Hydrological Research Letters 7(3), 73–78 (2013)
Published online in J-STAGE (www.jstage.jst.go.jp/browse/hrl). doi: 10.3178/hrl.7.73

A monitoring system for mountain glaciers and ice caps using 30 meter
resolution satellite data
Kazunari Nakano1, Yong Zhang1, Yoshihiro Shibuo2, Hironori Yabuki3 and Yukiko Hirabayashi1
1

Institute of Engineering Innovation, School of Engineering, the University of Tokyo, Japan
2
Earth Observation Data Integration & Fusion Research Institute, School of Engineering,
the University of Tokyo, Japan
3
Japan Agency for Marine-earth Science and Technology, Japan
Abstract:

Loss of volume in mountain glaciers and ice caps
(MG&IC) has been reported around the world in response
to atmospheric warming (IPCC, 2007), significantly
affecting terrestrial water resources (Immerzeel et al., 2010;
Kaser et al., 2010) and contributing to global sea-level rise
(Rignot et al., 2003; Radić and Hock, 2010; Church et al.,2011). To calculate current and future changes in glacier
volumes, one of the most important data sets is a complete
global glacier inventory with topographic attributes (e.g.,
minimum and maximum elevation, slope and aspect) for
each glacier. However, recent calculations of sea-level risedue to MG&IC melting relied on estimates of the total
volume of land ice scaled up from incomplete glacierinventories (e.g., Radić and Hock, 2010; Mernild et al.,2011). Hence, a globally complete and detailed glaciological
data set is urgently required.
The World Glacier Inventory (WGI) compiled informa-tion on MG&IC from aerial photography, map and satellite
imagery acquired during the 1960s and 1970s (WGMS,
1989). However, the WGI is not complete with respect todetailed glaciological information; only about 44% of
MG&IC and 23% of their area are inventoried (Dyurgerov
and Meier, 2005). Furthermore, the WGI makes change
detection for individual glaciers nearly impossible because
glaciers in the WGI are represented by point data rather thanglacier outlines (WGMS, 1989). For this reason, the Global
Land Ice Measurements from Space (GLIMS) project was
designed to generate a global survey of digital glacier
outlines from satellite data (Raup et al., 2007; Armstrong
et al., 2012). To date the GLIMS Glacier Database, hosted
at the National Snow and Ice Data Center (NSIDC) in
Boulder, CO, USA, contains records for over 96792 glacierswith a total area of 325968 km2 (Armstrong et al., 2012).
Although glacier inventories for British Columbia,
Caucasus, Heard Island, Iceland, Irian Jaya, and Switzerland
are complete in the GLIMS Glacier Database, the inventories
for other regions are not yet completed. Further, someinventoried glaciers in the GLIMS Glacier Database arelacking topographic parameters and some data are based on
somewhat old maps (Armstrong et al., 2012). In addition,
Cogley (2010) compiled an extended version of the WGI,
called WGI-XF, through compilation of existing inventories
including several older regional inventories that have been
documented in WGMS (1989) but not in the WGI, and new
inventories in Canada and the Sub-Antarctic. The WGI-XF
contains records for over 131000 glaciers, covering
approximately half of the MG&IC area (Cogley, 2010).
Although glacier outlines can be derived from multispectral
satellite data (e.g., Bayr et al., 1994; Sidjak and Wheate,
1999; Paul and Andreassen, 2009), these studies mainly
focused on regional scales. Such studies allow estimationof the fluctuations in glacier volumes in these regions, but
are insufficient for estimating global changes. A globally
complete inventory of glacier outlines, the Randolph GlacierInventory (RGI) has been published recently (Arendt et al.,2012). RGI primarily contains glacier outlines with little
additional metadata for each record, and does not give theexact date of the determination of all glacier outlines. Thereis no question that global glacier outlines in different timeseries rather than snapshot and with date information are
valuable.
Here we present a developed semi-automatic system toderive outlines of MG&IC (excluding glaciers in Greenland

Correspondence to: Yong Zhang, Institute of Engineering Innovation,
School of Engineering, the University of Tokyo, 2-11-16 Yayai, Bnkyo-ku,
Tokyo 113-8656, Japan. E-mail: zhyong@sogo.t.u-tokyo.ac.jp ©2013,
Japan Society fo Hydrology and Water Resources.

