Abstract
The cloud-free, wide-swath, day-and-night observation capability of synthetic aperture radar (SAR) has an important role in rapid landslide monitoring to reduce economic and human losses. Although interferometric SAR (InSAR) analysis is widely used to monitor landslides, it is difficult to use that for rapid landslide detection in mountainous forest areas because of significant decorrelation. We combined polarimetric SAR (PolSAR), InSAR, and digital elevation model (DEM) analysis to detect landslides induced by the July 2017 Heavy Rain in Northern Kyushu and by the 2018 Hokkaido Eastern Iburi Earthquake. This study uses fully polarimetric L-band SAR data from the ALOS-2 PALSAR-2 satellite. The simple thresholding of polarimetric parameters (alpha angle and Pauli components) was found to be effective. The study also found that supervised classification using PolSAR, InSAR, and DEM parameters provided high accuracy, although this method should be used carefully because its accuracy depends on the geological characteristics of the training data. Regarding polarimetric configurations, at least dual-polarimetry (e.g., HH and HV) is required for landslide detection, and quad-polarimetry is recommended. These results demonstrate the feasibility of rapid landslide detection using L-band SAR images.
Highlights
Landslides and slope failures induced by heavy rain and earthquakes caused two billion USD economic loss and ten thousand casualties in the last decade (International Federation of Red Cross and Red Crescent Societies 2016)
local incidence angle (LIA) dependency of the accuracy was more severe in sites: the heavy rain in Fukuoka Prefecture (site F) due to its steeper terrain than in site H
Proposed methods #5 and #6 using Pauli components with more case divisions with LIA provided better accuracy, in site H. These comparisons show that polarimetric parameters are useful to detect landslides in forested areas
Summary
Landslides and slope failures induced by heavy rain and earthquakes caused two billion USD economic loss and ten thousand casualties in the last decade (International Federation of Red Cross and Red Crescent Societies 2016). Czuchlewski ( 2003), Shimada et al (2014), Yonezawa et al (2012), and Watanabe et al (2016) employed eigenvalue parameters (Cloude and Pottier 1996) such as polarimetric entropy and alpha angle. Shimada et al (2014), Shibayama et al (2015), and Watanabe et al (2016) showed that polarimetric coherence between HH and VV polarization can be effectively used to detect landslides. Shibayama et al (2015) pointed out that polarimetric scattering mechanisms at landslides vary drastically with the local incidence angle (LIA) of radar, and proposed a decision tree classification that uses different thresholds according to LIA Most of the studies mentioned above basically applied a threshold to a polarimetric parameter to divide landslide areas and unchanged areas. Shibayama et al (2015) pointed out that polarimetric scattering mechanisms at landslides vary drastically with the local incidence angle (LIA) of radar, and proposed a decision tree classification that uses different thresholds according to LIA
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