SAR backscatter multitemporal compositing via local resolution weighting

Small, David

doi:10.1109/igarss.2012.6350465

Cited by 16 publications

(19 citation statements)

References 18 publications

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“…However, we suppose that mainly avalanches reaching below the wet snow line were detected and that likely many dry snow avalanches were missed because of their lower contrast to the surrounding snow. We think that avalanche mapping can be even improved with more advanced methods to combine different orbits, for example with local resolution weighing (Small, 2012). With that slopes facing off the radar are weighted stronger which does not only enhance the resolution but should also increase the avalanche visibility as the more omnidirectional scattering of the rough avalanche surface dominates the scattering of smooth snow slopes facing off the radar.…”

Section: Discussionmentioning

confidence: 99%

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Snow Avalanche Detection and Mapping in single, multitemporal, and multiorbital Radar Images from TerraSAR-X and Sentinel-1

Leinß¹,

Wicki²,

Holenstein³

et al. 2020

Preprint

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Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanches reports from a network of observers. During bad weather, however, information about occurred avalanches can be scarce or even be missing completely. To asses the potential of weather independent radar satellites we compared manual and automatic avalanche mapping results from high resolution TerraSAR-X (TSX) stripmap images and from medium resolution Sentinel-1 (S1) interferometric wide swath images. Within a selected test site in the central Swiss Alps the TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other but with TSX about 40 % more, mainly smaller avalanches were detected. However, S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 days with freely available acquisitions. With costly SPOT-6 images the Alps can be even covered in a single day at meter-resolution, at least for clear sky conditions. For the SPOT-6 and TSX mapping results we found a fair agreement but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, with radar, mainly the avalanche deposition zone was detected, whereas the release zone was well visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of the manually mapped new avalanches in the same image pair, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single orbit TSX change detection results. In a multiorbital S1 moasic covering entire Switzerland, we detected 7361 new avalanches which occurred during an extreme avalanche period around Jan 4th 2018.

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Section: Discussionmentioning

confidence: 99%

“…By the simple average slopes facing off the radar were mainly lightened up by the average with a bright layover area. We think that more advanced methods to merge radar images from multiple orbits, for example local resolution weighting (LRW) by Small (2012), should further improve avalanche mapping results.…”

Section: Multiorbital Compositementioning

confidence: 99%

Snow Avalanche Detection and Mapping in single, multitemporal, and multiorbital Radar Images from TerraSAR-X and Sentinel-1

Leinß¹,

Wicki²,

Holenstein³

et al. 2020

Preprint

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“…In the second stage, a temporal compositing was conducted to enhance spatial resolution, reduce noise, and mitigate the influence of speckle and viewing geometry [46]. This local resolution weighting (LRW) methodology merges all available RTC images from a specific time period into a single composite backscatter product.…”

Section: Image Differencing and Calculation Of Statisticsmentioning

confidence: 99%

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

2019

Self Cite

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Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer’s accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user’s accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user’s needs.

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“…Snow avalanches frequently threaten people and infrastructure in Switzerland and other mountainous countries. Every winter, dozens of people caught in avalanches suffer serious injuries or even die (Techel et al, 2016), and roads and railways have to be closed during periods of high avalanche danger. To inform about the current avalanche danger levels, ranging from 1 (low) to 5 (very high) on the European Avalanche Hazard Scale (Meister, 1995), the WSL Institute for Snow and Avalanche Research (SLF) publishes an avalanche bulletin twice a day during winter (SLF, 2018e).…”

Section: Introductionmentioning

confidence: 99%

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Leinß

Wicki

Holenstein

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m2 for TSX and 2000 m2 for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018.

show abstract

SAR backscatter multitemporal compositing via local resolution weighting

Cited by 16 publications

References 18 publications

Snow Avalanche Detection and Mapping in single, multitemporal, and multiorbital Radar Images from TerraSAR-X and Sentinel-1

Snow Avalanche Detection and Mapping in single, multitemporal, and multiorbital Radar Images from TerraSAR-X and Sentinel-1

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

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