Received 29 May, 2013
Accepted 13 August, 2013

We developed a monitoring system for deriving outlinesof mountain glaciers and ice caps (MG&IC) at a 30 mhorizontal resolution from Landsat Thematic Mapper (TM)and Landsat Enhanced Thematic Mapper plus (ETM+).
Location and area information at 30 m resolution was
obtained using a band ratio (TM4/TM5) and a thresholdvalue of TM3 with a 9 by 9 pixel average filter. The total
area and number of MG&IC were 449482 km2 and 414258,
respectively. The glacier outlines were similar to previous
satellite-derived products for different regions. Although thederived glacier area was similar to previous estimates atregional scales, it was overestimated in some parts ofScandinavia where available satellite images are limited andonly snowy season images can be used, and wasunderestimated in the western Himalayas and Caucasus
where the glacier outlines are derived with difficulty fromsatellite images because of the effect of debris cover. Our
system to monitor MG&IC has potential application in globalhydrological and land-surface models and estimates of global
sea-level rise.
KEYWORDS glacier outlines;
Landsat; global

satellite

monitoring;

INTRODUCTION—73—

K. NAKANO ET AL.
and Antarctica) from global satellite data with 30 mhorizontal resolution. The system consists of semi-automatic
data archiving and an automated mapping system runningin a Linux/UNIX system. With the automated mapping
system, our data set can be updated as new satellite data
become available and will form a time series of glacier
outlines, rather than a snapshot of location and extent.
Furthermore, apart from enhancing glacier inventories forregions where few direct measurements exist, the 30 m
global database of MG&IC, could be integrated into globalhydrological and land-surface models.

DATA
Satellite data
Landsat TM and Landsat ETM+ images were provided
in an orthorectified version (UTM projection, WGS84
datum) by the Global Land Cover Facility (GLCF). The
images were selected with as low snow and cloud cover as
possible to be suitable for glacier mapping. Each image
covers 185 km × 185 km and contains reflection intensity
information for each band. Satellite images including thelocations of MG&IG obtained from Hirabayashi et al.
(2010) or the GLIMS Glacier Data were analyzed. Overall,
we selected 334 Landsat TM images acquired during 1985–
1997 and 415 ETM+ images acquired during 1999–2003.

mountainous areas with steep slopes. A report about typicalartifacts and errors in the ASTER GDEM is given in METI/
NASA/USGS (2009), which indicates that the overall
accuracy of the global ASTER GDEM can be taken to beapproximately 20 m at 95% confidence interval, which isadequate for deriving glacier details for our global inventory.

Glacier inventory data
Glacier area on a continental scale from Radić and Hock(2010), the WGI-XF and the RGI were used to validate ourestimate in each region (Table I). Radić and Hock (2010)
calculated total glacierized area per region with complete
glacier inventory from the WGI-XF data set and withincomplete glacier inventory from a global grid cell ofglacierized area data set. The WGI-XF is an extended versionof the WGI, in which a set of explicit specifications has
been used to enhance the usefulness of the WGI data by
eliminating low-level inconsistencies (Cogley, 2010). The
RGI contains 170000 glacier outlines, which is acombination of both new and existing published glacier
outlines (Arendt et al., 2012). In addition, glacier inventories
for British Columbia, Caucasus, Switzerland, and the eastern
and western sides of the Himalayas (excluding India andPakistan) are complete in the GLIMS Glacier Database, andwere used to further evaluate our estimates.

