Mapping avalanches with satellites – evaluation of performance and
completeness

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

doi:10.5194/tc-2020-272

Cited by 5 publications

(37 citation statements)

References 34 publications

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“…The description of the generation of this SPOT6 database is presented by [7]. [8] confirmed the high potential of SPOT6-7 images in mapping avalanche outlines by using ground truth validation points. They studied two avalanche activity periods in 2018 and 2019, in a 180 km 2 area around Davos, and found: a Probability of Detection of 74 % (POD, events correctly detected by a human on the satellite data) and a Positive Predictive Value (percentage of identified events by manual detection that were an avalanche event) of 88 %.…”

Section: Introductionmentioning

confidence: 65%

“…While the use of optical data is limited by cloud cover, which should be as low as possible, usable data from radar satellites are independent of illumination and weather conditions ( [9], [10]). Moreover, optical remote sensing imagery can be expensive and only available on request for specific areas, as is the case of optical SPOT6-7 ( [8]). Sentinel-2 is free of charge, but [8] found it not suitable for avalanche mapping.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, optical remote sensing imagery can be expensive and only available on request for specific areas, as is the case of optical SPOT6-7 ( [8]). Sentinel-2 is free of charge, but [8] found it not suitable for avalanche mapping. The use of Synthetic Aperture Radar (SAR) is increasingly used in avalanche detections to obtain a continuity of observations.…”

Section: Introductionmentioning

confidence: 99%

“…The change detection with TerraSAR-X images detected more avalanche events than with the Sentinel-1 (small avalanches were missed due to the lower resolution of Sentinel-1 compared to TerraSAR-X). [8] used similar pre-processing steps of [20] and compared the reliability of avalanche detections of: SPOT-6, Sentinel-1 and optical Sentinel-2 images. [20] and [8] studies found that Sentinel-1 is effective in mapping avalanches (significant spatial and temporal coverage) despite the fact that these observations do not help to detect avalanches of small and medium sizes.…”

Section: Introductionmentioning

confidence: 99%

“…[8] used similar pre-processing steps of [20] and compared the reliability of avalanche detections of: SPOT-6, Sentinel-1 and optical Sentinel-2 images. [20] and [8] studies found that Sentinel-1 is effective in mapping avalanches (significant spatial and temporal coverage) despite the fact that these observations do not help to detect avalanches of small and medium sizes. [8] obtained a POD of 0.27 using Sentinel-1 manual mapping.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring

Karas

Karbou

Giffard‐Roisin

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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In this study, we develop a novel method to automatically detect areas of snow avalanche debris using a color space segmentation technique applied to SAR image time series through January 2018 in the Swiss Alps. Debris avalanche zones are detected assuming that these areas are characterised by a significant and localised increase in SAR signal relative to the surrounding environment. We undertake a sensitivity study by calculating debris products by varying the D-M reference images (a stable reference image taken several weeks before the event). We examine the results according to the direction of the orbit, the characteristics of the terrain (slope, altitude, orientation) and also by evaluating the relevance of the detection with the help of an independent SPOT database ([1]) including 18,737 avalanche events. Small avalanches are not detected by SAR images and depending on the orientation of the terrain some avalanches are not detected by either the ascending or the descending orbit. The detection results vary with the reference image; best detection results are obtained with some selected individual dates with almost 70 % of verified avalanche events using the ascending orbit.

show abstract

Section: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring

Karas

Karbou

Giffard‐Roisin

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Satellite detection of snow avalanches using Sentinel-1 in a transitional snow climate

Keskinen

Hendrikx

Eckerstorfer

et al. 2022

Cold Regions Science and Technology

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Snow Avalanche Susceptibility Mapping for Davos, Switzerland

Cetinkaya

Kocaman

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.

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Mapping avalanches with satellites – evaluation of performance and completeness

Cited by 5 publications

References 34 publications

Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring

Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring

Satellite detection of snow avalanches using Sentinel-1 in a transitional snow climate

Snow Avalanche Susceptibility Mapping for Davos, Switzerland

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