int. j. remote sensing, 2002, vol. 23, no. 4, 787–799
Changes in glacier area in Tyrol, Austria, between 1969 and 1992
derived from Landsat 5 Thematic Mapper and Austrian Glacier
Inventory data
F. PAUL
Department of Geography, University of Zurich–Irchel, Winterthurer Str. 190,
CH-8057, Zurich, Switzerland; e-mail: fpaul@geo.unizh.ch
(Received 20 December 1999; in Ž nal form 12 December 2000)
Abstract. Results are presented from the use of Landsat Thematic Mapper (TM)
imagery and data from the Austrian Glacier Inventory for area change detection
of a large number of glaciers. A trend analysis of glacier area between 1969, 1985
and 1992 was carried out for 235 glaciers in Tyrol, Austria. Ratio images from
TM channels 4 and 5 are thresholded to obtain a glacier mask as revealed by
analysis of the data for six sub-regions (Ötztal, Pitztal, Gurgl, Stubai North,
Stubai South and Zillertal). Glaciers with areas less than 1 km2 shrank signiŽ cantly between 1969 and 1992 (about ­ 35%). The total loss in area in this period
is about 43 km2 or ­ 18.6% of the area in 1969 (230.5 km2). Glaciers smaller than
1 km2 contribute 59% (25 km2) to the total loss although they covered only
one-third of the area in 1969.
1.
Introduction
Retreat of glaciers in the Alps since their last Holocene maximum extent (around
1850) is one of the clearest signals of recent global warming. While the large ice
sheets Antarctica and Greenland mainly have an active in uence on climate, mountain glaciers passively react to climatic changes. They are linked to the atmosphere
through mass and energy exchange which determine accumulation (gain of mass)
and ablation ( loss of mass) throughout the year. The mass balance taken at the end
of the ablation season is the immediate reaction to atmospheric conditions during a
year, whereas the change in length or area is the delayed reaction to a change in
local climate. Thus, glaciers integrate atmospheric conditions over some years and
are able to convert a gain of snow of a few meters in depth to a change in length of
a 100 m and more. Therefore, glaciers are considered as sensitive climatic indicators
(Haeberli 1995).
This is conŽ rmed in diVerent studies revealing signiŽ cant correlations between
temperature and precipitation over the year, and the mass balance of a glacier (e.g.
Kuhn 1981, Günther and Widlewski 1986). Field observations and numerical model
studies make it possible to determine the corresponding energy  uxes at the glacier
surface, permitting a direct comparison with natural or anthropogenic greenhouse
forcing (Oerlemanns 1994, Haeberli 1995).
Since 1972 the radiometer Multi-Spectral Scanner (MSS) and since 1984 the
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online © 2002 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/01431160110070708
788
F. Paul
radiometer Thematic Mapper (TM) have been acquiring calibrated data with high
spatial resolution (56 m×79 m and 30 m×30 m, respectively), achieving almost global
coverage. Earlier applications of Landsat images for investigation of glaciers used
photographic prints from MSS spectral channels. It was possible to derive the
transient snow line of numerous glaciers from these prints because snow and ice
show diVerent colours in false colour composites (FCCs) (Krimmel and Meier 1975,
Østrem 1975, Rott 1976). Later on, digital image processing techniques were applied
to MSS digital data to outline diVerent glacier facies in FCCs (Williams 1987), or
to classify sub-scenes using a decision tree classiŽ er (Della Ventura et al. 1987).
Some of the glacier studies with TM data used ratios of TM channels 4 and 5
(TM 4/TM 5) to separate glaciers from the surrounding terrain by thresholding the
ratio image or delineating diVerent glacier facies (Williams et al. 1991, Bayr et al.
1994). Problems arise with debris-covered parts of a glacier or some steep glaciers
facing north-west. For selected glaciers, Landsat-derived changes in length and area
show good coincidence with in situ measurements (Hall et al. 1992, Bayr et al. 1994).