METHODSASTER GDEM
Specific topographic inventory parameters such as
minimum, maximum, mean, and median elevation, meanslope, and mean aspect derived from a digital elevation
model (DEM) were used for analysis of the obtained glacier
outlines. For this study the Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER) Global
Digital Elevation Model (GDEM) with a horizontal
resolution of 1 arc-second (~30 m) was used. It was releasedin July 2009 by the Ministry of Economy, Trade and Industry(METI), Japan, and NASA, covering the earth’s surface
between 83°N and 83°S.
As recent studies focusing on comparing a pre-release
version of the ASTER GDEM and the DEM from the Shuttle
Radar Topography Mission (SRTM) have shown (Hayakawa
et al., 2008; Frey and Paul 2012), the ASTER GDEM has
higher resolution, fewer missing data, and better topographic
representation than the SRTM DEM, particularly in highMapping of MG&IC
Among methods for automated glacier mapping frommultispectral satellite data, the band ratio method is
considered the most efficient and convenient method (Paul,
2002; Raup et al., 2007; Paul and Andreassen, 2009). It
involves creating a thresholded ratio image from the raw
digital numbers of bands TM3 (0.63–0.69 μm) or TM4(0.76–0.90 μm) and TM5 (1.55–1.75 μm), relying on the
spectral contrast between ice and snow and other surfaces.
As expected from the spectral properties, glacier ice has amoderate reflectivity in the TM4 band (fine-grained snow
has high reflectance) and very low reflectance in the TM5band. The division of TM4 by TM5 yields high andconsistent values over ice and snow, and very low values
for the surrounding terrain. The glaciers can be therefore
distinguished from other surface types where the TM4/TM5
ratio is larger than 3.6 (Supplement Figure S1). Because of

Table I. Total area of MG&IC in each region from the study of Radić and Hock (2010) (RH10), the WGI-XF (Cogley,2010), the RGI (Arendt et al., 2012), and estimates from this study.
Region
North America
South America
IcelandCentral Europe
Caucasus
High Mountain Asia
New Zealand
Scandinavia
North and East Asia
Total

WGI-XF
(km2)

RH10
(km2)

RGI
(km2)

Area of this study(km2)

29,87920,64911,005
3,045
1,397
107,340
1,156
3,057
2,902
180,430

100,740
36,70011,005
3,045
1,397
114,330
1,156
3,057
2,902
274,332

104,315
38,135
11,0602,064
1,354
64,524
1,162
2,853
2,933
228,400

114,792
51,904
16,387
2,825
1,595
191,622
1,921
38,201
27,572446,819—74—

Number of this study
76,42031,217
31,217
3,259
2,709
193,191
2,456
46,01827,771
414,258

A GLOBAL SATELLITE-DERIVED GLACIER OUTLINES
spectral similarity with the surrounding terrain, some nonglacier areas at the edge of the image were identified as
glaciers after applying the threshold TM4/TM5 > 3.6(Supplement Figure S2). Also, some forested areas wereidentified as glaciers (Supplement Figure S2b). Hence, anadditional threshold of TM3 (DN > 50) was applied to
improve glacier mapping, exploiting the very highreflectivity of ice and snow in the TM3 band. The errors at
the edge of the image were removed after applying theadditional threshold in TM3 (Supplement Figure S2c). Asa last step of the main processing, a 9 × 9 pixel averagefilter was applied to remove misclassification of pixels andnoise, i.e. to eliminate isolated pixels and fill small gaps inglacier-covered regions (Supplement Figure S3). Finally,outlines of glaciers were obtained as an individual mass ofcontiguous glacier pixels. See Supplement Information S1
for the details.

Topographic information
Satellite-derived glacier outlines combined with the
ASTER GDEM were used to derive topographic parameters
for analyzing the glaciers, which include hypsometry,maximum, minimum and mean elevations. Note that the
ASTER GDEM is the same spatial resolution as Landsat
data (30 m), and also uses the same UTM coordinate system.
However there are still some points that do not match
between the two datasets. Although it is necessary to develop
a mosaic from different scenes from different years, ourstudy aimed to extract glacier outlines globally, and does
not focus on the point or small catchment scale. This makes
matching these points more difficult globally; hence we did
not match them in the edges of imageries. As previous
studies suggested (e.g., Frey and Paul, 2012; Hebeler and
Purves, 2009), although some grid points that may have an
influence on the hypsography of small glaciers are not equal
to each other, local differences in these data have only asmall influence on the hypsography of large glaciers.

