AVALANCHE AND SNOWFALL MONITORING WITH A GROUND-BASED SYNTHETIC APERTURE RADAR

Alberto Martinez-Vazquez, Joaquim Fortuny-Guasch

DG Joint Research Centre, European Commission Ispra, Italy

alberto dot martinez at jrc dot it, joaquim dot fortuny at jrc dot it Abstract— The feasibility of monitoring fast changes in the structure of the snow cover by means of the LISA (LInear SAr) ground-based synthetic aperture radar (GB-SAR) system has been investigated. During more than 350 days of data acquisition in the Swiss Alps, divided in the winters 2003-2004 and 2004-2005, the system has successfully monitored near 100 natural avalanches, 5 artificially triggered avalanches and the snowfall phenomena. With an acquisition rate of one image every 11 minutes, the LISA instrument has shown its ability to monitor localized (few square kilometers) changes of the snow cover. Keywords: Snow cover, snowfall, avalanche, GB-SAR, LISA, differential interferometry, structural changes. 1. INTRODUCTION

In the past few years, ground based synthetic aperture radar interferometry has proved its ability to monitor large man-made structures such as buildings, dams and bridges [1] as well as natural hazards like landslides [2]. Historically conventional space-borne SAR imagery has been used to map snow cover as well as to model and forecast snow melt runoff [3]. The work presented in this paper demonstrates the feasibility of monitoring the snow cover stability with techniques derived from space-borne SAR imagery, taking the advantages of ground based systems.

The deployment of the LISA instrument in the Sion valley (Switzerland) in November 2003 has provided more than 350 days of radar images at a rate of 1 image every 30 minutes for the winter 2003-2004 and 1 image every 11 minutes for the winter 2004-2005. These images are the basis of the results presented in section 4.

2. THEORY

The propagation of electromagnetic waves in snow is governed by the complex permittivity which is strongly dependent on the liquid water content. The penetration depth at the wavelength λ0 can be estimated from the real and imaginary parts of the complex relative permittivity.

The imaginary part of the permittivity of dry snow at C and L-band is of the order of 0.001 to 0.0001, whereas the real part depends only on the snow density [4], resulting in a typical penetration depth on dry winter snow of 20 m at C-band and more than 50 m at L-band [5].

The DInSAR (Differential Interferometric SAR) analysis conducted in this paper exploits the large penetration depth in dry snow, taking into account that the main contribution of backscattering from ground covered by dry winter snow stems from the ground surface. Thus, the snow acts as a layer which refracts and slightly attenuates the electromagnetic waves.

Synthetic aperture radar differential interferometry is based on the phase comparison of a pair of complex coherent radar images of the same scene taken at different instants of time. The interferogram is the basic operation that provides the phase shifts mentioned before. It is defined by the complex correlation function γ, and is computed pixel by pixel according to Eq. 1:

2

22

1

21

||||

*

II

II ⋅=γ (1)

The averaging operator ⟨⋅⟩ has been implemented as the two-dimensional convolution of the power signal with a square matrix of size 3×3 with all its components equal to one. The * operator represents the complex conjugate of the signal.

From the interferogram two products are obtained: the coherence (absolute value of γ), and the interferometric phase (angle of γ). A two-dimensional phase unwrapping algorithm has been applied to the interferometric phase. The algorithm implemented is an FFT-based solution of the unweighted least-squares phase unwrapping problem, that is simple to implement and computationally very efficient because it avoids the loops of the path-following methods. This is a critical point in the processing chain when dealing with the huge amount of images gathered with GB-SAR systems.

Before any processing, a co-registration has been applied to the raw data in order to reduce the observed rotation in the azimuth direction due to changes in the propagation medium (changes in the temperature and humidity of the air and snow). Choosing as reference the first image acquired on the day, the inverse rotation has been computed and applied to the rest of images by maximizing the cross-correlation of the signal’s complex modulus. Thanks to the zero padding technique, the sampling rate in the transformed domain has been increased to achieve sub-pixel resolution in the computation of the inverse rotation [6].

