Spectral Filtering of Multitemporal SAR Imagery for Multiscale Landslide Detection, Mapping, and Characterization

Spectral Filtering of Multitemporal SAR Imagery for Multiscale Landslide Detection, Mapping, and Characterization | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 March 2026 V3 Latest version Share on Spectral Filtering of Multitemporal SAR Imagery for Multiscale Landslide Detection, Mapping, and Characterization Authors : Manoj Thapa , Junle Jiang 0000-0002-8796-5846 [email protected] , Netra R Regmi , and Jake Walter 0000-0001-7127-9422 Authors Info & Affiliations https://doi.org/10.22541/au.175191581.15932054/v3 565 views 346 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Satellite synthetic aperture radar (SAR) imagery enables all-weather observation of landslide hazards, yet isolating landslide signals from pervasive noise in SAR intensity imagery remains challenging, particularly across diverse terrains and spatial scales. We develop a spectral filtering approach for C-band dual-polarized multitemporal SAR intensity imagery to enhance the detection of widespread landslides in Hokkaido and Hiroshima, Japan, triggered by earthquakes and rainfall events in 2018, respectively. Fourier spectral analysis of SAR intensity change images, constructed from post-event stacking from days to months, reveals characteristic separation between lower-frequency landslide signals (wavelengths >80–125 m) and higher-frequency noise (<60–70 m). Through spatial low-pass filtering, landslide areas are enhanced and distinguished from non-landslide areas, using uniform or adaptive percentile thresholds that vary by land surface type. Detected landslides are validated against independent inventories via pixel-based receiver-operating-characteristic curves and classification metrics. From low- to high-stacking conditions, filtered cases increase overall accuracy (89–94% in Hokkaido and 95–98% in Hiroshima) and precision (34–81% and 8–26%) compared to unfiltered cases. Detection performance varies with slope attributes and land surface conditions, with optimal metrics for moderate slopes (~6–18°) and forested terrains. Spectral filtering of noisier low-stacking images retains larger landslides and reduces misdetections for smaller ones, preserving power-law frequency-area scaling over ~10 3 –10 5 m 2 . Our approach facilitates near-real-time landslide characterization and continuous landscape monitoring. Supplementary Material File (manuscript r2.pdf) Download 12.79 MB File (supporting information r2.pdf) Download 14.51 MB Information & Authors Information Version history V1 Version 1 07 July 2025 V2 Version 2 12 November 2025 V3 Version 3 25 March 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords backscattering intensity change detection fourier spectral analysis geohazards geophysics landslides remote sensing Authors Affiliations Manoj Thapa School of Geosciences, The University of Oklahoma View all articles by this author Junle Jiang 0000-0002-8796-5846 [email protected] School of Geosciences, The University of Oklahoma View all articles by this author Netra R Regmi Oklahoma Geological Survey, The University of Oklahoma View all articles by this author Jake Walter 0000-0001-7127-9422 Oklahoma Geological Survey, The University of Oklahoma View all articles by this author Funding Information National Aeronautics and Space Administration 80NSSC22K1723 Netra Regmi Metrics & Citations Metrics Article Usage 565 views 346 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Manoj Thapa, Junle Jiang, Netra R Regmi, et al. Spectral Filtering of Multitemporal SAR Imagery for Multiscale Landslide Detection, Mapping, and Characterization. Authorea . 25 March 2026. DOI: https://doi.org/10.22541/au.175191581.15932054/v3 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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