Snow Properties Rs

8/7/2019 Snow Properties Rs

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Remote sensing based retrieval of snow cover properties

A. Schaffhauser a,, M. Adams a, R. Fromm a, P. Jrg a, G. Luzi b, L. Noferini b, R. Sailer a

a Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Innsbruck, Austriab Department of Electronics and Telecommunications, University of Florence, Italy

a b s t r a c ta r t i c l e i n f o

Article history:

Received 13 September 2007

Accepted 22 July 2008

Keywords:

Remote sensing

Terrestrial laser scanner

Ground based synthetic aperture radar

Snow depth

Snow water equivalent

In order to overcome the restrictions of conventional observation methods, novel remote monitoring

techniques such as terrestrial laser scanning (TLS) and ground based interferometric synthetic aperture radar

(GB SAR) are concurrently operated. Snow depth and snow water equivalent (SWE) or the snow mass onground are some of the key parameters in the assessment of avalanche hazard, for snow, snow drift and

avalanche modelling as well as model verification. While the TLS provides maps of the spatial snow depth

distribution, the GB SAR can in principle be used to retrieve snow depth and SWE. Remote sensing results are

compared to traditional field work, additionally advantages and limitations of the techniques are identified.

Finally, the applicability of the remote sensing based retrieval of these snow cover properties for snow and

snow avalanche applications is summarized.

2008 Elsevier B.V. All rights reserved.

1. Introduction

Traditional observation techniques (snow pits, probing, ultrasonic

snow depth sensors) provide primary input data for fore- and

nowcasting snow avalanche hazard in alpine regions. Because of

inhospitable weather conditions and inaccessibility due to avalanche

danger, in situ observations in avalanche terrain are rare. Remote

monitoring techniques offer the possibility to retrieve important snow

cover parameters such as snow depth and snow water equivalent

(SWE) from a safe distance. During the last decade substantial

progress has been made in the development of physically based

models (snow, snow drift and avalanches) and avalanche fore- and

nowcasting tools (e.g. Bartelt and Lehning, 2002; Sampl and Zwinger,

2004). High resolution retrieval of snow cover properties is needed as

model input, for model optimization and verification (Sailer et al.,

2008) and the forecast of avalanche danger.

Objectives of the study carried out in the framework of the

GALAHAD project (Advanced Remote Monitoring Techniques for

Glaciers, Avalanches and Landslides Hazard Mitigation) are (i) the

definition of the requirements for improved remote monitoring of

snow depth and SWE, (ii) the validation of the remote monitoring

observations and (iii) the evaluation of the fulfillment of the

requirements. GALAHAD is a European Union funded research project

focused on the development of advanced and innovative remote

monitoring techniques, namely GB SAR (ground based synthetic

aperture radar) interferometry and TLS (terrestrial laser scanning).

TLS is used in several applications such as scanning architecture

(Pfeifer and Rottensteiner, 2001), topography (digital elevation

models), landslides (Rowlands et al., 2003) and the derivation and

interpretation of geomorphologic structure (Deline et al., 2004;Conforti et al., 2005). The use of airborne laser scanners is gaining

importance in glaciological applications, in particular for the genera-

tion of glacier surface models (Baltsavias et al., 2001) and measure-

ments of ablation and accumulation of snow and ice at an annual time

scale (Lippert et al., 2006; Geist et al., 2003, 2005). However, adopting

the means of laser scanning for snow and avalanche research is rare,

up to nowonly few projects have been carried out. TheTLS technology

has been used for snowpack measurements within the SAMPLE

project (Snow avalanche monitoring and prognosis by Laser equip-

ment; Moser et al., 2001). Prokop et al. (in press) used TLS for the

determination of the spatial snow depth distribution on slopes. The

authors reported a mean deviation between TLS and tachymetry

(reference measurements) of 4.5 cm with a standard deviation of 2 cm

up to a distance of 300 m. Snow depth mapping in a forested area has

been carried out with airborne laser scanning (Deems and Painter,

2006).

