciers and glacierets that dominate the region, are
Data and methods
centered around the Tavan Bogd massif, which
itself is situated on the Mongolia–Russia–China
trinational border located at the ice-domed summit
of Nairamdal Uul (Friendship Peak, 4,180 m asl). It
was decided that these glaciers—for the sake of
physical, geographic, and hydrological continuity—would be included within this Mongolia-based
study as representative of Tavan Bogd glaciers,
despite the fact that several of the glaciers presented
here overlap or occur completely within the boundaries of Russia and/or China, and therefore fall
within the remit of those countries’ respective
GLIMS regional centers. Note that all glacier outlines submitted to GLIMS from this study reflect
the proper regional center within which the glacier
actually occurs. We have delineated both glacier
and regional center boundaries as best we can.
Apart from Potanin and Alexandra Glaciers,
there were no proper names or designations identified for the other glaciers in this region, and therefore we gave them provisional designations for the
sake of this work and future reference to it (Fig.
22.16; Table 22.7). The absence of glacier names
and designations reflects the paucity of surface
and satellite-based survey work conducted to date
for this remote group of glaciers.
Northwestern Mongolia hosted thick and more
widespread glacier ice during peak Pleistocene
glaciations (Fig. 22.14); this in part is evident from
the U-shaped valleys present within the Tavan
Bogd range. Lehmkuhl et al. (2011) indicated that
the ELA for this region during the LGM was at
3,019 m asl, or 381 m below its modern day value of
3,400 m asl. Today, meltwater from Tavan Bogd
feeds Tsagaan Gol (White River), Tsagaan-Us Gol
(White-water River), and Sogog Gol (Doe River),
which supplies the small scattering of ethnic
Kazakh villages dotting the region (including Sogog
village) with a seasonal surface water resource.
22.4
DATA AND METHODS
22.4.1 Topographic maps
Soviet topographic maps from 1970 to 1972 covering the study areas at scales 1:100,000, 1:200,000,
and 1:500,000 were downloaded from http://en.
poehali.org/maps (Table 22.1). They were manually
georeferenced using the World Geodetic System
1984 (WGS 84) reference frame. These maps aided
preliminary reconnaissance of the study areas
and evaluation of the glaciers at the time of map
487
publication. However, glacier margins were not
delineated from these maps due to their small scales
limiting the detail of smaller glaciers in the study
areas.
22.4.2 Satellite imagery
Satellite imagery including Corona, Landsat (MSS,
TM, ETMþ), Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER),
and EO-1 ALI (Earth Observing-1 Advanced Land
Imager) were reviewed for processing; however, the
availability of imagery covering the study areas was
limited by the number of scenes available, the poor
image quality of many scenes due to the presence of
clouds and snow cover in most imagery, and sometimes poor sensor gain settings for ASTER data.
ASTER imagery was downloaded from the Land
Processes Distributed Active Archive Center (LP
DAAC) located at the USGS Earth Resources
Observation and Science (EROS) Center; Landsat
imagery was downloaded from the Global Land
Cover Facility (GLCF) at the University of Maryland. EO-1 ALI imagery and additional Landsat
images were also acquired from the Earth Explorer
website (http://earthexplorer.usgs.gov/). For the
Munkh Khairkhan range, 11 ASTER Level 1B
and seven Landsat scenes were selected covering
1990 to 2008 (Table 22.1). For the Tavan Bogd
study area, two Landsat TM scenes (spanning 20
years) and four ASTER Level 1B scenes spanning 5
years were selected (Fig. 22.5 and Online Supplement 22.2). The TM scenes cover the entire Tavan
Bogd range, whereas only one ASTER scene covers
the entire range and the other three have partial
coverage. One cloud-free Landsat MSS scene from
September 1972 was examined, although not used
as a result of poor gain settings and poor spatial
resolution compared with the TM and ASTER imagery. ALI imagery was also too cloudy.
Each ASTER scene covers an area of 60 60 km
with 15 bands divided into three spectral resolution
groups: visible and near infrared (VNIR, bands 1, 2,
3N, 3B) with 15 15 m resolution; shortwave infrared (SWIR, bands 4–9) with 30 30 m resolution;
and thermal infrared (TIR, bands 10–14) with
90 90 m resolution. The imagery had already been
georeferenced using the Universal Transverse Mercator (UTM) projection. Landsat imagery selected
for this study used two sensors: Thematic Mapper
(TM) and Enhanced Thematic Mapper Plus
(ETMþ). TM scenes cover an area of 185 172
488
Recent glacier changes in the Mongolian Altai Mountains
Table 22.1. Data used in the Munkh Khairkhan study.
Satellite imagery
Scene ID
Date
Sensor
p141r027_5dt19900916
9/16/1990
Landsat TM
L5140027_02720060820
8/20/2006
Landsat TM
L5140028_02820060820
8/20/2006
Landsat TM
p140r027_7dk20000827
8/27/2000
Landsat ETMþ
p140r028_7dk20000827
8/27/2000
Landsat ETMþ
p141r027_7dk20020808
8/8/2002
Landsat ETMþ
L71141027_02720050816
8/16/2005
Landsat ETMþ
AST_L1B_00301022001050642_20090420191641_32169
1/2/2001
ASTER
AST_L1B_00304152001051039_20090420191630_31956
4/15/2001
ASTER
AST_L1B_00301282002050247_20090420191731_1285
1/28/2002
ASTER
AST_L1B_00310042002045519_20090420191651_32303
10/4/2002
ASTER
AST_L1B_00310032005045926_20090420191641_32170
10/3/2005
ASTER
AST_L1B_00308032006050008_20090420191731_1296
8/3/2006
ASTER
AST_L1B_00309042006050001_20090420191630_31951
9/4/2006
ASTER
AST_L1B_00301192007045416_20090420191741_1561
1/19/2007
ASTER
AST_L1B_00306052008050045_20090420191731_1303
6/5/2008
ASTER
AST_L1B_00311122008050033_20090420191701_343
11/12/2008
ASTER
AST_L1B_00311282008050048_20090420191741_1558
11/28/2008
ASTER
Soviet topographic maps
Map number
Scale
L-46-01
1:500,000
L-46-08
1:200,000
L-46-27
1:100,000
L-46-28
1:100,000
L-46-39
1:100,000
L-46-40
1:100,000
Data and methods
489
especially for multitemporal comparisons. Based on
reviewing the image quality of both Landsat and
ASTER imagery, the ideal seasonal window for
image collection over the two study areas is late
July to early September, coinciding favorably near
the end of the ablation season for late August and
September images.
22.4.3 GPS data
Figure 22.5. Tavan Bogd study area (dashed yellow
line) and international boundaries (red line). Landsat
TM 4, 3, 2 RGB composite images from (top) September 3, 1989 and (bottom) August 8, 2009. Both panels
of this figure can also be viewed in higher resolution as
Online Supplement 22.2.
km and ETMþ scenes cover an area of 183 170
km.
