Landslides in Kyrgyzstan captured by Sentinel-2

The giant Kurbu-Tash landslide in Kyrgyzstan occurred on 23-24 April 2017 and was highlighted by Dave Petley in the landslide blog of the American Geophysical Union.

Please read the erratum: https://www.cesbio.cnrs.fr/multitemp/?p=10566

The landslide can be seen in a Sentinel-2 image acquired 5 days after the event. I used a near-infrared composite to increase the contrast  between the vegetation and the bare soil.

I used this image to estimate that the length of the landslide is about 3.5 km [1]. The volume of the landslide was estimated to 2.8 million cubic meters by AKI press [2]. Local news reported there were no fatalities, but 5 days later, another landslide in Ayu village killed 24 people. I think that this landslide is also visible in the same Sentinel-2 imagery although it is less evident.

[1] Half of the length of the massive Glacier Bay landslide!

[2] This is still 20 times less than the first Aru glacier avalanche.

Plus d'actualités

Sentinel-2 reveals the surface deformation after the 2025 Myanmar earthquake

Sentinel-2 captured several clear-sky images of Myanmar before and after the 28 March 2025 earthquake. The animation below shows a 5-day apart sequence of images captured by Sentinel-2B and Sentinel-2C (10 m resolution) near the epicenter located close to Mandalay. The surface slip due to the earthquake follows the Sagaing Fault, a major fault in […]

Evolution de l’altitude de la ligne de neige au cours des 41 dernières années dans le bassin versant du Vénéon (Oisans)

Pour contribuer à caractériser les conditions hydrométéorologiques lors de la crue torrentielle qui a frappé la Bérarde en juin, j’ai analysé une nouvelle série de cartes d’enneigement qui couvre la période 1984-2024 [1]. Grâce à la profondeur temporelle de cette série, on constate que l’altitude de la ligne de neige dans le bassin versant du […]

Biophysical parameter retrieval from Sentinel-2 images using physics-driven deep learning for PROSAIL inversion

The results presented here are based on published work: Y. Zérah, S. Valero, and J. Inglada. « Physics-constrained deep learning for biophysical parameter retrieval from sentinel-2 images: Inversion of the prosail model« , in Remote Sensing of Environment, doi: 10.1016/j.rse.2024.114309. This work is part of the PhD of Yoël Zérah, supervised by Jordi Inglada and Silvia Valero. […]

Rechercher