Glacier mapping from multi-temporal optical remote sensing data within the Brahmaputra river
basin
R. Frauenfelder a, b, A. Kääb b *
b
a
Presently at: Norwegian Geotechnical Institute, Pb. 3930 Ullevaal Stadion, 0806 Oslo, Norway – rf@ngi.no
Department of Geosciences, University of Oslo, PO Box 1047 Blindern, 0316 Oslo, Norway – andreas.kaab@geo.uio.no
Abstract –
Glacier distribution and glacier changes were investigated in
the Upper Brahmaputra River Basin using remote sensing and
GIS in order to determine the influence of melting glaciers on
the long-term runoff of the Brahmaputra River (EU-project
BRAHMATINN). Current glacier distribution and glacier
changes since the 1970/80s were mapped using multi-temporal
optical remote sensing data from the Landsat series and the
Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER). Repeat glacier outlines were then
combined with the digital elevation model from the Shuttle
Radar Topography Mission (SRTM) and analyzed within a
GIS in order to assess spatio-temporal gradients in change in
glacier area. Our findings yield total glacier area losses
between -7 to -13% per decade in the Upper Brahmaputra
River basin (UBRB) for the period from 1970/80 to 2000;
c. 175 km3 w.e. glacier mass was lost, which equals
c. 7 km3 w.e. glacier mass loss per year. This corresponds to a
yearly average mass balance of more than -0.3 m w.e.
1.2 Upper Brahmaputra River Basin (UBRB)
The Brahmaputra River Basin (Figure 1) is the biggest transHimalayan river basin (A = 651’334 km2; L = 2’896 km),
encompassing parts of the territory, ecosystems, people,
economies and politics of China, Bhutan, Nepal, India and
Bangladesh. The Upper Brahmaputra River Basin (UBRB) in this
study is defined upstream of the town Guwahati in NE-India and
drains about 500’000 km2. The geography of the UBRB is
characterized by the alpine mountain system of the Himalaya and
the tropical Monsoon climate. The hydrology of the UBRB is
driven by the Monsoon cycle and has a positive annual water
balance with runoff regimes ranging from glacial-nival to pluvial.
http://www.brahmatwinn. uni-jena.de/
Keywords: glacier distribution, glacier change, Landsat, SRTM,
GIS, Brahmaputra
1. INTRODUCTION AND PRESENTATION OF STUDY
REGION
1.1 Motivation and aims
Alpine mountains are a key factor for the overall hydrological
regime, and major rivers in the world, such as the Brahmaputra in
Asia have their headwaters in alpine mountains. In humid parts of
the world, mountains provide 30% to 60% of downstream
freshwater, and in semi-arid and arid environments, this figure
adds up to 70% to 95%, in particular in the dry season when
glaciers provide large volumes of melt water. Glacier retreat and
permafrost thaw in high mountains have presently reached an
extent and speed that are without historical precedence. Glaciers in
the southern and central parts of the Himalayas are expected to be
especially sensitive to present atmospheric warming due to their
summer-accumulation type. This is likely to have substantial
impacts on the hydrological dynamics, resulting in a greater
variability in precipitation and stream flows, increasing intensity
of extreme events comprising water quantity and water quality.
The overall objective of the BRAHMATWINN** project is,
therefore, to enhance and improve capacity to carry out a
harmonized integrated water resources management (IWRM) for
the Brahmaputra River Basin.
Figure 1. Catchment area of the Brahmaputra river. Source:
www.earthtrends.wri.org
2. METHODS
In our study, current glacier distribution and glacier changes since
the 1960s are mapped using multi-temporal optical remote sensing
data from the Landsat series, the Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER) and CORONA.
Repeat glacier outlines are then combined with the Digital
Elevation Model (DEM) from the Shuttle Radar Topography
Mission (SRTM) and analyzed within a GIS in order to assess
spatio-temporal gradients in glacier tongue retreat and change in
glacier area. In this contribution we mainly focus on our findings
based on Landsat ETM+ data from around the year 2000 and
SRTM data. As a reference for the derived 2000 glacier data
serves the Chinese Glacier Inventory (CGI), which is based on
airphotos from the 1970s to 1980s in our study areas. In addition,
the analyses presented here focus on two detail areas. One was of
special interest for the overarching BRAHMATWINN project
* Corresponding author: Andreas Kääb, andreas.kaab@geo.uio.no
** The BrahmaTWinn project is funded through the Sixth Framework Programme of the European Commission (FP6), SUSTDEV-20053.II.3.6, contract no. 036592 (GOCE).
