CHAPTER
20
European Alps
Frank Paul, Yves Arnaud, Roberto Ranzi, and Helmut Rott
ABSTRACT
Glaciers in the Alps have been studied since the 17th
century, as they were located close to human settlements and catastrophic events (e.g., outburst floods
from glacier-dammed lakes) killed people and
devastated arable land. With increased physical
understanding of glaciers obtained during the
19th century, the first systematic measurements of
glacier fluctuations were started back in 1894. Over
the past decades, satellite measurements have
increasingly contributed to our understanding of
glaciers and their changes from a large variety of
sensors, techniques and research topics. In this
chapter we present how satellite data were applied
in four countries of the Alps (Austria, France, Italy,
and Switzerland) to learn more about glaciers and
their changes. By combining remote-sensing data
with field observations and numerical modeling,
several important results have been obtained. The
fields of research relevant to this chapter include
creation of glacier inventories and analysis of
glacier-specific area changes, determination of
elevation changes and flow velocity, mapping of
snow lines and mass balance assessment, as well
as studying supraglacial lakes, the thermal
properties of debris cover, and components of the
surface energy balance. Regional studies and field
evidence confirm that glacier area loss in the Alps
has been widespread and massive over the past
decades and was accompanied by major losses in
glacier volume.
20.1
REGIONAL CONTEXT
20.1.1 Geographic and topographic
characteristics
The Alps are located in the heart of Europe and
stretch over 1,200 km, from their southwestern end
close to the Mediterranean Sea toward the north
until they turn eastwards near the Mont Blanc massif, where they reach their highest elevation at 4,810
m asl. From here they stretch in a west–east direction for about 700 km until they slowly fade out at
their eastern end near Vienna. The Alps connect
six countries (Austria, France, Germany, Italy,
Slovenia, and Switzerland) and build a natural barrier for moist air masses from the north and south.
Consequently, intense orographic precipitation
occurs resulting in the Alps being the headwaters
of four major rivers (Danube, Po, Rhine, Rhone)
and being referred to as ‘‘water towers’’ (Viviroli
and Weingartner 2004).
The Alps are a comparatively young mountain
chain (mostly 15 Myr old) formed when the African
Plate collided with the European Plate. The main
phases of Alpine orogeny started in the Paleocene
to Eocene epochs. The geologic structure of the
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European Alps
Figure 20.1. Oberaarglacier in the Swiss Alps, a typical small-valley glacier (size 5 km 2 ) which contributes its
meltwater to a lake used for hydropower production (photo: F. Paul).
Alps is quite complex as older rock formations
slipped away from both sides of a central ridge
during the uplift of much younger granitic rocks
(Labhart 2005). Current tectonic uplift of about
1 mm per year is roughly compensated by elevation
loss due to weathering (Barletta et al. 2006). During
the peak of the last ice age about 20,000 years ago,
the entire massif was more or less covered by a
complex dendritic network of ice streams with only
the highest peaks exposed as nunataks (e.g., Jäckli
and Hantke 1970). In regions with hard rock (granite, gneiss), trim lines indicating this former extent
are sometimes well preserved (Kelly et al. 2004) and
many valleys still exhibit the characteristic landforms of a recently glaciated landscape that is well
suited for hydropower generation (Fig. 20.1). The
steep environmental gradients and the large variety
of lithospheric material over small distances results
in a high diversity of wildlife species (Burga et al.
2004), and the beautiful landscape attracts millions
of tourists each year.
20.1.2 Climatic conditions
The mountain region of the Alps contains a comparably high density of climate stations with several
very long-term (>200 years) records of temperature
and/or precipitation (Auer et al. 2007). This allows
very detailed analysis of the glacier–climate relationship back in time (e.g., Schöner et al. 2000).
In general, mean climatic conditions in the Alps
strongly depend on elevation and topographic
exposure to moist air masses. However, considerable regional variability is also related to the location of the Alps in the center of Europe, which is
influenced by at least five very different flow
patterns, causing pronounced seasonal variability
of temperature and the precipitation regime (Beniston 2006). The climate of the Alps is mainly influenced by: maritime westerly airflow (resulting in wet
and warm conditions); maritime northwesterly to
northerly flow (wet and cold); more continental
flow from the northeast (dry and cold); Mediterranean flow from the southwest (wet and warm);
and Mediterranean flow from the south to southeast (dry and warm). The resulting precipitation
pattern is complex and can only be explained when
orographic aspects are considered (Frei and Schär
1998). In Fig. 20.2 we show the precipitation pattern of the Alps based on the PRISM interpolation
(Schwarb et al. 2001) together with glacier outlines
from 2003 as derived from eight Landsat TM
Regional context 441
Figure 20.2. Color-coded map of mean annual precipitation for the greater Alpine region from the climatology by
Schwarb et al. (2001). Glacier outlines are shown in white and approximate country boundaries in black.
scenes. On average, there is abundant precipitation,
but climatic extremes (droughts in summer, floods
in summer and autumn, avalanches in winter) may
have severe regional impacts such as economic
losses or loss of life (e.g., Beniston 2007).
Since the end of the Little Ice Age (LIA) in the
1850s, mean annual temperature had increased by
about 1 C by the 1970s and by a further degree
since the mid-1980s. This is about twice the global
mean value and could partly be explained by the
higher continentality of the Alps (Beniston 2006).
There have been some decadal fluctuations of the
temperature (Fig. 20.3a) but there is also a clear
long-term (secular) trend. While temperature
decline in the 1960s is probably due to increased
pollution (global dimming), the sharp rise after
1985 could be due to so-called global brightening
(Wild et al. 2004). However, the net effect of this
radiative forcing on temperatures in the Alps (with
its much cleaner air) is subject to an ongoing debate
(e.g., Philipona et al. 2009). The consequences of
the temperature ‘‘jump’’ in the 1980s is also
reflected in the increasingly negative glacier mass
balances in the Alps (e.g., Haeberli et al. 2007).
High variability of precipitation from year to year
was observed at many places, and decadal variability could be recognized as well—however, a
long-term trend is not obvious (Fig. 20.3b). In this
respect, the decadal glacier fluctuations (length
changes) observed since the end of the LIA might
be related to changes in temperature and precipitation with the overall and long-term retreat being a
consequence of temperature increase only (e.g.,
Schöner et al. 2000).