3. INSTRUMENT AND TEST SITE

The system used to monitor the snow cover, LISA, is a ground based linear SAR fully developed and built at the Joint Research Centre (Ispra, Italy) of the European Commission. The main component of the instrument is a vector network analyzer, which is used to generate the stepped-frequency continuous wave (SF-CW) radar pulses and receive the coherent responses. A sled carrying the network analyzer, the power amplifier and the antennas is slid along a rail 5 m long in order to synthesize a linear aperture such that the azimuth resolution is obtained. This movement is directed by a linear positioner by means of a serial interface and the appropriate control software. A mobile phone is used to remotely control the automatic measurements through the public GSM network. Finally a personal computer operates all the systems and is used for data archiving and processing.

The radar was operated in vertical polarization with a central frequency of 5.83 GHz (C-band) and a bandwidth of 60 MHz, sampled in 1601 points. The aperture synthesized along the x-axis was 3.5 m long in 251 steps. The resulting data has been focused in a 601×601 pixels image over a 2200×2200 m rectangle by means of an FFT-based algorithm [7].

Fig. 1. Ortho-photo of the test site with the location and coverage of the instrument

The instrument has been used to monitor a Swiss test-site for avalanche experiments managed by the Swiss Federal

Institute for Snow and Avalanche Research (SLF, Davos) [8]. The site is located in the Sionne valley, in the Canton Valais of Switzerland, and consists of a concave shaped channel of 1200 m vertical drop and an average slope of 27 degrees. The avalanche path length is 2.5 km, starting from the highest elevation level at 2650 m a.s.l. and finishing at the level of 1450 m a.s.l.

The LISA system is located in the mountain opposite to the avalanche corridor at an approximate level of 1800 m a.s.l. In this way the instrument has direct visibility of the scene, covering an area of nearly 2 km by 2 km as can be seen in Fig. 1. Superimposed to the ortho-photo in Fig. 1 is the division of the focused area into 18 sectors to identify the starting position of the avalanches.

4. RESULTS

The LISA system has monitored a representative number of natural and artificially triggered avalanches. In Table I can be seen the artificial avalanches monitored by the instrument, where the size is expressed in meters according to a local system centred in the LISA position and oriented according to the radar field of view.

Table I. Artificial avalanches monitored Date Size Position 2004/01/19 10.31h 300x1800 1 2004/01/19 10.58h 400x1800 2 2005/02/17 10.09h 150x1600 1 2005/02/17 10.30h 100x400 1 2005/02/17 10.51h 300x2000 2

Thanks to the automatic and continuous measurements performed during the two winter campaigns, near 100 natural avalanches have been monitored and semi-automatically classified into a catalogue. The process has been carried out through image segmentation and classification techniques, with subsequent human supervision. Table II shows the biggest avalanches for the winter 2003-2004 (the nivological year is considered from 1st October 2003 to 30th September 2004), while Table III shows the biggest avalanches for the 2004-2005 winter.

Table II. Main natural avalanches monitored in the winter 2003-2004 Date Size Position 2004/01/15 05.54h 100x600 1 2004/01/15 09.31h 100x1400 1 2004/01/16 14.28h 100x300 4 2004/03/18 13.58h 100x1400 2 2004/04/07 02.06h 200x1000 2 2004/05/02 00.31h 100x400 1 2004/05/03 16.39h 100x800 4 2004/05/06 08.16h 200x400 1 2004/05/06 14.08h 100x800 2

Reflectivity images before and after the avalanches have also been analyzed. In some cases, the area affected by the avalanche presents a higher reflectivity after the avalanche due to the fact that the surface becomes rougher. Nevertheless, this phenomenon is only observed in a very limited number of the avalanches monitored in our field campaign, so the differential interferometry remains the principal technique in order to reveal avalanches in a time-series of radar images.