(Lhasa river basin), the other was chosen to test the
representativeness of the findings in the latter area (NW
Himalaya). The results are compared to a third study area
(Northern Buthan) which is one of the BRAHMATWINN sites,
but on which we do not report here in detail.
Glaciers are automatically mapped using ratios between Landsat
Thematic Mapper (TM and ETM+) near and short-wave infrared
channels, following an approach presented in detail by Paul et al.
(2002) and Kääb et al. (2002). Hereby, the different spectral
properties of glacier ice in the channels TM4 (near infrared) and
TM5 (short-wave infrared) is exploited (Figure 2, a–d). First, ratio
images from a channel division TM4/TM5 are computed (a). From
these ratio images, binary glacier masks (black = “glacier’’, white
= “other’’) are created by interactive thresholding (b). In the dry
northern slopes of the Himalayas, the threshold value for the ratio
image turns out to be c. 2.5.
The glacier maps are then filtered with a 3x3 median filter in order
to reduce noise (c) and the resulting raster areas are finally
converted into vector data (d, e). This vector data consists
basically of large contiguous ice masses which have to be divided
into individual glaciers prior to data retrieval. This is done by
intersecting this data with a vector layer of glacier basins. The
glacier basin layer is digitized manually (on-screen digitizing)
using the Landsat ETM+ as base data to discern the glacier basins
and the ice divides. Wherever possible, the CGI data was also used
as reference. Visual inspection of the glaciers and associated
glacier basins in GoogleEarth was used as an additional
information source. The intersection of the ice mass vector data
with the glacier basin layer yields individual glacier outlines,
again in vector format (f).
The final outlines of individual glaciers were compared to the
glacier outlines from the CGI and any suspect disagreements were
eliminated from the sub-set used for change analysis. Reasons for
such disagreements were, for instance, obvious geo-referencing or
digitizing errors in the CGI.
The accuracy of the method is high for snow covered and clean
glacier ice outside shadow areas. Misclassification typically
occurs for wetlands, lakes and rivers erroneously classified as ice,
and missing classification on middle- and side-moraines. These
errors can easily be eliminated by post-processing (i.e. manual
exclusion/inclusion of wrongly classified/missing areas). More
problematic are wrong classifications in cast shadow (induced by
the steep relief) and on debris-covered glaciers. Also these errors
have to be corrected manually during post-processing, but need a
high degree of glaciological expert knowledge.
3. RESULTS AND DISCUSSION
Figures 3 and 4 show the location of the two study areas, example
subsets of produced glacier outlines and the size distribution of
glaciers found in these areas. Tables A–D show the glacier
changes (in area and volume) found for both study areas. In the
NW-Himalayas the airphotos that form the base of the Chinese
Glacier Inventory (CGI) stem from 1980, the airphotos for the
Lhasa area are from 1970.
Figure 2. Glacier segmentation with Landsat Thematic Mapper
(TM and ETM+) imagery in the western Himalayas: (a) ratio
image from a division of channel TM4 and TM5, (b) glacier mask
(black = “glacier’’, white = “other’’) created from the ratio image
by interactive thresholding, (c) filtered (3x3 median filter) glacier
map, (d) resulting glacier areas, resp. ice mass areas; (e & f)
zoom-in of (d): (e) glacier areas (hatched) before intersection with
glacier basin layer and post-processing, (f) glacier areas (hatched)
after intersection and post-processing (see text for further
explanations).
We found significant differences in area change between the two
regions studied (cf. Figure 3 & 4; Tables A & B). These
differences can partly be attributed to differences in glacier size
distribution in the different study areas, different climatic
conditions (from the north slope of the Himalaya that is influenced
by Monsoon to the dry-continental plateau areas of Tibet), and
different glacier hypsography distributions. Especially the
significantly different glacier size distribution characteristics are
important for the up-scaling of our results.