While several ASTER scenes contained clouds,
snow, or shadows that limited analysis, Landsat
scenes from 1989 to 2009 proved to be of greater
use due to fewer clouds, snow, and shadows. Additionally, Landsat imagery covered a larger timescale
than ASTER imagery, and the near-anniversary
date pairs of Landsat images, acquired in the same
month over different years, allowed for high-quality
multitemporal comparison of the glaciers and landscape. The ETMþ sensor also had technical issues
affecting the quality of the data, including the wellknown Scan Line Corrector (SLC) failure of 2003,
which produced data gaps within individual scenes.
Although this problem can in part be corrected by
using older Landsat TM scenes to fill in the missing
data ‘‘gaps’’, such postprocessing introduces an
error in any quantitative analysis of the imagery,
For the Munkh Khairkhan study area, GPS observations were collected in summer 2009 using a
Trimble Recon XC GPS receiver and a Garmin
GPSMAP 76 CSx GPS receiver. Linear features
were collected along the terminus of Shuurkhai
Glacier and along the crest of moraines and till
ridges. Within this study area, 139 points and 46
line features were collected using the Trimble unit
and 94 points were collected using the Garmin unit.
Trimble GPS data were differentially corrected
using Trimble Pathfinder Office software. Base
station data used to differentially correct field
GPS data were selected from the Scripps Orbit
and Permanent Array Center (SOPAC) located in
Urumqi, China, 450 km away from the study area.
Unfortunately, this was the nearest station to the
study area that contained useful base data. After
differentially correcting the data, 116 of the 139
points collected had a position dilution of precision
(PDOP) of 5 (minimum 1.7, maximum 5, mean
2.9, standard deviation 0.8) and were used as
ground control points (GCPs). Often a PDOP value
of <4 is considered excellent, while 5–8 is acceptable, and >9 is poor (GISLab 2003); thus, a PDOP
of 5 was selected as a quality cutoff value. Although
this value is not very low, it does allow for a greater
number of points spread throughout the study
area; selecting a smaller PDOP value would result
in fewer points being available for comparison,
which would limit the extent for DEM assessment
throughout the study areas. The vertical error of
GPS data was calculated to be 7.7 m.
22.4.4 Pan-sharpening
Landsat images of the Munkh Khairkhan range
collected in August of 2000, 2002, and 2005 were
taken by the ETMþ sensor and included a panchromatic band 8, which was used for pan-sharpening to
improve image resolution employing ESRI ArcGIS
9.3. This improved the resolution from 30 30 m to
15 15 m allowing the imagery to be compared on
an equal footing with ASTER VNIR imagery.
490
Recent glacier changes in the Mongolian Altai Mountains
22.4.5
Glacier mapping
Both ASTER and Landsat imagery have multiple
bands covering various parts of the electromagnetic
spectrum allowing for the creation of visible, infrared, and thermal images all of which are used in
glacier mapping (Klein and Isacks 1998, Bishop et
al. 2003, Paul et al. 2004, Bamber 2006, Bolch et al.
2008, see also Chapter 4 of this book by Kääb et
al.).
For Landsat, one common false-color composite
(FCC) image combination is provided by TM
bands 5, 4, 3, which help delineate clean glacier
ice/snow and vegetation (Paul et al. 2004, Bolch
and Kamp 2006). Other useful FCC combinations
include TM bands 5, 4, 2, which also help differentiate water bodies (Klein and Isacks 1998); and
TM bands 7, 5, 4, which help to delineate snow/ice
from the surrounding landscape and to identify
changes in the landscape, including areas of saturated ground and stream drainage networks. The
same band combinations can also be applied to
ETMþ data, allowing for comparison between data
from both TM and ETMþ sensors. Similarly,
ASTER band composite 3N, 2, 1 can be closely
compared with ETMþ and TM bands 4, 3, 2.
To identify the spatial extent of snow/ice of the
Munkh Khairkhan study area, a land cover classification was performed for all TM and ETMþ
Landsat imagery (Table 22.1) using all bands as
input within the Maximum Likelihood Classification Spatial Analyst tool in ESRI ArcGIS 9.3 (Fig.
22.6). Around 3% of the area was misclassified as
water bodies, particularly areas of light snow along
the margins of the glaciers; these areas were
removed. The remaining total snow/ice area was
Figure 22.6. Munkh Khairkhan study area: an example of the Maximum Likelihood Supervised Classification of a
Landsat image used to delineate glacier snow and ice (August 16, 2005). Snow/ice are light blue, water bodies are
blue, vegetated areas are green. Note the misclassification of snow near glacier margins as water bodies.
Data and methods
then divided into two subclasses: glacial snow/ice,
and snow/ice not associated with glaciers. Glacial
snow/ice areas were delineated by creating polygon
shapefiles with the aid of topographic references,
including breaks in glacier margin slopes, topographic divides, and near each glacier’s headwall
that included the snow/ice area below the headwall
as defined by GLIMS (Raup et al. 2006). Area
calculations for each glacier polygon were
performed allowing quantitative comparison from
one year to the next. The excessive amount of lateseason off-glacier snow, clouds, and shadow present
in the ASTER imagery made it challenging to use
any classification methods. However, the snow and
shadows present in some ASTER scenes proved
useful for identifying landscape features by highlighting topographic features including ridges and
depressions.
Another method that has been used to delineate
clean glacier ice from its surroundings involves the
use of NIR and SWIR band ratios, especially bands
4 and 5 from Landsat TM or ETMþ sensors (Paul
et al. 2002, Kääb et al. 2003, Bolch and Kamp 2006,
Kamp et al. 2011). The ASTER band ratios of
bands 3 and 4 approximate the same spectral signature as Landsat TM and ETMþ bands 4 and 5.
They have also proved useful in mapping clean
glacial ice, and can be more accurate than Landsat
band ratios (Kääb et al. 2003, Kamp et al. 2011). A
continuing problem with using band ratios (and
some other spectrally based classification schemes)
is the difficulty in mapping debris-covered glaciers
(Bolch and Kamp 2006, Kamp et al. 2011). Landsat-based band ratios were used in this study and
the results for glacier mapping were similar to those
created using the Maximum Likelihood Classification.
For the Tavan Bogd range, a Landsat TM imager
pair, separated by 20 years (September 3, 1989 and
August 8, 2009) was used for glacier mapping
(Fig. 22.5 and Online Supplement 22.2). Although
ASTER’s VNIR band spatial resolution is 15 15
m, compared with 30 30 m for TM imagery, there
were no high-quality repeat ASTER image pairs
available for the entire Tavan Bogd range. ALI
imagery, with its 30 30 m spatial resolution in
VNIR/SWIR and 10 10 m panchromatic capability, was also too cloudy. In addition the time span
for the TM pair exceeded 20 years, compared with
only 5 years for the best ASTER (partial coverage)
pair.
