Details
| Original language | English |
|---|---|
| Article number | 104365 |
| Journal | International Journal of Applied Earth Observation and Geoinformation |
| Volume | 136 |
| Early online date | 18 Jan 2025 |
| Publication status | Published - Feb 2025 |
Abstract
The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.
Keywords
- Ensemble learning, InSAR, Landslide susceptibility mapping, Machine learning, Urban expansion
ASJC Scopus subject areas
- Environmental Science(all)
- Global and Planetary Change
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Environmental Science(all)
- Management, Monitoring, Policy and Law
Sustainable Development Goals
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In: International Journal of Applied Earth Observation and Geoinformation, Vol. 136, 104365, 02.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Landslide susceptibility assessment of the Wanzhou district
T2 - Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map
AU - Zhou, Chao
AU - Gan, Lulu
AU - Cao, Ying
AU - Wang, Yue
AU - Segoni, Samuele
AU - Shi, Xuguo
AU - Motagh, Mahdi
AU - Singhc, Ramesh P.
N1 - Publisher Copyright: © 2025 China University of Geosciences
PY - 2025/2
Y1 - 2025/2
N2 - The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.
AB - The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.
KW - Ensemble learning
KW - InSAR
KW - Landslide susceptibility mapping
KW - Machine learning
KW - Urban expansion
UR - http://www.scopus.com/inward/record.url?scp=85215427150&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2025.104365
DO - 10.1016/j.jag.2025.104365
M3 - Article
AN - SCOPUS:85215427150
VL - 136
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
SN - 1569-8432
M1 - 104365
ER -