Figure 5 shows percentual decadal glacier area changes in three
study areas investigated within the BRAHMATWINN project. In
the NW Himalayas (area 1) percentual decadal glacier area change
amounted to c. -8%/10 yr, in the SW Nyainqentanglha range near
Lhasa (area 2) the value is c. -7%/10 yr. In the area of Northern
Buthan (area 3, not presented here in detail) percentual decadal
glacier area change was -9 to -13%/10 yr for the period 1976–
2000.
Table B. %-Glacier change 1970–2000 per glacier size class in
the SW Nyainqentanglha mountain range (see also Figure 4).
*)
Note that in this study area, glacier size class no. 7 contains only
1 glacier.
Glacier size class
1 (< 0.1 km2)
2 (> 0.1 km2 to < 0.5 km2)
3 (> 0.5 km2 to < 1.0 km2)
4 (> 1.0 km2 to < 5.0 km2)
5 (> 5.0 km2 to < 10.0 km2)
6 (> 10.0 km2 to < 20.0 km2)
7 (> 20.0 km2) *)
Area change
1970–2000
(%)
-63.1
-41.1
-30.5
-18.5
-11.2
-5.8
+1.9
Figure 3. Satellite-derived glacier area delineation, exemplified
for a site within the study area in the NW Himalayas (area 1).
Symbology: yellow lines = glacier outlines derived by using ratios
between TM4 (near infrared) and TM5 (short-wave infrared)
channels; green lines = Chinese Glacier Inventory (CGI) from c.
1980. The pie chart visualizes the number of glaciers in this study
area per size class.
Table A. Glacier change in % over 1980–2000 per glacier size
class in the NW Himalayas (see also Figure 3).
Glacier size class
1 (< 0.1 km2)
2 (> 0.1 km2 to < 0.5 km2)
3 (> 0.5 km2 to < 1.0 km2)
4 (> 1.0 km2 to < 5.0 km2)
5 (> 5.0 km2 to < 10.0 km2)
6 (> 10.0 km2 to < 20.0 km2)
7 (> 20.0 km2)
Area change
1980–2000
(%)
-68.5
-45.1
-30.9
-22.7
-14.8
-16.7
-10.0
Figure 5. Mean glacier area changes in the two study areas
reported here (1 = NW Himalayas, 2 = SW Nyainqentanglha) and
in Northern Buthan (= 3; part of the BRAHMATWINN project
but not reported in detail here) shown as decadal percentual
changes.
Our results are in good agreement with numbers reported from
other studies: In Pumqu (Tibet), mean glacier area changes
amounted to -8%/10yr for the period 1970-2001 (Jin et al. 2005).
S. Donghui reports glacier area changes of up to -3.5 (-2.5 to 4.5)%/10yr for the Tibetean plateau (personal communication,
2006). Karma et al. (2003) studied glacier changes in Buthan and
found mean glacier area changes in the order of -9%/10yr for the
period 1963-1993.
Glacier volume cannot be measured directly from space. We use
two different empirical relations between glacier area and mean
glacier thickness to estimate glacier volumes. The large
discrepancy between both methods underlines that these
approaches for glacier volume estimation can only yield rough
first-order estimates. We estimate the according ice volume loss to
be on the order of 20%, or roughly -0.3 m water equivalent per
year as average over both study areas and both volume estimate
methods (Table D).
Figure 4. Satellite-derived glacier area delineation, exemplified
for a site within the study area in the SW Nyainqentanglha
mountain range, northwest of Lhasa (area 2). Symbology: yellow
lines = glacier outlines derived by using ratios between TM4 (near
infrared) and TM5 (short-wave infrared) channels; green lines =
Chinese Glacier Inventory (CGI) from c. 1970. The pie chart
visualizes the number of glaciers in this study area per size class.
Using the CGI and our inventory results we upscale the above
glacier change to the entire UBRB and estimate it to be on the
order of 15% area loss per decade since the 1970s, or, in other
words, 7 km3 w.e. per year (0.015 mm a-1 sea level equivalent).