RESULTS
In total, 414258 mapped glaciers with a total area of
449482 km2 were identified in the 30 m global glacierdatabase (Supplement Figure S4). Of all the regions defined(Table I), High Mountain Asia contains the largest ice mass
outside of Antarctica and Greenland, representing ~42.6%of the total area of MG&IC, while the area in the Caucasus,
Central Europe and New Zealand is relatively small,
representing less than 1% of the total area.
The distribution of glacier area with elevation in Central
Europe, the Caucasus, the Himalayas, North America andSouth America is depicted in Figure 1. Most of the glacierarea occurs between 4500 m and 6000 m a.s.l. in the
Himalayas, and glaciers in Central Europe and the Caucasus
are distributed mainly in the altitudinal range of 2000 to
4000 m a.s.l. (Figure 1). In North America and SouthAmerica, the elevation of most glaciers is relatively low,
about 1500 m a.s.l., and some ice is also found between
3000 m and 5000 m a.s.l. (Figure 1).
The glaciers of each region are classified into size bins,
and the total area per size bin is determined. Similar toprevious studies (e.g., Raper and Braithwaite, 2005; Radić

Figure 1. Glacier area-elevation distribution in the Caucasus,
Himalayas, Central Europe, North America and SouthAmerica in 100 m elevation binsand Hock, 2010) we assign the upper boundaries for eacharea size bin to be 2n km2 with n ranging from −3 to 15.
This means that the smallest size bin contains glaciers ofless than 0.125 km2 while the largest size bin contains
glaciers between 16384 km2 and 32768 km2. The cumulative
number distribution of MG&IC for each size bin in Central
Europe, the Caucasus, High Mountain Asia, North America
and South America is shown in Figure 2. Our estimates
were larger than those in the WGI-XF (Figure 2). Note that
the WGI-XF does not cover all glaciers in these regions,and its average map year is 1964 with standard deviationof eleven years and a time range from 1901 to 1993 (Cogley,
2010; Radić and Hock, 2010), whereas our glacier databaseis derived from Landsat TM or ETM+ images acquired
during recent decades. Furthermore, our results for small
glaciers are more complete than other glacier inventories(e.g., WGMS, 1989; Cogley, 2010). In accordance with ourestimation, small glaciers with area ≤ 1 km2 in the Central
Europe, Caucasus, High Mountain Asia, North America andSouth America account for 69% of the total number of
MG&IC (Figure 2). Especially in High Mountain Asia small
glaciers account for ~44% of the total number (Figure 2e).

DISCUSSION
Error assessment
The accuracy of the mapped glacier outlines is an
important but difficult topic in glacier mapping from satellite
data (Raup et al., 2007; Racoviteanu et al., 2010), especiallyin regions with shadow, clouds, seasonal snow, proglaciallakes and debris cover. A comparison of glacier area fromdifferent databases indicated that the total area of MG&IC
is fairly similar to previous estimates with the exception of
Scandinavia and North and East Asia (Table I). Althoughimages were selected with acquisition dates prior to theonset of heavy snowfall (approximately October 1 in the
Northern Hemisphere and April 1 in the Southern
Hemisphere) as late in the melt season as possible, only afew satellite images are acquired in May or June in—75—

K. NAKANO ET AL.

Figure 2. Cumulative number of glaciers for each size bin in (a) North America, (b) South America, (c) Central Europe,(d) the Caucasus, and (e) High Mountain Asia. Blue and red colours denote our glacier database and the WGI-XF inventory
Scandinavia and North and East Asia. Consequently,
seasonal snow may be identified as glacier pixels, resulting
in overestimation in these regions. The relatively simple
assumption of the onset of snowfall may lead to theoverestimation of glacier area where snow season is out of
the assumed periods, such as in the low latitude regions of
the Southern Hemisphere where dry season is from May toAugust (Sicart et al., 2011). In addition, the area of debriscovered glaciers was underestimated in the western
Himalaya and Caucasus where debris-covered areas arelarge, and are excluded from glacier area by this method.
Although several methods for debris-cover mapping havebeen applied to different debris-covered glaciers (e.g.,
Bishop et al., 2000; Paul et al., 2004a; Racoviteanu et al.,2010), there is no single best method for debris-cover
mapping that can be applied to large regions without somemanual corrections of the resulting outlines. Combinationof our method and state-of-the-art techniques to extract
spatial distribution of debris using satellite information (e.g.,
Zhang et al., 2011) may improve our inventory over debriscovered glaciers.
The method used in this study is efficient in delineating
clean ice in a timely manner, but might not work properly
for ice in shadow. Ice in shadow needs to be delineatedmanually or using algorithms customized and tested for suchcases (Racoviteanu et al., 2010). In addition, we applied a9 × 9 pixel average filter to remove misclassification of
pixels and noise, i.e. to eliminate isolated pixels and fillsmall gaps in glacier-covered regions, with the consequencethat this process maybe lead to an underestimate of the areaof small glaciers.

