Dynamic spatiotemporal quantification of avalanches in the Central Tianshan Mountains by integrating air–space–ground collaborative sensing and snow field–terrain filters

Abstract

Accurate spatiotemporal data on avalanches is crucial for the development and safety of the Central Tianshan Mountains. However, the existing inventory has problems such as low spatial resolution and limited usability. To gather high precision avalanche distribution data and analyze their spatiotemporal distribution patterns, a collaborative ‘air-space-ground’ sensing network, was employed alongside a ‘snow field-terrain’ filter to construct an avalanche inventory database. The study found that avalanche has lower reflectivity and a rougher textured surface in contrast to undisturbed snow. This led to the identification of 639 avalanches in winter and 1,083 in spring from SuperView-1 images, with accuracy exceeding 0.91, while the QADI (Quantity and Allocation Disagreement Index) remained below 0.08. The results revealed that almost all avalanches are distributed in mountainous terrain with low relief, on sunny and semi-sunny slopes with an altitude of 2,662–3,346 m and a slope of 16°–48°, as well as in grassland and bare land. New avalanches in spring are mainly distributed where the increase in solar radiation is moderate and the snow depth increases by 0.44–0.59 m. The high-precision avalanche inventory offers spatial information service function, aiding decision-makers to seize development opportunities.

Keywords

• Central Tianshan
• ‘air-space-ground’ sensing network
• SuperView-1
• ‘Sf-t’ filter
• QADI