Table III. Main natural avalanches monitored in the winter 2004-2005 Date Size Position 2005/01/18 19.30h 100x1400 1 2005/01/18 20.34h 100x1000 1 2005/01/18 23.35h 200x300 1 2005/01/19 07.46h 200x300 1 2005/01/21 23.36h 100x300 1 2005/01/22 04.35h 300x1600 2 2005/01/22 19.31h 100x300 1 2005/01/23 04.36h 100x400 1 2005/01/23 05.50h 200x400 1 2005/01/23 10.07h 200x800 1 2005/01/23 12.15h 200x600 2 2005/01/24 02.50h 100x300 1 2005/01/24 17.48h 200x1800 1 2005/01/25 04.29h 150x1000 1 2005/03/07 05.00h 100x200 1 2005/03/13 04.55h 100x300 1 2005/03/15 05.47h 100x300 14 2005/03/15 14.25h 100x1200 1 2005/03/19 18.00h 150x800 11 2005/03/23 06.19h 100x200 9 2005/03/24 20.39h 100x600 7 2005/03/24 23.38h 100x800 7

2005/03/28 18.44h 100x800 7 2005/04/08 15.15h 150x1000 5 2005/04/09 12.46h 100x400 1 2005/04/09 14.27h 100x400 2 2005/04/17 09.50h 100x300 1 2005/04/17 13.50h 100x600 1 2005/04/18 09.49h 100x500 2 2005/04/28 15.37h 100x300 1

4.1. Artificial avalanches The following maps show the interferometric coherence (Fig. 2-a) and phase (Fig. 2-b) of three artificially triggered

avalanches on the 17th February 2005. The area shown corresponds to the square in Fig. 1 rotated by about 45 degrees.

Fig. 2. Artificial avalanches

The two white zones on the top right of the coherence images correspond to radar shadow areas, where no signal can

be received. They are better seen in the ortho-photo in Fig. 1 as the two prominences at the right of the LISA perpendicular axis.

Before the avalanches are triggered, the interferograms present a very high average coherence modulus (0.97) due to excellent weather conditions. The three avalanches produce a clear reduction of the coherence (Fig. 2-a) on the affected paths, decreasing the average value to 0.83. On the other hand, the interferometric phase (Fig. 2-b) becomes completely random on the avalanche paths, while a slight instability is also observed on the top-right part of the phase.

Another interesting phenomenon observed is how the displaced snow has accumulated on the base of the mountain, where a small forested area is present. This is seen as a uniform decrease in the coherence (very bottom of the image) plus a random component (in y = 850 to 1000 m) due to the powder cloud coming from the movement of the snow. A similar behaviour is also observed in the interferometric phase.

4.2. Natural avalanches Fig. 3 shows the interferometric coherence and phase of three natural avalanches monitored the 23rd of January 2005.

In this case the avalanches are smaller than the artificial ones presented in the previous subsection. The two shadow areas with null coherence and phase, characteristic of the test site, can be seen again. The natural avalanches are situated in the top-left part of the maps (two of them) and in the top-right part (the third one). This last avalanche can be observed more clearly in Fig. 3-b as a random interferometric phase.

When observing the time series of differential interferograms immediately after the avalanches (natural or artificial) a slight uniform increase of the interferometric phase is appreciated. The interpretation of this behavior is not yet completely clear.

Fig. 3. Natural avalanches

4.3. Snowfall Finally, we present in Fig. 4 the snowfall phenomena as seen from our GB-SAR at C-band. The master image for the

computation of the differential interferograms is chosen to be half an hour before the results of Fig. 4-a and 4-b and one hour before the results of Fig. 4-c and 4-d. The interval between these results is then 30 minutes.

Fig. 4. Snowfall in two stages

The local meteorological station recorded an increase of the snow height of approximately 5cm between the 15.04h and the 16.08h of the 18th of January 2005.

