Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area
2.2. Rainfall Data Sources and Classification
2.3. Interpretation of Landslide Areas and Analysis of Their Changes
2.3.1. Landslide Inventory
2.3.2. Multitemporal Changes in Landslide Areas and Extraction of Explanatory Factors
2.4. Establishment of the Landslide Susceptibility Model
2.4.1. Model and Factor Selection
- Elevation (unit: meters): The altitude above sea level is directly calculated from the DEM for all of Taiwan with a 20 m resolution provided by the Ministry of the Interior.
- Slope (unit: degrees): Calculated from the DEM data of the Ministry of the Interior with the 20 m resolution by using ArcGIS 10.7 (Spatial Analyst of extension tool).
- Topographic roughness (unit: meters): The degree of topographic change in the area. Topographic roughness is calculated by using ArcGIS 10.7, with the standard deviation of the elevation values in a circular window (3 × 3 pixel, moving window) as a measure of the degree of elevation variation in the area [39]. A schematic diagram of the circular window is shown in Figure 2, and the standard deviation (SD) is calculated as follows:
- Slope roughness (unit: degrees): The variations of slope within the area. Using the slope roughness, it is possible to find out whether a landslide would occur in the landscape with drastic slope variations. The calculation method and tool are the same as the topographic roughness, and is calculated as follows:
- Aspect sine value and cosine value (unitless): There will be numerical discontinuities if the aspect is expressed as an angle (e.g., 0° = 360°), and thus the aspect can be decomposed into an X vector and a Y vector, i.e., the aspect sine value and aspect cosine value, respectively. A positive sine value indicates the degree to the east, and a negative sine value indicates the degree to the west, a positive cosine value indicates the degree to the north, and a negative cosine value indicates the degree to the south. The value can be calculated from the DEM data by using ArcGIS 10.7 (Spatial Analyst of extension tool).
- NDVI before landslides (unitless): NDVI is the most common index for evaluating the surface vegetation status in an area [40,41,42,43]. The NDVI value before the flood season can be used as a quantitative value for the vegetation status before landslides. It was calculated from SPOT satellite imagery by Erdas Imagine 4.0.
- Maximum accumulated rainfall in 24 h (unit: mm): In previous studies, it was often believed that rainfall intensity is related to landslide events [44,45]. However, the choice of rainfall intensity differed based on the different regions or research objectives. In previous studies on rainfall thresholds for landslides in southwestern Taiwan, the accumulated rainfall in 6, 12, 24, 48, and 72 h in autumn during different years was adopted in the variance analysis, and it was found that the 24 h accumulated rainfall had the lowest variation coefficient [46], i.e., the internal changes and differences in data corresponding to the 24 h accumulated rainfall are relatively small. The rainfall value is calculated by the Kriging method.
- Topographic wetness index (TWI, unit: m2): The ability of the terrain to control soil moisture. The steeper the slope, the faster the runoff speed, the lower the degree of infiltration, and thereby, the lower the soil moisture content [47]. Because the watershed area is relatively large and the slope is mild in low and flat areas, the soil water content is relatively high. If the water content under the slope is large, the pore water pressure of soil increases, and the soil is affected by the lifting force of water. The value can be calculated by using ArcGIS 10.7. As a result, the probability of slope failure and sliding becomes higher, and is expressed as follows:
- Geology (unitless): Different geological conditions affect the strength of rock mass, joint density, joint structures, etc. We used the local geological formations (Figure 1) as a categorical variable in the model. The data are transferred from vector data to grid data.
- Distance to the river (unit: meters): The distance from any point to the river. Landslides may occur on both sides of the riverbank. Continuous erosion on the foundation slope makes the area unstable, and a landslide is likely to occur during high-intensity rainfall. The distance from the river channel is obtained by analysis using ArcGIS 10.7 (path distance), which is to calculate the flow path from the river channel to the landslide.