Area-altitude distribution for MG&IC—76—

The altitude distribution as a function of area for glaciersA GLOBAL SATELLITE-DERIVED GLACIER OUTLINES
Table II. Average correlations of the glacier area-altitude distribution between this study and the models assumed shaped
distributions for each glacier.
Central Europe

Caucasus

Himalayas

North America

South America

0.64
0.76

0.65
0.76

0.63
0.75

0.40
0.46

0.30
0.38

1/3 shifted normal distribution
Triangle distribution

has been modelled in previous studies to estimate glaciermass balance according to altitude (e.g., Raper et al., 2000;
Paul et al., 2004b; Raper and Braithwaite, 2006; Hirabayashi
et al., 2010). Because of limited hypsometric data for
glaciers, these studies assumed relatively simple shapes toestimate the area-altitude distribution of glaciers. Raper et al.
(2000) and Raper and Braithwaite (2006) assumed a linearly
increasing function from the terminus to mean altitude anda linearly decreasing function above that altitude toapproximate the area-altitude distribution (hereafter called
the Triangle distribution). Hirabayashi et al. (2010) assumed
that the altitude distribution of a glacier has a normal
distribution curve that is shifted by one third of the range
between the maximum and minimum altitudes. We evaluatedthe existing area-altitude models using our glacier databaseby calculating correlations for our estimate and the two areaaltitude assumptions (the Triangle distribution and 1/3
shifted normal distribution) for each 50 m altitude interval(Supplement Figure S5). The altitude distribution was
obtained for the entirety of each glacier. The averagecorrelations between the altitude distribution of glaciers andeach assumed shape distribution in Central Europe, theCaucasus, the Himalayas, North America and South America
shows that correlations are low in North America and SouthAmerica (Table II). Overall, there is a better match with the
Triangle distribution than with the 1/3 shifted normal
distribution in these regions (Table II).

CONCLUSIONS
A 30 m global outline of MG&IC was derived fromLandsat TM and ETM+ images, which are available for
immediate use. Our database contains records for over
414258 glaciers with a total area of 449482 km2. Although
there are errors due to limitations in satellite images andapplied methods in some regions such as Scandinavia, North
and East Asia, and the western Himalayas and Caucasus,
our database matches well with previous satellite-derived
estimates at regional scales, which is the appropriate scaleneeded for monitoring future fluctuations in MG&IC.
Evaluation of published area-altitude models showed that
the Triangle distribution resembles those obtained from ourdatabase in the Alps, the Caucasus, and the Himalayas. In
particular, the developed 30 m glacier outlines can easily
connect with other gridded data such as temperature or
precipitation data to aid in the development of land surface
modelling. We expect that our glacier outlines, which canbe updated from recent satellite record in a semi-automaticway, can be used for a wide range of scientific purposes.

ACKNOWLEDGEMENTS
The research was supported by funding programs fornext-generation world-leading researchers by the Japan
Society for the Promotion of Science (JSPS) and Core
Research for Evolutional Science and Technology (CREST)
project of the Japan Science and Technology Agency (JST).

SUPPLEMENTS
Detail explanation of a method to extract glacier outline
from Landsat TM/ETM+ images (Supplement Information
S1, Supplement Figure S1, S2 and S3), spatial distributionof derived outlines of MG&IC (Supplement Figure S4) andan example of the area-altitudinal distribution for the glacier(Supplement Figure S5) are given in the supplementarymaterials.

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