Landslide Susceptibility Mapping Constrained by InSAR-Derived Deformation Using Multi-Source Data Integration.
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| Title: | Landslide Susceptibility Mapping Constrained by InSAR-Derived Deformation Using Multi-Source Data Integration. |
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| Authors: | Han, Xudong1 (AUTHOR), Song, Wei1,2 (AUTHOR) songwei@mail.cgs.gov.cn, Pan, Shuhua1,3 (AUTHOR), Cao, Chen1,2 (AUTHOR), Bao, Yiding2,3 (AUTHOR) |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2540. 25p. |
| Subjects: | Radar interferometry, Multisensor data fusion, Machine learning, Back propagation, Random forest algorithms, Geospatial data, Landslide hazard analysis, Probability density function |
| Geographic Terms: | Guizhou Sheng (China), China |
| Abstract: | Highlights: What are the main findings? An LSM framework constrained by InSAR-derived deformation was proposed by integrating SBAS-InSAR deformation analysis, multi-source geo-environmental data, and machine learning. An RF-BPNN combined model was used for susceptibility mapping, and Shapley value analysis identified slope, elevation, rainfall, and relief as the dominant factors controlling landslide susceptibility prediction. What are the implications of the main findings? The KDE-PCC-based classification adjustment provides a quantitative way to incorporate observation-period deformation activity into susceptibility classification threshold optimization. The Xiangle and Namu landslide cases illustrate the adjustment effects of incorporating InSAR-derived deformation constraints into susceptibility classification. Landslide susceptibility mapping (LSM) is fundamental to disaster prevention and spatial risk management in mountainous regions. However, conventional LSM approaches that rely mainly on static landslide influencing factors and empirical classification thresholds may have limited temporal relevance and interpretability. In response to these limitations, this study proposed an LSM framework constrained by interferometric synthetic aperture radar (InSAR)-derived deformation information. Wangmo County, Guizhou Province, China, was selected as the study area. Multi-source data, including small baseline subset InSAR (SBAS-InSAR) deformation results, optical remote sensing imagery, geo-environmental factors, and field investigation data, were used to construct and validate four machine learning models: logistic regression (LR), random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN). The validation results showed that the RF and BPNN models performed better than the LR and SVM models in terms of AUC, accuracy, precision, recall, and F1-score. Accordingly, an RF–BPNN combined model was constructed using an equal-weight averaging strategy. Shapley value analysis indicated that terrain- and rainfall-related factors made dominant contributions to landslide susceptibility prediction, a finding consistent with the landslide development characteristics in the study area. InSAR-derived deformation information was extracted from 31 Sentinel-1A images using SBAS-InSAR. A classification adjustment strategy based on kernel density estimation (KDE) and the Pearson correlation coefficient (PCC) was then used to identify the susceptibility classification scheme with relatively high spatial consistency with deformation activity during the observation period. The optimized classification scheme achieved a PCC value of 0.65, compared with 0.61 for the natural breaks classification, indicating a modest improvement in the spatial consistency between susceptibility zoning and deformation activity. The Xiangle and Namu landslides were used as representative cases to illustrate the adjustment effects of the deformation-constrained classification scheme. The proposed framework provides a practical approach for incorporating observation-period InSAR-derived deformation information into regional LSM and can support landslide monitoring and decision-making in complex terrains. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? An LSM framework constrained by InSAR-derived deformation was proposed by integrating SBAS-InSAR deformation analysis, multi-source geo-environmental data, and machine learning. An RF-BPNN combined model was used for susceptibility mapping, and Shapley value analysis identified slope, elevation, rainfall, and relief as the dominant factors controlling landslide susceptibility prediction. What are the implications of the main findings? The KDE-PCC-based classification adjustment provides a quantitative way to incorporate observation-period deformation activity into susceptibility classification threshold optimization. The Xiangle and Namu landslide cases illustrate the adjustment effects of incorporating InSAR-derived deformation constraints into susceptibility classification. Landslide susceptibility mapping (LSM) is fundamental to disaster prevention and spatial risk management in mountainous regions. However, conventional LSM approaches that rely mainly on static landslide influencing factors and empirical classification thresholds may have limited temporal relevance and interpretability. In response to these limitations, this study proposed an LSM framework constrained by interferometric synthetic aperture radar (InSAR)-derived deformation information. Wangmo County, Guizhou Province, China, was selected as the study area. Multi-source data, including small baseline subset InSAR (SBAS-InSAR) deformation results, optical remote sensing imagery, geo-environmental factors, and field investigation data, were used to construct and validate four machine learning models: logistic regression (LR), random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN). The validation results showed that the RF and BPNN models performed better than the LR and SVM models in terms of AUC, accuracy, precision, recall, and F1-score. Accordingly, an RF–BPNN combined model was constructed using an equal-weight averaging strategy. Shapley value analysis indicated that terrain- and rainfall-related factors made dominant contributions to landslide susceptibility prediction, a finding consistent with the landslide development characteristics in the study area. InSAR-derived deformation information was extracted from 31 Sentinel-1A images using SBAS-InSAR. A classification adjustment strategy based on kernel density estimation (KDE) and the Pearson correlation coefficient (PCC) was then used to identify the susceptibility classification scheme with relatively high spatial consistency with deformation activity during the observation period. The optimized classification scheme achieved a PCC value of 0.65, compared with 0.61 for the natural breaks classification, indicating a modest improvement in the spatial consistency between susceptibility zoning and deformation activity. The Xiangle and Namu landslides were used as representative cases to illustrate the adjustment effects of the deformation-constrained classification scheme. The proposed framework provides a practical approach for incorporating observation-period InSAR-derived deformation information into regional LSM and can support landslide monitoring and decision-making in complex terrains. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18152540 |