Unfortunately, all of the above-mentioned studies apply their algorithms only to a
small number of glaciers and only emphasize the large potential of Landsat MSS
and TM data for glacier studies, especially in remote areas or where Ž eld data
are poor.
In a Ž rst attempt to document general trends in the glaciated area, the available
algorithms were applied to 377 glaciers in the European Alps to derive changes in
area between 1973, 1985 and 1992 (Paul 1995). Although some good results with
MSS data (from 1973) were achieved, comparison with 1985 and 1992 (from TM)
is diYcult because of the diVerent spatial resolution of the sensors and diVerent
glacier mapping methods. Therefore, data from the Austrian Glacier Inventory (AGI)
are used in this survey for comparison with TM data (path 193, row 27) from 30
September 1985 and from 17 September 1992 (cf. Ž gure 1). Based on the physical
properties of ice and snow, thresholding of a TM 4/TM 5 ratio image is used for
glacier mapping and a statistical analysis of glacier changes is carried out.
2.
Physical properties
Since glaciers are the result of the metamorphosis of snow over many years, their
spectral properties are very similar to those of snow, except for areas where the
glacier is covered by debris. The spectral properties of snow were studied in situ
(Grenfell et al. 1981) and modelled numerically (e.g. Warren 1982). The re ection of
fresh snow reaches 95% in the visible part of the spectrum and closely resembles a
Lambertian re ector. According to Hall et al. (1989), the re ection in this spectral
range is virtually independent of grain size, but depends strongly on contamination
by soot and dust. In the near-infrared (between 0.7 and 1.5 mm), however, re ection
decreases strongly with increasing wavelength and the dependence of re ection from
grain size increases. On the other hand, the dependence of re ection on contamination
decreases. Between 1.5 and 2.2 mm, the re ection of snow is very low, whereas it is
high for water clouds and ice clouds. There is only minor dependence of re ection
on contamination, but a strong one on grain size around 1.8 and 2.2 mm (Dozier
1989).
While TM 4 is located in the near-infrared part of the spectrum (0.76–0.90 mm),
TM 5 is situated in the middle-infrared part (1.55–1.75 mm). Thus, re ection of snow
and ice is much higher in TM 4 than in TM 5. Over other terrain (without ice or
snow) re ection is higher in TM 5 than in TM 4, and in regions with cast shadow
Changes in glacier area in T yrol, Austria
789
Figure 1. Orientation map showing location of study site and outline of TM scene (path
193, row 27).
or over clear water bodies re ection is low in both channels. Hence, dividing TM 4
by TM 5 reveals high values over ice and snow but low ones over other terrain,
shadow and clear water bodies in the ratio image. Snow and ice can, thus, be
separated from other terrain with a threshold. As a consequence of the higher
re ection of turbid water in TM 4, pro-glacial lakes are partly included in the glacier
mask after thresholding.
3.
Deriving glacier outlines with TM
An area of about 20 km2 (including the glacier Hochjochferner) in the southern
‘Ötztal Alps’ near the Italian border was selected for the testing of digital image
processing techniques. This region shows many glacial and periglacial features such
as glaciers with debris cover of changing thickness, areas with snow and bare ice
exhibiting varying illumination geometries, steep glacier areas with crevasses, terrain
with vegetation, bare rock, and a small lake. Figure 2 shows the Hochjochferner as
seen from a nearby mountain on 17 August 1991 for comparison with Ž gures 3(a)–( f ),
which show diVerent image processing steps.
