A Novel Dataset Replenishment Strategy Integrating Time-Series InSAR for Refined Landslide Susceptibility Mapping in Karst Regions
Abstract
:1. Introduction
2. Study Area and Data
2.1. Overview of the Study Area
2.2. Conditioning Factor Spatial Database
2.2.1. Data Sources
2.2.2. Factor Selection and Processing
3. Methodology
3.1. Frequency Ratio Method
3.2. SBAS-InSAR Technology
3.3. XGBoost Algorithm
3.4. SHAP Algorithm
3.5. PDP Algorithm
3.6. Sampling Strategies
- (1)
- Calculate the frequency ratio (FR) value for each interval of each conditioning factor to obtain an FR value attribute dataset based on raster cells.
- (2)
- Create an initial expansion sample set based on FR values. Filter samples using geographic environment indicators: select raster cells with a minimum FR value greater than 1 as expansion positive samples, totaling 955; select raster cells with a maximum FR value less than 1 as expansion negative samples, totaling 99.
- (3)
- Use the deformation rate along the slope direction obtained by SBAS-InSAR technology to refine the expanded sample dataset. The deformation rate along the line of sight (LOS) of the satellite imaging is initially deduced using SBAS-InSAR technology. However, there is some bias between this deformation information and the actual surface deformation. Therefore, the LOS deformation rate is converted to the slope direction deformation rate using a relevant formula and interpolated in ArcGIS with the inverse distance weighting method. Only negative deformation rate values are retained to obtain the final deformation rate distribution along the slope direction (Figure 7). If the deformation rate along the slope direction is less than 0, it indicates deformation in that direction; if greater than 0, it indicates no deformation. Considering surface spatial deformation factors fully, the final expanded positive samples are those with slope direction deformation rates less than 0, totaling 359, and the final expanded negative samples are those with deformation rates greater than 0, totaling 16.
- (4)
- The buffer-controlled sampling (BCS) method was used to generate random non-landslide samples. These samples, along with the expanded set of positive and negative samples and historical landslide samples, were merged to achieve a 1:1 ratio of landslide to non-landslide samples. This process created the final dataset.
4. Results
4.1. Models’ Performance Test and Comparison
4.1.1. Confusion Matrix
4.1.2. ROC Curve and AUC Value
4.2. Comparison of Landslide Susceptibility Mapping
4.3. SHAP Interpretability Results
5. Discussion
5.1. Effectiveness of Combining FR and SBAS-InSAR Sample Extension Strategies
5.2. Analysis of the Primary Contributing Factors of Landslide Susceptibility in Karst Landscapes Based on SHAP Modeling
5.3. Impact Analysis of Significant Contributing Factors Based on PDP Modeling
5.3.1. One-Factor Dependence Analysis of Distance from Mining Sites
5.3.2. One-Factor Dependence Analysis of NDVI
5.3.3. One-Factor Dependence Analysis of Surface Deformation Rate
5.3.4. One-Factor Dependence Analysis of Land Surface Temperature
6. Conclusions
- (1)
- The sample expansion strategy combining the frequency ratio (FR) and SBAS-InSAR technology interpretation results effectively improves model prediction accuracy and stability. The sample expansion strategy reduces the similarity between negative and positive samples, mitigating the impact of sample imbalance on model training. The optimized model can identify high-risk areas more accurately, proving the method’s effectiveness and practicality
- (2)
- The XGBoost-SHAP-PDP algorithm constructs a comprehensive interpretation framework for the landslide susceptibility assessment model, allowing exploration and interpretation from multiple perspectives. Distance from mining sites, lithology, NDVI, distance from faults, and land surface temperature are the top five contributing factors. The vulnerability of lithology and the destructive effect of mining activities on slope stability are important reasons for frequent landslides in karst areas. Meanwhile, factors such as average annual rainfall, soil erosion modulus, and surface deformation rate also greatly influence landslide occurrence.