20.1.3 Glacier characteristics
According to the national glacier inventories that
were compiled from aerial photography and topographic maps during the 1960s to 1970s, the Alps
contain about 5,150 glaciers covering an area of
2,900 km 2 with sizes between 0.01 and 87 km 2
(Zemp et al. 2008). While most glaciers are small
(80% are smaller than 1 km 2 ), there are a few
(0.6%) that are larger than 10 km 2 but cover
22% of the total glacier area (Fig. 20.4). This size
distribution has several consequences. For example,
the total number of glaciers in an inventory strongly
depends on the minimum size recorded and on the
way a glacier entity is defined. Some inventories
have aggregated several nearby individual ‘‘glaciers’’ (partly including perennial snow fields) into
one total glacier with a relatively larger size. While
very small ice patches do not contribute substantially to total area (and are thus often neglected in
statistical applications), they could make a large
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European Alps
Figure 20.3. Homogenized time series of mean
annual (a) temperature and (b) precipitation for the
climate station Sils (Grisons, Switzerland) since
1864 (data from MeteoSwiss). The smoothed lines give
10-year running means and the straight line is a linear
regression. Figure can also be viewed as Online Supplement 20.1.
contribution to total area change (Paul et al.
2004a), and their existence still provides climatic
information. From a climatic point of view and
our interpretation of inventories, it makes a lot of
difference whether such small glaciers exist and are
counted, exist but are not counted, do not exist at
all, or recently existed but have disappeared.
Indeed, many of these small entities only survive
due to specific local topographic conditions (shading, avalanches, debris, etc.) and are thus somewhat
decoupled from climate change. However, such
effects can only be assessed if they are included in
the sample.
Assuming a total glacier volume of about 100 to
130 km 3 in the Alps in the 1970s (e.g., Haeberli and
Hoelzle 1995, Paul et al. 2004a) and keeping the
above total area in mind, a mean glacier thickness
of about 35 to 45 m can be calculated. However,
this value is strongly influenced by the thickness
values of the largest glaciers; the majority of glaciers
(<1 km 2 ) are much thinner. This has important
consequences for future glacier evolution under
further increasing temperatures. With current mean
mass losses of about 1 m water equivalent per year
(¼ 1,000 kg m 2 yr1 ), many of the glaciers in the
Alps may disappear within a few decades (Zemp et
al. 2006). A recent study by Paul et al. (2011)
revealed that in 2003 glacier cover in the entire Alps
was about 2,100 km 2 , which is nearly 30% less than
in the 1970s.
Climatic classification of the glaciers in the Alps
can be related to precipitation at the equilibrium
line (Ohmura 2001) or the mass balance gradient
(Kuhn 1984). With regard to the precipitation distribution depicted in Fig. 20.2, most glaciers fall
into a transitional climate type (i.e., between
maritime and continental). However, along the
northern rim of the Alps many glaciers experience
more maritime conditions, and in the drier parts of
the Alps (southern Valais, Ötztal Alps) more continental conditions. In general, glaciers in the Alps
are temperate with the exception of those parts of
glaciers that are at high altitudes (>3,500 m) which
are cold or thin parts at the tongue that could be
considered polythermal (Suter et al. 2001).
Figure 20.4. Percentage of glacier count and area covered for seven distinct size classes and all glaciers in the Alps
according to the World Glacier Inventory (WGMS, 1989).
Regional context 443
20.1.4
Glacier observations
The first detailed glacier observations in the Alps
were initiated by catastrophic events (repeated
glacier lake outbursts) as a result of advancing
glaciers during the LIA (e.g., Tufnell 1984). In the
course of the 19th century, scientific interest focused
on the causes of glacier fluctuations and the physical nature of glaciers (e.g., Hess 1904, see also this
book’s Prologue by Jeff Kargel). Extensive field
campaigns and measurements were performed on
several glaciers in the Alps (cf. Lliboutry 1964).
Systematic observations of glacier length changes
were initiated at the Sixth Geological Congress in
Zurich by A. Forel in 1894 and have continued until
today (Haeberli 1998). A valuable source of information with respect to glacier inventory data was
compiled near the end of the LIA (around 1850/60)
by the first accurate systematic cartographic surveys
(cf. Mayer 2010). In some regions (e.g., in Switzerland) the quality of the cartography was sufficient
to allow reconstruction of former glacier extents
with good accuracy (Maisch et al. 2000).
The first overall assessments of glacier cover
based on mapping of cartographic material of that
time were performed by Richter (1888) for Austria
and Jegerlehner (1903) for Switzerland. In the 1960s
to 1970s glacier inventories were compiled from
aerial photography (and some earlier topographic
maps) following a systematic scheme and using
standardized coding to allow computer-based
analysis of the entries on punch cards (UNESCO
1970). The results of this analysis were published by
Müller et al. (1976) for Switzerland, by Vivian
(1975) for France, and by the Italian Glaciological
Committee (CGI, 1962) for Italy. The first systematic calculation of glacier change from the LIA
maximum extent up to the 1973 inventory was published by Maisch et al. (2000). They used the abovementioned topographic maps from the end of the
LIA and additional field surveys to reconstruct
former glacier outlines and topography (100 m contours). Digitization of both glacier extents (LIA and
1973) by Wipf (1999) for a part of Switzerland
enabled the data to be automatically analyzed
within a geographic information system (GIS). This
digital inventory was extended to include all Swiss
glaciers in the course of the new Swiss glacier inventory (Kääb et al. 2002, Paul et al. 2002). The glacier
inventory was based on the semiautomated classification of multispectral satellite data (Landsat TM)
from 1998/99 and was combined with a digital elevation model (DEM) to obtain topographic glacier
attributes such as minimum, mean, and maximum
elevation (Paul et al. 2009b). It also served as a pilot
study for GLIMS and contributed to the design of
the database. The inventory of Austrian glaciers
was presented by Patzelt (1980) for the reference
year 1969 and by Lambrecht and Kuhn (2007) for
the reference year 1998, in both cases based on
aerial photogrammetry surveys. A new assessment
was made by Citterio et al. (2007) for Italy.
20.1.5 Satellite data
The Landsat Thematic Mapper (TM) sensor has
become popular in recent years as a tool to create
glacier inventories (e.g., Williams and Ferrigno
1988, Andreassen et al. 2008, Bolch et al. 2010,
Le Bris et al. 2011). This is mostly due to the large
swath area covered by one scene (180 185 km),
the long record of data available (starting in 1982),
adequate spatial resolution (30 m) to map the
target, and a shortwave infrared (SWIR) band capable of accurately classifying glaciers (Paul and
Hendriks 2010). Further important advantages of
Landsat are the continuous acquisition of scenes (in
contrast to the programmed on-demand mode of
other satellites) and the free availability of all Landsat data from the USGS archive in an orthorectified
version (Wulder et al. 2012). Combined with the
free availability of the SRTM DEM and the
ASTER GDEM, a new era of glacier inventory
creation has started (Paul 2010).