The estimation of snow parameters can benefit from microwave

remote sensing based on passive (radiometry) and active (scattero-

metry, SAR) radar techniques. Different algorithms have been devel-

oped during the last years for the retrieval of SWE, e.g. Shi and Dozier

(2000) for a radar algorithm. Dry snow layers at longer wavelengths (L

to C band) can be considered almost transparent with a moderate

volume scattering depending on the frequency. For dry snow

conditions the penetration depth for the C band amounts to about

20 m. In contrast to theTLS, themaincontribution to thebackscattered

signal stems from the snow/ground interface. When targeting wet

Cold Regions Science and Technology 54 (2008) 164175

Corresponding author.

E-mail address: [email protected] (A. Schaffhauser).

0165-232X/$ see front matter 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.coldregions.2008.07.007

Contents lists available at ScienceDirect

Cold Regions Science and Technology

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snow, attenuation occurs due to the presence of liquid water, thus the

interaction becomes morecomplexand the penetration depth reduces

dramatically to a few centimetres. Higher frequencies show an

increased sensitivity to dry snow properties, but have a limited ability

of penetrating a wet snow cover. In the last years the capability of

mapping snow cover by means of SAR images from satellite has been

widely investigated. In particular C band data have been suggested forthe classification and the discrimination of bare surface and snow

covered area (e.g. Bernier and Fortin, 1998). In order to retrieve the

SWEof dry snowcoverfrom C bandinterferometricdata available from

spaceborne platforms, a retrieving approach has beeninvestigated and

applied to ERS interferometric data by Guneriussen et al. (2001).

Changes of the snow properties between two consecutive interfero-

metric SAR images cause changes of the interferometric phase. Several

further studies demonstrated the capability of spaceborne and

airborne SAR systems for the retrieval of dry snow properties

(Koskinen, 2001; Rott et al., 2004). A similar approach has been

applied for a ground based interferometer by Martinez-Vazquez and

Fortuny-Guasch (2006) and by Luzi et al. (2007). Avalanche tracks

appear as zones of high degradation of the coherence, in interfero-

metricphase maps they show up as areas of random noise. In additionan algorithm to retrieve the depth of dry snow was developed and

validated (Martinez-Vazquez and Fortuny-Guasch, 2006).

The requirements for improved remote monitoring of snow depth

and SWE were defined as follows. The operational range of the

instruments must cover the entire avalanche track, in order to

determine the avalanche mass balance. Additionally the range must

allow the installation of the sensors in a safe distance of the target,

providing observation geometry suitable for the specific applications.

Continuous observations with adequate spatial (less than 5 m) and

temporal resolution (1 h to 1 day) are required for snow cover models,

avalanche dynamics models, forecast of avalanche hazard and snow

redistribution studies. The accuracy of the observations has to be high

enough to resolve significant snowpack changes in order to observe the

snowpack evolution during a storm (accumulation, wind influence and

settlement), before and after avalanche events and lies at about 0.1 m

(mean absolute error, MAE). The acceptable error of the SWE retrieval is

about20%. Observational and technicalrequirementsare summarizedin

Table 1.

2. Test site

TheGALAHADprojecttest sitefor snowand avalanche observations

is located in the Wattener Lizum (province of Tyrol, Austria), a training

centre of the Austrian Army (Fig. 1). Thestudy area (Fig. 2) is equipped

with four automatic weather stations (AWS). The Meteo Slope AWS is

in theline of sight of theremote monitoring instruments in the central

part of the target slope. Ultrasonic snow depth measurements deliver

ground truth observations for the verification of the remote sensing

data. Snow depth observations with snow stakes are also available forvalidation. Both remote monitoring instruments (TLS and GB SAR) are

installed in the valley bottom at an altitude of 2040 m, sheltered by a

wooden hut. The distance to the boundary of the target area, the

Tarntaler Kpfe (peak at 2757m) is about 1900 m andwithin therange

of both remote monitoring instruments (Fig. 2). The difference in

Table 1

Observational requirements for the use of SAR and TLS for snow avalanche applications

and degree of fulfillment

Requirements Rating

GB SAR TLS

Observation range

Measurement range 1.5 to 3 km Medium Medium

Field of view 30 High High

Accuracy/resolution Accuracy Spatial

resolution

Temporal

resolution

Rating

GB SAR TLS

Snow depth changes 0.1 m b5 m 1 h to 1 d Low High

SWE 20% b5 m 1 h to 1 d Low

Instrumental requirements

All weather

measurement

Independency to air humidity,

cloudiness, precipitation, snow

drift, etc.

