TY - JOUR

T1 - Dynamic susceptibility mapping of slow-moving landslides using PSInSAR

AU - Jiaxuan, H.

AU - Mowen, X.

AU - Atkinson, P.M.

PY - 2020/8/1

Y1 - 2020/8/1

N2 - A landslide susceptibility map (LSM) is a valuable tool for landslide assessment and land use management. This research proposes a landslide susceptibility dynamic map (DLSM) to increase LSM utility and update the predicted map in a time series. Slope units, as basic mapping units, were produced to define the landslide boundaries and simplify the mapping in the DLSM. The permanent scatterer interferometric synthetic aperture radar (PSInSAR) technique was used to estimate the line of sight velocity (V los). This was then reprojected into the velocity in the steepest slope direction (V slope) to avoid the influence of geometric distortion. The DLSM was produced by integrating, using slope unit aggregate values, the susceptibility (probability) of landsliding predicted by logistic regression based on eight spatial covariates and the V slope predicted using the PSInSAR technique. The DLSM is a dynamically changing susceptibility map in which susceptibility is increased in certain months, particularly where surface velocity increases following the rainy season. The proportion of the study area classified with extremely high susceptibility increased from 22.2% to 44.8% after the rainy season. The DLSM, thus, potentially improves the prediction reliability for slow-moving landslides and, in particular, can help to avoid false negatives. The DLSM can be applied in areas for which radar data are available and can provide more reliable and readily interpretable results to decision-makers. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

AB - A landslide susceptibility map (LSM) is a valuable tool for landslide assessment and land use management. This research proposes a landslide susceptibility dynamic map (DLSM) to increase LSM utility and update the predicted map in a time series. Slope units, as basic mapping units, were produced to define the landslide boundaries and simplify the mapping in the DLSM. The permanent scatterer interferometric synthetic aperture radar (PSInSAR) technique was used to estimate the line of sight velocity (V los). This was then reprojected into the velocity in the steepest slope direction (V slope) to avoid the influence of geometric distortion. The DLSM was produced by integrating, using slope unit aggregate values, the susceptibility (probability) of landsliding predicted by logistic regression based on eight spatial covariates and the V slope predicted using the PSInSAR technique. The DLSM is a dynamically changing susceptibility map in which susceptibility is increased in certain months, particularly where surface velocity increases following the rainy season. The proportion of the study area classified with extremely high susceptibility increased from 22.2% to 44.8% after the rainy season. The DLSM, thus, potentially improves the prediction reliability for slow-moving landslides and, in particular, can help to avoid false negatives. The DLSM can be applied in areas for which radar data are available and can provide more reliable and readily interpretable results to decision-makers. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

KW - Decision making

KW - Land use

KW - Logistic regression

KW - Magnetic susceptibility

KW - Mapping

KW - Synthetic aperture radar

KW - Dynamic susceptibility

KW - False negatives

KW - Geometric distortion

KW - Interferometric synthetic aperture radars

KW - Land-use management

KW - Landslide susceptibility

KW - Permanent scatterers

KW - Surface velocity

KW - Landslides

KW - dynamic analysis

KW - landslide

KW - mapping method

KW - prediction

KW - slope dynamics

KW - slope stability

KW - spatiotemporal analysis

KW - synthetic aperture radar

U2 - 10.1080/01431161.2020.1760398

DO - 10.1080/01431161.2020.1760398

M3 - Journal article

VL - 41

SP - 7509

EP - 7529

JO - International Journal of Remote Sensing

JF - International Journal of Remote Sensing

SN - 0143-1161

IS - 19

ER -