- (3)
- Introducing factors such as soil erosion modulus, surface deformation rate, and land surface temperature significantly improves the model’s prediction ability. Applying these factors helps to understand and evaluate the spatial pattern of landslide disasters more comprehensively.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Name | Data Sources | Typology | Resolution |
---|---|---|---|
Historical landslide data | Chinese Academy of Sciences, Center for Resource and Environmental Data and Sciences | Vector (spatial) | - |
DEM | Geospatial data cloud platform | Raster | 30 m |
Landsat-8 data | Geospatial data cloud platform | Raster | 30 m |
Geological data | National Geological Information Data Center (NGIDC) | Vector (spatial) | 1:200,000 |
Waters | Chinese Academy of Sciences, Center for Resource and Environmental Data and Sciences | Vector (spatial) | 1:100,000 |
Roads | Chinese Academy of Sciences, Center for Resource and Environmental Data and Sciences | Vector (spatial) | 1:100,000 |
Multi-year average rainfall | Chinese Academy of Sciences, Center for Resource and Environmental Data and Sciences | raster | 1000 m |
Administrative subdivision (e.g., of provinces in counties) | National Geographic Information Public Service Platform | Vector (spatial) | 1:100,000 |
Provenance | National Address Library | Vector (spatial) | - |
Soil data | World Soil Database (HWSD) | Raster | 1000 m |
Sentinel-1 data | EarthData ASF Data Search | Raster | 30 m |
Normalized Difference Vegetation Index (NDVI) | National Science and Technology Resources Sharing Service Platform | Raster | 30 m |
Categories | Conditioning Factors | Classification/Unit | Classification Criteria |
---|---|---|---|
Topographic and geomorphologic factors | Elevation (Ele)/m | 11 | (1) 1051–1427; (2) 1917–2012; (3) 1427–1580; (4) 2012–2099; (5) 1580–1711; (6) 1711–1818; (7) 1818–1917; (8) 2099–2189; (9) 2189–2299; (10) 2299–2439; (11) 2439–2744 |
Slope/° | 10 | (1) 0–5; (2) 5–10; (3) 10–15; (4) 15–20; (5) 20–25; (6) 25–30; (7) 30–35; (8) 35–40; (9) 40–50; (10) >50 | |
Surface cutting depth (SCD)/m | 10 | (1) 0~31; (2) 31~47; (3) 47~61; (4) 61–75; (5) 75~90; (6) 90~107; (7) 107~125; (8) 125~148; (9) 148~180; (10) >180 | |
Aspect | 10 | (1) plane (−1); (2) north (0–22.5); (3) northeast (22.5–67.5); (4) east (67.5–112.7); (5) southeast (112.5–157.5); (6) south (157.5–202.5); (7) southwest (202.5–247.5); (8) west (247.5–292.5); (9) northwest (292.5–337.5); (10) north (337.5–360) | |
Surface cover factors | Soil bare rate (SBR) | 7 | (1) <−80; (2) −80 to −55; (3) −55 to −36; (4) −36 to −24; (5) −24 to −16; (6) −16 to −11; (7) −11 to −1 |
Soil erosion modulus (SEM) | 9 | (1) <304; (2) 304–989; (3) 989–1749; (4) 1749–2661; (5) 2662–3802; (6) 3802–5171; (7) 5171–6920; (8) 6920–9581; (9) >9581 | |
Land surface temperature (LST)/° | 9 | (1) <16; (2) 16–24; (3) 24–28; (4) 28–31; (5) 31–33; (6) 33–36; (7) 36–39; (8) 39–43; (9) 43–54 | |
NDVI | 9 | (1) <0.20; (2) 0.20–0.33; (3) 0.33–0.43; (4) 0.43–0.52; (5) 0.52–0.59; (6) 0.59–0.66; (7) 0.66–0.74; (8) 0.74–0.83; (9) 0.83–0.99 | |
Soil type (ST) | 18 | (1) Brown soil; (2) Lakes and waters; (3) Rock; (4) alluvial soil; (5) Yellow soil; (6) paddy soil; (7) Acidic purple clay; (8) Limestone; (9) Yellow-brown loamy soil; (10) Yellow-red soil; (11) black lime; (12) Yellow-brown soil; (13) mountainous red soil; (14) Red soil; (15) Red loamy soil; (16) Brick red soil; (17) calcium phosphate; (18) Dark brown soil | |
Hydrological factors | Distance from waters (DFW)/m | 9 | (1) <500; (2) 500–1000; (3) 1000–1500; (4) 1500–2000; (5) 2000–2500; (6) 2500–3000; (7) 3000–4000; (8) 4000–9000; (9) >9000 |
Average annual rainfall (AAR)/mm | 9 | (1) <1007; (2) 1007–1023; (3) 1023–1036; (4) 1036–1052; (5) 1052–1070; (6) 1070–1090; (7) 1090–1112; (8) 1112–1137; (9) >1137 | |
