THE CORRELATION OF GLACIER MASS BALANCE AND SAR BACKSCATTER ON SVARTISEN, NORWAY

Ian A. Brown(1)

, Miriam Jackson(2)

, Matthias Braun(3)

, Rune Engeset(2)

(1) INK, Stockholm University, 10691 Stockholm (Sweden), Email: ian.brown@natgeo.su.se

(2)NVE, Majorstua, Oslo (Norway), Email:mja@nve.no

(2)ZFL,Bonn University, Bonn (Germany), Email:mabra@uni-bonn.de

ABSTRACT

Mass-balance measurements describe the difference

between accumulation and ablation and therefore the

annual climate response of a glacier. Such

measurements are the standard observation of glacier

‘health’ and when coupled with frontal, area or volume

observations can describe the dynamic response of a

glacier to climate over longer time scales. Mass balance

measurements are made in situ and are logistically

intensive. Satellite-based techniques have yet to replace

traditional measurements although the subject has

received a great deal of attention. Here we report on the

testing of a proxy algorithm that was developed for

Svalbard glaciers and uses Synthetic Aperture Radar

imagery. Applied to the complex topography of two

icecaps the method failed to correlate glacier facies area

with mass balance observations. Our experiment shows

more novel approaches are needed to find functional

glacier monitoring solutions.

1. INTRODUCTION

Glaciers respond to climate on time scales ranging from

several hours to millennia.. Very short term responses

include snow melt and runoff generation associated with

a Föhn wind or an increase in the heat flux caused by

changes in cloud cover. Such events are mostly of

interest to the scientific community. On the monthly,

seasonal, annual and sub-decadal time scales changes in

glacier size, mass balance and runoff may be off interest

to a wider range of people.

Environmental managers, for examples those

responsible for reporting Natura 2000 Habitat data

under the European Commission’s Habitat Directive,

may have an interest in glacier change data. Under

Natura 2000 glacier data must be reported to the

commission every six years. Water resource managers

and regulators may also have a demand for glacier data

given the importance of glacier melt to European, north

and south American and Asian rivers. Such demand

may exhibit high temporal resolution or Near Real Time

(NRT) dimensions. Other interested parties may include

industry (hydropower generators), local communities,

particularly in developing economies, and international

groups such as UNESCO’s GRID offices, NGOs and

scientific groups.

The response of a glacier to climate on a seasonal to

annual scale is best described by mass balance

measurements. These in situ measurements determine

winter accumulation, summer ablation and calculate the

net balance (sum of accumulation and ablation) at points

on the glacier surface. Networks of individual

observations can be dense (hundreds of observations) to

sparse (a few observations only). Such measurements

have been the backbone of glacier monitoring since the

1940s providing a more direct relationship between

annual climate and glacier response than the older

frontal observations. Mass balance programs are

expensive, time consuming and potentially dangerous.

They also cover few glaciers and rarely manage to

represent the entire spatial variability across large

glaciers.

Satellite observations complement but cannot replace

mass balance measurements (Dowdeswell, et al. 1997).

Interferometric Synthetic Aperture Radar (InSAR),

Radar altimetry (RA) and Laser Altimetry (also known

as LiDAR) can measure surface elevation change at

different sensitivities over wide areas. Such techniques

however may require an optimal, and not always

available, degree of data acquisition, or may suffer from

lower than necessary spatial resolution. SAR

backscatter is sensitive to changes in grain size, density

and liquid water content. Forster et al. (1999) found a

correlation between accumulation and backscattering

the dry snow facies of Greenland but other work has

shown that the sensitivity of SAR to non-mass balance

parameters is greater than the backscatter sensitivity to

seasonal changes in mass balance (Forster et al. 1999;

Brown et al., 2004; De Ruyter de Wildt et al., 2003).

Other parameters such as snowline or glacier facies can

be derived from optical and SAR images (Partington,

19998; Brown et al., 1999; Engeset and Ødegård, 1999;

Rau et al., 2000). These parameters may be employed to

add a spatial dimension to mass balance measurements

or to provide related data where mass balance data are

_____________________________________________________

Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

absent. Modelling and/or future in situ observations may

improve the utility of such observations. As yet though

there is not direct method of measuring glacier mass

balance by satellite and therefore proxies have become

widely used and are regularly analysed with reference to

mass balance data.

