Backscatter Characteristics of Snow Avalanches for Mapping With Local Resolution Weighting

Abstract

Snow avalanches cause a sudden change of snow properties making them detectable with synthetic aperture radar (SAR). However, steep alpine terrain combined with the slant view geometry of SAR sensors complicates detection: the avalanche brightness depends on the incidence angle and the observed area is limited by radar layover and shadow. Likewise, the spatial resolution varies strongly with the local incidence angle relative to the terrain. To increase the avalanche brightness and to improve the imaging coverage and resolution we apply local resolution weighting (LRW) on Sentinel-1 (S1) backscatter images from ascending and descending orbits. LRW merges acquisitions by averaging them, weighted with the local groundrange resolution. To analyze the relative avalanche brightness with respect to the local incidence angle and polarization, and to quantify the benefit of LRW, we created a dataset of 914 manually drawn avalanche outlines based on S1 imagery of an extreme avalanche event on January 4, 2018 in the Swiss Alps. We show that avalanches appear brightest at slopes facing away from the radar at local incidence angles of 55 ± 20 • ; such slopes are weighted considerably stronger through LRW. With a processing pipeline for avalanche segmentation using a fixed threshold on the backscatter difference we obtain a higher F1 score (0.75) with LRW compared to an unweighted orbit average (F1 = 0.68) or single orbit acquisitions (F1 = 0.5). For automatic segmentation, we used the manually drawn dataset for training and testing of a deep neural U-Net and achieved a F1 score of 0.81 on LRW backscatter differences.

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

“…Also, this limits the effects of layover, which can obscure avalanche debris. This result matches the findings of Tompkin and Leinss (2021) whose work with exclusively wet snow avalanches showed a 4 dB increase in avalanche debris backscatter change for incidence angles between 40-70° for VV polarization and 2 dB for VH polarization. (Figure 6).…”

“…In Wyoming and Utah, due to prevailing wind direction and less solar warming, more avalanches are noted in field observations on east facing aspects. This predominance of east facing avalanches means that in ascending images, there were more paths that faced away from the sensor leading to higher incidence angles and, based on this study and Tompkin and Leinss (2021), to higher detection rates. Generally, this relationship suggests that depending on the predominant aspects of avalanches in a region, either flight direction (ascending or descending), will yield higher detection rates.…”

“…More recent research has explored this relationship between incidence angle and increased backscatter. Tompkin and Leinss (2021), exploring a dataset of exclusively wet snow avalanches, found that, compared to an optimal incidence angle of ~55°, backscatter was reduced by 4 dB for incidence angles below 40° in VV polarization (vertically transmitted and received signal). They also found that backscatter intensity also decreased for incidence angles over 70 degrees.…”

Snow Avalanche Frequency Estimation (SAFE): 32 years of monitoring remote avalanche depositional zones in high mountains of Afghanistan

“…Vickers et al (2016) conducted one of the first studies utilizing Sentinel-1 products to detect avalanche debris by developing an unsupervised classification. This technology seems very promising for avalanche detection (Eckerstorfer et al, 2017;Malnes et al, 2015;Martinez-Vazquez and Fortuny-Guasch, 2008;Schaffhauser et al, 2008;Tompkin and Leinss, 2021;Yang et al, 2020). Using TerraSAR-X and Sentinel-1 products, Leinss et al (2020) mapped avalanches, demonstrating the potential of radar products in snow hazard detection.…”

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

“…Backscatter values provide information on terrain roughness and any change indicates that a mass movement or a significant erosion event occurred in a given area. This technology seems very promising for avalanche detection (Eckerstorfer et al, 2017;Malnes et al, 2015;Martinez-Vazquez and Fortuny-Guasch, 2008;Schaffhauser et al, 2008;Tompkin and Leinss, 2021;Yang et al, 2020). However, the acquisition of frequent radar images is too recent to use this technique to detect historical avalanches.…”