Topographic wetness index (TWI) | 6 | (1) 2.08–4.88; (2) 4,88–6.33; (3) 6.33–8.13; (4) 8.13–10.48; (5) 10.48–13.95; (6) 13.95–30.62 | |
Human activity factors | Distance from mining site (DFMS)/m | 10 | (1) <2300; (2) 2300–4141; (3) 4141–5889; (4) 5889–7545; (5) 7545–9201; (6) 9201–10,858; (7) 10,858–12,698; (8) 12,698–14,722; (9) 14,722–17,943; (10) >17,943 |
Distance from road (DFR)/m | 9 | (1) <428; (2) 428–909; (3) 909–1436; (4) 1436–2029; (5) 2029–1723; (6) 2723–3535; (7) 3535–4555; (8) 4555–6208; (9) >6208 | |
Geological factors | Lithology (Li) | 8 | (1) C1: lower Carboniferous; (2) C1P1: Carboniferous and Permian juxtaposition; (3) C2: upper Carboniferous; (4) D3C1: Devonian and Carboniferous juxtaposition; (5) P2: Upper Permian; (6) T1: Upper Triassic; (7) T2: middle Triassic; (8) T3:Lower Triassic |
Distance from fault (DFF)/m | 9 | (1) <500; (2) 500–1000; (3) 1000–1500; (4) 1500–2000; (5) 2000–2500; (6) 2500–3000; (7) 3000–4000; (8) 4000–9000; (9) >9000 | |
Surface deformation rate (SDR)/mm·a−1 | 9 | (1) <−78; (2) −78 to −50; (3) −50 to −35; (4) −35 to −23; (5) −23 to −12; (6) −12 to −1; (7) −1 to 9; (8) 9~26; (9) >26 |
Parameter | Corresponding Value |
---|---|
Acquisition of the satellite | Sentinel-1A |
Orbit | Ascending orbit |
Resolution/m | 5 × 20 |
Polarization mode | VV + VH |
Revisit period/d | 12 |
Incidence angle/(°) | 38.99 |
Impact time | February 2017–December 2017 |
Number of images/views | 34 |
Actual Value | Predicted Value | Actual Value | Predicted Value | ||
---|---|---|---|---|---|
Landslide | Non-Landslide | Landslide | Non-Landslide | ||
Landslide | 43 | 5 | Landslide | 149 | 12 |
Non-Landslide | 15 | 41 | Non-Landslide | 23 | 136 |
Sample | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Before Expanding Sample | 0.8077 | 0.8913 | 0.7321 | 0.8039 |
After Expanded Sample | 0.8906 | 0.9189 | 0.8553 | 0.8860 |
Sample | Susceptibility Class | Probability of Landslide Occurrence | Grid Number (pcs) | Area Ratio (%) | Number of Landslide Points (pcs) | Density (pcs/km2) |
---|---|---|---|---|---|---|
Before Expanding Sample | Very Low Susceptibility Zone | <0.257 | 977,062 | 27.545 | 3 | 0.003412 |
Low Susceptibility Zone | 0.257–0.392 | 849,144 | 23.938 | 13 | 0.017011 | |
Medium Susceptibility Zone | 0.392–0.521 | 828,716 | 23.362 | 18 | 0.024134 | |
High Susceptibility Zone | 0.521–0.662 | 565,749 | 15.949 | 54 | 0.106054 | |
Very High Susceptibility Zone | >0.662 | 326,629 | 9.208 | 85 | 0.289149 | |
After Expanded Sample | Very Low Susceptibility Zone | <0.123 | 1,530,467 | 43.145 | 1 | 0.000726 |
Low Susceptibility Zone | 0.123–0.230 | 1,042,708 | 29.394 | 20 | 0.021312 | |
Medium Susceptibility Zone | 0.230–0.418 | 547,520 | 15.435 | 34 | 0.068998 | |
High Susceptibility Zone | 0.418–0.648 | 281,614 | 7.938 | 61 | 0.240676 | |
Very High Susceptibility Zone | >0.648 | 144,991 | 4.087 | 57 | 0.436809 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Yang, Y.; Ma, X.; Ding, W.; Wen, H.; Sun, D. A Novel Dataset Replenishment Strategy Integrating Time-Series InSAR for Refined Landslide Susceptibility Mapping in Karst Regions. Water 2024, 16, 2414. https://doi.org/10.3390/w16172414
Yang Y, Ma X, Ding W, Wen H, Sun D. A Novel Dataset Replenishment Strategy Integrating Time-Series InSAR for Refined Landslide Susceptibility Mapping in Karst Regions. Water. 2024; 16(17):2414. https://doi.org/10.3390/w16172414
Chicago/Turabian StyleYang, Yajie, Xianglong Ma, Wenrong Ding, Haijia Wen, and Deliang Sun. 2024. "A Novel Dataset Replenishment Strategy Integrating Time-Series InSAR for Refined Landslide Susceptibility Mapping in Karst Regions" Water 16, no. 17: 2414. https://doi.org/10.3390/w16172414