Glacier mapping was achieved using a semiautomated algorithm that included the following
491
steps: TM images were subset to include only the
region of interest (Fig. 22.5). Co-registration was
unnecessary as the pair was well co-registered with
a relative image-to-image accuracy exceeding <0.5
pixel. Dark object subtraction (DOS) was completed for each VNIR/SWIR band to remove the
effects of path radiance within visible bands (e.g.,
TM1, TM2) and to subsequently boost the signalto-noise ratio, particularly within areas of shadows,
when applying band ratio techniques (Crippen
1988). Several low-turbidity lakes within the scenes
were selected as the ‘‘dark objects’’. A snow/ice
mask was generated using a thresholded normalized
difference snow index (NDSI ¼ [TM2 TM5]/
[TM2 þ TM5]) (Hall et al. 1987). On visual inspection of the normalized image, an NDSI threshold of
>0.6 was selected to represent pixels of ice and
snow and a binary raster snow/ice mask was
created. Several water bodies, such as lakes, ponds,
and streams were misclassified as ice/snow, and
subsequently removed by applying a water mask
computed by the TM band ratio: TMVIS /TMNIR
(TM2/TM4) (Kargel et al. 2005). Unsupervised
classification, principal component analyses, and
generation of a normalized difference water index
(NDWI) were also trialled to isolate an effective
water mask; however, no water classes were consistently identified, and therefore any remaining areas
of water were manually removed when misclassified
as glacier ice.
Next, a median filter was applied to the NDSI
binary classification image to remove noise and
small isolated pixel areas of transient snow cover.
The resultant snow/ice binary raster was vectorized
and polygons were generated that represented individual and clustered glaciers. A minimum (snow/
ice) filter was applied which removed snow/ice polygons <0.1 km 2 , many of which appeared to be
transient snow patches, with possibly a few glacierets. Additionally, any remaining nonsupraglacial
water bodies and transient snow patches were
manually removed, and previously excluded debris-covered glacier areas manually added to the
ice polygon areas. The 2009 image contained noticeably more transient snow patches which required
manual removal; some nonglacier patches were
identified by intersecting polygons generated from
the less snow-covered 1989 image with polygons
from 2009. Finally, the 13 largest glacier basins
were manually delineated using the underlying original TM images and an ASTER GDEM2 mosaic.
Glacier area was calculated for the 13 largest
glaciers, and for total ice within the subset area,
492
Recent glacier changes in the Mongolian Altai Mountains
both for the earlier and later TM images. All work
was completed using Leica’s ERDAS Imagine
image-processing software and ESRI ArcGIS 9.3.
22.4.6 Error analysis (area accuracy and
change precision)
The primary source of error is inaccurate delineation of glacier ice extent, both from semiautomated
and manual classifications. Systematic errors
associated with the semiautomated classification
of glacier ice in the Tavan Bogd and Munkh Khairkhan study regions are primarily based on deficiencies in the classification algorithm. These might
include factors such as consistent misclassification
of debris-covered ice (which could make measured
areas smaller than the actual glacier), selection of
NDSI threshold values (which could make the
measured value either systematically too small or
too large), and the vectorization of classified ice
from raster-based data, among others. Fortunately,
Tavan Bogd contains very little debris-covered
glacier area, and most glaciers have relatively clean
ice surfaces and high-contrast contact with adjacent
land cover, and so systematic errors in glacier
margin location are likely less than 1 pixel. Of the
13 largest glaciers, debris cover ranges from virtually 0 to 8%, with most glaciers covered by only
2%. The more heavily debris-covered areas have
been manually classified by on-screen digitizing of
vector polygons, but elsewhere we used automated
classification algorithms. It was much the same in
the Munkh Khairkhan region: smaller glaciers were
present with clean ice and only minor areas were
obscured by debris.
When optimal classification conditions prevail
(i.e., debris-covered glaciers are absent), there
appears to be a general underestimation of glacier
ice by a maximum of 1 pixel, and probably no cases
involve overestimation of glacier area. This error
likely has a variety of causes, including deficiencies
in the classification algorithm listed above, among
others. In any case, we take a conservative assessment of our measurements and define the systematic error as 1 pixel. The percentage error of area
determinations, Aer , then is proportional to sensor
resolution and is given by:
Aer ¼ 100% ðn mÞ=Agl
ð22:1Þ
where n ¼ number of pixels defining the perimeter
of the glacier area, m ¼ spatial resolution of the
sensor bands applied expressed as an area of the
pixel (e.g., 900 m 2 for a 30 30 m TM pixel),
Agl ¼ the area of the glacier, and the factor 100 is
there to convert to percentage.
For the Tavan Bogd region we calculated systematic errors, assuming an uncertainty of 1 pixel
(either 1 pixel too wide or too narrow), for the
largest 13 glaciers ranging from 5.1 to 15.3%, with
most other large glaciers falling near the 10% mark.
Systematic error was more substantial for small
glaciers. We measured glaciers down to 0.01 km 2 ;
however, due to the ballooning of errors at small
glacier sizes, we cut off our tabulated glaciers at 0.1
km 2 , where systematic errors calculated by eq.
(22.1) were 50%. Total area error of all glaciers
combined in the Tavan Bogd region in 1989 was
14%. These calculations were also applied to
glaciers in the Munkh Khairkhan region with a
resulting error of 16% in 1990 and 14% in 2006.
Recall, however, that this represents the maximum estimated error and is likely an error biased
toward underestimation of glacier area (i.e., there is
probably more ice present than reported). Furthermore, measured areas tend to be consistent as far as
classifications of both the earlier and later datasets
are concerned. Therefore, ice area percentage
change from 1989 to 2009 represents a far more
robust value than the absolute accuracy of any
given glacier area. Much like the classic definition
of precision versus accuracy, where repetition of
measurements made on different but similar datasets results in precision of values fit well within the
envelope representing accuracy, the same pertains
to many kinds of scientific measurements, where
relative precision greatly exceeds absolute accuracy.
Thus, it is possible to measure area differences that
are smaller than systematic error limits. We believe
that both accuracy and precision reporting in our
scientific discipline would mark an improvement.
In cases where glacier margins comprise clean ice
and any debris-covered fringe is much less than a
pixel wide, as is the case for some Mongolian
glaciers, classifications based on either automated
or manual delineation can be better than 1 pixel.
Furthermore, in these cases, the error may tend to
be random (i.e., there is equal probability that any
given glacier extent measurement will be located
either inside or outside the actual glacier margin,
perhaps by as much as a pixel either way). Where
this occurs, area determination error is much less
than 1-pixel systematic error. If random errors are
1 pixel, then glacier area error is reduced by a
factor n 1=2 when compared with systematic error.
Hence, after reducing the equation, random error
percentage, which we define as area precision, Ap , is
Data and methods
given by:
Ap ¼ 100 ðn 1=2 mÞ=Agl
ð22:2Þ
If a glacier is delineated by 100 pixels (or polygon
vertices)—along the perimeter, for instance—random error—precision from eq. (22.2)—is one tenth
that of systematic error—accuracy from eq. (22.1).
For manual digitization, there may be fewer vertices
of the vectorized polygon representing the glacier,
but systematic error may still be defined by equation (22.1). However, percentage precision for
manual digitization, Ap;m , is instead given by:
Ap;m ¼ 100 ð j 1=2 Þ ðn mÞ=Agl
ð22:3Þ
where j ¼ the number of manual vertices of the
vectorized polygon. In our work, because there
were so few debris-covered glaciers and relatively
little need for manual digitization, precision can
generally be given by eq. (22.2), and eq. (22.3) is
irrelevant. Random errors for Tavan Bogd glacier
area determinations range between 0.13 and 6.7%,
with the largest errors associated with smallest area
glaciers (0.1 km 2 ). Random errors for the 13
largest glaciers range between 0.13 and 0.50%.