2.4.2. Model Calibration and Validation
Model Calibration
Model Validation
2.4.3. Landslide Risk and Critical Rainfall under Different Rainfall Scenarios
3. Results
3.1. Long-Term Rainfall Variations
3.2. The Multitemporal Changes in Landslide Areas
3.3. Landslide Susceptibility Model and Explanatory Factors
4. Discussion
4.1. Rainfall Hot Spots and Landslides
4.2. Landslide Risk Ranking and Simulation of Critical Rainfall Scenarios
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Probability of Landslide | Risk Level |
---|---|
>0.8 | Serious |
0.6–0.8 | High |
0.4–0.6 | Moderate |
0.2–0.4 | Low |
<0.2 | Slight |
Year | Landslide Area (ha) | Proportion (%) | Count | Average Landslide Area (ha) |
---|---|---|---|---|
2003 | 842.49 | 0.62 | 1063 | 0.78 |
2004 | 2155.56 | 1.58 | 2560 | 0.84 |
2005 | 2661.70 | 1.96 | 2694 | 0.98 |
2006 | 2677.86 | 1.97 | 2608 | 1.02 |
2007 | 2600.72 | 1.91 | 2020 | 1.28 |
2008 | 1266.00 | 0.93 | 1281 | 0.98 |
2009 | 9472.67 | 6.96 | 4615 | 2.05 |
2010 | 8467.19 | 6.22 | 7337 | 1.09 |
2011 | 6946.50 | 5.11 | 4998 | 1.38 |
2012 | 6069.76 | 4.46 | 4861 | 1.24 |
2013 | 6177.93 | 4.54 | 6685 | 0.88 |
2014 | 6352.23 | 4.67 | 4717 | 1.34 |
2015 | 4786.04 | 3.52 | 3855 | 1.24 |
2016 | 5401.57 | 3.97 | 3248 | 1.66 |
2017 | 5890.22 | 4.33 | 3527 | 1.67 |
2018 | 5951.46 | 4.38 | 5360 | 0.85 |
Tested Model | Parameters | β value | S.E, | Wald | Sig. | Exp(β) |
---|---|---|---|---|---|---|
2005 | Slope | 0.040 | 0.003 | 199.911 | 0.000 | 1.040 |
Aspect of sin | 0.544 | 0.038 | 209.682 | 0.000 | 1.724 | |
Aspect of cosine | −0.541 | 0.038 | 202.804 | 0.000 | 0.582 | |
Topographic wetness index | 9.136 | 0.014 | 97.286 | 0.000 | 1.146 | |
Distance to river | −0.001 | 0.000 | 177.706 | 0.000 | 0.999 | |
Previous NDVI | −7.108 | 0.180 | 1561.588 | 0.000 | 0.001 | |
Continuous maximum 24-hourly rainfall | 0.001 | 0.000 | 39.043 | 0.000 | 1.001 | |
Geology | 268.817 | 0.000 | ||||
Constant | −0.260 | 0.282 | 0.853 | 0.356 | 0.771 | |
2009 | Elevation | −3.34 × 10−4 | 0.000 | 64.078 | 0.000 | 1.000 |
Slope | 0.032 | 0.002 | 227.551 | 0.000 | 1.032 | |
Aspect of sin | 0.730 | 0.027 | 745.453 | 0.000 | 2.075 | |
Aspect of cosine | −1.019 | 0.030 | 1162.476 | 0.000 | 0.361 | |
Distance to river | −0.001 | 0.000 | 140.098 | 0.000 | 0.999 | |
Topographic wetness index | 0.211 | 0.010 | 443.191 | 0.000 | 1.235 | |
Previous NDVI | 0.002 | 0.000 | 44.848 | 0.000 | 1.002 | |
Continuous maximum 24 hourly rainfall | −0.728 | 0.115 | 40.142 | 0.000 | 0.483 | |
Geology | 535.879 | 0.000 | ||||
Constant | −3.251 | 0.350 | 86.235 | 0.000 | 0.039 | |
Mixed | Elevation | −2.28 × 10−4 | 0.000 | 38.028 | 0.000 | 1.000 |
Slope | 0.049 | 0.005 | 115.588 | 0.000 | 1.050 | |
Terrain roughness | −0.008 | 0.003 | 8.245 | 0.004 | 0.992 | |
Aspect of sin | −0.571 | 0.025 | 532.984 | 0.000 | 1.769 | |
Aspect of cosine | −0.903 | 0.027 | 1084.046 | 0.000 | 0.405 | |
Distance to river | −4.93 × 10−4 | 0.000 | 120.778 | 0.000 | 1.000 | |
Topographic wetness index | 0.194 | 0.009 | 435.909 | 0.000 | 1.214 | |