In Ž gures 3(a) and (b), TM channels 4 and 5 are shown after histogram equalization. As expected from the spectral properties, glacier ice is light grey (snow is white)
in TM 4, while glacier ice appears nearly black (snow is dark grey) in TM 5. Shaded
areas have low re ection and appear black in both channels. Figure 3(c) gives the
result of the division of TM 4 by TM 5, yielding high and similar grey levels over
ice and snow, and very low grey levels for the surrounding terrain. Thresholding of
this image is shown in Ž gure 3(d), where the grey levels darker or brighter than the
threshold are colour coded. The blue and green pixels were removed (turned to
790
F. Paul
Figure 2. The Hochjochferner in the ‘Ötztaler Alps’ as seen from a nearby mountain
(17 August 1991). The snow-covered areas are white and glacier ice is grey. The
diVerent elevations of the four accumulation basins are also recognizable. The areas
with snow are similar to those in Ž gure 3( f ).
white together with the other, even darker, pixels), the red and yellow pixels are
brighter and were not removed (turned to black). This thresholding is to some degree
subjective, and in the process of removing pixels enclosing only terrain, some mixed
pixels also disappear. However, the errors introduced by this procedure are small,
because the number of terrain pixels assigned to the glacier class is nearly the same
as the number of glacier pixels assigned to the terrain class. Because of spectral
similarity with the surrounding terrain, it is not possible to include the morainecovered parts of a glacier automatically. Hence, these areas were added manually
before comparison with AGI data.
After applying a 3×3 median Ž lter to the black and white glacier mask, isolated
pixels are removed (red in Ž gure 3(e)) and isolated gaps are Ž lled ( blue). If glaciers
are not too small, this produces a better glacier mask by removing snow patches.
The number of pixels added and subtracted to a single glacier by the median Ž lter
lies in a similar range. The delineation of the glacier mask was derived with an edge
extraction Ž lter, performed by the image-processing software (KHOROS 1991). In
Ž gure 3( f ), the boundary of the glacier mask is shown in cyan on a TM 3, 2 and 1
(as RGB) composite image. The snow-covered area is displayed in yellow.
Figure 3. The results of the algorithm for the Hochjochferner derived from the Landsat TM
scene 193-27 acquired on 17 September 1992: (a) and (b) the region in TM 4 and TM
5 after histogram equalization, (c) ratio image of TM 4 and TM 5, (d) the ratio image
after thresholding (see text for colours), (e) the eVect of a 3×3 median Ž lter on the
black and white glacier mask ( blue pixels were added, red pixels were deleted), ( f )
delineation of the glacier (cyan) derived from (e), and snow-covered area (yellow) on
a TM 3, 2 and 1 (as RGB) composite image. Landsat TM data: © ESA.
Changes in glacier area in T yrol, Austria
791
(a)
(b)
(c)
(d)
(e)
(f)
792
F. Paul
4. Results and discussion
4.1. Data handling
A selection of 235 glaciers was used for statistical analysis of trends in glacier
area between 1969 and 1992 using the data from the AGI and the TM scenes
mentioned above. Most of the AGI data were compiled from vertical aerial photographs taken in 1969 and a small part also from oYcial topographic maps with the
state of the glaciers in 1969. The TM scenes were chosen because they are among
the Ž rst and last (maximum time lag in between) available images for the study with
respect to cloud cover and snow conditions. Both scenes were acquired near the end
of the ablation period with about 6 dry and hot weeks during July and August and
3 weeks of continued sunshine before the dates of acquisition. Thus, nearly all
remaining snow Ž elds adjacent to the glaciers had disappeared, one essential condition for glacier mapping. The scenes were subdivided into six regions (Ötztal, Pitztal,
Gurgl, Stubai North, Stubai South and Zillertal ) including the main glacierized
regions.
If two (or more) glaciers are connected within a single basin without a distinct
border, their margin was derived from topographic maps of scale 1:25 000 and laid
over the sub-scene before extracting the single glacier (cf. Ž gure 7). Two glaciers with
doubtful outlines and three glaciers with parts of their area in cast shadow were
rejected from statistical analysis. Furthermore, 30 glaciers smaller than 0.1 km2 in
1969 were excluded. Also 28 glaciers in Italy are not considered, since Italian glacier
data are not part of the AGI. Because of the spectral similarity with the surrounding
terrain automated mapping of debris cover on glaciers is very diYcult. To correct
for that, debris cover was added manually for 15 glaciers. If debris cover was only
one pixel in size and of arbitrary length, the pixels were added to the glacier surface
after median Ž ltering. Two glaciers had to be excluded because of the uncertain limit
of debris-covered ice. If glaciers split up after 1969, they were treated as a single
glacier in the following years.