Here we test a method for the extraction of the

accumulation area of a glacier that was demonstrated on

glaciers on Svalbard (König et al. 2004). The method

classified the glacier surface with class area strongly

correlated to the glacier net balance. The advantage of

the technique lies in the strong correlation with mass

balance and automated nature of the processing. The

former provides a link to established glaciological

methods and the latter supports better operational

remote sensing. As Norway has a large glacier mass

balance programme, mainly related to the importance of

hydropower in Norway, there was a large impetus for

finding an automated method that could be used for

glacier monitoring, and possibly applied to additional

glaciers. The existence of a good mass balance records

as well as complementary data made Engabreen an ideal

test site for testing this method.

2. OPERATION GLACIER MONITORING

Glacier observations have historically been the preserve

of scientists working in Universities or government

research establishments. Using aerial photographs,

optical-thermal and later, microwave satellite data,

observations have focused on questions of science.

Observations of glaciers have been used to explain

climate change, the impact of climate variations on

glacier size and/or extent, and the seasonal melt

progression and/or potential runoff derived from

glaciers. Climate changes studies require observations

separated by a long time period or an observation with

reference to a proxy such as geomorphological

interpretations. The second application of glacier

observations typically requires annual or multi-annual

intervals between observations. The third application

uses observations to derive data over a melt season of

typically four months or less and may require weekly to

monthly data or data acquired on an opportunistic basis.

In the 1990s glaciological remote sensing increased

dramatically [I’d put this after the technology bit – new

sensor systems etc.] as awareness of climate change

was expressed in greater demand for glaciological data

and climate impact observations. At the same time new

sensor systems, including high resolution optical-

thermal instruments, Altimeters and Synthetic Aperture

Radars (SARs) improved the quality of data and the

range of parameters that could be extracted from them.

Finally investments in the Ground Segment by agencies

such as ESA and NASA and the growth of the internet

radically improved access to data.

Since the 1990s operational glacier monitoring

programs have been established. Some, for example the

Global Land Ice Measurements from Space (GLIMS)

program essentially codify, compile and promote

traditional activities within a network; in this case

annual or multi-annual glacier area and extent mapping.

Other networks or programs involve coherent attempts

to generate comparable products to improve spatial

coverage of individual observations and/or to provide a

benchmark from which quality can be evaluated and

standard algorithms employed. Such programs have

included European Commission funded Framework

research and ESA sponsored algorithm groups. A

further category of programs, such as the Polarview

Glacier Monitoring Service, provide operational

monitoring products to end-users based on their needs

which may include fast turn around or Near Real Time

(NRT) products. Such services break with the past in

that they are services responding to a market need and

that they are not products aimed at the producer but the

end-user.

The Polarview Glacier Service provides operational

glacier monitoring products to end-users in Europe and

north America. The typical end-user is a public body

(e.g. national or local government), which has the task

of monitoring climate impact monitoring, water

resource management or another form of environmental

monitoring. The typical service delivered to such users

comprises surface-type or facies maps acquired at the

interval that is most appropriate. Service delivery is

normally uneven and focused on the melt season: late

summer. The service has the potential to deliver NRT

products although this is not the normal case. Historical

products exploiting image archives are also available

enabling users to build time series of observations.

Services under ESAs GMES Service Element must

provide operational services that are validated and

documented. Research and Development, for example

to improve product quality, integrate new sensors or

systems, or develop new products cannot be performed

under GMES and yet is a vital contributor to the

ongoing success of the GMES Service Element

Services. The organisations behind the Polarview

Glacier Service performs research to improve the

quality of products, help users employ the products to

their fullest potential and improve the efficiency and

reliability of the service.