High Low

Temperature range 35 C to +50 C High Low

Resistance Against snow/ice, rain, rime,

moisture

High Medium

Weight Por table system ( 1 to 2 per sons) is

needed, the system has to be mobile to

ensure versatileand multi-purposeuse

Low Medium

Scan rate High scan rate with low pulse time is

needed to ensure the required

temporal resolution

High Low

Power consumption Low electric power consumption is

asked to ensure self-contained power

supply (i.e. solar radiation)

Medium Medium

Fig.1. Location of the Wattener Lizum test site (province of Tyrol, Austria).

165A. Schaffhauser et al. / Cold Regions Science and Technology 54 (2008) 164175


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data acquisition. The best results are obtained during clear nights, in

the absence of precipitation, fog or drifting snow. Under these

conditions the intensity of the backscattered signal is generally higher

than in bright daylight. The quality control of the dataset excludes

images strongly affected by fog or snowfall ( Jrg et al., 2006).

Measurements with a reflection coefficient below 0.008 are referred

to as invalid distance measurements. Above this threshold, the

magnitudeof thereflection coefficient does not influence the accuracy

of the distance measurements. The spatial resolution is chosen in a

way that ensures a trade-off between acquisition time and required

spatial resolution (0.5 to 1.0 points per m2 depending on the

inclination, the distance and the orientation of the slope). For each

point of the TLS point cloud, the vertical distance to the ground (snow

depth), represented by a high resolution Triangulated Irregular

Network, was calculated. Snow depth changes are differences

between two retrievals.

Fig. 4. Snow depths differences [m] between 9 February and 14 February 2007 retrieved by TLS (UTM32, units are m).

Fig. 3. Snow depth [m] measured by TLS on 14 February 2007 (UTM32, units are m).



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3.2. Measurements

Examples for the determination of snow depth (Fig. 3) and snow

depth changes (Figs. 4 and 5) demonstrate the potential of the TLS

technique. In alpine regions snow fall is generally accompanied by

wind causing snow redistribution, erosion on wind exposed, and snow

depositionin leeward terrain.The snowdepth distribution in the lower

part of thetarget area (Fig. 3) reveals theinfluenceof thewindfromthe

first snow fall in autumn to 14 February 2007. Wind exposed areas like

ridges are characterized by a shallow snow depth (less than 0.25 m).

Dominant and even moderate depressions are filled up with snow,

redistributed by wind. In those areas the snow depth is greater than

1.0 m with peak values of more than 2.5m in thegullies.White regions

containing no data are out of sight of the instrument. The snow depth

Fig. 6. Snow depth profile [m] across the slope (24 April 2007) obtained from TLS measurements and tachymetry observations.

Fig. 5. Snow depth difference [m] betweentwo TLS acquisitionsbeforeand after an artificiallyreleased snow avalanche on25 April 2007. Thereleaseareapolygon is indicated as a red

line (UTM32, units are m). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

168 A. Schaffhauser et al. / Cold Regions Science and Technology 54 (2008) 164175


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changes from 9 to 14 February 2007 are shown in Fig. 4. This period is

characterized bya moderatesnowfall(0.25m at theMeteo Slope AWS)

at the beginning and subsequent settlement. Up to 0.3 m snow is

accumulated in the dominant gully, whereas on the homogeneous part

of the slope the snow depth increases less than 0.20 m. Furthermorethe moderate depressions in the centre of the slope are filled by

redistributed snow. On 25 April 2007 an avalanche was artificially

released. Prior to and after the avalanche event TLS measurements of

the entire target area were carried out in order to calculate the snow

depth changes in the release area as well as in the deposition zones

(Fig.5). In the release areaat analtitudeof 2420m to2540m the meansnow depth loss is approximately 1.0 m. Not only thesnowdepositions

Fig. 8. Time series of snow depths measured by TLS compared to (a) observations of stake 9 (accuracy0.1 m, indicated by the error bar) and (b) ultrasonic measurements at the

Meteo Slope AWS.

