Many causative factors for landslide susceptibility analysis are directly or indirectly derived from topographic data. These include topographic-related factors, like slope gradient, topographic roughness, curvature, etc., location-related factors, like distance to road which may be counted from different distance scale, and triggering factors, like rainfall value and earthquake intensity which are interpolated by cokriging with topographic data. A factor calculated from topographic data of different measurement scale will output a different value, and high resolution data is not always directly outputting an effective factor for interpreting landslides. Each factor may exist an optimal measurement scale which can best interpret the landslide distribution at a specific area and for a specific purpose. The LiDAR digital terrain model is the highest accuracy and highest resolution terrain data currently available. The resolution is as high as 1m. But we know from past research and the experience of this research team, using high-resolution digital terrain models does not yield better analysis results. It is necessary to consider the optimal measurement scale of each landslide causative factor. If we can get the best scale factor for analysis, and coupled with the inherent accuracy of the LiDAR digital terrain model, it is bound to build a better model of landslide susceptibility.In the last year project proposed by this team, we have selected the Shihmen Reservoir catchment area and the Zengwen Reservoir catchment area as two study areas and select four Typhoon-triggered landslide inventories at each catchment to analyze the scale effect and to determine the optimal measurement scale of each susceptibility factor. During the study, we learned that the validity of the factors is greatly affected by the accuracy of the digital terrain model and the accuracy of landslide mapping. Therefore, this year it is planned to focus on the selection of high-precision LiDAR digital terrain model and re-mapping and careful inspection of each polygonal object induced by rainfall in high-resolution satellite image. Hope we can clearly find the best measurement scale of each factor in each rainfall-induced landslide inventory. And establish the landslide susceptibility model of each rainfall event with the best validity factor. Compare the success rate of the model established by the best validity factor to the success rate of the model established by the original factor. We also use cross-validation to understand the growth of prediction rate.The study area is still selected in the upper catchment area of Zengwen Reservoir, but for the rainfall events, typhoon Hebe, typhoon Toraji, typhoon Mindulle, 20050515 heavy rain, 20060609 heavy rain, typhoon Morakot, 20110718 heavy rain, 20120610 heavy rain, 20150523 heavy rain, totaling 9 independent rain events will be used to study optimal measurement scale for each causative factor, and to compare of the success rate and prediction rate of the landslide susceptibility models. By using high-precision digital terrain model and rainfall-induced landslide data of different sizes to refine the selection of the best measurement scale of factors, a refined event landslide susceptibility model is made. We will share the results with the world.
|Effective start/end date||1/08/20 → 31/07/21|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- landslide susceptibility analysis
- susceptibility factor
- optimal measurement scale
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