Study on the intensity and coherence information of high-resolution ALOS-2 SAR images for rapid massive landslide mapping at a pixel level

A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick response, no contact and broad coverage. This study aimed to conduct a comprehensive exploration on the intensity and coherence information of three Advanced Land Observing Satellite-2 (ALOS-2) SAR images, for rapid massive landslide mapping in a pixel level, in order to provide a reference for future applications. Applied data were two pre-event and one post-event high-resolution ALOS-2 products. Studied area was in the east of Iburi, Hokkaido, Japan, where massive shallow landslides were triggered in the 2018 Hokkaido Eastern Iburi Earthquake. Potential parameters, including intensity difference (d), co-event correlation coeffcient (r), correlation coeffcient difference (δr), co-event coherence (γ), and coherence difference (δγ), were first selected and calculated based on a radar reflection mechanism, to facilitate rapid detection. Qualitative observation was then performed by overlapping ground truth landslides to calculated parameter images. Based on qualitative observation, an absolute value of d (dabs1) was applied to facility analyses, and a new parameter (dabs2) was proposed to avoid information loss in the calculation. After that, quantitative analyses of the six parameters (dabs1, dabs2, r, δr, γ and δγ) were performed by receiver operating characteristic. dabs2 and Dr were found to be favorable parameters, which had the highest AUC values of 0.82 and 0.75, and correctly classified 69.36% and 64.57% landslide and non-landslide pixels by appropriate thresholds. Finally, a discriminant function was developed, combining three relatively favorable parameters (dabs2, δr, and δγ) with one in each type, and achieved an overall accuracy of 74.31% for landslide mapping.