Fig. 7. Scatterplot of snow depths obtained by TLS and geodetic survey (24 April 2007). The solid line indicates the linear fit to the data set (correlation coefficient of 0.9).



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Fig. 9. Scatterplot of snow depth acquired by several TLS surveys against observations of ten snow stakes (with a spyglass) and snow depths at the Meteo Slope AWS (ultrasonic

sensor) during the winter 2006/2007. The solid line indicates the linear fit to the data set (correlation coefficient of 0.98).

Fig. 10. Deviation of the TLS observations from the tachymetrically assessed values duringfive surveys in December 2007 and January 2008. Sample size and mean absolute error are

given in the x-axes labels.



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in the run out zone but also the accumulation islands along the

avalanche track are captured by TLS measurements. Furthermore the

erosion areas in thelower part of theavalanche trackare apparent. The

snow in the gullies was entirely eroded.

3.3. Validation

Traditional point measurements (geodetic survey, snow stakes and

ultrasonic sensor) are available for the verification of the TLS

measurements. The geodetic survey (tachymetry) was done on 24

April 2007 along profiles with 107 single points distributed over the

lower part of the investigation area (Fig. 2). The coordinates of the

snow surface and the ground (x,y,z) were measured by a total station

(tachymeter) at the stakes location and along six profiles. Snow depth

at each point was derived from these measurements. Although there

are some uncertainties in the detection of the most representative

ground point for example due to abrupt changes in the micro relief,

it is assumed that the geodetic survey leads to the most accurate snow

depth measurements. The snow depth derived from TLS observationsis an interpolated value (weighted inverse distance) within a radius of

1.5 m covering approximately 3 to 6 points, whereas the geodetic

survey refers to single point measurements. Fig. 6 depicts a snow

depth transect derived from the geodetic survey and TLS measure-

ment across the slope. The MAE (TLS minus geodetic measurements)

is 0.12 m. As all geodetic survey profiles with 107 points in total are

entirely covered by the TLS window, the MAE between both

measurement principles at one point in time is 0.14 m (Fig. 7). TLS

snow depth and the snow depth derived from the geodetic survey are

well correlated (correlation coefficient 0.90).

To verify snow depth retrievals obtained with TLS during winter

2006/2007 continuous snow depth observations of the Meteo Slope

AWS and the snow stakes are used. Although the temporal resolution

of the snow stake observations (read of by a spyglass) is relatively

Fig. 11. Accumulated coherence map between 9 February 2007 and 19 February 2007.

Table 2

GB SAR measurement parameters

Central frequency 5.948 GHz

Band 25 MHz

Transmitted power 20 dBm

Polarisation VV

Maximum target distance 2000 m

Frequency number 401

Linear scansion length 1.3 m

Linear scansion point number 111

Measurement time 14 min

Spatial resolution 2 2 m



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coarse compared to the ultrasonic measurements, all major snow

depth changes of the winter are reproduced. Again the TLS data are

interpolated by the inverse distance squared method with 1.5 m

search radius to the positions of the stakes and the Meteo Slope AWS.

Fig. 8a compares the snow depth observed at snow stake 9 and the

corresponding TLS measurement during winter 2006/2007 (25

simultaneous measurements). The MAE between both observations

is 0.12 m. Thecomparison of the snow depth data from allsnow stakes

and the corresponding TLS data show a MAE of 0.15 m. Snow depths,obtained by TLS and the ultrasonic sensor show an acceptable match

throughout the entire period (Fig. 8b) with a MAE of 0.06 m (25

coincident measurements). The scatterplot of the snow depth

obtained by TLS against the snow depth observed at the ten snow

stakes and the ultrasonic sensor is depicted in Fig. 9. The positive bias

for the TLS measurements results from an underestimation caused by

the snow stake observations (correlation coefficient of 0.98).