Previous NDVI | 0.000 | 0.000 | 9.400 | 0.002 | 1.000 | |
Continuous maximum of 24-hourly rainfall | −2.238 | 0.078 | 815.912 | 0.000 | 0.107 | |
Geology | 484.502 | 0.000 | ||||
Constant | −1.594 | 0.198 | 65.137 | 0.000 | 0.203 |
2005 Tested Model | |||
Observed value | Predicted value | ||
Non-landslide | Landslide | Percent accuracy (%) | |
Non-landslide | 4114 | 886 | 82.28 |
Landslide | 1336 | 3664 | 73.28 |
Overall accuracy (%) | 77.78 | ||
AUC for 2005 (calibration) | 0.895 | ||
AUC for 2009 (validation) | 0.619 | ||
2009 Tested Model | |||
Observed value | Predicted value | ||
Non-landslide | Landslide | Percent accuracy (%) | |
Non-landslide | 5479 | 2521 | 68.49 |
Landslide | 1940 | 6060 | 75.75 |
Overall accuracy (%) | 72.12 | ||
AUC for 2005 (validation) | 0.639 | ||
AUC for 2009 (calibration) | 0.788 | ||
Mixed Tested Model | |||
Observe value | Predicted value | ||
Non-landslide | Landslide | Percent accuracy (%) | |
Non-landslide | 6280 | 2720 | 69.78 |
Landslide | 2714 | 6286 | 69.84 |
Overall accuracy (%) | 69.81 | ||
AUC for 2005 (calibration) | 0.811 | ||
AUC for 2009 (calibration) | 0.772 |
Risk Level | 300 mm | 600 mm | 900 mm | |||
---|---|---|---|---|---|---|
Area (m2) | Ratio (%) | Area (m2) | Ratio (%) | Area (m2) | Ratio (%) | |
Slight | 114,693.90 | 84.32 | 105,002.14 | 77.19 | 90,025.42 | 66.18 |
Low | 121,30.90 | 8.92 | 18,398.50 | 13.53 | 28,030.10 | 20.61 |
Moderate | 3827.80 | 2.81 | 5725.76 | 4.21 | 8877.96 | 6.53 |
High | 2619.56 | 1.93 | 3202.48 | 2.35 | 4294.92 | 3.16 |
Serious | 2753.64 | 2.02 | 3696.92 | 2.72 | 4797.40 | 3.53 |
Risk Level | 300 mm | 600 mm | 900 mm | |||
---|---|---|---|---|---|---|
Area (m2) | Ratio (%) | Area (m2) | Ratio (%) | Area (m2) | Ratio (%) | |
Slight | 62,596.74 | 46.02 | 58,465.12 | 42.98 | 54,292.16 | 39.91 |
Low | 47,089.60 | 34.62 | 47,831.00 | 35.16 | 48,456.60 | 35.62 |
Moderate | 20,203.90 | 14.85 | 22,658.60 | 16.66 | 25,081.60 | 18.44 |
High | 5127.64 | 3.77 | 5878.16 | 4.32 | 6785.92 | 4.99 |
Serious | 1007.92 | 0.74 | 1192.92 | 0.88 | 1409.52 | 1.04 |
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Tseng, C.-W.; Song, C.-E.; Wang, S.-F.; Chen, Y.-C.; Tu, J.-Y.; Yang, C.-J.; Chuang, C.-W. Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan. Remote Sens. 2020, 12, 3855. https://doi.org/10.3390/rs12233855
Tseng C-W, Song C-E, Wang S-F, Chen Y-C, Tu J-Y, Yang C-J, Chuang C-W. Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan. Remote Sensing. 2020; 12(23):3855. https://doi.org/10.3390/rs12233855
Chicago/Turabian StyleTseng, Chun-Wei, Cheng-En Song, Su-Fen Wang, Yi-Chin Chen, Jien-Yi Tu, Ci-Jian Yang, and Chih-Wei Chuang. 2020. "Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan" Remote Sensing 12, no. 23: 3855. https://doi.org/10.3390/rs12233855
APA StyleTseng, C. -W., Song, C. -E., Wang, S. -F., Chen, Y. -C., Tu, J. -Y., Yang, C. -J., & Chuang, C. -W. (2020). Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan. Remote Sensing, 12(23), 3855. https://doi.org/10.3390/rs12233855