Apart from glacier mapping and DEM generation (Chapters 4 and 5), several further glaciological
applications (such as mapping of velocity fields or
snow cover) can be performed with satellite data
(e.g., Paul et al. 2009a), particularly when somewhat higher resolution optical sensors such as
SPOT and ASTER (e.g., Berthier et al. 2005, Kääb
2005) or microwave sensors are considered as well
(e.g., Strozzi et al. 2002). Some results from these
additional studies carried out in the Alps are presented below. Very high spatial resolution satellite
data (<1 m) such as those available from Ikonos,
GeoEye, or QuickBird are expensive over larger
regions, and have thus mainly been used on individual glaciers (e.g., to validate mapping results
from Landsat; Paul et al. 2013).
While many cloud-free scenes from the end of the
ablation period are available from TM and ETMþ
over the Alps, very few provide appropriate glaciermapping conditions (i.e., no snow apart from that
on glaciers). This is actually the single most important requirement for glacier inventory creation, as
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European Alps
adverse snow conditions can lead to significant
overestimation of glacier area (e.g., Paul and
Andreassen 2009). It is thus recommended to use
only scenes with the best snow conditions for glacier
inventory work, even if some image mosaicking due
to cloud cover is required. Due to the large number
of years with negative glacier mass balances in the
past 30 years, such useful Landsat scenes have been
acquired for parts of the Alps about once every 4 to
6 years throughout that period. By contrast, a small
number of scenes (fewer than 10 in total) from
ASTER have been acquired in the past 10 years
over the Alps that could be used for glacier inventory work. Nevertheless, less optimal scenes still
have value for other glaciological studies (see Section 20.5).
20.2
AUSTRIA
20.2.1 Regional context
The highest peaks and main glacier areas of the
Austrian Alps are distributed along the main drainage divide extending east–west between the river
basins of the Inn and Salzach in the north and
the Adige (along the border to Italy) and the Drau
(in Eastern Tyrol and Carinthia) in the south (Fig.
20.5). The highest elevations in these mountain
groups, which are composed of igneous and metamorphic rocks, range between 3,250 and 3,798 m
(Grossglockner). In the Northern Calcareous Alps,
close to the border with Germany, there are several
small glaciers. With one exception (Parseierspitze,
Lechtaler Alpen), the highest peaks in these mountain ranges do not reach 3,000 m. Mountain groups
with a total glacier area of at least 10 km 2 are
marked in Fig. 20.5.
Based on the 1969 glacier inventory, the equilibrium line altitude (ELA) of individual glaciers in the
Austrian Alps has been estimated to range between
2,600 and 3,100 m in elevation (Gross et al. 1977).
Differences in the ELA are caused mainly by
regional and local variations in precipitation and
by the topographic characteristics of glaciers. Precipitation decreases from both northern and southern rims towards the inner Alpine zone. The driest
region is in the Ötztal Alps mountain group with
ELAs between 2,900 and 3,100 m.
Figure 20.5. MODIS false-color image with the main glacier-covered mountain groups in Austria.
Austria
445
Table 20.1. Glacier areas of the main mountain groups (Fig. 20.5) in the Austrian Alps and
total area, based on aerial photogrammetry, for the reference years 1969 and 1998 (from
Lambrecht and Kuhn 2007).
Mountain group
20.2.2
Area 1969
(km 2 )
Area 1998
(km 2 )
Area change
(km 2 )
Relative change
(%)
Ankogel-Hochalmgruppe
19.9
16.1
3.7
18.8
Glockner Gruppe
68.9
59.8
9.1
13.2
Ötztaler Alpen
179.7
148.1
31.6
17.6
Silvrettagruppe
24.2
20.3
3.9
16.1
Sonnblickgruppe
12.8
9.7
3.0
23.7
Stubaier Alpen
63.1
53.0
10.1
15.9
Venedigergruppe
93.4
81.0
12.5
13.3
Zillertaler Alpen
65.6
51.7
13.9
21.2
Other mountain groups
39.6
29.9
9.7
24.4
Total glacier area
567.1
469.7
97.4
17.2
Austrian glacier inventories
The first complete inventory of Austrian glaciers
was compiled for the reference year 1969, based
on photogrammetric analysis of aerial photographs
(Patzelt 1980). The second complete inventory was
based on aerial photos taken between 1996 and
2002; the images for most glaciers were acquired
in 1997 and 1998 (Lambrecht and Kuhn 2007).
The 1969 inventory was reanalyzed to obtain a
homogeneous basis for the study of temporal
changes. In the original 1969 inventory (Patzelt
1980) the firn areas above the bergschrund (the
crevasse that forms when moving ice separates from
the unmoving ice or firn set against the rock backwall of a high mountain glacier) were not included.
This resulted in a total glacier area of 542 km 2 10
km 2 for 925 glaciers. The reanalyzed glacier area of
1969 amounted to 567 km 2 and the upscaled 1998
area was 470 km 2 , corresponding to a decrease of
17% relative to the 1969 area (Lambrecht and
Kuhn 2007). Total area for the mountain groups
with the main glacier areas are quoted in Table 20.1.
Gross (1987) estimated the maximum glacier area
for 1850 using historic sources (e.g., maps, paintings) and mapping of moraine positions. He reports
an area decrease of 46.4% in 1969 relative to the
1850 extent. The decrease between 1969 and 1998
was a further 9.5% reduction over the 1850 area.
The 1969 and 1998 glacier inventories apply a cutoff
size threshold of 0.01 km 2 . The majority of glaciers
in the Austrian Alps are smaller than 1 km 2 and
only five are larger than 10 km 2 . The largest glacier
is Pasterzen Kees in the Glockner mountain group,
which covered 19.8 km 2 in 1969 and had decreased
to 18.0 km 2 by 2000 (Kellerer-Pirklbauer et al.
2008).
20.2.3 Satellite-based study of glaciers in
the Stubaier Alpen
Satellite images offer the possibility to update
glacier inventories more frequently than photogrammetric surveys based on aerial photography.
In the Austrian Alps satellite images have been used
for mapping the glaciers of the Stubaier Alpen
mountain group as a contribution to the GLIMS
database (Schicker 2006). Suitable satellite images
close to the end of the ablation period are available
from September 30, 1985 (Landsat 5) and from
August 23, 2003 (ASTER). The ratio of visible to
near-infrared reflectance was used for automated
segmentation of glacier areas. These areas were then
manually corrected for debris-covered ice surfaces
and possible errors in shadow zones. Fig. 20.6
shows the 1985 Landsat image’s glacier boundaries.
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European Alps
Figure 20.6. Landsat 5 TM image of September 30,
1985 (bands 5, 4, 3 as RGB) with glaciers of the
Stubaier Alpen in Austria outlined in white. The largest
glaciers are (1) Sulzenau, (2) Sulztal, (3) Alpeiner, and
(4) Lisenser. Figure can also be viewed as Online Supplement 20.2.