The radar images confirm, as expected, the fact that the coherence deteriorates in a progressive mode and in a distributed way all along the observed area. In the case of avalanches, on the contrary, the coherence deteriorates abruptly and in a very well localized zone.

Concerning the interferometric phase, it changes smoothly in the case of snowfall. Nevertheless, the phase at the bottom of Fig. 4-b and 4-d presents a random behaviour because this area corresponds to a forest. Differential interferograms are very noisy in forested areas because of the movement of the branches and leaves, that is naturally increased when snowing. When avalanches occur, on the other hand, the phase behaves randomly because of the fast velocity of the phenomenon.

5. CONCLUSIONS

The semi-automatic classification of the radar images has enabled the creation of a catalogue of the avalanches which occurred in the whole test site (2km × 2km). This technique inherits the all-weather remote sensing advantages of the radar systems. Thus, the problems inherent to the current visual and seismic methods are overcome: miss of visual images under bad weather conditions or on nocturnal hours, miss of seismic data out of the area covered by the geophones and necessity to access the area for the installation of the instruments.

The archive available (more than 33000 SAR images), corresponding to two winters and including signatures of natural and artificially triggered avalanches as well as of snowfall, should allow the study of other interesting phenomena related to the snow cover. Together with the ground truth data, it may also lead to the automatic discrimination of avalanches with respect to heavy snowfall, or snow drift because of the weather conditions, and to the identification of some new precursors of avalanches based on ground-based SAR imagery.

6. ACKNOWLEDGEMENTS

The authors would like to thank Urs Gruber and François Dufour from SLF (Davos, Switzerland) and Giuseppe Antonello, David Shaw, Dario Tarchi, Marco Basso and Jorge Morgado from DG JRC (Ispra, Italy) for their collaboration in this work.

7. REFERENCES

1. M. Pieraccini, D. Tarchi, H. Rudolf, D. Leva, G. Luzi, C. Atzeni, “Interferometric radar for remote monitoring of buildings deformations.” Electronic Letters, vol.36, no.6, pp.569-570, 2000.

2. D. Leva, G. Nico, D. Tarchi, J. Fortuny-Guasch, A.J. Sieber. “Temporal analysis of a landslide by means of a ground-based SAR interferometer.” IEEE Transactions on Geoscience and Remote Sensing, vol.41, April, pp.745-752, 2003

3. Jiancheng Shi, J. Dozier, H. Rott. “Active microwave measurements of snow cover progress in polarimetric SAR.” International Geoscience and Remote Sensing Symposium, IGARSS '94. vol.4, August, pp.1922-1924, 1994.

4. C. Matzler. “Microwave Permitivity of Dry Snow.” IEEE Transactions on Geoscience and Remote Sensing, vol.34, no.2, March, 1996.

5. H. Rott, T. Nagler, R. Scheiber. “Snow Mass Retrieval by means of SAR Interferometry.” 3rd FRINGE Workshop, European Space Agency, Earth Observation, 2003.

6. A. Martinez-Vazquez; J. Fortuny; U. Gruber; “Monitoring of the Snow Cover with a Ground-Based Synthetic Aperture Radar”. 4th EARSeL Special Interest Group on Land Ice & Snow (LIS-SIG) Workshop eProceedings 4 2/2005, Bern (Switzerland), February 2005.

http://las.physik.uni-oldenburg.de/eProceedings/vol04_2/04_2_martinez1.html 7. J. Fortuny, A.J. Sieber. “Fast Algorithm for a Near-Field Synthetic Apperture Radar Processor.” IEEE Trans.

Antennas and Propagation, vol. 42, pp.1458-1460, Oct. 1994. 8. F. Dufour, U. Gruber, B. Sovilla, P. Bartelt. “A new Swiss test-site for avalanche Experiments in the Vallée de la

Sionne / Valais”. http://www.wsl.ch/slf/schnee-lawinen/Lawinendynamik/VdlS/vdls-haupt-en.html