Random errors for Munkh Khairkhan glacier areas
are similarly only a fraction of systematic errors.
The assumption of random error still probably
underestimates accuracy (accuracy is worse than
estimated) even when there are ideal clean ice
boundaries, because there will still be some amount
of systematic error, perhaps half a pixel at best.
However, random error is easily calculated, and it
probably gets as close as possible to an evaluation
of repeatability of measurements made on different
but very similar images (e.g., same sensor systems,
similar time of year, and the same classification
algorithm), and hence may be used to define what
changes may be deemed reliable.
Lessening large systematic errors for future work
would require their accurate identification and mitigation within the classification algorithm. Providing multispectral sensors with higher spatial
resolution would help greatly toward this aim.
22.4.7 Digital elevation models
Digital elevation models (DEMs) play an important
part in many morphometric glacier–mapping,
terrain analysis, and surface process–modeling
methods, particularly for creating such parameters
493
as slope, aspect, and curvature (Bishop et al. 2003,
Kamp et al. 2003, Paul et al. 2004, Bolch 2005,
Surazakov and Aizen 2006, Toutin 2008, see also
Chapter 4 of this book by Kääb et al.). DEMs used
for this study included the new ASTER Global
Digital Elevation Map (GDEM—a product of the
Japanese Space Agency, METI, and NASA) Version 1 dataset—made available in June 2009—and
the SRTM dataset. Both of these datasets are available to download free: ASTER GDEM data were
accessed from LPDAAC, while SRTM data were
downloaded from the Consortium for Spatial Information–Consultative Group for International
Agriculture Research website (CGIAR-CSI 2008).
The more recent ASTER-based GDEM2 upgraded
dataset, now available, was unavailable at the time
our analysis was conducted. For the Munkh Khairkhan study area, 11 ASTER scenes were processed
at the University of Nebraska-Omaha using SilcAst
1.01 software to generate DEMs without GCPs.
These DEMs were used to generate a new ‘‘mean
ASTER DEM’’ (see below) that provided additional detail of the study area surface. The ASTER
GDEM, SRTM DEM, and mean ASTER DEM
were then assessed for accuracy to determine which
would be used for geomorphometric analysis.
DEMs do not always have the requisite accuracy
needed for geomorphometric or dynamical analysis,
and inaccurate DEMs can result in misleading
information that would seriously compromise morphometric analysis (Toutin 2008). Clouds, shadows,
and other artifacts often contribute to inaccurate
DEM generation; therefore, DEMs must be examined for errors. To do this, SilcAst-generated
ASTER DEMs were compared against GPS field
observations and topographic map spot elevations
to assess the accuracy of DEM elevation estimates
(Kumler 1994, Bishop and Shroder 2004). In the
Munkh Khairkhan study area, the elevations of
Munkh Khairkhan peak at 4,208 m asl and other
surrounding peaks were compared with elevations
derived from each SilcAst-generated DEM. Elevations that were significantly higher (in some cases
>100 m) suggested that clouds or other problematic issues were present; therefore, these scenes were
excluded from further analysis. Other artifacts were
also observed in SilcAst-generated ASTER DEMs
(Fig. 22.7); they appeared to be associated with
shadows mainly in the Shuurkhai Valley, due, it
appears, to seasonal and time-of-day Sun angle in
relation to the east–west trend of the range and
associated steep north-facing slopes. These shadows
created pseudo-hills and pseudo-flat surfaces in the
494
Recent glacier changes in the Mongolian Altai Mountains
Figure 22.7. Munkh Khairkhan study area. (A) SilcAst-generated DEM from the ASTER scene on November 28, 2008. (B) SilcAst-generated ASTER DEM from
the ASTER scene on January 2, 2001. Note how the
shadows create artifacts within the DEM including
pseudo-hills (A) and pseudo-flat surfaces with jagged
edges (B), typically representative of a lake or other
body of water. Figure can also be viewed in higher
resolution as Online Supplement 22.3.
DEM, which adversely affected geomorphometric
analysis.
For the Munkh Khairkhan study area, 3 of the 11
SilcAst-generated DEMs were selected (ASTER
scenes from January 2, 2001, April 15, 2001, and
November 28, 2008). Differentially corrected GPS
points with PDOP values of <5 were overlaid on all
three DEMs in ArcGIS. The elevation cell value for
each underlying DEM was then compared with the
corresponding GPS point. Individually, the scenes
have relatively low accuracy with mean absolute
error (MAE) values from 9.7 to 11 m and root mean
square error (RMSE) values from 12–18 m when
compared with GPS elevation values (Fig. 22.8;
Table 22.2). There was a maximum elevation error
of 129 m between GPS and DEM elevation values
according to the SilcAst November 28, 2008 dataset. Based on these values, it was determined that a
single SilcAst-generated DEM dataset could not be
used for geomorphometric analysis.
To compensate for the lack of accuracy of each
individual SilcAst-generated ASTER DEM, a
method similar to that proposed by Muller (2008)
was used, in which the three SilcAst-generated
ASTER DEMs were averaged to produce a new
mean ASTER DEM (Fig. 22.9). This helped to
smooth the errors of all three DEMs resulting in
a more accurate mean ASTER DEM with a horizontal error of 15 m and a vertical error of 10 m
(Fig. 22.8; Table 22.3). Following the same methods
used to analyze each individual SilcAst-generated
DEM, differentially corrected GPS points were
placed on top of the ASTER GDEM, mean
ASTER DEM, and SRTM DEM. Again, the
elevation cell value of each underlying DEM was
compared with the corresponding GPS point. When
compared with the ASTER GDEM and SRTM
DEM, the mean ASTER DEM’s accuracy proved
to be better than the ASTER GDEM dataset, but
not as accurate as the SRTM DEM dataset. However, the mean ASTER DEM had the highest
spatial resolution of 15 15 m allowing topographic features to be shown in greater detail than
was the case with the SRTM DEM. Based on this
comparison between the three types of DEMs, the
newly created mean ASTER DEM was selected
for generating DEM-derived datasets and geomorphometric analysis.
For the Tavan Bogd study area, several data tiles
from the ASTER GDEM2 dataset (made available
from NASA and METI in October 2011) were
mosaicked and used as a basemap, this was additional to mosaicked SRTM tiles that were infilled
with GDEM2 data (necessary for occasional zones
where SRTM data are missing). Both DEM
products provided topographic guidance for the
delineation of glacier basins (Fig. 22.3).
Data and methods
495
Figure 22.8. Munkh Khairkhan study area. (Top) Sample distribution of DEM elevation values between 3,000 and
3,200 m asl for three ASTER DEMs generated using SilcAst compared with GPS elevation values. (Bottom) Sample
distribution of DEM elevation values between 3,000 and 3,200 m asl for mean ASTER DEM, ASTER GDEM, and
SRTM DEM compared with GPS elevation values.
496
Recent glacier changes in the Mongolian Altai Mountains
Table 22.2. Munkh Khairkhan study area: MBE, MAE,
and RMSE values for three ASTER DEMs compared
with differentially corrected GPS elevation values.