The glacier masks were derived with the algorithm presented above and single
glaciers were extracted with the scissor function of the public domain software
package XPAINT. The number of ice pixels were counted for each glacier and each
year. Hence, glacier areas (in km2) were calculated by multiplication with the pixel
size of the TM sensor (900 m2). To group the glaciers into four area classes (<1 km2,
1–5 km2, 5–10 km2, >10 km2) an average size of all 3 years is calculated. The changes
between 1969 and 1985, 1985 and 1992, and 1969 and 1992 were derived for each
glacier using the area in the 3 individual years. Furthermore, the eight diVerent
expositions were token from the AGI data. Hence, several statistical analyses of the
data are possible. A summary for all regions is given in table A1 (see Appendix).
4.2. Area change
A comparison of relative changes in glacier area for all area classes, the three
time intervals, and the separation into the six regions is displayed in Ž gure 4. All
regions together in the class smaller than 1 km2 (1–5 km2) endure a loss of area of
about ­ 18.5% (­ 4.7%) between 1969 and 1985 (with respect to the area of 1969),
and of about ­ 20% (­ 7.7%) between 1985 and 1992 (with respect to the area of
1985). The changes in the classes 5–10 km2 and larger than 10 km2 are roughly in
the same range as in the class 1–5 km2. Glaciers larger than 1 km2 double their
relative area decrease between 1985 and 1992 compared with the period 1969–1985.
In total (all regions and all classes), there is nearly the same loss of area between the
Changes in glacier area in T yrol, Austria
793
Figure 4. Relative changes in glacier area (percent) for six regions, separated into area classes
and the time periods 1969–1985, 1985–1992 and 1969–1992, using data from the AGI
(1969) and Landsat TM (1985 and 1992).
794
F. Paul
two smaller time periods (19.8 and 23.0 km2, respectively). For the whole time period
1969–1992 the total loss of area is nearly 43 km2 or –18.6%, with respect to the area
of 1969. The contribution of glaciers smaller than 1 km2 to this loss is about 25 km2
or 59%, although they cover only one-third of the area in 1969.
In Ž gure 5 the dependence of the relative area change between 1985 and 1992
from the glacier area in 1985 is shown. There is a large scatter of relative area
changes (+10 to ­ 98%) for glaciers smaller than 0.5 km2, glaciers with sizes between
0.5 and 3 km2 show a scatter between ­ 10 and ­ 40%, and the relative area changes
of glaciers larger than 3 km2 vary only between –5 and –8%, conŽ rming other studies
over longer time periods (Maisch et al. 1999). Thus, glaciers smaller than 0.5 km2
react very individually, while all glaciers larger than 3 km2 show nearly the same
relative change in area.
The relative area changes versus exposition of the ablation area is depicted in
Ž gure 6. There is only little diVerence when using the exposition of the accumulation
area. The average values show no signiŽ cant dependence of area change with glacier
exposition. Nevertheless, in some regions there is a larger loss of area for glaciers
facing east to south-west. This may not be representative, however, because of the
small numbers of south and south-west facing glaciers (cf. table A1). Inclusion of the
Italian glaciers may be helpful to conŽ rm a trend.
On the basis of particular points, the relative displacement of both scenes was
evaluated and the glacier masks were shifted according to this values. As an example,
glacier shrinkage is illustrated in Ž gure 7, which shows a portion of the sub-scene
Ötztal with area changes between 1985 and 1992 in red and green (loss and gain of
area, respectively), the glacier outline (cyan) and the snow-covered area (yellow).
Table A2 (see Appendix) presents some additional data for the numbered glaciers.