Figure 20.7. ASTER image of August 23, 2003
(bands 4, 3, 2 as RGB) for a part of the Stubaier Alpen
with glacier outlines from 2003 in white. Red outlines
refer to the 1985 extents shown in Fig. 20.6.
In this cloudless scene a total of 116 glaciers were
mapped. Four glaciers, annotated in Fig. 20.6, are
larger than 3 km 2 . The largest, Sulzenauferner
(No. 2), covered an area of 4.9 km 2 in 1969, but
had decreased to 4.1 km 2 by 2003, by which date the
glacier had split into two separate parts (Fig. 20.7).
In the 1985 image, snow covered 31% of the glacier
area, which is a clear indication of negative mass
balance. When considered as the accumulation area
it should be about twice as large for a balanced
budget. The percentage of snow cover on a glacier
clearly decreases with decreasing glacier size
(Schicker 2006).
At the end of the record warm summer of 2003
there was no accumulation area on any of the
Stubai glaciers (Fig. 20.7). The ASTER image of
August 23, 2003 shows very low reflection for all
glaciers. On some of the larger glaciers small firn
areas persisted from previous years, but these areas
belonged to the ablation zone of the mass balance
year 2002/2003. As the image is partly cloud covered, only 88 of the 116 glaciers of the 1969 inventory could be remapped. Of these, 14 very small
glaciers had disappeared by 2003. Overall changes
are listed in Table 20.2. Aerial photos of the Stubai
Alps for the second glacier inventory were acquired
Table 20.2. Glacier area in the Stubai Alps based on aerial photogrammetry in the years 1969 and 1997
(Lambrecht and Kuhn 2007), and analysis of satellite images: Landsat TM, 30 September 1985, and ASTER,
23 August 2003 (Schicker 2006). The numbers are shown for the 88 glaciers covered by all four dates, and for the
116 glaciers covered by three dates.
Count
Glacier area
(km 2 )
Change of the 1969 area
(%)
1969
1985
1997
2003
69–85
85–97
97–03
85–03
116
63.1
62.3
52.2
—
1.3
16.0
—
—
88
54.1
54.4
47.2
36.9
þ0.6
13.3
19.0
32.3
France 447
Figure 20.8. Area change in percent of total glacier
area per year for the Stubaier Alpen (Austria), referring
to the 1969 glacier size, for three periods: 1969–1985,
1985–1997, 1997–2003 (Schicker, 2006).
in 1997, and the numbers in the table refer to this
year.
Comparison of the three inventories shows that
glacier area changed little between 1969 and 1985.
Some of the larger glaciers went through a small
advance such that the sample of 88 glaciers revealed
overall a small increase in area during this period.
The full sample of 116 glaciers showed a small
retreat, primarily because this sample included a
higher percentage of small glaciers. The change
between 1985 and 1997 was more significant, with
glacier area decreasing by 13.3 and 16.0% (relative
to the 1969 glacier area) for the two samples, respectively. Between 1997 and 2003 the retreat accelerated with 19% of the 1969 glacier area (respectively,
21.8% of the 1997 glacier area) being lost during
these 6 years. The relation between glacier size and
retreat shows a clear dependence on glacier size
(Fig. 20.8), with increasing relative loss in area
for decreasing glacier size.
20.2.4 Conclusion
Satellite-based studies on Austrian glaciers during
the 1980s and 1990s place the main focus on the
development of methods for retrieval of glacier
parameters, using optical and radar imagery (e.g.,
Rott and Markl 1989, Floricioiu and Rott 2001).
The focus of recent work involving satellite data is
on complementing and updating glacier inventories.
Two complete inventories of Austrian glaciers are
available for 1969 and 1998 based on aerial photogrammetry. For parts of the glacierized region in
Austria, late summer satellite images are available
from multiple years, which reduces the time scale
needed to study glacier changes. Analysis of the
Stubaier Alpen mountain group, using satellite
images from 1985 and 2003 and the photogrammetric inventory of 1969, shows very little area
change between 1969 and 1985. Ongoing major
glacier shrinkage started about 1985. In the 12 years
between 1985 and 1997 the glacier area decreased
by 17%, and in the 6 years between 1997 and 2003
there was a further 19% loss. Thus, in as little as
18 years the area of glaciers in the Stubaier Alpen
decreased by about one third. However, relative
change is dependent on glacier size and the degree
of dominance of small glaciers in the region. Overall
area loss for all glaciers in Austria is thus slightly
lower, as quoted in Table 20.1, between 1969 and
1998. The remarkable acceleration of retreat during
the last few years coincides with a series of very
warm summers. These changes are also clearly evident in glacier mass balance measurements carried
out on six Austrian glaciers, with a record loss of
about 2,000 kg m 2 for the specific mass balance in
2002/2003.
20.3
FRANCE
20.3.1 Introduction
The most recent inventory covering French
glaciers was conducted in the 1970s during compilation of a glacier inventory for the Western Alps
under the aegis of UNESCO. At the time, almost
900 glaciers covering a total area of around 568 km 2
were unevenly distributed among seven larger
mountain groups: Mont Blanc (178 km 2 ),
Dauphiné (135 km 2 ), Grand Paradiso (100 km 2 ),
Vanoise (85 km 2 ), Dorsale Frontière (57 km 2 ),
Dents du Midi-Aiguilles Rouges (8 km 2 ), and the
Meridional Alps (4 km 2 ) (Vivian 1975).
Since the 1970s no systematic glacier inventory
has been compiled for France. Though satellite data
from the Terra ASTER and Landsat TM/ETMþ
sensors were frequently used to create glacier
inventories as part of GLIMS (e.g., for Switzerland), there simply were not enough suitable images
covering French glaciers, mainly due to frequent
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European Alps
cloud cover, adverse snow conditions, or inadequate image characteristics (sensor gain, angle of
view). The situation now has changed in that more
appropriate images have been acquired by the
Landsat sensors and are currently being used to
create an updated French glacier inventory. An
inventory for the 1980s based on Landsat TM
images of September 28, 1985, October 17, 1986,
September 9, 1987, and August 27, 1988 is in process. This inventory will be updated for 2003 using
ETMþ images of August 13, 2003, SPOT-5 images
of August 23, 2003, and finally with aerial photos of
2008 and 2010 taken by the French National Geographic Institute.
In the past, French scientists gave priority to
detailed study of selected benchmark glaciers
(Argentière, Mer de Glace, Saint Sorlin, and
Gébroulaz) as part of the French glacier survey
observatory (ORE GLACIOCLIM). Mass balance
and length fluctuations are measured for these
glaciers at least biannually in the field. Additional
observations conducted by GLACIOCLIM include
volumetric mass balance with aerial photogrammetry, surface velocity, thickness variations, front
cartography, and meteorological observations. Several of these studies make extensive use of satellite
images and are described in the following subsections.