In order to minimize the measurement uncertainties (e.g. micro

relief, snow free DEM), tachymetry measurements of the snow surface

were carried out in the validation area prior to the TLS scans. Fig. 10

shows the deviation of the TLS observations from the tachymetric

assessed values at different times in December 2007 and January

2008. The MAE for these five surveys is 0.05 m, with a standard

deviation of 0.04 m, at a total sample size of 349 points.

Taking into account the uncertainties of the different measurements

principlesthe postulated requirementsare fulfilled in thevalidationarea

in up to 1 km distance to the instruments. The TLS technique has the

capability to deliver reliable snow depth observations.

4. Ground based synthetic aperture radar (GB SAR) interferometry

4.1. Technique

The snow mass distribution along an avalanche track (start mass

and potential entrainment mass) is one of the key input parameters

for avalanche dynamics simulations. The potential risks and high

spatial variability characterize in situ SWE observations (snow pits).

Fig.12. Snow depths (full line) and mean snow density (thick full line) calculated from

GB SAR phase shift data between 9 February 2007 and 19 February 2007. The dashed

line shows the snow depth recorded by the Meteo Slope AWS (ultrasonic sensor).

Fig.13. SWE changes between 9 February 2007 and 14 February 2007 derived from TLS and GB SAR observations (UTM32, units are m).



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They are not suitable for the estimation of the snow mass distribution

along an avalanche path. SWE (product of snow depth and the mean

density of a snow column) is defined as the height of the equivalent

water column [mm] or as mass over a unit surface area [kg/m2]. Snow

parameters are retrieved between two SAR image acquisitions by

means of phase shifts. The application of interferometry is related to

the interferometric degree of temporal coherence. The decorrelation is

strongly influenced by the presence of wet snow, melting and

refreezing play an important role. The application of this techniqueis therefore limited to periods with dry snow conditions. Because of

the high temporal resolution, better coherence is obtained by the GB

SAR observations than by satellite monitoring.

The GB SAR system of the University of Florence (Italy) consists

of continuous-wave step-frequency radar using a vector network

analyzer HP8653D as a transceiver unit (C-Band) and two antennas,

one to transmit the radar signal and the other to receive the

backscattered power. The antennas move at discrete position incre-

ments on a horizontal rail (1.3 m long) to scan the synthetic aperture.

Themoving apparatus is positioned on a stableconcreteplatform.Radar

images are acquired in real time and focused by means of a portable

personal computer, using standard SAR techniques. The system can

continuously work, sending data to a web server via a GPRS standard

mobile phone or a wireless connection. The radar parameters are listed

in Table 2.

According to Guneriussen et al. (2001) the interferometric phase

shift of a microwave signal crossing through a growing layer of dry

snow can be expressed as:

/ 4

z cos

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0 sin

2

q !; 1

where is the wavelength in the atmosphere, z the change in snow

depth, the incidence angle and the real part of the permittivity. In

the case of dry snow, the imaginary part of the permittivity can be

neglected. The real part depends only on the snow density s, which

can be approximated forsb500 kg/m3 to s=1+1.6s (snow densities

in g/cm3), such that Eq. (1) becomes:

/ 4

z cos

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 1:6s sin

2

q : 2

For local incident angles lower than approximately 60, Eq. (2) can

be reduced to a linear relationship between the interferometric phase

difference and the changes of the SWE; for the ERS case,

Guneriussen et al. (2001) proposed the following expression:

/ 4

0:87SWE: 3

While used for space- and airborne observation, Eq. (3) is not

applicable for GB SAR measurements. The geometry is completely

different from the satellite case monitoring a slope from below leads

to higher incidence angles. Incident angles are generally higher than

60 at the Wattener Lizum test site, the assumptions leading to Eq. (3)

are no longer valid, therefore the Eq. (2) must be applied.

4.2. Retrievals and validation

The analysis of the cumulative coherence map (Fig. 11) shows that

the period from 9 February to 19 February 2007 fulfils the main

requirement for the application of the GB SAR technique. Dry snow

conditions prevail the region used for the data analysis (lower

central part) is characterized by high coherence.