The isolated red areas (mostly snow patches) persisted in 1985 but not in 1992. The
Figure 5. Relative changes in glacier area between 1985 and 1992 against glacier size in 1985
for the six sub-regions.
Changes in glacier area in T yrol, Austria
795
Figure 6. Relative changes in glacier area versus exposition of the ablation area for the six
sub-regions and three time intervals: A=1969–1985, B=1985–1992 and C=1969–1992.
red area to the left of glacier 22 (a part of the Niederjochferner) received special
attention in 1991, when ‘Ötzi’, the Stone Age man, was discovered there.
4.3. Error discussion
The following points have to be considered if TM-derived glacier areas are
compared with the data from the AGI: debris cover, line of division of connected
glaciers, glaciers in cast shadow, snow Ž elds adjacent to the glacier, and the diVerent
map scale for creation of the area data in the AGI and with TM. The largest
manually added debris cover reaches 20% of the entire glacier area. If the outer side
of this debris cover outline is wrong, an area error of 3% results. The outline
precision is estimated by shifting the relatively longest division line between the two
smallest glaciers by one pixel. In this worst case the error in the derived glacier areas
reaches 4%. The Hochjochferner and the Stockferner were rejected because it was
not possible to decide where the divide between individual glacier parts were located.
Three glaciers were discarded because they show an increase in area only in zones
with cast shadow. Obvious snowŽ elds adjacent to a glacier were omitted during the
extraction process. Since snow conditions in 1985 are slightly worse than 1969 or
1992, it is possible that snowŽ elds at the highest parts of a glacier may have been
assigned to the glacier in 1985 but not in 1969 or 1992. Thus, real loss of area might
be somewhat larger between 1969 and 1985 and somewhat smaller between 1985
and 1992.
Because data in the AGI were compiled from aerial photographs with about a
10 times better spatial resolution than TM, an increase in area of about 5% may
result for a glacier with a size of 0.1 km2 compared to the AGI data, inferred by the
change of scale. This behaviour is common for natural surfaces with a fractal
dimension. The diVerence will strongly decrease with increasing glacier size.
Moreover, on average the median Ž lter will decrease the size of glaciers smaller than
796
F. Paul
Figure 7. Glacier changes in a small part of the southern ‘Ötztal Alps’ between 1985 and
1992. The image is the result of combining the digital glacier masks from both years
and adding the diVerences to the TM image acquired on 17 September 1992 ( bands
3, 2 and 1 as RGB). Loss of area is shown in red and gain of area in green. The snowcovered area (yellow) and the glacier margin from 1992 (cyan) is also shown. See table
A2 for the glacier numbers. Landsat TM data: © ESA.
0.1 km2 up to 50%. Hence, 30 glaciers with a size of less than 0.1 km2 in 1969 were
rejected. The expositions and counts of the 28 omitted Italian glaciers are distributed
as follows: N: 2, E: 5, SE: 11, S: 6, SW: 4. Thus, they would greatly increase the
number of glaciers in the south sector.
The absolute accuracy of TM-derived areas is diYcult to estimate since similar data
for 1985 or 1992 were not available. However, a comparison with a manually derived
Changes in glacier area in T yrol, Austria
797
outline on a SPOT Pan image of the Gries Glacier, Switzerland, reveals that absolute
accuracy obtained with TM is within 1% if debris cover is added manually to the
glacier surface. Totally, the accuracy increases with glacier size and may vary between
5% for the smallest glaciers and 1% for the largest.
5.
Conclusion and perspectives
The ratio between TM channels 4 and 5 was thresholded to produce a glacier
mask. The algorithm has been discussed in previous studies, but always applied only
to a small number of glaciers. Here, it was used for 235 glaciers in the Tyrolean Alps
to calculate their areas in 1985 and 1992 which were then compared with the data
from the Austrian Glacier Inventory of 1969.
