Mapping avalanches with satellites – evaluation of performance and completeness

Hafner, Elisabeth D.; Techel, Frank; Leinß, Silvan; Bühler, Yves

doi:10.5194/tc-15-983-2021

Cited by 40 publications

(40 citation statements)

References 42 publications

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“…• SLF avalanche cadastre, which has a focus on the regions Davos and Zuoz in Grisons (Schweizer et al, 2020;Hafner et al, 2021).…”

Section: Validationmentioning

confidence: 99%

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Automated avalanche hazard indication mapping on state wide scale

Bühler

Bebi

Christen

et al. 2022

Preprint

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Abstract. Snow avalanche hazard mapping has a long tradition in the European Alps. Hazard maps delineate areas of potential avalanche danger and are only available for selected areas where people and significant infrastructure are endangered. They have been created over generations, at specific sites, mainly based on avalanche activity in the past. For a large part of the area (90 % in the case of the Canton of Grisons) no maps are available. This is a problem when new territory with no or incomplete historical record is to be developed. It is an even larger problem when trying to predict the effects of climate change at the state scale where the historical record may no longer be valid. To close this gap, we develop an automated approach to generate spatial continuous hazard indication mapping based on a digital elevation model for the canton of Grisons (7105 km2) in the Swiss Alps. We calculate eight different scenarios with return periods ranging from frequent to very rare as well as with and without taking the protective effects of the forest into account. This approach combines the automated delineation of potential release areas, the calculation of release depths and the numerical simulation of the avalanche dynamics. This procedure can be applied worldwide, where high spatial resolution digital elevation models, detailed information on the forest and data on the snow climate are available, enabling reproducible hazard indication mapping also in regions where no avalanche hazard maps yet exist. This is invaluable for climate change studies. The simulation results are validated with official hazard maps, by assessments of avalanche experts and by existing avalanche cadastres derived from manual mapping and mapping based on satellite datasets. The results for the canton of Grisons are now operationally applied in the daily hazard assessment work of the authorities. Based on these experiences, the proposed approach can be applied for further mountain regions.

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“…• SLF avalanche cadastre, which has a focus on the regions Davos and Zuoz in Grisons (Schweizer et al, 2020;Hafner et al, 2021).…”

Section: Validationmentioning

confidence: 99%

“…• Avalanches mapped based on optical and radar satellite imagery for two large avalanche periods in January 2018 and January 2019 (Bühler et al, 2019;Bründl et al, 2019;Hafner et al, 2021;Bühler et al, 2021).…”

Section: Validationmentioning

confidence: 99%

Automated avalanche hazard indication mapping on state wide scale

Bühler

Bebi

Christen

et al. 2022

Preprint

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“…The development of these algorithms depends on a robust data set of observed avalanche events to determine appropriate terrain characteristic thresholds which define avalanche release areas. Such datasets can be created through recording of manual observations or generated by applying satellite mapping (Lato et al, 2012;Bühler et al, 2019;Hafner et al, 2021). The combination of terrain characteristics varies between the different algorithms, but some common parameters are slope angle, curvature, roughness, and aspect.…”

Section: Potential Avalanche Release Area Modellingmentioning

confidence: 99%

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

Sykes

Haegeli

Bühler³

2021

Preprint

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Abstract. Potential avalanche release area (PRA) modelling is critical for generating automated avalanche terrain maps which provide low-cost large scale spatial representations of snow avalanche hazard for both infrastructure planning and recreational applications. Current methods are not applicable in mountainous terrain where high-resolution elevation models are unavailable and do not include an efficient method to account for avalanche release in forested terrain. This research focuses on expanding an existing PRA model to better incorporate forested terrain using satellite imagery and presents a novel approach for validating the model using local expertise, thereby broadening its application to numerous mountain ranges worldwide. The study area of this research is a remote portion of the Columbia Mountains in southeastern British Columbia, Canada which has no pre-existing high-resolution spatial data sets. Our research documents an open source workflow to generate high-resolution DEM and forest land cover data sets using optical satellite data processing. We validate the PRA model by collecting a polygon dataset of observed potential release areas from local guides, using a method which accounts for the uncertainty of human recollection and variability of avalanche release. The validation dataset allows us to perform a quantitative analysis of the PRA model accuracy and optimize the PRA model input parameters to the snowpack and terrain characteristics of our study area. Compared to the original PRA model our implementation of forested terrain and local optimization improved the percentage of validation polygons accurately modelled by 11.7 percentage points and reduced the number of validation polygons that were underestimated by 14.8 percentage points. Our methods demonstrate substantial improvement in the performance of the PRA model in forested terrain and provide means to generate the requisite input datasets and validation data to apply and evaluate the PRA model in vastly more mountainous regions worldwide than was previously possible.

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“…However, this approach only covers high elevations areas while our study aims to detect avalanches proximate to local communities at lower elevations (typically valleys). Manual and visual approaches, despite the time consuming process, can also be applied to detect avalanches using high resolution images (e.g., SPOT-6), mid-resolution (e.g., Sentinel-2A and B images), or even Google Earth images (Singh et al, 2020;Yariyan et al, 2020;Hafner et al, 2021). Terrain parameters like slope gradient and curvature have also been added to the avalanche detection process using Digital Elevation Models (DEM) combined with Landsat-8 images (Bühler et al, 2018;Singh et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we used optical data to detect avalanches on a long-term basis and built an open-access script in Google Engine interface: Snow Avalanche Frequency Estimation (SAFE). Landsat-5, 7 and 8 products were used as their resolution (30 m, i.e., minimum detectible size of 900 m²) is sufficient to detect larger avalanches (Abermann et al, 2019;Eckerstorfer et al, , 2014Hafner et al, 2021;Singh et al, 2019Singh et al, , 2020Smith et al, 2020;Yariyan et al, 2020). Our objective is to automatically map annual avalanche occurrence over the past 32 years using Landast-5, 7 and 8 image archives in the Amu Panj basin of Afghanistan.…”

Section: Introductionmentioning

confidence: 99%

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

Caiserman¹,

Sidle²,

Gurung

2022

Preprint

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Abstract. Snow avalanches are the predominant hazards in winter in high elevation mountains. They cause damage to both humans and assets but cannot be accurately predicted. Until now, only local maps to estimate snow avalanche risk have been produced. Here we show how remote sensing can accurately inventory large avalanches every year at a basin scale using a 32-yr snow index derived from Landsat satellite archives. This Snow Avalanche Frequency Estimation (SAFE) built in an open-access Google Engine script maps snow hazard frequency and targets vulnerable areas in remote regions of Afghanistan, one of the most data-limited areas worldwide. SAFE correctly detected of the actual avalanches identified on Google Earth and in the field (Probability of Detection 0.77 and Positive Predictive Value 0.96). A total of 810,000 large avalanches occurred since 1990 within an area of 28,500 km2 with a mean frequency of 0.88 avalanches/km2yr−1, damaging villages and blocking roads and streams. Snow avalanche frequency did not significantly change with time, but a northeast shift of these hazards was evident. SAFE is the first robust model that can be used worldwide and is capable of filling data voids on snow avalanche impacts in inaccessible regions.

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Mapping avalanches with satellites – evaluation of performance and completeness

Cited by 40 publications

References 42 publications

Automated avalanche hazard indication mapping on state wide scale

Automated avalanche hazard indication mapping on state wide scale

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

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