20.3.2 Examples of remote sensing–based
studies in the French Alps
In the study by Berthier et al. (2004) glacier thickness changes on Mer de Glace were measured by
comparing DEMs derived from satellite optical
images. Differential DEMs for 2000 to 2003 derived
from aerial photos and satellite images are shown in
Fig. 20.9. They calculated the rate of thickness
change for different periods using satellite-derived
DEMs acquired in 1994, 2000, 2003 and one from
aerial photography from 1979 (Fig. 20.10). Below
2,100 m, the thinning rate changed from 1 m
yr 1 0.4 m yr1 between 1979 and 1994 to 4.1 m
yr 1 1.7 m yr1 between 2000 and 2003. Analysis
further revealed that at lower altitudes the thinning
rate has increased during the last 10 years whereas
at higher altitudes the study could not reach any
conclusion because poor image contrast in the
accumulation area had the effect of reducing the
accuracy of the DEMs.
A further study by Berthier et al. (2005) developed a method to map surface velocity fields on
Figure 20.9. Differential DEMs for the 2000–2003 period derived from aerial photos (left panel) and satellite
images (right panel). A debris-covered area on the glacier is surrounded by a white circle. The white lines (right
panel) locate the topographic profiles. A histogram of thickness change for the 2,000–2,050 m altitude interval is
drawn in black in the center of the figure. The distribution is well approximated by a Gaussian curve (blue line).
Figure can also be viewed as Online Supplement 20.3.
France 449
Figure 20.10. Rate of thickness change on the lower
Mer de Glace for the last 25 years. Error bars were not
added to preserve the clarity of the figure. Figure can
also be viewed as Online Supplement 20.4.
mountain glaciers by cross-correlation of SPOT-5
optical images. It was applied to two glaciers in the
Mont Blanc area (Mer de Glace and Argentière).
The accuracy of the satellite measurements has been
assessed by comparison with a nearly simultaneous
differential GPS survey. As long as the images have
a similar viewing geometry and correlate well, the
accuracy of displacement measurements is of the
order of 0.5 m. The horizontal displacement of
glaciers in the Mont Blanc area between August
23 and September 18, 2003 is depicted in Fig.
20.11. The highest speeds occur on the steep icefalls
of the Mer de Glace, Bosson, and Brenva Glaciers
with velocities over 500 m yr1 .
Figure 20.11. Horizontal displacement of glaciers of the Mont Blanc area between August 23 and September 18,
2003 for the entire Mont Blanc area. The white outlines encompass the Argentière, Brenva (Italy), and Mer de Glace
glaciers. Where the correlation coefficient is too weak, pixels appear in gray. The white dots are the ablation stakes
measured by DGPS. The black line is a longitudinal profile of the Mer de Glace glacier. Figure can also be viewed as
Online Supplement 20.5. Reprinted from Remote Sensing of Environment (Berthier et al. 2005) with permission of
Elsevier.
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European Alps
Figure 20.12. Computed ELA obtained from remote sensing versus ELA observed from field measurements.
Confidence intervals of the observed ELA are obtained from linear regression of the mass balance according to
stake altitude. Confidence intervals of the computed ELA depend on the pixel size of the images used (<30 m in this
study) and on the glacier slope across the ELA (<20% for the three glaciers). As they do not exceed 6 m they are not
shown in the figure. Reprinted from the Journal of Glaciology (Rabatel et al. 2005) with permission of the
International Glaciological Society.
By using the topographic transverse surveys
available for Mer de Glace, Argentière, and Glacier
du Tour, Berthier et al. (2006) assessed the usefulness of SRTM for monitoring glacier volume variations and revealed a clear altitude-related bias for
SRTM elevations of glaciers. If SRTM is the more
recent of the two topographies under comparison,
the volume loss would be overestimated (and vice
versa). However, in a recent study by Paul (2008)
the bias was attributed to the different cell sizes of
the two compared DEMs as well as the generally
increasing steepness of the terrain towards higher
altitudes. The elevation of steep terrain that has a
convex curvature is always underestimated in the
lower resolution DEM.
Rabatel et al. (2005) developed a method for
snow line delineation from remotely sensed images
that were acquired at the end of the hydrological
year to calculate the equilibrium line altitude (ELA)
and glacier mass balances of three well-documented
glaciers (Argentière, Gébroulaz, and Saint Sorlin).
Computed ELA versus ELA observed from ground
measurements for the three glaciers is compared in
Fig. 20.12. Cumulative mass balance from remote
sensing versus field measurements for Argentière
Glacier between 1993 and 2002 is compared in
Fig. 20.13. Considering the uncertainties in the
measurement of both values, the agreement is
rather good.
Rabatel et al. (2008) applied the same methodology to Glacier Blanc (Ecrins region) where they
reconstructed the annual ELA and mass balance
from a 25-year time series of satellite images.
ELA derived from remote-sensing data versus
ELA derived from ground measurements is compared in Fig 20.14. This graph confirms that the
snow line measured using satellite images can be
used as an accurate proxy for the ELA (as already
Italy 451
Figure 20.13. Cumulative mass balance bðtÞ derived
from remote sensing (cumulative computed) and from
field measurements (cumulative glacier-wide ground)
for Argentière Glacier between 1994 and 2002. Figure
can also be viewed as Online Supplement 20.6.
Reprinted from the Journal of Glaciology (Rabatel et
al. 2005) with permission of the International Glaciological Society.
Figure 20.15. Comparison of mass balance series of
French alpine glaciers. Glacier Blanc (black diamonds)
with vertical bars (black line) representing the 95%
confidence interval. Other glaciers (triangles) represent
the average of a centered mass balance series of five
monitored glaciers in the French Alps (Argentière,
Tacul, Gébroulaz, Saint Sorlin, and Sarennes), and vertical bars (dashed line) represent the 95% confident
interval. Reprinted from the Journal of Glaciology
(Rabatel et al. 2008) with permission of the International Glaciological Society.
shown in Rabatel et al. 2005). The mass balance
anomaly series of Glacier Blanc derived by remote
sensing versus the mean of other ground-monitored
French alpine glaciers (Argentière, Gébroulaz,
Saint Sorlin, Sarennes, and Tacul, ) is compared
in Fig. 20.15. There is good overall agreement
between both series, and modeled Glacier Blanc
mass balance values for Glacier Blanc are always
within the confidence interval of other monitored
glaciers.
20.4
ITALY
20.4.1 Introduction
Figure 20.14. Comparison between ELA derived
from remote-sensing data and ELA derived from
ground measurements. Uncertainty bars on the ELA
based on ground measurements represent the confidence intervals obtained from linear regression of the
mass balance according to stake altitude. Reprinted
from the Journal of Glaciology (Rabatel et al. 2008)
with permission of the International Glaciological
Society.
