IJG  Vol.6 No.8 , August 2015
Spatial Variation of Soil Depth and Shallow Slope Failures in Sangun Mountains, Fukuoka Prefecture, Japan
ABSTRACT
Shallow slope failure is often induced by rainfall infiltration in a soil mantle overlying a less permeable bedrock. Soil depth is an important input parameter in slope stability analysis. This paper provides the spatial variation of soil depth and the occurrence of slope failure in Sangun mountains area. The spatial pattern of soil depth was simulated by proses based model using airborne laser survey data (LiDAR data) and Geographic Information System (GIS) function. As a function for soil production, we use in the study area a numerical model developed by Dietrich et al. (1995) to predict the local spatial variation of the depth of soil. The soil depth data measured at 20 locations that represent morphological variability are used as a sample data set to test the model results. Furthermore, the soil depth variations are compared to the slope failure distribution in the whole area. Slope failure locations in the study area are identified from interpretation of aerial photographs and field surveys. Fifty-five of slope failures are considered for slope failure hazard analysis. Therefore, the slope failures occur more frequently at soil depth intervals in the ranged from 1.01 m to 1.5 m.

Cite this paper
Pachri, H. , Mitani, Y. , Ikemi, H. and Jiang, W. (2015) Spatial Variation of Soil Depth and Shallow Slope Failures in Sangun Mountains, Fukuoka Prefecture, Japan. International Journal of Geosciences, 6, 813-820. doi: 10.4236/ijg.2015.68065.
References
[1]   Hanamura, et al. (2004) Heavy Rain Disaster in Kyushu Area on July 2003. Japan Society of Engineering Geology, 25, 30-31.

[2]   Costa, J.E. and Wieczorek, G.F. (1987) Debris flow/Avalanches: Process, Recognition and Mitigation. Reviews in Engineering Geology, 7, 239 p.

[3]   Catani, F., Segoni, S. and Falorni, G. (2010) An Empirical Geomorphology-Based Approach to the Spatial Prediction of Soil Thickness at Cathment Scale. Water Resources Research, 46, W05508.
http://dx.doi.org/10.1029/2008WR007450

[4]   Ho, J.Y., Tun Lee, K., Chang. T.C., Wang, Z.Y. and Liao, Y.H. (2012) Influences of Spatial Distribution of Soil Thickness on Shallow Landslide Prediction. Engineering Geology, 124, 38-46.
http://dx.doi.org/10.1016/j.enggeo.2011.09.013

[5]   Dietrich, W.E., Reiss, R., Hus. M.L. and Montgomery, D.R. (1995) A Process-Based Model for Colluvial Soil Depth and Shallow Landsliding Using Digital Elevation Data. Hydrological Processes, 9, 383-400. http://dx.doi.org/10.1002/hyp.3360090311

[6]   Park, S.J., McSweeney, K. and Lowery, B. (2001) Identification of the Spatial Distribution of Soils Using a Process-Based Terrain Characterization. Geoderma, 103, 249-272.
http://dx.doi.org/10.1016/S0016-7061(01)00042-8

[7]   David, R. (2006) Subsurface Exploration Using the Standard Penetration Test and the Cone Penetrometer Test. Environmental & Engineering Geoscience, XII, 161-179.

[8]   Vahed, G, Husaini, O. and Bujang, K.H. (2009) A Study of the Weathering of the Seremban Granite. Electronic Journal of Geotechnical Engineering, 14.

[9]   Duzgoren-Aydin, NS. and Aydin, A. (2006) Chemical and Mineralogical Heterogeneities of Weathered Profiles: Implications for Landslide Investigation. Natural Hazards and Earth System Sciences, 6, 315-322. http://dx.doi.org/10.5194/nhess-6-315-2006

 
 
Top