4.7 Article

Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA

期刊

REMOTE SENSING OF ENVIRONMENT
卷 124, 期 -, 页码 348-359

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2012.05.025

关键词

Landslide; Monitoring; Detection; ALOS/PALSAR; Synthetic aperture radar (SAR); Interferometric SAR (InSAR)

资金

  1. China Scholarship Council
  2. Cascades Volcano Observatory
  3. Natural Science Foundation of China (NSFC) [41072266]
  4. Ministry of Land & Resources, China [1212011220186, 121201122014]
  5. USGS Volcano Hazards Program

向作者/读者索取更多资源

Multi-temporal ALOS/PALSAR images are used to automatically investigate landslide activity over an area of similar to 200 km by similar to 350 km in northern California and southern Oregon. Interferometric synthetic aperture radar (InSAR) deformation images, InSAR coherence maps, SAR backscattering intensity images, and a DEM gradient map are combined to detect active landslides by setting individual thresholds. More than 50 active landslides covering a total of about 40 km(2) area are detected. Then the short baseline subsets (SBAS) InSAR method is applied to retrieve time-series deformation patterns of individual detected landslides. Down-slope landslide motions observed from adjacent satellite tracks with slightly different radar look angles are used to verify InSAR results and measurement accuracy. Comparison of the landslide motion with the precipitation record suggests that the landslide deformation correlates with the rainfall rate, with a lag time of around 1-2 months between the precipitation peak and the maximum landslide displacement. The results will provide new insights into landslide mechanisms in the Pacific Northwest, and facilitate development of early warning systems for landslides under abnormal rainfall conditions. Additionally, this method will allow identification of active landslides in broad areas of the Pacific Northwest in an efficient and systematic manner, including remote and heavily vegetated areas difficult to inventory by traditional methods. Published by Elsevier Inc.

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