View in EDS HTML Full Text PDF Full Text

Early Detection of Geohazards in Alpine Regions Using Seasonally Partitioned InSAR: A Case Study of the Eastern Himalayan Syntaxis.

Saved in:
Bibliographic Details
Title: Early Detection of Geohazards in Alpine Regions Using Seasonally Partitioned InSAR: A Case Study of the Eastern Himalayan Syntaxis.
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]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194587064
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Early Detection of Geohazards in Alpine Regions Using Seasonally Partitioned InSAR: A Case Study of the Eastern Himalayan Syntaxis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Hao-Liang%22">Li, Hao-Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Xiu-Jun%22">Dong, Xiu-Jun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dongxiujun@cdut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Qiang%22">Xu, Qiang</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ou%2C+Ou%22">Ou, Ou</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yi-Shan%22">Li, Yi-Shan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jie%22">Liu, Jie</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sima%2C+Jing-Song%22">Sima, Jing-Song</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1843. 26p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Alpine+regions%22">Alpine regions</searchLink><br /><searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Seasonal+temperature+variations%22">Seasonal temperature variations</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Landslides%22">Landslides</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Himalaya+Mountains%22">Himalaya Mountains</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194587064
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18111843
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1843
    Subjects:
      – SubjectFull: Radar interferometry
        Type: general
      – SubjectFull: Alpine regions
        Type: general
      – SubjectFull: Emergency management
        Type: general
      – SubjectFull: Seasonal temperature variations
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Landslides
        Type: general
      – SubjectFull: Himalaya Mountains
        Type: general
    Titles:
      – TitleFull: Early Detection of Geohazards in Alpine Regions Using Seasonally Partitioned InSAR: A Case Study of the Eastern Himalayan Syntaxis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Hao-Liang
      – PersonEntity:
          Name:
            NameFull: Dong, Xiu-Jun
      – PersonEntity:
          Name:
            NameFull: Xu, Qiang
      – PersonEntity:
          Name:
            NameFull: Ou, Ou
      – PersonEntity:
          Name:
            NameFull: Li, Yi-Shan
      – PersonEntity:
          Name:
            NameFull: Liu, Jie
      – PersonEntity:
          Name:
            NameFull: Sima, Jing-Song
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1