Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam
2. Landslide Inventory
3. Landslide Causative Factors
- Topography is intrinsically associated with landslides by slope gradient and other factors, such as weathering, precipitation, soil thickness, etc. Hence, topography strongly affects landslides [28,29]. Ban Khoang is a mountainous area where the microclimate is quite predominant. Hence, the aspect is considered an indirect landslide causative factor in this study. A digital elevation map (DEM) of the study area with a pixel size of 10 m by 10 m was obtained by using inverse distance weighted interpolation in QGIS 3.6 from elevation points and contours of a topographic map, scale 1:10,000, published by the Cartographic Publishing House, Vietnamese Ministry of Natural Resources and Environment (2019). Then the aspect map of Ban Khoang commune (Figure 4A) was developed based on the Aspect tool inside QGIS 3.6 software.
- In most landslide studies, slope gradient is considered a principal causative or triggering factor. A slope map was derived from the DEM using the slope function tool of QGIS 3.6. The slope map is in the form of a raster map with the same 10 m pixel size as the DEM, but was converted to vector by separating the slope angles into six classes: (1) flat-gentle slope (<5°), (2) fair slope (5–15°), (3) moderate slope (15–25°), (4) fairly moderate slope (25–35°), (5) steep slope (35–45°), and (6) very steep slope (>45°). The map of slope classes of Ban Khoang commune is displayed in Figure 4B.
- Geology and slope instability are strongly associated [30,31]. Hence, a geological map of Ban Khoang (Figure 4C) was derived from the map of geology and mineral resources of the Lao Cai sheet group, scale 1:50,000 by Lap et al. (2003) . Figure 4C displays the distribution of geological classes in Ban Khoang commune in Sa Pa district, Lao Cai province of Vietnam.
- Geomorphology is considered an essential factor related to landslide occurrence in the study area. Based on the analyses of the topological characteristics, geological structures, neotectonic movements, and morphometries, six geomorphological units can be identified in the study area by  (Figure 4D).
- Soil is an essential factor of slope instability in many settings [34,35]. A digital map of soil was derived from previous work in Lao Cai province carried out by the National Institute of Agriculture Planning and production (2019), identifying three types of soil mechanics in the study area, i.e., (1) outcrop, (2) reddish-yellow humus soil on claystone, and (3) reddish-yellow humus soil on magma rocks (Figure 4E). The soil depth map (Figure 4F) was derived based on the soil depth information based on the map of soil mechanics.
- Neotectonics contribute to slope instability by fracturing, faulting, jointing, and deforming foliation structures [36,37]. For this study, faults were extracted from the map of geology and mineral resources scale 1:50,000. Additionally, lineaments were interpreted from free available Landsat 8 captured by NASA in 2020. The fault and lineament density was calculated as the total length of faults and lineament per 1 km2 (See Figure 4G).
- Studies have shown that the proximity to drainage axes with intensive gully erosion is an important factor controlling the occurrence of landslides [38,39]. A map of river density was derived on the basis of the digitizing river and stream courses on the topographic map and interpolation in QGIS software (version 3.6). A map of the river density class (Figure 4H) was created by subdividing the river density range values into five classes: (1) <1000 m/km2, (2) 1000–2000 m/km2, and (3) 2000–3000 m/km2, (4) 3000–4000 m/km2, and (5) >4000 m/km2.
- Vegetation augments slope stability primarily in two ways: (1) by removing soil moisture through evapotranspiration and (2) by providing root cohesion to the soil mantle . A land-use map was obtained from the land-use map of Lao Cai published by the land administration department of the Ministry of Natural Resources and Environment, 2019 . The land use composed of 10 land-use classes is displayed in Figure 4I.
4. Method for Landslide Susceptibility Analysis
- For the slope factor, there is an obvious distinction between classes with slope angles 5–15° and >45° compared to other classes. This indicates that landslides in the study area are mainly occurring in areas with slope angles 5–15° and >45°.
- The class of fault density of 1000–1500 m/km2 has the highest Wij value (0.2835) compared to the remaining classes from all causative factors; hence, it has the highest impact on landslides in the study area.
