Monitoring the Jianshanying landslide in a karst mountainous area of Guizhou by optical remote sensing

Landslides in karst mountainous areas of southwest China induced by underground mining can cause huge economic losses and casualties. Such hazard is characterized by large deformation gradients and serious collapse of the surface. This work takes the Jianshanying mining-induced landslide as an example to realizes the quantitative 2D time-series deformation monitoring of the landslide by optical sub-pixel correlation methods based on multiple optical images. First, multiple historical archives of Google earth images and UAV images are used to detect the damage of the surface, showing that the 1# landslide area has undergone significant surface deformation in 2013, becoming increasingly serious over time. Then, the 2D deformation time series of the 1# landslide area from July 31, 2016 to March 22, 2020 is inverted based on the Sentinel-2 data. Results indicate that the horizontal deformation in this area tended to be larger in the slope direction as well as in the east-west and north-south directions over time. The deformation continued to increase over time, and the maximum cumulative deformation reached 44 m and -58 m, respectively. Therefore, the large-gradient deformation monitoring of the landslide disaster in Guizhou karst mountainous areas can be realized by using optical remote sensing technology, which is important to the monitoring and early warning of landslides in similar areas.