Snow avalanche mapping using sentinel-1 SAR change detection

Daniker Chepashev (), Natalya Denissova (), Olga Petrova (), Gulzhan Daumova (), Gulzhan Daumova () and Aigerim Kalybekova ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 6, 1478-1488

Abstract: Snow avalanches pose a persistent threat to mountain communities, yet systematic inventories remain scarce where cloud cover and rugged topography limit optical remote sensing. Leveraging the all-weather capability of C-band radar, we design an automated workflow that transforms Sentinel-1 imagery into a season-scale avalanche record for the Zailiysky Alatau range (Northern Tien Shan, Kazakhstan). A dual-polarization Interferometric-Wide pair acquired in March–April 2024 was co-registered in Google Earth Engine, speckle-suppressed with an adaptive Enhanced Lee filter, and converted to a VV–VH polarization-difference layer. Temporal differencing highlighted fresh debris as negative anomalies. Layover, radar shadow, and permanent water were masked using the 30 m SRTM DEM and the JRC Global Surface Water product. Further, decision tree classifiers were used for delineation of avalanche from non-avalanche pixels. Validation against PlanetScope (3 m) and Sentinel-2 (10 m) imagery acquired within ± 2 days returned a detection completeness. Results confirm that Sentinel-1 change detection can retrieve most medium-to-large avalanches even under persistent cloud cover, offering a cost-free complement to sparse field observations in Central Asia. The workflow fully implemented in a cloud platform requires no scene-specific tuning and is transferable to other snow-covered mountain regions for near-real-time hazard assessment.

Keywords: Change detection; Decision-tree classification; Sentinel-1 SAR; Snow avalanche. (search for similar items in EconPapers)
Date: 2025
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