Early Detection of Geohazards in Alpine Regions Using Seasonally Partitioned InSAR: A Case Study of the Eastern Himalayan Syntaxis.
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| Title: | Early Detection of Geohazards in Alpine Regions Using Seasonally Partitioned InSAR: A Case Study of the Eastern Himalayan Syntaxis. |
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| Authors: | Li, Hao-Liang1 (AUTHOR), Dong, Xiu-Jun1,2 (AUTHOR) dongxiujun@cdut.edu.cn, Xu, Qiang1,3 (AUTHOR), Ou, Ou1,2 (AUTHOR), Li, Yi-Shan2,3 (AUTHOR), Liu, Jie1,3 (AUTHOR), Sima, Jing-Song1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1843. 26p. |
| Subjects: | Radar interferometry, Alpine regions, Emergency management, Seasonal temperature variations, Remote sensing, Landslides |
| Geographic Terms: | Himalaya Mountains |
| Abstract: | Highlights: What are the main findings? A novel seasonal-partition Stacking-InSAR method is proposed, which separately processes winter and summer interferograms based on InSAR coherence variation to mitigate seasonal decorrelation in alpine regions. Applied in the eastern Himalayan syntaxis, the method identified over 26% more geohazards than conventional Stacking-InSAR, with less than 19% overlap between hazards detected in winter and summer, highlighting strong seasonal activity differences. What are the implications of the main findings? The method enables more accurate and comprehensive early detection of both high-altitude concealed hazards (best detected in summer) and riverside landslides (best detected in winter), directly supporting disaster prevention in high, cold mountains. It demonstrates that incorporating qualified long-temporal-baseline interferometric pairs can improve the detection of slow-creeping slopes, and the framework is particularly suitable for regions with significant seasonal InSAR coherence variations. In alpine mountain regions, significant seasonal surface changes reduce InSAR coherence over long time spans, hindering geohazard identification. This study proposes a method for geohazard detection based on InSAR seasonal coherence variation. First, time-series interferograms and coherence maps are generated from Sentinel-1 imagery. Each year is then partitioned into summer, transition, and winter seasons by analyzing the spatial migration of high-coherence zones. Interferometric pairs from the transition season are further screened and reassigned to summer or winter groups according to their coherence characteristics. Stacking-InSAR is applied separately to the summer and winter datasets to derive seasonal deformation rates; long-temporal-baseline pairs (60–120 days) that maintain sufficient coherence are selectively incorporated to improve the detectability of slow-moving slopes. Finally, geohazards are identified by combining the summer and winter deformation results. Applied in the eastern Himalayan syntaxis, the method showed that less than 19% of geohazards were detectable in both seasons, indicating seasonal variations in geohazard activity. Moreover, it identified approximately 29% more geohazards on average than traditional Stacking-InSAR using all interferograms. Thus, the proposed approach enables more accurate and effective geohazard detection in cold mountains, supporting disaster prevention and mitigation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel seasonal-partition Stacking-InSAR method is proposed, which separately processes winter and summer interferograms based on InSAR coherence variation to mitigate seasonal decorrelation in alpine regions. Applied in the eastern Himalayan syntaxis, the method identified over 26% more geohazards than conventional Stacking-InSAR, with less than 19% overlap between hazards detected in winter and summer, highlighting strong seasonal activity differences. What are the implications of the main findings? The method enables more accurate and comprehensive early detection of both high-altitude concealed hazards (best detected in summer) and riverside landslides (best detected in winter), directly supporting disaster prevention in high, cold mountains. It demonstrates that incorporating qualified long-temporal-baseline interferometric pairs can improve the detection of slow-creeping slopes, and the framework is particularly suitable for regions with significant seasonal InSAR coherence variations. In alpine mountain regions, significant seasonal surface changes reduce InSAR coherence over long time spans, hindering geohazard identification. This study proposes a method for geohazard detection based on InSAR seasonal coherence variation. First, time-series interferograms and coherence maps are generated from Sentinel-1 imagery. Each year is then partitioned into summer, transition, and winter seasons by analyzing the spatial migration of high-coherence zones. Interferometric pairs from the transition season are further screened and reassigned to summer or winter groups according to their coherence characteristics. Stacking-InSAR is applied separately to the summer and winter datasets to derive seasonal deformation rates; long-temporal-baseline pairs (60–120 days) that maintain sufficient coherence are selectively incorporated to improve the detectability of slow-moving slopes. Finally, geohazards are identified by combining the summer and winter deformation results. Applied in the eastern Himalayan syntaxis, the method showed that less than 19% of geohazards were detectable in both seasons, indicating seasonal variations in geohazard activity. Moreover, it identified approximately 29% more geohazards on average than traditional Stacking-InSAR using all interferograms. Thus, the proposed approach enables more accurate and effective geohazard detection in cold mountains, supporting disaster prevention and mitigation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111843 |
