Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides
Abstract
1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources Used in This Study
2.3. Description and Analysis of Landslide Inventory
3. Methodology
3.1. The Infinite Slope Stability Model
3.2. The Infiltration Model
3.3. The Root Cohesion Inversion Model
3.4. The Infinite Slope Stability Model Considering the Influence of Land Use and Rainfall
4. Experimental Results
4.1. Analysis of Root Cohesion Inversion Results
4.2. Validation of Root Cohesion Inversion Accounting for Spatial Heterogeneity
4.3. Dynamics of Landslide Susceptibility Across Different Land Uses During a Rainfall Event
4.4. Impact of Root Cohesion on Rainfall Thresholds for Landslides
5. Discussion
5.1. Uncertainties in Satellite-Derived Root Cohesion Models for Landslide Susceptibility Assessment
5.2. Complex Role of Root Cohesion in Slope Stability Under Various Conditions
6. Conclusions
- (1)
- Root cohesion spatial heterogeneity is critical: Inversion of root cohesion using satellite-derived tree height and biomass revealed significant spatial variability. Secondary forests exhibited substantially higher cohesion values than farmland, attributable to greater tree height and stand age. This underscores the limitation of uniform root parameter assumptions.
- (2)
- Spatially explicit cohesion mapping enhances validation: Using high-resolution Gaofen imagery to construct a validated landslide inventory, the model incorporating heterogeneous root cohesion achieved superior prediction accuracy. This significantly outperformed models assuming uniform root cohesion or neglecting root reinforcement, confirming the value of remote sensing-based inversion for susceptibility assessment.
- (3)
- Land use governs dynamic susceptibility: Time-resolved analysis during an extreme rainfall event (>250 mm/day) identified farmland as the most vulnerable land use, with susceptibility escalating markedly during peak rainfall. Secondary forests exhibited greater resilience, attributed to their well-developed root systems.
- (4)
- Integrated approach enables scalable assessment: Combining satellite data with physics-based modeling provides a scalable and transferable methodology for landslide susceptibility mapping, particularly valuable in data-scarce or inaccessible regions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Type | Format | Spatial Resolution | Source |
---|---|---|---|
DEM | Raster | 12.5 m × 12.5 m | https://search.asf.alaska.edu/#/?zoom=3¢er=125.336020,22.365939, accessed on 8 May 2024 |
Soil depth | Raster | 1 km × 1 km | https://daac.ornl.gov/SOILS/guides/Global_Soil_Regolith_Sediment.html, accessed on 8 May 2024 |
Soil type | Raster | 1 km × 1 k m | https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/, accessed on 8 May 2024 |
Land use | Raster | 30 m × 30 m | https://zenodo.org/records/5816591, accessed on 8 May 2024 |
Above ground biomass | Raster | 30 m × 30 m | https://www.scidb.cn/en/detail?dataSetId=f48c4983dbd84a4c9c287111ac91c5aa, accessed on 8 May 2024 |
Tree height | Raster | 30 m × 30 m | https://www.3decology.org/2023/06/21/forest-tree-height-map-of-china-2/, accessed on 8 May 2024 |
Tree age | Raster | 30 m × 30 m | https://figshare.com/articles/dataset/CHINA_FAGE_30m_7z/21627023/7, accessed on 8 May 2024 |
Land Use | Forest | Grassland | Bare Land |
---|---|---|---|
(m/d) | 2.4192 | 0.792 | 0.144 |
Soil Type | (kPa) | (°) | ||
---|---|---|---|---|
Min | Max | Min | Max | |
Loamy sand | 10 | 20 | 31 | 34 |
Sandy loam | 10 | 20 | 31 | 34 |
Loam | 10 | 20 | 28 | 32 |
Silty (sandy) loam | 10 | 20 | 25 | 32 |
Sandy clay loam | 10 | 20 | 25 | 32 |
Sandy clay loam | 10 | 20 | 31 | 34 |
Clay loam | 10 | 20 | 18 | 32 |
Clay | 10 | 20 | 18 | 28 |
Sandy clay | 10 | 20 | 31 | 34 |
Silty (sandy) clay | 10 | 20 | 18 | 32 |
Scenario | Susceptibility | % of Predicted Areas | % of Landslide | LR | %LR |
---|---|---|---|---|---|
without root cohesion | Very high | 7.1 | 16.61 | 2.3409 | 36.20994 |
High | 15.6 | 23.22 | 1.4885 | 23.02469 | |
Moderate | 17.68 | 19.71 | 1.1149 | 17.2457 | |
Low | 14.05 | 12.39 | 0.8821 | 13.64466 | |
Very low | 43.74 | 27.92 | 0.6384 | 9.875015 | |
with uniform root cohesion assignment | Very high | 8.92 | 19.41 | 2.1772 | 33.0801 |
High | 10.77 | 17.11 | 1.5892 | 24.1461 | |
Moderate | 14.02 | 17.25 | 1.2302 | 18.6915 | |
Low | 13.87 | 12.92 | 0.9311 | 14.14702 | |
Very low | 50.57 | 33.07 | 0.6539 | 9.935274 | |
with inverted root cohesion | Very high | 5.02 | 13.01 | 2.5897 | 36.66001 |
High | 13.28 | 22.51 | 1.6947 | 23.99032 | |
Moderate | 16.8 | 20.14 | 1.1988 | 16.97031 | |
Low | 14.69 | 14.07 | 0.958 | 13.56153 | |
Very low | 48.36 | 30.12 | 0.6229 | 8.817825 |
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Miao, Z.; Xiong, Y.; Cheng, Z.; Wu, B.; Wang, W.; Peng, Z. Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides. Sensors 2025, 25, 4221. https://doi.org/10.3390/s25134221
Miao Z, Xiong Y, Cheng Z, Wu B, Wang W, Peng Z. Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides. Sensors. 2025; 25(13):4221. https://doi.org/10.3390/s25134221
Chicago/Turabian StyleMiao, Zelang, Yaopeng Xiong, Zhiwei Cheng, Bin Wu, Wei Wang, and Zuwu Peng. 2025. "Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides" Sensors 25, no. 13: 4221. https://doi.org/10.3390/s25134221
APA StyleMiao, Z., Xiong, Y., Cheng, Z., Wu, B., Wang, W., & Peng, Z. (2025). Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides. Sensors, 25(13), 4221. https://doi.org/10.3390/s25134221