The present study supports other Ž ndings (e.g. Böhm 1993) that small Alpine
glaciers have been generally retreating between 1969 and 1992, especially between
1985 and 1992. In this study it was found that the period of glacier advance in the
European Alps between 1965 and 1985 was accompanied by a strong shrinkage
(­ 18.5%) of 190 glaciers smaller than 1 km2. Obviously, the distribution in size of
the glaciers with annually repeated length measurements is not representative to
describe the behaviour of glaciers smaller than 1 km2 (Hoelzle et al. 1999). A total
of 28 glaciers showed a gain of area between 1969 and 1985. Only six of them belong
to the Austrian length measurement network and all of them showed an advance in
this period. The massive retreat of glaciers since 1985 was conŽ rmed with the
Landsat-derived area changes.
To conclude, more Landsat TM data should be processed to derive global glacier
data and the related changes in recent decades. Apart from the manual delineation
of debris cover this is a relatively easy task for a large number of glaciers and
especially suited for application in remote areas. It will provide a better statistical
basis for further analysis and applications such as hydrological modelling (Haefner
et al. 1997). Also, the importance of glaciers as reservoirs of drinking water, for
irrigation, and for energy production purposes underlines the urgency of such a
study. It is hoped that the successful start of Landsat 7 ETM+ and the sensor
ASTER on-board the platform Terra will surpass the Ž nancial limitations of
previous studies.
Attempts are presently being made within the GLIMS programme (Global Land
Ice Measurements from Space) to overcome the diYculties presented in this study
and to establish a global land-ice inventory from space. The preparation of the
related Swiss Glacier Inventory for the year 2000 will focus especially on fusion
techniques, including satellite imagery of diVerent spatial and spectral resolution,
aerial photography, and integration of digital elevation models for retrieval of glacier
parameters. The corresponding management and analysis of the glacier data derived
will be GIS-based for such a large dataset.
Acknowledgements
I gratefully acknowledge the anonymous reviewers for their helpful comments.
Moreover, I would like to thank Dr Stephan Bakan, Max-Planck-Institut for
Meteorology in Hamburg, for reading the manuscript and for his support in obtaining
the Landsat TM data used in this study. Thanks are also extended to Prof. Dr
Hartmut Grassl, former head of the World Climate Research Program (WCRP) in
Geneva, for his inspiration to carry out a satellite-derived glacier survey, and to
Dr Andreas Kääb, University of Zurich, for many useful comments.
F. Paul
798
Appendix
Table A1. Summary of the data for the six regions, the eight expositions and the four area
classes. For each set the number of glaciers, area, and relative change in area is given.
Area (km2)
Relative change in area (%)
Count
1969
1985
1992
69–85
26
54
43
37
45
30
43.35
44.01
45.94
24.58
33.16
39.42
41.49
40.70
42.80
21.42
28.73
35.90
37.50
35.16
38.15
19.15
25.06
33.15
­
Exposition (ablation area)
N
52
NE
56
E
29
SE
31
S
5
SW
2
W
26
NW
34
81.44
43.08
15.97
22.46
2.80
1.70
13.79
49.22
75.46
38.61
14.83
20.62
2.25
1.54
11.45
46.29
68.92
33.47
12.55
17.23
1.85
1.41
9.94
42.79
Area class
0–1 km2
1–5 km2
5–10 km2
>10 km2
190
38
5
2
72.24
96.40
39.71
22.11
58.86
92.08
38.91
21.20
47.07
85.05
36.50
19.56
All
235
230.46
211.05
188.18
Region
Ötztal
Pitztal
Gurgl
Stubai N
Stubai S
Zillertal
85–92
­
4.3
7.5
­ 6.8
­ 12.9
­ 13.4
­ 8.9
­
­
­
­
­
­
­
­
18.5
4.5
­ 2.0
­ 4.1
­
­
8.4
13.5
20.1
17.0
22.1
24.4
15.9
­
­
­
­
­
­
8.7
13.3
­ 15.4
­ 16.4
­ 17.8
­ 8.4
­ 13.2
­ 7.6
­
­
20.0
7.6
­ 6.2
­ 7.8
­
10.8
­
7.4
10.4
7.1
8.2
19.6
9.5
17.0
6.0
­
­
9.6
13.6
­ 10.9
­ 10.6
­ 12.8
­ 7.6
­
­
69–92
­
15.4
22.0
21.4
23.3
33.9
17.1
27.9
13.1
­
­
­
­
­
­
­
34.8
11.8
­ 8.1
­ 11.6
­
­
­
18.3
Table A2. Area, relative change in area and exposition for the numbered glaciers in Ž gure 6.