The results of image processing of seven ASTER
satellite images of the Italian Alps (Fig. 20.16) were
among the first contributions to the GLIMS
database. In the Italian Alps most glaciers are of
small to medium size, with only four being larger
than 10 km 2 : Forni, Miage, Mandrone, and Lys.
Because of the prevailing climatic conditions during
the last two decades, with low winter precipitation
and high air temperatures, most Italian glaciers are
now shrinking. In this section we start of by show-
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European Alps
Figure 20.16. ASTER images and acquisition dates
(dd.mm.yy) of the Italian Alps processed within the
GLIMS project: (1) Sabbione, (2) Pustertal, (3) Dolomites, (4) Belvedere, (5) Miage, (6) Adamello, and (7)
Calderone, a remnant glacier in the Apennines. See this
book’s Chapter 33 by Kargel et al. for details on Calderone Glacier.
ing evidence of this retreat detected after processing
satellite-derived outlines of 56 glaciers in three
different areas. Because several glaciers are covered
by debris, thus preventing efficient use of visible and
infrared bands for glacier mapping, we developed
ad hoc algorithms to map debris-covered glaciers
using thermal infrared data. Finally, we present the
results of applying satellite image data to energy
balance modeling in the Adamello region.
20.4.2 Glacier retreat: glaciers in the
Sabbione, Pustertal, and
Dolomites regions
The first systematic inventory of Italian glaciers
dates back to the early 1960s (CGI, 1962) when
almost 1,000 glaciological units were classified from
field surveys and aerial photos. The resulting
inventory, published in four volumes by the Italian
Glaciological Committee and the National
Research Council, has been a reference for successive studies including those conducted in recent
years (Citterio et al. 2007). In the 1980s, data for
most, if not all, of the glaciers were updated and
archived in the World Glacier Inventory, compiled
by the World Glacier Monitoring Service (WGMS,
1989). These data have been used in this study as a
baseline for the assessment of area changes in the
last two decades of the 20th century.
Previous pioneering studies of glacier classification from satellite images in the Italian Alps were
based mainly on Landsat MSS data (Della Ventura
et al. 1986, 1987, Serandrei-Barbero and Zanon
1993). Later, Landsat TM data were used to detect
glacier change in the Breonie Alps (Serandrei
Barbero et al. 1999) and the Ortler Alps (Binaghi
et al. 1997). In this section we show areal change
statistics detected from three ASTER images of the
Sabbione, Pustertal, and Dolomites regions (see
Fig. 20.17). Particular benefits resulting from the
use of the higher spatial resolution ASTER images
compared with Landsat TM images relate to the
improved ability to detect very small glaciers a
few hectares (10 2 km 2 ) in size. For the Sabbione
and Pustertal images (Figs. 20.17 and 20.18) glacier
outlines were delineated using VNIR and SWIR
band ratios (e.g., ASTER bands 3/4), with
1:10,000-scale maps derived from aerial photogrammetry used as a reference. However, problems
were encountered in getting an image of the Dolomites (region 3 in Fig. 20.16) because of shadows
and debris cover. Hence the thermal infrared (TIR)
band was applied instead (see below).
Selecting a group of 56 glaciers ranging in size
from 0.01 to 2.69 km 2 , average area loss of 41%
was recognized compared with glacier extents from
the 1980s as reported in WGMS (1989). Least
squares regression indicates an average 34% shrinkage in total glacier area, but some small glaciers
increased in size, maybe due to misclassification
of snow (Fig. 20.19). For many glaciers, marked
retreat is clearly visible, as shown in Figs. 20.17
and 20.18 where areal change in the Southern Sabbione (2.69 versus 3.64 km 2 ), Western Sabbione
(1.23 versus 1.98 km 2 ), and Collalto/Hochgall Kees
(0.42 versus 0.83 km 2 ) Glaciers are depicted.
20.4.3 The Belvedere and Miage debriscovered glaciers
Field measurements and energy balance modeling
simulating the 2002 and 2003 melt seasons were
performed for the Belvedere Glacier. They indicate
that in the hours before noon, the temperature of
the surface down to a depth of some tens of centimeters thick is on average 4.5 C colder than for
other debris deposits (Taschner and Ranzi 2002).
The thermal signature of both Landsat ETMþ and
ASTER images were similar and coincided with the
debris thickness distribution as mapped from field
Italy 453
Figure 20.17. ASTER image of the Sabbione glacier area (No. 1 in Fig. 20.15) acquired on August 24, 2001. The
box shows Southern Sabbione Glacier (now measuring 2.69 km 2 ) and Western Sabbione Glacier (1.23 km 2 ), both
of which have been retreating from Sabbione Lake since the 1990s.
measurements (Diolaiuti et al. 2003). Albedo maps
for the Belvedere Glacier were derived from an
ASTER image, by calculating incident radiation
using the distributed radiative transfer model that
is briefly described in Ranzi and Rosso (1991) and
in Ranzi et al. (2010). Based on data in these two
papers, an atmospheric emissivity of 0.93 was
assumed, which was combined with radiance-atthe-sensor measurement to derive the albedo.
These albedo maps were useful in simulating
cumulative inflow into the ephemeral Lago Effimero supraglacial lake, using an energy balance
model (Ranzi and Rosso 1991, Cagnati et al.
2004, Macelloni et al. 2005) which was modified
to simulate debris-covered ice. This lake raised
some concern because of its remarkable volume
change observed at the end of summer 2002 (see
Fig. 20.20), which was related to surge-type movement of the glacier (Haeberli et al. 2002). The lake
posed a potential threat to downstream infrastructure and to the mountain village of Macugnaga
and was thus intensively kept under observation
for some years. Cumulative melt estimated for the
basin upstream of the lake from April 1, 2002/2003
until June 30, 2002 and June 18, 2003 was 350 and
276 mm, respectively (Ranzi et al. 2004). These
values are in agreement for both years with the
volumes of the lake measured after drainage. A
further comparison made using the energy balance
model confirmed close agreement between simulated and measured cumulative melt (320 mm for
the August to September 2003 months) as collected
at an energy balance station that was installed on
the debris-covered part of the glacier.
ASTER-derived surface temperatures were used
for mapping the margins of both Belvedere and
Miage Glaciers. Due to the cooler surface temperature of debris on ice compared with debris on bare
ground (Fig. 20.21), a surface temperature threshold could be used to discriminate both types of
debris. The debris itself was mapped by first classifying the debris-free ice from a SWIR/VNIR band
ratio and then applying an NDVI threshold to the
remaining area to discriminate debris from vegetation. Knowledge of surface geomorphological
features, obtained from in situ surveys and maps,
is finally used for manual delineation of the glacier
boundary. By applying an enhanced band ratio
algorithm the capability and limitations of a TIRbased method in discriminating the two debris-
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European Alps
Figure 20.19. Scatter plot of area change of 56 glaciers located in regions No. 1, 2, and 3 (see Fig. 20.15).