Problems encountered during our analyses are largely connected
to uncertainties of the Chinese Glacier Inventory (CGI). The CGI
dataset is an invaluable source for comparison, yet it has some
deficiencies which complicate change detection analyses. The CGI
data were produced from published maps, dating from 1981
through 2002 (personal communication B. Raup, GLIMS). The
airphotos used for producing these maps are from 1970 and 1980
for our two study areas. Little is known, however, about the used
methodology to digitize the original map data, about the date when
the glacier outlines contained in the maps were mapped, and about
the datasets’ general accuracy. Errors found during our analyses
were: (a) local distortions in geo-referencing, and (b) missing
glaciers. The distortions manifested themselves mainly in two
ways: either as differences in geospatial accuracy on different
sides of mountain ranges, or as “shifting” geospatial accuracies
within a given region. In addition, the watershed delineation in the
CGI is quite coarse, which caused problems for the delineation of
the individual glacier basins.
Our findings show that the Upper Brahmaputra river basin has lost
roughly 20% of its water reserves bound in glaciers during the last
20–30 years. While such glacier melt leads to increased dryseason runoff on the short term, the long-term consequences of
decreasing ice resources could be a decline of river runoff
contribution from glaciers in the dry season. Modelling of and
adaptation strategies to these changes in the physical environment
and their socio-economic consequences is the purpose of the
BRAHMATWINN project.
Table C. Absolute change in glacier area and glacier volume
1970/80–2000 in the NW Himalayas (area 1) and the SW
Nyainqentanglha mountain range (“Lhasa”; area 2). Methods for
volume estimation: 1) Maisch 1992, 2) Driedger & Kennard 1986.
Jin, R., X. Li, T. Che, L. Wu, and P. Mool, “Glacier area changes
in the Pumqu river basin, Tibetan Plateau, between the 1970s and
2001”, Journal of Glaciology, vol. 51, no. 175: p.p. 607–610,
2005.
Area 1
Area 2
Area 1
Area 2
Glacier
area
1970/80
Glacier
area
2000
(km2)
449.4
535.3
(km2)
372.6
429.5
Glacier
volume
1970/80
Glacier
volume
2000
(km3)
12.91)/27.72)
13.0/19.6
(km3)
10.6/22.4
10.4/15.6
Area
change
1970/80–
2000
(km2)
-76.7
-105.8
Volume
change
1970/80–
2000
(km3)
-2.3/-5.3
-2.6/-4.0
Area
change
1970/80–
2000
(%)
-17.1
-19.8
Volume
change
1970/80–
2000
(%)
-17.6/-19.2
-20.0/-20.3
Table D. Total and yearly mean net glacier mass balance
1970/80–2000 in the NW Himalayas (area 1) and the SW
Nyainqentanglha mountain range (“Lhasa”; area 2) as derived
from the volume change estimates shown in Table C. Methods for
volume estimation: 1) Maisch 1992, 2) Driedger & Kennard 1986.
Area 1
Area 2
Net mass balance1)
mm w.e.
1970/80–
per year
2000
-5.49
-0.27
-5.40
-0.18
Net mass balance2)
mm w.e.
1970/80–
per year
2000
-12.97
-0.64
-8.25
-0.27
REFERENCES
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Hochstand von 1850 im Gebiet der östlichen Schweizer Alpen”,
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Brahmatwinn project – Twinning European and South Asian River
Basins to enhance capacity and implement adaptive management
approaches. http://www.brahmatwinn.uni-jena.de/
Chinese Glacier Inventory. http://wdcdgg.westgis.ac.cn/DATA
BASE/Glacier/glacier_inventory.asp
World Glacier Inventory. http://nsidc.org/data/docs/noaa/
g01130_glacier_inventory/
Watersheds of the world: Asia and Oceania – Brahmaputra
watershed. www.earthtrends.wri.org
4.
CONCLUSIONS
Extrapolation of our findings to the entire UBRB yield total
glacier area losses from 1970/80 to 2000 between -7 to -13% per
decade. In the period 1970/80–2000, c. 175 km3 w.e. glacier mass
was lost in the UBRB, which equals c. 7 km3 w.e. glacier mass
loss per year. This corresponds to a yearly average mass balance
of more than -0.3 m w.e. which would have contributed c. 0.4 mm
to sea level rise since 1970/80 (0.015 mm a-1).
ACKNOWLEDGEMENTS
Landsat data from United States Geological Survey (USGS),
orthorectification: Geodata Solutions, Jena. Chinese Glacier
Inventory (CGI) from Institute of Tibetan Plateau Research
through Global Land Ice Measurements from Space database
(GLIMS).