SilcAst
SilcAst
SilcAst
11/28/2008 4/15/2001 1/2/2001
Table 22.3. Munkh Khairkhan study area: MBE, MAE,
and RMSE values for three types of DEMs compared
with differentially corrected GPS elevation values.
Mean
MBE (m)
2.8
6.6
7.7
3.8
MAE (m)
11.0
11.0
9.7
10.0
RMSE (m)
18.0
14.0
12.0
14.0
MBE ¼ mean bias error, MAE ¼ mean absolute error, and
RMSE ¼ root mean square error.
22.4.8 DEM-derived datasets
Several topographic-based parameters and derivatives can be extracted from a DEM including slope,
curvature, aspect, hillshading, and solar radiation
ASTER GDEM Mean DEM
SRTM
MBE (m)
4.7
3.5
3.3
MAE (m)
8.6
6.8
6.2
RMSE (m)
11.0
9.7
9.5
MBE ¼ mean bias error, MAE ¼ mean absolute error, and
RMSE ¼ root mean square error.
insolation, which are useful for geomorphological
mapping and glacial analysis (Bishop et al. 2003,
Kamp et al. 2003, Paul et al. 2004, Bolch and Kamp
2006, Surazakov and Aizen 2006). All of these datasets were generated from the mean ASTER DEM
using ESRI ArcGIS.
Figure 22.9. Munkh Khairkhan study area: the resulting mean ASTER DEM covering the Shuurkhai Valley created
from three ASTER DEMs using SilcAst. Artifacts within the DEM (Fig. 22.7) have been smoothed out in the final
product.
Data and methods
For slope, the same thresholds that Bolch and
Kamp (2006) used to delineate glaciers in the Alps
and Tien Shan were applied, and these classes
helped to identify some of the landforms within
the study areas. However, smaller features were
often obscured, making it difficult to delineate the
extent of larger features, including glaciers. Plan
curvature and profile curvature assisted in delineating ridges and valleys within the study area. Aspect
was an aid to identification of areas exposed to
direct sunlight and areas that were shaded.
Hillshading is often used to improve the visual
aspect of a DEM. Typically, the (default) light
source is set at an azimuth of 315 and a solar zenith
angle of 45 . While hillshading did help to identify
some features in this study, it often obscured others,
especially if the landform was near a larger and
steeper feature such as a cliff, or if the orientation
of the landform minimized shading due to shape.
To improve the identification of surface features,
hillshading was performed from eight azimuths
(0, 45, 90, 135, 180, 225, 270, and 315 ) and a solar
zenith angle of 45 . All hillshades were then merged
to compute a mean hillshade (Fig. 22.10). This
method is comparable with the multidirectional,
oblique-weighted (MDOW) shaded relief image
developed by Mark (1992), which incorporates four
azimuths (225, 270, 315, and 225 ), a zenith of 30 ,
and weighting using a generalized aspect map. This
method improves the visual quality of hillshade,
while still maintaining shadows since the four
azimuths used are between 225 and 360 . By
applying light from eight azimuths equally spaced
around a circle, shadows were minimized and the
resulting hillshade improved geovisualization,
helped to identify additional features, and
smoothed some of the textural differences present
in standard hillshade. It was especially useful in
delineating cirques and features in east to westoriented valleys.
22.4.9 Geomorphometric analysis
Geomorphometric analysis, using the surface toolset within ESRI’s ArcGIS Spatial Analyst toolbox
to generate datasets, was performed on ASTER
DEM-derived datasets. This allowed comparison
of geomorphic parameters covering the study areas.
Similar methods have been used to delineate
glaciers and landforms in high mountainous regions
of the world (Bishop et al. 2003, Kamp et al. 2003,
Paul et al. 2004, Bolch and Kamp 2006). So that
different features could be delineated, each dataset
497
Figure 22.10. Munkh Khairkhan study area: comparison between typical hillshade (top) and mean
hillshade from eight different directions using a zenith
angle of 45 (bottom). The same mean ASTER DEM
has been draped over both using the same color palette
and transparency level, while both hillshades are using
the same black to gray palette with a minimum–maximum stretch applied underneath.
was individually analyzed to identify patterns that
matched field observations.
Morphometric glacier-mapping (MGM) methods use DEMs and satellite imagery as input to
delineate the extent of glacierization in highmountain regions (Bolch 2005, Bolch and Kamp
2006, Kamp et al. 2011). This method is useful
for larger glaciers consisting mostly of clean ice;
however, smaller glaciers, like those found in the
Munkh Khairkhan region, and debris-covered
glaciers present a problem. Often manual delineation or other methods are needed for these types of
glaciers (Bolch and Kamp 2006, Bane 2009). MGM
uses a classification of satellite imagery together
with a classification of various geomorphometric
498
Recent glacier changes in the Mongolian Altai Mountains
parameters. Slope is often a critical parameter used
to identify glaciers and delineate them from other
land cover classes. Usually, a threshold value of 24
is used to identify the smooth parts of a glacier;
however, this value makes it difficult to determine
the boundary between the toe of a terminus and an
outwash plain (Bolch and Kamp 2006).
Variations in slope classification schemes did
seem to identify features including U-shaped valleys, cirques, and hanging valleys; however, smaller
features were often obscured. Slope combined with
elevation, hillshading, and imagery analysis helped
to identify both present and past extents of glacial
activity within the study area. Breaks in glacierized
and unglacierized terrain were determined based on
the texture of slope and hillshading. Unglacierized
terrain appears rugged at fine scales, while glacierized terrain has a smoother texture at fine scales
associated with glacial erosion of the surface. Elevation combined with hillshade products was used to
delineate terminal moraines from the surrounding
surface. Similarly, draping imagery over hillshade
improved the visual reorganization of surface landforms. By combining these methods meso and
macroscale features could be analyzed.
Present ELA calculations were performed using
the toe-to-summit altitude method (TSAM)
because it is easy to use in remote areas and has
proved useful for other glaciers in Mongolia (Benn
and Lehmkuhl 2000, Bader et al. 2008).
22.5
RESULTS
22.5.1 Glacial change in the Munkh
Khairkhan range
Results from five different years between 1990 and
2006 showed that the snow/ice area of the Munkh
Khairkhan range shrank by 30%, losing 12 km 2
in area (Fig. 22.11; Table 22.4). Glaciers make up
only a small portion of the total snow/ice area, but
even so show a mean loss in area of 13% (Table
22.5). However, one glacier, Khar Tsunkh,
Figure 22.11. Munkh Khairkhan study area: multitemporal (1990–2006) comparison of snow/ice area overlays on
DEMs.
Results
Table 22.4. Munkh Khairkhan study area: changes in
snow/ice/glacial areas between 1990 and 2006.
Year
Total
snow/
Ice/
Glacial
area
(km 2 )
Snow/
Ice
area
Glacial Glacial
area area of
total
area
(km 2 )
(km 2 )
(%)
1990
39.7
32.9
6.8
17.1
2000
29.8
23.9
5.9
19.8
2002
26.4
21.0
5.4
20.3
2005
29.4
23.4
6.0
20.3
2006
27.6
21.7
5.9
21.4
D1990–2006 (km 2 ) 12.1
11.2
0.9
N/A
30.5
34.1
13.1
þ4.3
D1990–2006 (%)
increased in area by 6%. In 1990, glaciers made
up 17% of the total snow/ice area, and in 2006
this area increased to 21%, while snow/ice areas
not classified as glacial decreased, from 33% in
1990 to 22% in 2006 (Table 22.4). The greatest
losses in glacial area and changes in ELA can be
found in glaciers located farther to the east (Fig.
