Upgrading of the subjective landslide hazard evaluation scheme in Sri Lanka

2018

Geosciences

The infiltration of rainwater into soil slopes leads to an increase of porewater pressure and destruction of matric suction, which causes a reduction in soil shear strength and slope instability. Hence, surface moisture and infiltration properties must be direct inputs in reliable landslide hazard assessment methods. Since the in situ measurement of pore pressure is expensive, the use of remotely sensed soil moisture is practically feasible. Downscaling improves the spatial resolution of soil moisture for a better representation of specific local conditions. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a better accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. This model provides a good classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil slide hazard. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can provide the threshold probability for identifying locations with a high soil slide hazard is proposed.

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Section: Background Of the Studymentioning

confidence: 99%

An Improved Data-Driven Approach for the Prediction of Rainfall-Triggered Soil Slides Using Downscaled Remotely Sensed Soil Moisture

2018

Geosciences

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“…Conversely, the lack of vegetation cover would create favorable conditions for erosion and slope failure. Moreover, the destruction of vegetation cover due to deforestation, construction and urbanization invariably enhances the potential for erosion and landsliding [2]. Hence, timely identification of changes in land cover, particularly the reduction of forest cover due to deforestation, is vital to landslide risk mitigation.…”

Section: Introductionmentioning

confidence: 99%

“…In landslide hazard evaluation, it is important to first identify the factors which contribute to landslide occurrence. There are two types of factors that can affect the potential for landslide occurrence at a given location: (1) factors that can be attributed to that location and (2) factors that trigger mass soil movement [3]. Location-dependent causative factors consist of land cover, slope angle, soil type, rock type, land form, hydrological factors, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Location-dependent causative factors consist of land cover, slope angle, soil type, rock type, land form, hydrological factors, etc. [2]. If the conditions of the above attributes are favorable for landsliding, such as low vegetation cover, landslides can be triggered by rainfall, earthquakes, 2 of 14 volcanic activity, wildfire, human activity, etc.…”

Section: Introductionmentioning

confidence: 99%

“…If the conditions of the above attributes are favorable for landsliding, such as low vegetation cover, landslides can be triggered by rainfall, earthquakes, 2 of 14 volcanic activity, wildfire, human activity, etc. [2]. Rainfall-triggered landslides are mostly caused by conditions that promote sudden increases in pore water pressures and the soil overburden.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

2016

Geosciences

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques-k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated.

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Stochastic Investigation of the Feasibility of Using Remotely Sensed Moisture Data for Rainfall Induced Landslide Hazard Assessment

Advancing Culture of Living With Landslides

2017