Natural Hazards and Earth System Sciences Automated detection of snow avalanche deposits : segmentation and classification of optical remote sensing imagery

Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object–oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits. 1 Avalanche hazards Mountainous countries, such as Switzerland, Norway and Canada, are annually subjected to natural hazards originating in alpine terrain. Such hazards include rockfall, landslide, debris flow, flash floods, snow and ice avalanches. The impacts of such hazards range from delays and financial loss through road and rail line closures, the destruction of property and infrastructure, to loss of life. Of these major geologically and meteorologically controlled natural hazards, snow avalanches have caused the greatest number of causalities in Norway during the last decades (Jaedicke et al., 2008). The same is reported from Switzerland for the period of 1997– 2007 (Harvey and Zweifel, 2008). In the same time period, an estimated 1020 causalities have been reported as a direct result of snow avalanches in the entire European Alps and 250 per year worldwide (Schweizer, 2008). Specifically, 155 fatalities from snow avalanche accidents were reported in Canada during the period 1997–2007 (Jamieson et al., 2011). Snow avalanches are the result of unstable masses of snow that detach from a mountain or hillside. Snow avalanches can occur in various meteorological situations, within variable terrain and can manifest themselves in many forms, the common division among avalanches being loose snow versus slab and wet versus dry snow avalanches. Also powder clouds can be the component of any dry moving avalanche. The natural release of an avalanche is typically dependant on parameters such as snowfall, temperature, wind direction and speed, snowpack conditions, slope orientation, terrain and vegetation (Armstrong and Armstrong, 1987; Gubler and Bader, 1989; McClung and Schaerer, 1993). Figure 1 portrays two dry snow avalanches, one naturally triggered avalanche deposit (a) and one artificially triggered at the Norwegian Published by Copernicus Publications on behalf of the European Geosciences Union. 2894 M. J. Lato et al.: Automated detection of snow avalanche deposits Fig. 1. Deposit of a naturally released snow avalanche (a); Artificially triggered avalanche in transit at NGI’s research station at Ryggfonn (b). Both sites are located within the Strynefjell region, Sogn og Fjordane, Norway. Photos courtesy of: Krister Kristersen (a) and Hedda Breien(b), Norwegian Geotechnical Institute. Geotechnical Institute’s research station at Ryggfonn, Stryn, Western Norway (b). 2 Need for more complete and precise avalanche data Snow avalanches, in general, are poorly mapped, which is commonly due to the remote location of their occurrence. Often avalanches are only reported if they cause an obstruction to public infrastructure, damage to personal property, or are witnessed and reported by local observers. However, decisions regarding, e.g., the closure of roads and rail lines and the setting of warning levels, rely on information that is derived from knowledge of historic events in combination with metrological data of the recent past and expected future (Bellaire et al., 2011). The general practiced routine for mapping snow avalanches relies on two main techniques. The first technique involves a field mission to map the extent and location of avalanche start-zones and runout-zones by hand, by amateur photographs, or with a GPS device. These field missions are typically conducted during the peak avalanche period of the year as snow depth and layers are also recorded in the field. Problems related to this method are: poor accessibility of the terrain due to avalanche danger, only small areas can be surveyed, and surveys can only be conducted in good weather. The second commonly employed technique for mapping snow avalanches is the visual analysis and digitising of aerial photographs or optical remote-sensing imagery (Scott, 2009). Both methods require expert involvement and visual identification of a fallen snow avalanche. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical imagery would greatly assist in the development of accurate, spatially widespread, detailed maps of zones historically prone to avalanches. Such maps could be used to estimate regional susceptibility. This would provide decision makers with knowledge of the frequency of avalanches as well as details regarding the size and extent of historical events. This increase in knowledge and access to data will have two pertinent applications: short-term forecasting and mitigation (e.g., road/railway closures, evacuations), and long-term planning (e.g., transport network planning, mitigation measure design, municipal planning). Forecasting, mitigation and planning are critical for maintaining public safety, and managing public developments into the future through the use of susceptibility modelling. Another application is the use of such data in modelling: recently developed numerical avalanche simulation tools such as RAMMS (Christen et al., 2010), SAMOS (Sampl and Zwinger, 2004) or ELBA+ (Keiler et al., 2006) require accurate avalanche release and run-out information for calibration and validation of the models. Nat. Hazards Earth Syst. Sci., 12, 2893– 2906, 2012 www.nat-hazards-earth-syst-sci.net/12/2893/2012/ M. J. Lato et al.: Automated detection of snow avalanche deposits 2895 Table 1.Selection of VHR spaceborne and airborne sensors potentially suited for snow avalanche mapping. Note: for space-borne sensors, only commercial ones are listed. Spatial resolution is given at nadir. Spaceborne Sensor Lifetime Bands Spatial resolution (m) Swath Revisit time panchromatic multi-spectral (km) (days) IKONOS 1999p, b, g, r, nir 0.8 3.6 11 3 QUICKBIRD 2001p, b, g, r, nir 0.6 2.4 16.5 1 to 3.5 GeoEye-1 2008b, g, r, nir 0.5 2 15.2 3 WorldView-1 2007p 0.5 – 17.6 1 to 5 WorldView-2 2009p, 8 ms 0.46 2 16.4 1 to 4 ORBVIEW-3 2003p, 4 ms 1 4 8 <3 SPOT 5 p, g, r, nir, swir 2.5, 5 10 60 2 to 3 FORMOSAT-2 2004p, b, g, r, nir 2 8 24 daily KOMPSAT-2 2006p, b, g, r, nir 1 4 15 2 to 3 KOMPSAT-3 2012p, b, g, r, nir 0.7 2.8 15 2 to 3 Cartosat 2B 2010 p 0.8 – 9.6 4 Pléiades 1 2011 p, b, g, r, nir 0.7 2.0 20 daily Pléiades 2 2012 p, b, g, r, nir 0.7 2.0 20 daily