22.11; Tables 22.5 and 22.6). Results showed that
there was a mean ELA increase of 1.4 m yr1
between 1990 and 2006 (Table 22.6).
Climate data from 1997 to 2008 for the Munkh
Khairkhan range were evaluated to see whether
temperature and precipitation might correlate with
499
snow/ice area change. It appears that both temperature and precipitation may have impacted the
amount of snow/ice present in the area, especially
during 2005 when there was a decrease in annual
temperature and an increase in annual precipitation
leading to an increase in snow/ice area (Figs. 22.12
and 22.13).
During the LGM it is estimated that 106 km 2 of
glacial ice were held in the Shuurkhai Valley and its
associated tributary valleys, while in the Doloo
Nuur Valley and its contributing valleys there were
38 km 2 of glacial ice. The Shuurkhai Valley
Glacier is estimated to have extended 26 km from
its source toward the east, while the Doloo Nuur
Valley Glacier is estimated to have extended for 23
km to the north (Fig. 22.14). The ELA during the
LGM was 3,450 m asl for the Doloo Nuur Valley
Glacier and 3,300 m asl for the Shuurkhai Valley
Glacier. A comparison of LGM ELAs with present
ELAs shows an increase of 260 m for the Doloo
Nuur Valley Glacier and 375 m for the Shuurkhai
Valley Glacier. By using cross-sectional data
extracted from the mean ASTER DEM together
with trimlines viewed in satellite imagery of the
Shuurkhai Valley, the LGM glacier was estimated
to be 125–150 m thick.
22.5.2 Glacial change in the Tavan Bogd
range
From 1989 to 2009 the total area of glacier ice
within the Tavan Bogd study area decreased from
213 to 204 km 2 , or by 4.2% over the 20 years (Fig.
22.15 and Online Supplement 22.4). Total area
of the 13 largest glaciers over the same period
Table 22.5. Munkh Khairkhan study area: snow/ice/glacial area (km 2 ) of selected glaciers for 1990–2006.
Year
Doloo Lake
Glacier
Shuurkhai
Glacier
Khar Tsunkh
Glacier
Arts
Glacieret
Arts
Glacier
Total glacial
area
1990
2.94
0.84
1.49
0.26
1.24
6.77
2000
2.55
0.81
1.44
0.20
0.90
5.91
2002
2.25
0.77
1.37
0.15
0.80
5.34
2005
2.65
0.77
1.55
0.18
0.82
5.96
2006
2.53
0.81
1.58
0.17
0.80
5.88
D1990–2006 (km 2 )
0.41
0.03
þ0.09
0.10
0.44
0.89
D1990–2006 (%)
13.86
4.08
þ6.19
36.25
35.74
13.13
4,200
4,060
4,060
3,780
3,850
Shuurkhal
Khar Tsünkh
Arts Glacieret
Arts Glacier
3,200
3,350
3,210
3,290
3,220
3,525
3,565
3,635
3,675
3,710
(m)
ELA
8/20/2006
Summit
Toe
elevation elevation
(m)
(m)
Doloo Nuur
Glacier
ELA: TSAM method
3,200
3,330
3,210
3,280
3,210
Toe
elevation
(m)
3,525
3,555
3,635
3,670
3,705
(m)
ELA
8/16/2005
3,190
3,320
3,190
3,270
3,200
Toe
elevation
(m)
3,520
3,550
3,625
3,665
3,700
(m)
ELA
8/8/2002
3,190
3,280
3,180
3,270
3,200
Toe
elevation
(m)
3,520
3,530
3,620
3,665
3,700
(m)
ELA
8/27/2000
3,150
3,270
3,170
3,260
3,200
Toe
elevation
(m)
25
22
Average
40
20
15
10
(m)
1.38
1.56
2.5
1.25
0.94
0.63
(m/yr)
DELA DELA
3,500
3,525
3,615
3,660
3,700
(m)
ELA
9/16/1990
44
50
80
40
30
20
(m)
DToe
Table 22.6. Munkh Khairkhan study area: ELA calculations for individual glaciers using TSAM method after Benn and Lehmkuhl (2000).
2.75
3.13
5
2.5
1.88
1.25
(m/yr)
DToe
500
Recent glacier changes in the Mongolian Altai Mountains
Results
501
Figure 22.12. Munkh Khairkhan study area: comparison of mean annual temperature (village of Munkh Khairkhan) and total snow/ice area from 1990 to 2008. Climate data from 1990 to 1996 are missing from the record
(MASPL 2009).
Figure 22.13. Munkh Khairkhan study area: comparison of total annual precipitation (village of Munkh Khairkhan) and total snow/ice area from 1990 to 2008. Climate data from 1990 to 1996 are missing from the record
(MASPL 2009).
decreased from 142 to 132 km 2 , or 6.5% (Table
22.7, Fig. 22.16 and Online Supplement 22.5).
The largest glaciers all decreased in area with percent losses ranging between 4.3 and 14.1% over the
20 years. The largest and most studied glaciers in
the Tavan Bogd range, Potanin Glacier and Alexandra Glacier, lost 1.13 and 1.20 km 2 of ice area
from 1989 to 2009, respectively (4.3 and 8.1%
area, respectively). Generally, the larger glaciers
lost a smaller percentage of ice, although this rela-
tionship becomes noisy among the population of
smallest glaciers. There does not appear to be any
aspect-related correspondence with ice loss. Debris
cover for these largest glaciers ranges from effectively 0 to 7.9%; and total debris cover decreased by
14% from 1989 to 2009. Between 1989 and 2009,
Potanin Glacier receded by 516 m at a mean rate of
25.8 m yr1 . Over the same period, the adjacent
Alexandra Glacier receded by 653 m at a mean rate
of 32.7 m yr1 .
502
Recent glacier changes in the Mongolian Altai Mountains
Figure 22.14. Munkh Khairkhan study area: general
geomorphological map of today (top) and approximation of glacial extent during the LGM (bottom) based
on data collected during the field campaign. Geographic features can be seen in Fig. 22.2. The black
area was not studied for the project.
22.6
DISCUSSION
22.6.1 Munkh Khairkhan range
Glacier mapping using a multimethod approach
including fieldwork, satellite imagery, and DEM
analyses allowed landscape features to be delineated. However, many of the glacial landforms
mapped were identified manually through interpretation of satellite imagery and DEMs due to
the limited performance of automatic methods such
as morphometric glacial mapping (Bolch and Kamp
2006, Bane 2009, Kamp et al. 2011). Fieldwork, in
particular, allowed first-hand understanding of the
processes at work on the surface that cannot be seen
in satellite imagery or DEMs. GPS observations
proved to be critical for GIS-based mapping of
small landform features that would not be visible
in satellite imagery. The use of GPS to map moraine
ridges in the field greatly improved the validity of
each moraine ridge position, allowing comparison
with satellite imagery and DEM-derived datasets
including slope, curvature, and hillshading. Larger
landform features with associated GPS observations were easy to identify in both ASTER and
Landsat imagery, while locations without GPS
observations were delineated based on imagery
interpretation and comparison with slope, curvature, aspect, and elevation datasets.