In the Exposition column ‘acc’ and ‘abl’ stand for exposition of the accumulation and
ablation area, respectively. WGI=World Glacier Inventory.
Relative change in
area (%)
Area ( km2)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
WGI code
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
4J143
OE 134
OE 133
OE 132
OE 131
OE 130
OE 129
OE 128
OE 127
OE 126
OE 125
OE 124
OE 123
OE 122
OE 120
OE 119
OE 117
OE 116
Name of glacier
Plattei
Vernagt
Guslar Gr.
Guslar Mi.
Guslar Kl.
Kesselwand
Hintereisw.
Vernaglwand N
Vernaglwand S
Hintereis
Rofenberg W
Rofenberg E
Latsch
Kreuz S
Kreuz M
Kreuz N1
Eis
1969 1985 1992
0.27
9.56
1.76
1.04
0.19
4.28
0.56
0.42
0.64
9.47
0.55
0.23
0.25
0.84
0.68
0.42
1.25
0.20
9.52
1.69
0.96
0.18
4.33
0.61
0.44
0.52
9.14
0.49
0.20
0.14
0.78
0.63
0.44
1.17
0.11
8.80
1.47
0.76
0.11
4.10
0.51
0.35
0.46
8.51
0.42
0.14
0.07
0.69
0.55
0.35
1.05
69–85
­
­
­
­
­
26.0
­ 0.4
­ 3.9
­ 7.6
­ 7.6
1.2
8.8
5.4
18.1
­ 3.5
10.8
12.1
42.8
­ 7.3
­ 7.7
3.9
­ 6.5
Exposition
69–92
acc abl
42.8
­ 7.6
­ 13.0
­ 21.3
­ 36.4
­ 5.4
­ 16.0
­ 20.9
­ 11.7
­ 6.9
­ 13.8
­ 29.8
­ 53.5
­ 11.4
­ 12.9
­ 18.8
­ 9.8
­ 57.7
­ 8.0
­ 16.4
­ 27.3
­ 41.2
­ 4.2
­ 8.5
­ 16.7
­ 27.7
­ 10.1
­ 23.1
­ 38.3
­ 73.4
­ 17.9
­ 19.6
­ 15.6
­ 15.6
SE
S
E
NE
N
SE
SE
E
SE
E
NW
N
SE
NW
NW
N
N
­
85–92
SE
SE
SE
NE
N
E
SE
SE
SE
NE
NW
N
SE
NW
NW
NW
N
Changes in glacier area in T yrol, Austria
18
19
20
21
22
4J143
4J143
4J143
4J143
4J143
OE 115
OE 114
OE 113
OE 112
OE 111
Rotkar
Noe. Say
Say
Sue. Say
Niederjoch
All
0.47
0.14
0.39
0.13
2.92
36.46
0.44
0.07
0.40
0.09
2.66
35.10
0.31
0.02
0.36
0.05
2.38
31.58
­ 7.1
49.4
2.3
­ 31.9
­ 8.8
­ 3.7
­
­
29.1
75.9
­ 9.5
­ 44.9
­ 10.6
­ 9.3
­
799
­ 34.1
­ 87.8
­ 7.4
­ 62.5
­ 18.5
­ 13.4
NE NE
E
E
NE NE
NE NE
N N
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