3. GLACIER FACIES MAPPING

The standard product of the Polarview Glacier Service

is a surface-type or ‘facies’ map. These products

classify the glacier surface into, for example,

percolation zone/firn area, bare ice zone (in winter), wet

snow, or exposed bare ice (in summer). In mass balance

terms the percolation zone or firn area corresponds to a

time-integrated accumulation area (where the period of

integration may be on decadal to sub-decadal

timescales). This should not be confused with the

accumulation area which is defined as the region in

which net accumulation exceeds net ablation. The bare

ice zone similarly is a time-integrated ablation area in

that it represents a region where averaged over (at least)

several years ablation exceeds accumulation. The

products allow an end-user to track the progress of the

melt season and compare products over annual time

steps to infer climate impacts. The products are based

on the classification of SAR backscatter using

supervised classification methods or a decision-tree

approach. These approaches require operator input and

are therefore not as easily processed as fully automated

products. Operator time is also a major cost. Automated

processes are therefore desirable.

3.1. Data Processing

A typical SAR based facies classification might include

the following processing steps. First the data is ingested

and calibrated. The geographic distortion is then

corrected for the effects of topography and incidence

angle (orthorectification). Then the data may be filtered

to remove speckle and perhaps smooth the high

variability of pixel to pixel backscatter. Finally a

decision tree or supervised classification scheme is

applied. A SAR segmentation and classification

algorithm provides a good basis for the classification of

glacier facies in SAR data. This approach identifies

boundaries in the image and creates objects or segments

which are then averaged. The process is iterative

allowing the user some control over segment size.

Following segmentation a supervised classification can

be performed based on the identification of training

samples (i.e. segments) which represent the surface

classes to be classified. For a masked glacier region in a

winter SAR image two or three classes may be

sufficient. Firn and bare ice facies (under a dry snow

cover) are standard a boundary class may also be

included if the firn line is fuzzy. In this case the third

output class must be analysed to determine whether it

corresponds to the accumulation or ablation area or is a

fuzzy boundary. König et al. (2004) argue that due to

the presence of large accumulations of superimposed ice

at the ELA three surface zones or facies can be

identified in winter SAR images of Svalbard glaciers.

They note a backscatter progression from high through

medium to low backscatter corresponding to the firn

area, Superimposed ice zone and the ablation area.

Elsewhere facies zonation at the firn limit is considered

to be a direct transition from the percolation zone (firn)

to the bare ice facies (Rau et al. 2000, Brown et al.

2004).

4. Testing the K-means Classifier

Given the potential advantages of an automated or

unsupervised classification scheme with a strong

correlation to mass balance the k-means approach of

König et al. (2004) was tested with a view to adopting

the methodology for operational use. The test site was

the twin icecaps of West and East Svartisen [I think western and eastern Svartisen is better, but no

biggie] in northern Norway (Fig.1). These glaciers are

the second and fourth largest glaciers in mainland

Norway and their runoff contributes to the largest

hydropower reservoir in Norway; a country with 99% of

its electricity generation provided by hydropower. Two

catchments on West Svartisen have been monitored by

the Norwegian Water Resources and Energy

Directorate. Engabreen a 38 km2 valley glacier

descending steeply to the west has had mass balance

programme since the 1970s. Storglombreen, with an

area of 60 km2 and flowing from the northern part of

West Svartisen in a north-easterly direction has had two

five-year mass balance programmes, the most recent

terminating in 2004.

The data for the test comprised of eighteen ERS-1 and -

2 SAR and Envisat ASAR scenes acquired in the

winter-spring period between 1996 and 2005. The

ASAR scenes were VV polarised image products

comparable with the ERS PRI images that comprised

the bulk of the dataset. Several other scenes were

Figure 1. A location map showing the West and East Svartisen icecaps and the Engabreen catchment with the

mass balance stakes referred to in the text. "Storglombreen is the catchment immediately to the west of Engabreen"

available but were discarded during processing because

of the obvious presence of liquid water in the snow pack

affecting backscatter. The SAR images were calibrated

using ESA BEST software and orthorectified to within

about one pixel or better. Given the large topographic

range this orthorectification RMS error cannot be

considered valid for all regions of scene but rather

represents the average RMS across the scene. This

results in better coregistration over flat regions such as

the icecap plateau (or are you including eastern

Svartisen here?) relative to the steep topography of the

margins.

Slope effects and incidence angle variation were

ameliorated using a correction based on the sine and

cosine of the incidence angle along and across track

(Ulander, 1996). The corrected images were classified

using the k-means classifier with three classes as

described by König et al. (2004). The third class was

expected to represent firn edge gradation, a fuzzy

boundary, rather than superimposed ice as there is no

evidence of this existing on Engabreen.. An example of

a classification image is shown in Fig.2.

