Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing.
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| Title: | Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing. |
|---|---|
| Authors: | Zeng, Tongxiao1 (AUTHOR) tongxiaozeng@nnu.edu.cn, Zhang, Jun1 (AUTHOR) jun.zhang@njnu.edu.cn, Chen, Yulin1 (AUTHOR) yulin_chen@nnu.edu.cn, Zhu, Shaonan2 (AUTHOR) zhushaonan@njupt.edu.cn |
| Source: | Remote Sensing. Aug2024, Vol. 16 Issue 16, p3089. 18p. |
| Subjects: | Rainfall reliability, Landslide prediction, Climate change, Rainfall, Remote sensing, Landslides |
| Abstract: | With global climate change and increased human activities, landslides increasingly threaten human safety and property. Precisely extracting large-scale spatiotemporal information on landslides is crucial for risk management. However, existing methods are either locally based or have coarse temporal resolution, which is insufficient for regional analysis. In this study, spatiotemporal information on landslides was extracted using multiple remote sensing data from Emilia, Italy. An automated algorithm for extracting spatial information of landslides was developed with NDVI datasets. Then, we established a landslide prediction model based on a hydrometeorological threshold of three-day soil moisture and three-day accumulated rainfall. Based on this model, the locations and dates of rainfall-induced landslides were identified. Then, we further matched these identified locations with the extracted landslides from remote sensing data and finally determined the occurrence time. This approach was validated with recorded landslides events in Emilia. Despite some temporal clustering, the overall trend matched historical records, accurately reflecting the dynamic impacts of rainfall and soil moisture on landslides. The temporal bias for 87.3% of identified landslides was within seven days. Furthermore, higher rainfall magnitude was associated with better temporal accuracy, validating the effectiveness of the model and the reliability of rainfall as a landslide predictor. [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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 179355402 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zeng%2C+Tongxiao%22">Zeng, Tongxiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tongxiaozeng@nnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jun%22">Zhang, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jun.zhang@njnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yulin%22">Chen, Yulin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yulin_chen@nnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Shaonan%22">Zhu, Shaonan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhushaonan@njupt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2024, Vol. 16 Issue 16, p3089. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Rainfall+reliability%22">Rainfall reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Landslide+prediction%22">Landslide prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Landslides%22">Landslides</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With global climate change and increased human activities, landslides increasingly threaten human safety and property. Precisely extracting large-scale spatiotemporal information on landslides is crucial for risk management. However, existing methods are either locally based or have coarse temporal resolution, which is insufficient for regional analysis. In this study, spatiotemporal information on landslides was extracted using multiple remote sensing data from Emilia, Italy. An automated algorithm for extracting spatial information of landslides was developed with NDVI datasets. Then, we established a landslide prediction model based on a hydrometeorological threshold of three-day soil moisture and three-day accumulated rainfall. Based on this model, the locations and dates of rainfall-induced landslides were identified. Then, we further matched these identified locations with the extracted landslides from remote sensing data and finally determined the occurrence time. This approach was validated with recorded landslides events in Emilia. Despite some temporal clustering, the overall trend matched historical records, accurately reflecting the dynamic impacts of rainfall and soil moisture on landslides. The temporal bias for 87.3% of identified landslides was within seven days. Furthermore, higher rainfall magnitude was associated with better temporal accuracy, validating the effectiveness of the model and the reliability of rainfall as a landslide predictor. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs16163089 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 3089 Subjects: – SubjectFull: Rainfall reliability Type: general – SubjectFull: Landslide prediction Type: general – SubjectFull: Climate change Type: general – SubjectFull: Rainfall Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Landslides Type: general Titles: – TitleFull: Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zeng, Tongxiao – PersonEntity: Name: NameFull: Zhang, Jun – PersonEntity: Name: NameFull: Chen, Yulin – PersonEntity: Name: NameFull: Zhu, Shaonan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 16 – Type: issue Value: 16 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
