Deriving slope movements for an imminent landslide along the Jinsha river

Abstract

Abstract. Landslides are major hazards that may pose serious threats to mountain communities. Even landslides in remote mountains could have non-negligible impacts on populous regions by blocking large rivers and forming megafloods. Usually, there are slope deformations before major landslides occur, and detecting precursors over large mountain regions is important for screening possible landslide disasters. In this work, we applied multi-temporal optical remote sensing images (Landsat 7 and Sentinel-2) and an image correlation method to detect sub-pixel slope deformations of a slope. Along the Jinsha river, this slope is located downstream the famous Baige landslide near the Mindu town, Tibet Autonomous Region. We used DEM derived aspect to restrain background noises in image correlation results. We found the slope remained stable from November 2015 to November 2018 and moved significantly from November 2018 to November 2019. We used more data to analyse slope movement in 2019 and found retrogressive slope movements with increasingly large deformations near the river bank. We also analysed spatial-temporal patterns of the slope deformation from October 2018 to February 2020 and found seasonal variations in slope deformations. Only the slope foot moved in dry seasons, whereas the entire slope activated in rainy seasons. Until 24 August 2019, the size of the slope with displacements larger than 3 m is similar to that of the Baige landslide. However, the river width at the foot of this slope is much narrower than the river width at the foot of the Baige landslide. We speculate it may continue to slide down and could threaten the Jinsha river. Further modelling works should be done to check if the imminent landslide could dam the Jinsha river and measures be taken to mitigate possible dammed breach flood disasters. This work illustrates the potential of using optical remote sensing to monitor slope deformations over large remote mountain regions.