Figure 2. A k-means classification of an ERS-2 SAR

image acquired on November 31st 2004. Three classes

were chosen in this case representing the firn area (blue)

ablation area (red) and a boundary zone (green)

assumed to be thin firn and therefore part of the

accumulation area. – see comment later

Of the eighteen scenes classified about half exhibited a

reliable result over the entirety of the two icecaps.

Given the steep topography, a strong precipitation

gradient and an undulating surface on parts of the West

Svartisen icecap it is not surprising that artifacts were

found in the classification images. Two major problems

existed. Topographic effects were still evident in the

results suggesting the topographic correction applied

failed to fully remove the topographic signal in the SAR

data. Given the presence of very steep nunataks on East

Svartisen, and very steep slopes on some of the glacier

margins this is perhaps not surprising. A second

problem, and a more serious one for the evaluation of

the classification results, was the persistent

misclassification of a large region of the Engabreen

tongue due to extensive crevassing.

Figure 3. Two examples of the k-means classification

for the Engabreen catchment. The upper image (24-12-

1993), appears a successful classification nevertheless

has a small region with an erroneous classification of

firn as a result of creavssing and the bare ice zone

extends above the plateau lip normal to topographic

contours. The lower image (29-10-1995) shows how

crevassing can affect a classification with a large region

of the lower glacier classified as shallow firn (green)-.

In both cases the classification result has been clumped

using 5 x5 filter to improve visual interpretation.

4.1. Relationship Between Classified Area and Mass

Balance

Obvious misclassifications were not found across entire

images and affect only small portions of the data. Thus

it was possible to attempt to correlate the area, in pixels

or metres2 under a particular class with mass balance

parameters. König et al. (2004) found a strong

correlation between the accumulation area in classified

images and net balance. Here we chose the ablation area

rather than accumulation area in an attempt to exclude

misclassifications around nunataks.

The longest mass balance dataset available was that

from Engabreen (Fig. 4). The period studied here, from

1996-2005, contains both positive and negative net

balance years making it particularly suitable for the

validation of the method. Mass balance data from

Storglombreen from 2000-2005 were not used due to

the shortness of the time series. Instead the

classification images of the Storglombreen catchment,

extracted from the larger images of West and East

Svartisen, were correlated with data from Engabreen.

Stake 105 on is located at high elevation close to the ice

divide between the two catchments making it suitable

for comparison with Storglombreen and Engabreen

data.

Mass balance Engabreen 1970 - 2006

-5

-4

-3

-2

-1

0

1

2

3

4

5

1970 1975 1980 1985 1990 1995 2000 2005

Bal

ance

(m w

.e.)

-5

-4

-3

-2

-1

0

1

2

3

4

5

Winter balance Summer balance Net balance

Mean

Figure 4. The mass balance time series from Engabreen ( Kjøllmoen et al, 2007). The latter part of the dataset

contains both positive and negative net balance years.

The ablation area classified by the k-means method did

not correlate strongly with the mass balance data. Area-

averaged annual net balance, summer balance and

winter balance series and local parameters at stake

locations on Engabreen did not correlate with the

classified ablation area (Table 1). The best correlations

returned were between the classified ablation area for

the whole of the two icecaps and the summer balance at

stake 105 (r2=0.47). The correlation between the

classified ablation area of Engabreen and summer

balance at stake 105 was r2= 0.31. This suggests that

using the wider area performs some form of spatial

averaging reducing the classification error. Net balance

at stake 105 was weakly correlated with the ablation

area of the two icecaps (r2=0.37).

Mass

Balance

Series

Classified

Ablation Area

Correlation

(r2)

Sample

Size

Engabreen

Averaged bn

Engabreen 0.05 10

Engabreen

Averaged bs

Engabreen 0.10

10

Engabreen

Averaged bw

Engabreen 0.00 10

Engabreen

Averaged bn

Storglombreen

0.08 9

Engabreen

Averaged bs

Storglombreen

0.01 9

Engabreen

Averaged bn

W&E Svartisen 0.30 10

Engabreen

Averaged bs

W&E Svartisen 0.31 10

Stake 105 bn W&E Svartisen 0.37 10

Stake 105 bn Engabreen 0.30 10

Stake 105 bs W&E Svartisen 0.47 10

Stake 105 bs Engabreen 0.31 10

Table 1. The relationship between different mass

balance parameters measured on Engabreen and the

classified ablation area on different parts of Svartisen.

