A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China

The M s 8.0 Wenchuan earthquake of 2008 dramatically changed the terrain surface and caused long-term increases in the scale and frequency of landslides and debris flows. The changing trend of landslides in the earthquake-affected area over the decade since the earthquake remains largely unknown. In this study, we were able to address this issue using supervised classification methods and multitemporal remote sensing images to study landslide evolution in the worst-affected area (Mianyuan River Basin) over a period of ten years. Satellite images were processed using the maximum likelihood method and random forest algorithm to automatically map landslide occurrence from 2007 to 2018. The principal findings are as follows: (1) when compared with visual image analysis, the random forest algorithm had a good average accuracy rate of 87% for landslide identification; (2) postevent landslide occurrence has generally decreased with time, but heavy monsoonal seasons have caused temporary spikes in activity; and (3) the postearthquake landslide activity in the Mianyuan River Basin can be divided into a strong activity period (2008 to 2011), medium activity period (2012 to 2016), and weak activity period (post 2017). Landslide activity remains above the prequake level, with damaging events being rare but continuing to occur. Long-term remote sensing and on-site monitoring are required to understand the evolution of landslide activity after strong earthquakes.