Engineering

Flooding (psychology)

Geotechnical engineering

Geomorphology

Cartography

Interferometric synthetic aperture radar

Telecommunications

Vegetation (pathology)

Computer science

GNSS augmentation

Seismology

Geology

Psychology

Geography

Hydrology (agriculture)

Global Positioning System

Synthetic aperture radar

Psychotherapist

Hydropower

Remote sensing

Medicine

GNSS applications

Terrain

Landslide

Electrical engineering

Pathology

Engineering Geology

Landslide hazards along the Jinsha River corridor pose serious threats to the lives and property of local residents and can affect the safety of hydropower facilities because of the large size, number, strong activity, and disaster chain characteristics that occur following such events (such as a landslide creating a dammed lake which fails and leads to flooding), thus attracting widespread attention in both China and the rest of the world. As there is currently no complete landslide inventory map that covers the entire Jinsha River corridor, in this study the Sentinel-1 and ALOS/PALSAR-2 datasets were employed to detect and map active landslides along the entire Jinsha River corridor. Complex geomorphological conditions, such as the humid climate, dense vegetation, and steep terrain, pose great challenges to conventional InSAR-based landslide mapping methods, which can lead to a high probability of mis-judgements and omissions of landslides. Therefore, we propose a new InSAR-based procedure that can be used to conduct large-area landslide mapping through the integration of surface deformation and geomorphological features. More than 360 SAR images covering the Jinsha River corridor were processed and more than 900 active landslides were detected and mapped over the entire Jinsha River corridor for the first time. In particular, several large-scale landslides with a length and/or width of >1 km were found. Our results show that the landslides over the Jinsha River corridor are mainly located in three high earthquake-prone areas and reservoir areas, and that the landslides are mainly distributed at elevations of 1500–2000 m a.s.l. and have slope angles of 15–25°. Moreover, the deformation time series results indicate that the heavy rainfall in the summer and the rapid decline of water level in the Jinsha River might be two significant factors that accelerate the deformation of active landslides and reactivate unstable slopes. The findings in this research can be directly applied to landslide hazard mitigation and prevention along the entire Jinsha River corridor. In particular, the proposed procedure can be used for the efficient and systematic mapping of active landslides in other regions with similarly complex geomorphological conditions.

Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China