In order to improve upon the classification a Narenda-

Goldstein clustering method was also tested (Nareda

and Goldstein 1988). This method used 10 or fewer

classes identified by the classification algorithm. In

general the classification resulted in several firn classes,

a boundary zone and an ablation zone class. Where

several classes were returned over one facies they were

aggregated. The Narenda-Goldstein method improved

slightly upon the k-means method. The Engabreen

catchment ablation areas as classified by the method

were correlated with the local net balance at stakes 17,

38 and 105 with r2 values of 0.42, 0.47 and 0.48

respectively (n=9). These classifications were not

however correlated with the local summer balance

measured at the stakes. The classifications were

correlated with area average net balance but not summer

balance (r2 values of 0.53 and 0.02 respectively, n=9).

5. Discussion

The consistent correlation of the Narenda-Goldstein

classifier with net balance but failure to correlate even

weakly with the summer balance is counter intuitive.

The delineation of the lower catchment may be one

source of error alternatively the summer balance may

not be represented as strongly in the net balance of a

catchment that receives quite large volumes of

precipitation with significant local variation in spatial

distribution. The more simple k-means approach may

reduce the complexity of the classification smoothing

natural variation.

Given the difficulty of accurately classifying all parts of

the icecaps simultaneously neither the k-means nor the

Narenda-Goldstein unsupervised approaches offer a

significant improvement in the efficiency of the SAR

image processing. The supervised segmentation-

classification approach whilst requiring more operator

time also provides an opportunity to perform

rudimentary analysis and quality control. The

segmentation images also act as a locally smoothed

SAR image with no speckle. As such they have a

potential utility in themselves (Fig.5).

Figure 5. An example of a segmentation imageand

subset showing the difference between strong volume

scattering from firn and weaker surface scattering from

the bare ice zones.

As a preliminary exploration of these data the mean

backscatter of the segments in which stakes 38 and 105

lay were correlated with backscatter. The assumption

was that the spatial smoothing associated with the

generation of the segmentation images might provide a

better basis for correlation than backscatter in a filtered

SAR image. The segmented images did not in fact

provide an alternative to classification in terms of their

correlation with mass balance parameters. Generally the

image data provide on early winter and one late winter

scene for each year. The difference between these

scenes was correlated with local specific summer

balance at stakes 38 and 105 (r2 values of 0.73 and 0.46

respectively) and weakly with local net balance (r2

values of 0.33 and 0.34). At stake 38 there was a

correlation with mean winter balance calculated over all

images available for that year (r2= 0.53). It is uncertain

if these relationships are stable or replicable (or just

random).

6. CONCLUSIONS

Our goal was to evaluate the potential of unsupervised

classification methods following the successful

application of a k-means classifier on SAR data over

Svalbard. Whilst such approaches are simplistic they do

offer the potential for the automation of SAR facies

mapping. However, the testing of two algorithms on

West and East Svartisen shows that artefacts related to

topography remain a challenge and a limitation on the

application of classifiers. Ultimately whilst

unsupervised classification may work well on valley

glaciers with simple geometries and across track

orientation on glaciers with more complex topography

and different geometries such methods do not perform

well. More sophisticated methods are required; the

coupling of mass balance models and SAR,

segmentation and spatial-temporal composites,

polarimetric datasets, and improved topographic

correction are likely to improve our ability to process

SAR data over glaciers in the future.

7. ACKNOWLEDGEMENTS

This research was an R & D activity in support of

GMES (Polarview) service development. It was

supported by NVE, FP6 EC INTEGRAL project

(Contract No. SST3-CT-2003-502845) the Universities

of Bonn and Stockholm, ESA and the Swedish National

Space Board. Additional research was done under the

Envisat project AOE 601. 8. REFERENCES

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