Generally, one of the remaining unknown parametersz and s in

Eq. (2) can be estimated or must be provided by other measurements

in order to overcome the restrictions caused by the scan geometry:

Snow depth differences z are observed by the ultrasonic snow depth

sensor located in the target area. Together with the GB SAR phase data

at the pixel closest to the station, the mean snow density of the new

layer is computed according to Eq. (2). On the other hand snow depth

changes between consecutive GB SAR acquisitions are retrieved under

Fig. 14. Snow depth differences between 9 February 2007 and 14 February 2007 calculated from GB SAR data, assuming a constant snow density of 120 kg/m3

(UTM32, units are m).



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the assumption of a constant mean snow density (snow pits). Fig. 12

shows the mean density of the new snow layer between 9 February

and 19 February 2007 (thick line), the snow depth delineated from the

GB SAR measurements (thin line) and the ultrasonic snow depth

observation (dashed line). Light snowfall on 10 February 2007 leads to

a snow density of about (80 kg/m3), during the snowfall from 13

February 2007 till the afternoon of 14 February 2007 snow depth

increases about 0.25 m. The mean snow density fluctuates due to thenoisy signal of the ultrasonic snow depth sensor. The obtained values

of 100 kg/m3 are typical for new snow. Subsequent settlement of the

new snow layer results in snow depth decreases, the mean snow

density increases from 100 kg/m3 to close to 200 kg/m3. Density

profiles recorded in safe distance at the valley bottom yield densities

between 100 to 120 kg/m3 for the freshly fallen snow and values

between 180 and 220 kg/m3 for the settled snow. The snow depth

increase during thesnowfall is well reproduced by the GB SAR, but the

amount of new snow and the subsequent settling is underestimated

due to the assumption of a constant density of 120 kg/m 3.

Analogous to the integration of the ultrasonic snow depth

measurements, spatially distributed TLS observations and GB SAR

phase shift data areused to derivemean snow density andsubsequent

SWE changes. TLS observations are available on 9 February and 14February 2007. Fig.13 shows the SWE of the snow layer formedduring

the period under consideration, higher snowaccumulation (SWE up to

40 mm) appears near gullies (snow redistribution caused by wind),

unrealistically high SWE values occur in regions of lower coherence.

The spatial distribution of the snow depth changes (Fig. 14) can again

approximately be derived from GB SAR phase shift data by assuming a

constant snow density of 120 kg/m3. The TLS system is able to deduct

snow depth changes with the required accuracy (see last section), so

that the capability of the GB SAR approach for snow depth retrieval is

validated by comparing Fig.14 with the snow depth changes observed

with the TLS (Fig. 4). The GB SAR snow depth retrieval algorithm is

able to reproduce the magnitude of the snow depth changes, but can

currently not reproduce the spatial snow depth distribution accu-

rately. Underestimation occurs especially in the upper region of the

scanned area. The snow density varies slightly along the slope.

Therefore the SWE is computed by means of TLS snow depth data

under the assumption of a constant snow density (120 kg/m3).

Analogous to the snow depth the GB SAR derived SWE (Fig. 13) is

validated by the comparison with the TLS approach, depicted in

Fig. 15. GB SAR SWE estimates are less accurate than SWE obtained

from TLS data (Fig. 15). Generally, the GB SAR retrievals are highly

uncertain, but the measurements do not depend on weatherconditions and the acquisition time is relatively short.

5. Conclusions

Two remote monitoring techniques were concurrently used for

measuring snow depth and SWE. Whereas the TLS technique just

provides information on the snow depth, GB SAR interferometry offers

the opportunity to deduce SWE. Snow depth, derived from TLS, was

validated with other observation techniques (tachymetry, snow stakes

and ultrasonic measurements) in a validation area in up to 1 km

distance to the instrument. Considering the measurement uncertain-

ties of the applied techniques, the TLS observations are within the

requested limits (Table 1). The observation error grows with

increasing range because of the divergence of the laser beam. Forthe enhancement of the accuracy at greater ranges the beam

divergence must be reduced. The successful operation of the

instrument requires optimum visibility during the acquisition;

erroneous records occur at instrument temperatures below 15 C.