Glacier size as mapped from the two ASTER images of
August 24, 2001 and September 17, 2003 is compared
with values reported in the World Glacier Inventory
(WGMS, 1989), which was updated in the mid-1980s.
Figure 20.18. ASTER image of the Pustertal glacier
area (No. 2 in Fig. 20.15) acquired on September 17,
2003. The box shows Collalto/Hochgall Kees Glacier,
now measuring 0.42 km 2 (in green), compared with its
boundaries from two aerial surveys (in blue and cyan)
in the 1990s. Figure can also be viewed as Online
Supplement 20.7.
covered lobes of the glacier from other surfaces was
ascertained. The study by Mihalcea et al. (2008)
confirmed the qualitative observations of Ranzi et
al. (2004) on Miage Glacier and showed a positive
correlation between ASTER-derived surface temperatures and debris thickness (the thicker, the warmer). The results confirm the potential of remotesensing techniques for mapping ‘‘black’’ glaciers
Figure 20.20. Belvedere Glacier (No. 4 in Fig. 20.15) with the dramatic change of Effimero Lake visible from the
August 24, 2001 (left) and July 19, 2002 (right) ASTER images. For scale, the images are about 3,500 m from top to
bottom.
Italy 455
Figure 20.21. Thermal signature of debris-covered
Belvedere Glacier (No. 4 in Figure 20.15). The thicker
the debris cover the warmer the satellite-derived temperature.
with similar geomorphological features worldwide.
A recent overview on the various techniques for
mapping debris-covered glaciers is given by Shukla
et al. (2010).
20.4.4 Albedo and energy balance of
Mandrone Glacier
Analysis of ASTER images from 2002 and 2003
indicate a retreat of the terminus of Mandrone
Glacier (a member of the Adamello group of
glaciers; image 6 in Fig. 20.16) of 165 m since
1997 and of 2,085 m since the end of the LIA.
The altitude of the terminus increased from 1,780 m
at the end of the LIA to the current 2,580 m asl.
Mandrone Glacier—the most important and largest
glacier in the Adamello group—was 13.4 km 2 in size
in 2003. It has also been the object of two applications of satellite image processing. Based on global
radiation measurements recorded at the automatic
weather station at Passo della Lobbia (3,020 m asl),
the parameters needed to produce a 3D radiative
model of incoming direct and diffuse shortwave
radiation were calibrated. Using the modeling
scheme adopted to estimate the albedo of Belvedere
Glacier, the ice albedo of Mandrone Glacier was
calculated from radiance-at-the-sensor data of an
ASTER image that was acquired on August 23,
2003 when the glacier was almost snow free (Ranzi
and Taschner 2005). Snow albedo was simulated by
a model tested by point measurements (Ranzi and
Rosso 1991). A distributed energy balance (Ranzi et
al., 2010) and water balance model was applied to
simulate melt seasons over the 1995–2009 period.
The model was tested at the point scale using snow
data (Cagnati et al. 2004, Macelloni et al. 2005) and
compared with ablation stake measurements. Runoff data were the main means of verification, but
simulated snow line position compared with that
derived from the ASTER image acquired on June
20, 2003 was also used (Fig. 20.22). With a mean
glacier mass loss of about 1,300 kg m 2 yr1 over
the 15-year period, continuous retreat can be
expected in the coming decades, with the glacier
Figure 20.22. Computed melt rate (left) over snow-covered and ice-covered regions around the Adamello group
of glaciers (No. 5 in Fig. 20.15) and an ASTER satellite image from June 20, 2003 for comparison. Arrows indicate
the position of the snow line. Figure can also be viewed as Online Supplement 20.8.
456
European Alps
evolving towards a new smaller equilibrium size and
shape (Coppola et al. 2012; Grossi et al. 2013).
20.5
SWITZERLAND
20.5.1 Methods for glacier inventory
creation
A new Swiss glacier inventory was derived from
Landsat TM data acquired in 1998/1999 (Paul
2007). Several glacier-mapping methods were compared in this study, and GIS-based methods for
automated data retrieval were developed (Paul et
al. 2002). In this and subsequent studies it was
concluded that the most simple method
(thresholded band ratios with the raw data of
TM3 or TM4 and TM5) provided the most accurate
mapping results (Paul et al. 2002, Paul and Kääb
2005). This was confirmed by other studies (e.g.,
Albert 2002). The method is based on the large
differences in spectral reflectance of ice and snow
in the SWIR compared with the VNIR (Hall et al.
1987, Dozier 1989). While clean and dirty glacier ice
could be routinely mapped (even in cast shadow),
debris-covered glacier parts (with respect to a 30 m
pixel) are more problematic as they exhibit the same
spectral characteristics as the surrounding rock
walls and need to be manually corrected. In regions
with accurate DEM data available, geomorphometric techniques can be combined with terrain
classification and neighborhood analysis to map
debris cover (Paul et al. 2004b), and in some cases
thermal infrared data can be used to map supra-
glacial debris based on temperature, as described
in the previous section. However, manual editing
of the outlines remains mandatory with these
methods.
The approach for creating glacier inventory data
by GIS-based processing has the special advantage
of being repeatable and comparable with former
and also future inventories. For this purpose, a
glacier basin vector layer that defines ice divides
and includes all the glacier parts that belong to a
former glacier was digitized (Fig. 20.23a). This
basin layer was then digitally intersected with available glacier outlines (from 1973, 1985, 1992, and
1998/1999) to compare the same entities in all cases
(Kääb et al. 2002, Paul et al. 2002). Topographic
parameters for each of the individual glaciers (Fig.
20.23b) were then obtained through digital combination with a DEM, basically by using the concept
of zone statistics, in which each glacier entity represents a zone. This facilitates calculation of statistical
properties (e.g., minimum, maximum, mean, and
median elevation) for each zone (i.e., glacier entity)
from an underlying value grid (i.e., DEM, slope,
aspect, radiation, etc.). and store them in the
attribute table for each glacier (Paul et al. 2002).
However, there is still the time-consuming step of
manual digitization of central flow lines for each
glacier to determine their lengths. Due to massive
geometry changes (rock outcrops, tongue separation, disintegration) in the past 20 years it was in
many cases not possible to use the central flow line
from 1973 for length change determination in the
1998/1999 dataset (cf. Paul et al. 2007).
Figure 20.23. (a) Glacier catchments (thick black lines) from manual delineation for extraction and identification
of individual glaciers. (b) Individual glaciers color-coded and converted back to raster format for zonal (glacierspecific) calculations using various input grids.