Due to the relatively small size of glaciers in the
Munkh Khairkhan range the most useful method
for delineating them was to create a polygon feature
class derived from maximum likelihood classification of snow/ice from Landsat imagery. The thresholds for slope that Bolch and Kamp (2006) used to
delineate glaciers were applied and found to be
useful for outlining ablation areas on Doloo Nuur,
Shuurkhai, and Khar Tsunkh Glaciers. However,
accumulation areas on all glaciers and slopes on the
smaller glaciers, such as Arts Glacieret and Arts
Glacier, were not easily detected using slope classification, thus making it difficult to delineate them,
similar to the results of Bane (2009). We therefore
relied on the polygon feature classes created from
maximum likelihood classification of Landsat
imagery to delineate these features. Although this
method worked best at delineating small glaciers
and snow/ice areas, two limitations exist for
accurate glacier mapping: spatial resolution of the
imagery and debris cover present on two of the
glaciers. Small glaciers require finer resolution
imagery to delineate their areas: in the case of Arts
Glacieret and Arts Glacier misclassification of only
a few pixels can alter area calculations by a large
percentage. Debris cover is present along the western edges of Doloo Nuur Glacier and Khar Tsunkh
Glacier near their termini. Parts of these two
glaciers were not classified as snow/ice and were
not included in calculations because their spectral
signature varied from clean snow/ice and was
dependent on how much debris was present. While
the area measurements may not be precise, they do
provide an approximation that is useful in detecting
change.
From data collected for total snow/ice and glacial
snow ice area, it is evident that glaciers in the
Munkh Khairkhan range fluctuated in area
between 1990 and 2006. Overall, there has been a
decrease in total snow/ice area by 12 km 2 (30%)
(Fig. 22.11; Table 22.4). This decrease in area
showed that between 1990 and 2006 the glaciers
in the Munkh Khairkhan range reduced in area
at an annual rate of 0.3–2% and receded by
1.3–5.0 m yr 1 . Only Khar Tsunkh Glacier
increased in area (by 6%, Table 22.5). For glaciers
located in the Tavan Bogd, Turgen, Kharkhiraa,
and Tsambagarav ranges, Kadota and Davaa
(2007) calculated an annual recession rate of
Discussion
503
Figure 22.15. Tavan Bogd study area: glacier extents for the 20-year period derived from Landsat TM imagery.
International boundaries are delineated by the red lines. Note the dead ice zone and lake development below the
termini of Potanin Glacier and Alexandra Glacier (base TM 4, 3, 2 RGB composite from August 8, 2009). Figure can
also be viewed in higher resolution as Online Supplement 22.4.
0.3–1% between 1970 and 2000. Khrutsky and
Golubeva (2008) calculated annual recession by
0.85% yr1 in the Turgen range and 1.2%
yr1 in the Kharkhiraa range, and a general annual
recession rate of 2.5–5.7 m between 1969 and 2002.
Although both total snow/ice area and glacial
area are decreasing, as time progresses glacier area
becomes a larger part of total snow/ice area as
snow/ice outside glacier areas continue to decrease
(Table 22.4). Similar results were found in the
Russian Altai in which flat-top glaciers lost more
area than valley glaciers (Kadota and Davaa, 2007).
This is due to the sheltering effect of the topography
of cirques and the east–west orientation of the
Shuurkhai Valley on the north side of the Munkh
Khairkhan range (Fig. 22.2). The spatial distribution of glaciers within the region also affects the
change in glacial area and ELA. In general, ELAs
decrease from west to east, and the greatest losses in
glacial area and change in ELA can be found in
smaller glaciers located to the east (Figs. 22.11;
Tables 22.5 and 22.6). This is due to the orographic
climatic effect of the Munkh Khairkhan range,
which blocks westerlies and their precipitation,
resulting in reduced precipitation from west to east
through the Mongolian Altai (Lehmkuhl et al.
2004). Although warming in the Mongolian Altai
is responsible for glacier retreat, precipitation has a
larger influence on smaller glacier fluctuations than
temperature, especially when considering the local
topography.
There is a very close relationship between ELA
and local climate, and fluctuations in ELA are
important indicators of glacier response to climate
change (Lehmkuhl et al. 2004). ELAs in 2006 calculated for the Munkh Khairkhan range were
504
Recent glacier changes in the Mongolian Altai Mountains
Table 22.7. Tavan Bogd study area: selected glacier change for 1989–2009.
Glacier_ID (name)
1989
2009
1989 to 2009 1989 to 2009
Area
(km 2 )
Debris cover
(km 2 )
Debris
(%)
Area
(km 2 )
Debris cover
(km 2 )
Debris
(%)
Area change
(km 2 )
Change
(%)
TB-001
5.31
0.00
0.00
4.98
0.00
0.00
0.33
6.1
TB-002
12.28
0.00
0.04
11.52
0.00
0.00
0.76
6.2
TB-003
9.12
0.22
2.46
8.47
0.23
2.69
0.65
7.1
TB-004
5.86
0.20
3.41
5.32
0.15
2.77
0.54
9.3
TB-005
23.34
1.81
7.76
22.27
1.75
7.86
1.07
4.6
TB-006
9.92
0.27
2.69
9.18
0.21
2.25
0.74
7.5
TB-007
4.96
0.00
0.04
4.26
0.01
0.21
0.70
14.1
TB-008
11.30
0.08
0.73
10.49
0.13
1.23
0.80
7.1
TB-009 (Alexandra)
14.73
0.69
4.71
13.53
0.22
1.59
1.20
8.1
TB-010 (Potanin)
26.28
0.11
0.42
25.16
0.24
0.94
1.13
4.3
TB-011
9.82
0.00
0.02
9.34
0.00
0.00
0.48
4.9
TB-012
5.70
0.00
0.02
5.08
0.00
0.00
0.62
10.9
TB-013
2.91
0.00
0.02
2.68
0.00
0.00
0.22
7.6
Totals
141.52
3.40
2.41
132.28
2.92
2.21
9.24
6.53
between 3,525 and 3,710 m asl; Klinge (2001) and
Lehmkuhl et al. (2004) reported ELAs between
3,000 and 3,600 m asl. As the area of a glacier
decreases near its terminus, its ELA increases in
elevation. Mean ELA increase for the Munkh
Khairkhan range was 22 m between 1990 and
2006 (Table 22.6). LGM ELAs were also calculated
for comparison with 2006 ELAs. Results showed an
increase of 260 m for the Doloo Nuur Valley
Glacier and 375 m for the Shuurkhai Valley
Glacier. These values are of the same order as those
found by Lehmkuhl et al. (2004), Kadota and
Davaa (2007), and Bader et al. (2008) for other
parts of the Mongolian Altai.
Understanding how total snow/ice area and
climate data fluctuate overall is hindered by the lack
both of consistent climate records for the Munkh
Khairkhan range and of clean, complete, and repetitive satellite imagery covering the study area.