- Cam Đường formation (Wij = 0.7893) are distinctly more favorable for landslides compared to the other geological formations (Wij ≤ 0.5110).
- For the geomorphological factor, denudational and erosional slope on metamorphic rocks, Quaternary sediment, also favor landslides.
- For the land-use factor, natural reforestation land is most favorable for landslide occurrence. Other classes seem to have very little or no influence for landslides.
5. Results and Discussion
6. Validation of Landslide Susceptibility Map
- A total of 75% of the observed landslides in the study area is selected at random (see Figure 18). These areas form the training data set. The actual selection was made arbitrarily without considering causative factors. It was only taken into account to spread the training data set as evenly as possible over the study area.
- On the basis of the training data set, a new LSZ map based on the statistical index method for the whole study area was created (see Figure 19).
- The remaining 25% of the observed landslides in the study area is used to evaluate the correctness of the new LSZ map.
Conflicts of Interest
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|Landslide Causative Factors||Landslide|
|% Occ.||No. of|
|Fault and lineament density|
|Reddish-yellow humus soil on magma rocks||1629||61.99||401,940||75.38||−0.1957|
|Reddish-yellow humus soil on claystone||999||38.01||119,576||22.43||0.5277|
|Ancient planation surface||93||3.54||59,101||11.08||−1.1417|
|Denudational and erosional slope on metamorphic rocks||1597||60.77||193,797||36.35||0.5140|
|Denudational and erosional slope on granite rocks||489||18.61||256,604||48.13||−0.9502|
|Erosional steps in front of mountain||22||0.84||14,599||2.74||−1.1850|
|Sa Pả formation||262||9.97||32,774||6.15||0.4836|
|Cam Đường formation||169||6.43||15,573||2.92||0.7893|
|Yê Yên Sun complex||571||21.73||69,501||13.03||0.5110|
|Po Sen complex||163||6.20||183,843||34.48||−1.7154|
|Đá Đinh formation||312||11.87||64,195||12.04||−0.0140|
|Bản Nguồn formation||1151||43.80||167,316||31.38||0.3334|
|Protection reforestation land||1089||41.44||252,972||47.44||−0.1353|
|Annual crop land||2||0.08||33,860||6.35||−4.4242|
|Natural reforestation land||585||22.26||39,883||7.48||1.0906|
|Rural residential land||16||0.61||8229||1.54||−0.9302|
|Unused mountain land||682||25.95||152,292||28.56||−0.0958|
|Mountain land without forest||0||0.00||12,683||2.38||0.0000|
|River and spring land||13||0.49||2660||0.50||−0.0085|
|Protection forest land||0||0.00||125||0.02||0.0000|
|Landslide Causative Factors||LSIMin||LSIMax||LSIRange||LSIDev|
|Fault and lineament density||−3.2296||0.2835||3.5131||−1.4628|
|Accuracy of Prediction||Observed Landslide|
|Accuracy of Prediction||Landslide Training Data Set||Landslide Validating Data Set|
|Number||Percentage (%)||Area (km2)||Percentage %||Number||Percentage %||Area (km2)||Percentage %|
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Thanh, L.N.; Fang, Y.-M.; Chou, T.-Y.; Hoang, T.-V.; Nguyen, Q.D.; Lee, C.-Y.; Wang, C.-L.; Yin, H.-Y.; Lin, Y.-C. Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam. Water 2022, 14, 2814. https://doi.org/10.3390/w14182814
Thanh LN, Fang Y-M, Chou T-Y, Hoang T-V, Nguyen QD, Lee C-Y, Wang C-L, Yin H-Y, Lin Y-C. Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam. Water. 2022; 14(18):2814. https://doi.org/10.3390/w14182814Chicago/Turabian Style
Thanh, Long Nguyen, Yao-Min Fang, Tien-Yin Chou, Thanh-Van Hoang, Quoc Dinh Nguyen, Chen-Yang Lee, Chin-Lun Wang, Hsiao-Yuan Yin, and Yi-Chia Lin. 2022. "Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam" Water 14, no. 18: 2814. https://doi.org/10.3390/w14182814