GB SAR phase shift measurements cannot be converted directly into

SWE changes due to the observation geometry. The mean density or

the snow depth changes between two consecutive acquisitions has to

be estimated or provided by complementary measurements (TLS,

ultrasonic snow depth sensors). The implemented retrieval algorithm

is able to roughly reproduce the magnitude of the snow depth/SWE

changes, butit is notin the position to provide the spatial snow depth/

SWE distribution accurately. Even if the GB SAR retrievals are

uncertain, the technology provides observations independent from

the weather with a relatively short acquisition time (15 min)

Fig.15. SWE changes between 9 February 2007 and 14 February 2007 derived from TLS data under the assumption of a constant snow density of 120 kg/m 3 (UTM32, units are m).



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compared toTLS. GB SARmeasures the phase shift relative to an initial

condition. Assumptions on the snowpack are necessary for the

initialisation of the GB SAR measurements. TLS observations provide

initial snow depth and SWE (assumption of constant snow density) at

the beginning of the GB SAR observation period.

The rating (low/medium/high) of the accuracy of the instruments

and the fulfillment of the requirements is summarized in Table 1.

Currently the proposed techniques are useful for scientific purposes;

they provide input to snow avalanche dynamics models, snow driftstudies and snow models. Snow data in high temporal resolution are

available for more sophisticated fore- and nowcasting tools. The

techniques are not adequate for operational use (snow and avalanche

related themes), where accurate real time information under all

weather conditions is required. Recent advances of laser technology

permit a reduction of the TLS acquisition time by a factor of 10

accompanied by an enhancement of the observation range to up to

4 km. Taking into account, that the technique fails during a snow

storm, TLS can operationally be used for the monitoring of avalanche

tracks. Further research is needed into the development of algorithms,

which are able to overcome the restrictions caused by the GB SAR's

observational geometry.

Acknowledgements

The GALAHAD project is funded by the European Union (Specific

Targeted Research Project FP6-2004-Global-3, N. 018409). The authors

would like to thank colonel Knoll, head of the military training camp

in Wattener Lizum, and his team for the support and the two

anonymous reviewers for the detailed and valuable comments.

References

Baltsavias, E.P., Favey, E., Bauder, A., Bsch, H., Pateraki, M., 2001. Digital surfacemodelling by airborne laser scanning and digital photogrammetry for glaciermonitoring. Photogrammetric Record 17 (98), 243273.

Bartelt, P., Lehning, M., 2002. A physical SNOWPACK model for the Swiss AvalancheWarning Services. Part 1: numerical model. Cold Regions Science and Technology35,123145.

Bernier, M., Fortin,J.P.,1998. Thepotential of timeseries C-Band SARdata to monitordry

and shallow snow cover. IEEE Transactions on Geoscience and Remote Sensing 36(1), 226245.

Conforti, C., Deline, P., Mortara, G., Tamburini, A., 2005. Terrestrial Scanning LidarTechnology applied to study the evolution of the ice-contact image lake (MontBlanc, Italy). Proceedings of the 9th Alpine Glaciological Meeting, Milan, Italy.

Deems, J., Painter, T.H., 2006. Lidar measurement of snow depth: accuracy and errorsources. International Snow Science Workshop (ISSW) Proceedings, Telluride, USA,pp. 300338.

Deline, P., Diolaiuti,G., Kirkbride, M.P., Mortara,F., Pavan,M., Smiraglia, C., Tamburini, A.,2004. Drainage of ice-contact Miage Lake (Mont Blanc Massif, Italy) in September2004. Geografia Fisica e Dinamica Quaternaria 27 (2), 113119.

Dozier, J., Painter, T.H., 20 04. Multispectral and hyperspectral remote sensing of Alpinesnow properties. Annual Review of Earth and Planetary Sciences 32, 465494.