Although these data are lacking, some correlations
can be made. Decreases in total annual precipitation from 1997 to 2001 and increased mean annual
temperature correspond to a decrease in total snow/
ice area. Increases in total annual precipitation
from 2002 to 2005 together with decreases in mean
annual temperature during the same time period are
linked to an increase in total snow/ice area (Figs.
22.12 and 22.13). However, this short time frame is
only a subset of the larger timescale over which
glaciers fluctuate.
Geomorphic processes are dynamic and change
in accordance with retreat in response to climate
change. As ice is removed from the Munkh Khairkhan range, the granitic and metamorphic cirque
walls and valley floors may begin to exfoliate. This
would lead to increased mass-wasting events, produce talus slopes in unglacierized areas, increase the
amount of debris deposited onto glaciers, and allow
increased mobilization of sediment. Furthermore,
increased melting of the glacier will result in more
meltwater entering regional hydrology. This could
potentially be beneficial for livestock and agriculture, but over decades or centuries—as the glaciers
continue to shrink—the availability of meltwater
Discussion
505
Figure 22.16. Tavan Bogd study area: glacier extents for the largest glaciers. International boundaries are
delineated by the red lines. Note the dead ice zone and lake development below the termini of Potanin Glacier
and Alexandra Glacier (base TM 4, 3, 2 RGB composite from August 8, 2009). Figure can also be viewed in higher
resolution as Online Supplement 22.5.
will decrease. Any increase in meltwater would also
result in increased erosion along rivers and higher
sediment loads, which would alter the landscape
adjacent to the rivers.
22.6.2 Tavan Bogd range
Similar to losses in glacier area reported for the
Munkh Khairkhan range, glaciers in the Tavan
Bogd range have also experienced ice loss in the
latter part of the 20th century and early 21st century. Total area of glacier ice within this study area
decreased from 213 to 204 km 2 , or by 4.2% over 20
years; this figure includes losses in total area of the
13 largest glaciers in Tavan Bogd, which over the
same period decreased by 6.5%. This rate of ice
area loss is consistent with the 6% decrease in
glacier area reported between the 1960s and the
1990s (Baast 1999).
Additionally, ablation (end of season) measurements collected by Kadota et al. (2011) on Potanin
Glacier indicate that rapid thinning is occurring
and perhaps accelerating, particularly at the lower
portion and terminus areas where thinning rates
averaged 2.6 m yr1 from 2004 to 2009; this,
coupled with low surface velocities measured in
the lowermost part of the glacier, suggests that ice
influx is considerably less than ablation, resulting in
the observed wastage and retreat. Potanin Glacier
retreated 90 m between September 2003 and September 2009, a mean retreat rate of 15 m yr1
(Kadota et al. 2011). Our satellite-based study indicates that between 1989 and 2009 Potanin Glacier
receded by 516 m, a mean rate of 25.8 m yr1 , and
by 5.1% of its total length. Over the same period the
adjacent Alexandra Glacier receded by 653 m, a
mean rate of 32.7 m yr1 , and by 7.7% of its total
length. In this respect, early 21st century retreat at
Potanin Glacier may be decelerating.
Continuous, long-term climate data are sparse in
this remote region, and the nearest climate records
are for a station in Olgii, 150 km to the east and at
506
Recent glacier changes in the Mongolian Altai Mountains
a lower elevation (1,715 m asl) than the glaciers.
Olgii climate data can be considered regional proxy
data for the Tavan Bogd range. Mean air temperature in Olgii from 1961 to 2008 has increased by
2 C (a trend of 4.2 C per 100 years), including a
rise in mean temperature of 1 C from 1989 to
2008 (Konya et al. 2010).
Precipitation values are not immediately available for this region, although surface meteorological stations have been in place on Potanin
Glacier since 2007/2008 (Konya et al., 2010).
Nevertheless, the sharp rise in regional mean annual
air temperature is consistent with a state of disequilibrium and the glacier wastage trend observed
in Tavan Bogd in the latter part of the last century
and up to recent years. Clearly, Potanin Glacier and
other glaciers in the Tavan Bogd range are in a state
of disequilibrium as a result of changing climate,
and sustained and continued warming will likely
lead to further shrinkage. Additional monitoring,
from both field and satellite-based studies, is
required to quantify and characterize the likely continuation of glacier ice loss in the Tavan Bogd range
and its inevitable impacts on the ecosystem and
communities that rely on this resource.
22.7
CONCLUSIONS
Much of the work undertaken in this study focused
on evaluating methods to improve the data quality
available to DEMs and applying them in geomorphometric analysis. The increasing availability of
different digital elevation datasets such as ASTER
GDEM and SRTM facilitates comparison and
quality assessment of the sources of elevation data.
Three individual ASTER DEMs were selected,
processed, and averaged together for this study.
The end result was a mean ASTER DEM comparable with the SRTM dataset, but with higher resolution. The only downside to this method was the
continued impact of artifacts on DEM accuracy.
Based on field studies, satellite imagery, and
DEM analyses, glaciers in the Munkh Khairkhan
and Tavan Bogd ranges were responsible for the
deposition of glacial till and large moraines
throughout their respective valleys. These glaciers
were also responsible for the formation of U-shaped
valleys, cirques, and hanging valleys like those that
occupy the north side of the Munkh Khairkhan
range.
The glaciers in both Munkh Khairkhan and
Tavan Bogd were mainly retreating in the 1990s
and 2000s. However, caution must be used due to
the relatively limited time frame. This is especially
true for the Munkh Khairkhan range, where the
results could also be interpreted as showing the
glaciers remaining stagnant between 2000 and 2006.
These glacial changes were most likely associated
with warming temperatures and changes in precipitation over the last few decades. Climate change
affects different parts of the world at different rates,
and the current increase in annual temperatures is
causing most glaciers in the Mongolian Altai region
to decrease in area, as evidenced in the Tavan Bogd
range. Precipitation is also a key factor in glacial
area fluctuation, especially for small glaciers
impacted by orographic effects.
This study has barely scratched the surface of the
opportunities for glacier research that exist in the
Mongolian Altai. Nevertheless, it should serve as a
benchmark for future work. The Mongolian Altai is
situated at the climatic boundary between the
Atlantic influence from the west and the Pacific
influence from the east. It is also a transition zone
between deserts and steppe to the south and boreal/
taiga forest to the north. It can thus be considered a
region particularly sensitive to small-scale climatic
changes. Ultimately, however, it will fall to the
people living here to deal with any alterations to
their environment. Studies on environmental
change should be coupled with studies on cultural
change, especially with regard to mitigation strategies and social vulnerability.
22.8
ACKNOWLEDGMENTS
This project was made possible through the
generosity of the University of Montana and the
American Center for Mongolian Studies. Ulrich
Kamp thanks the Alexander von Humboldt Foundation, Germany, for awarding a research fellowship and the faculty of the Institute for Space
Sciences at Freie Universität Berlin for their hospitality. We thank the Global Land Ice Measurements from Space (GLIMS) program for
providing ASTER imagery free of charge; and Jeffrey Olsenholler, University of Nebraska-Omaha,
for ASTER DEM generation. ASTER data courtesy of NASA/GSFC/METI/Japan Space Systems,
the U.S./Japan ASTER Science Team, and the
GLIMS project.
References 507
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