Switzerland
20.5.2
Results
The overall results from the 1998/1999 Swiss glacier
inventory were published in Paul et al. (2004a). We
thus focus here on some of the most interesting
results. For a sample of 938 glaciers, area changes
from 1973 to 1998/1999 were calculated and
extrapolated to all 2,057 Swiss glaciers. The resulting relative area change is 16%. This change
mostly occurred after 1985, which gives a mean
decadal change that is twice as high (14%) as
for the period starting in 1973 (6.4%). Compared
with mean decadal change since 1850 (2.2%) the
1985 to 1998/1999 area loss rate is about six times
higher. There is a strong dependence between relative area change and initial glacier size (the smaller
the glacier the larger the change), which implies that
mean area changes obtained for other regions
depend on both the time period and the size classes
considered in the respective sample. The scatter in
relative area change values strongly increases
towards smaller glaciers (Fig. 20.24), implying that
only a large sample gives a representative picture
of ongoing changes. Most importantly, glaciers
smaller than 1 km 2 contribute about 40% to total
457
area loss although they cover only 15% of the total
area.
Visual analysis of the outlines from 1973 and
1998/1999 reveals the extreme variability of size
changes from glacier to glacier. Quite often, glaciers
with very little to no change are located directly
next to glaciers with strong changes or complete
disappearance (Fig. 20.25). As also visible in this
figure, in some regions well-shaded and northfacing glaciers shrank much more than their
south-facing neighbors with little shading. This
high variability over short spatial scales implies
the importance of complementing from time to time
the information on glacier reaction to climate
change as derived from length change measurements of selected glaciers with a new inventory to
get the complete picture of changes. Many glaciers
lost area due to separation from formerly connected
tributaries, emerging rock outcrops, shrinkage
along their entire perimeter and disintegration.
All these observations are indirect but strong indicators of massive downwasting (cf. Paul et al. 2007).
Multitemporal analysis revealed that length/area
changes could be tracked with TM at about 5-year
intervals in this region (i.e., with changes being two
pixels or larger).
Figure 20.24. (a) Scatter plot of relative change in glacier area from 1973 to 1998 versus glacier size in 1973 for a
sample of 713 Swiss glaciers. Mean change for specific size classes (red line) increases towards smaller glaciers.
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European Alps
Figure 20.25. Glacier outlines in the Rheinwald Region for 1850 (blue), 1973 (yellow), and 1999 (black) on a
TM5, 4, 3 false-color composite from 1999 illustrating the high local variability in area change.
Several glacier-specific parameters and their
changes were analyzed in more detail by Paul
(2007). As an example, the spatial variability of
median glacier elevation for all Swiss glaciers >1
km 2 is depicted in Fig. 20.26. The elevation values
are much lower along the northern Alpine rim,
where precipitation is much higher than for the
drier inner-alpine valleys (see Fig. 20.2). Another
example is the change in the area–elevation distribution of the glacierized surface from 1973 to 1998
as illustrated in Fig. 20.27. The figure reveals that
the largest absolute area losses have taken place
where most of the area is located (from 2,700 to
3,000 m), while the greatest relative losses occurred
at much lower altitudes. If this change is used as a
first-order approximation of a future trend, the
evolution of ice loss for different altitudes could
be determined (Paul et al. 2004a, Zemp et al. 2006).
20.5.3 Conclusions
Our analysis of glacier change, as determined by
satellite data, in the Swiss Alps revealed widespread
area loss and an accelerating trend in the recent
past. This area loss goes hand in hand with major
loss of glacier volume that can be detected by differencing satellite-derived DEMs. As far as glacier
inventory creation from multispectral satellite data
is concerned, we identified the following points that
are still challenging:
—the limited number of appropriate scenes regarding snow conditions;
—the workload required for manual editing of
debris-covered glaciers;
—the rapid geometry changes that reduce the comparability of glacier parameters through time;
—automated determination of length changes that
are comparable with field measurements.
Most of the abovementioned challenges also occur
on a global scale (e.g., Paul and Andreassen 2009,
Racoviteanu et al. 2009, Svoboda and Paul, 2009).
It is hoped that these and future studies will help to
address the key issues and solve them.
Synthesis and outlook
459
Figure 20.26. Color-coded median elevation for all glaciers in Switzerland. The wetter northern Alpine rim with
low ELAs (blue colors) can be clearly distinguished from the drier inner Alpine valleys with higher ELAs (yellow to
red colors). Figure can also be viewed as Online Supplement 20.9.
Figure 20.27. Area–elevation distribution (hypsography) of the glacierized surface from 1973 and 1998
for a sample of 713 Swiss glaciers.
20.6
SYNTHESIS AND OUTLOOK
Glaciological studies in the Alps have a long
tradition. There have been more than 150 years
of observations accompanied by high-quality
documentation and topographic maps. Accordingly, the knowledge we have on glaciers and how
they change through time in the Alps is unique and
has contributed considerably to understanding the
processes involved in climate change and the assessment of their effects. Using data from today’s vast
collection of multispectral satellite sensors available
since the mid-1980s, many pioneering glaciological
studies have also been performed in the Alps (see
the references). The country-specific sections in this
chapter illustrate to some extent the large variety of
sensors used and the various glaciological applications developed. In addition to the (semiautomated)
mapping of glacier outlines, the creation of glacier
inventory data, and related change assessment, the
following topics have also been presented here: elevation changes, velocity fields, snow line positions,
and automated debris-cover determination. These
and similar studies should help extend in space and
time the sparse field measurements, and thus these
studies might help to assess the measurements’ regional representativeness and aid improvements in
global assessments.
In view of Landsat data—now freely available
from the USGS archive—and the global ASTER
GDEM, it is expected that a detailed and complete
global glacier inventory can be created. This is supported by recent projects like GlobGlacier (Paul et
al. 2009a) which mapped about 25,000 glaciers in
several key regions of the world, including all
glaciers in the Alps in 2003 (Paul et al. 2011).
New efforts are needed to harmonize and standardize data processing for glacier inventories (Paul et
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European Alps
al. 2009b). Future satellites such as the recently
launched Landsat 8 satellite from NASA and
USGS or ESA’s Sentinel 2 will support operational
glacier monitoring from space.
20.7
ACKNOWLEDGMENTS
The work of F. Paul is supported by a grant
from the ESA project GlobGlacier (21088/07/IEC). R. Ranzi thanks Stefan Taschner and Laura
Iacovelli for their valuable help in processing some
of the ASTER images and the financial support
from a MURST ‘‘Monitoring of Glaciers’’ grant
and another from the CARIPANDA project.
ASTER data courtesy of NASA/GSFC/METI/
Japan Space Systems, the U.S./Japan ASTER
Science Team, and the GLIMS project.
20.8
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