Geist, T., Lutz, E., Sttter, J., 2003. Airborne laser scanning technology and its potentialfor applications in glaciology. International Archives of Photogrammetry, RemoteSensing and Spatial Information Science XXXIV (3W13), 101106.

Geist, T., Elvehy, H., Jackson, M., Sttter, J., 2005. Investigation on intra-annualelevation changes using multitemporal airborne laser scanning data case studyEngabreen, Norway. Annals of Glaciology 42, 195201.

Guneriussen, T.,Hgda, K.H.,Johnson, H., Lauknes, I., 2001. InSAR forestimating changesin snowwaterequivalentof dry snow. IEEETransactions on Geoscience and RemoteSensing 39 (10), 21012108.

Jrg, P., Fromm, R., Sailer, R., Schaffhauser, A., 2006. Measuring snow depth with aterrestrial laser ranging system. Proceedings for International Snow ScienceWorkshop (ISSW), Telluride, USA, pp. 452460.

Koskinen, J.T., 2001. Snow monitoring using interferometric TOPSAR data. Proceedingsof IGARSS 2001, Sydney, Australia, pp. 28662868.

Lippert, J., Wastl, M., Sttter, J., Moran, J., Geist, T., Geitner, C., 2006. Measuring andmodellingablation and accumulation on glaciers in Northern Iceland.Zeitschrift frGletscherkunde und Glazialgeologie 89, 8798.

Luzi, G., Pieraccini, M., Noferini, L., Mecatti, D., Macaluso, G., Atzeni, C., Jrg, P., Sailer, R.,2007. Microwave interferometric measurements over a snow covered slope: anexperimental data collection in Tyrol (Austria). Proceedings of IGARSS 2007Barcelona, Spain.

Martinez-Vazquez, A., Fortuny-Guasch, J., 2006. Snow cover monitoring in the SwissAlps with a GB-SAR. IEEE Geoscience and Remote Sensing Society Newsletter 138,1114.

Moser, A., Geigl, B., Steffan, H., Baur, A., Paar, G., Fromm, R., Schaffhauser, H., Kck, K.,

Schnhuber, M., Randeu, W.L., 2001. SAMPLE Snow Avalanche Monitoring andPrognosis by Laser Equipment. Final Report. EU Target Area II regional supportfunded, Styrian Government Ref. AAW 11 L 6 97 /5. 116 pp.

Pfeifer, N., Rottensteiner, G., 2001. The Riegl Laser Scanner for the surveyof the Interiorsof Schnbrunn Palace. In: Grn, A., Kahmen, H. (Eds.), Optical 3-D MeasurementsTechniques V, pp. 571578.

Prokop, A., Schirmer, M., Rub, M., Lehning, M. and Stocker, M., in press. A comparison ofmeasurement methods: terrestrial laser scanning, tachymetry and snow probingfor the determination of the spatial snow-depth distribution on slopes. Annals ofGlaciology, 49.

Rott, H., Nagler, T., Scheiber, R., 2004. Snow mass retrieval by means of SARInterferometry. Proceedings of the FRINGE2003 Workshop, ESA/ESRIN, Frascati,Italy, ESA SP-550.

Rowlands, K.A.,Jones, L.D.,Whitworth, M., 2003.Landslidelaser scanning:a newlook atan old problem. Quarterly Journal of Engineering Geology and Hydrogeology 36,155157.

Sailer, R., Fellin, W., Fromm, R., Jrg, P., Rammer, L., Sampl, P., Schaffhauser, A., 2008.Snow avalanche mass-balance calculation and simulation-model verification.Annals of Glaciology 48, 183192.

Sampl, P., Zwinger, T., 2004. Avalanche simulations with SAMOS. Annals of Glaciology38, 393398.

Shi, J., Dozier, J., 2000. Estimation of snow water equivalent using SIR-C/X-SAR, Part I:inferring snow density and subsurface properties. IEEE Transactions on Geoscienceand Remote Sensing 38 (6), 24652474.

Weitcamp, C., 2005. Range-resolved Optical Remote Sensing of the Atmosphere.Springer, New York.


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