International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 157–164, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-157-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 157–164, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-157-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

GIS-ASSISTED RAIN-INDUCED LANDSLIDE SUSCEPTIBILITY MAPPING OF BENGUET USING A LOGISTIC REGRESSION MODEL

M. N. Cruz, K. C. Medina, A. S. Cabriga, F. Mendoza, and A. C. Blanco M. N. Cruz et al.
  • Department of Geodetic Engineering, College of Engineering, University of the Philippines, Diliman, Quezon City, Philippines

Keywords: NDVI, LAI, TWI, causative factors, DEM, susceptible, hazard

Abstract. Landslides are a major concern in disaster risk reduction and management in Southeast Asia due to the region’s geographic location and setting. These are massive downward movement of rock, soil and/or debris under the influence of gravity. Benguet, lying within the Cordilleran mountains of the Philippines, is landslide prone. The increasing demand for sustainable development and expansion of human settlements and infrastructures deems landslides as a problem for the mountainous province. More than half of Benguet’s land area is highly susceptible to landslides. Hence, landslide potential identification and assessment, associated with topography, is vital in ensuring efficiency while minimizing collateral damage and unwanted casualties. This study developed a logistic regression model to map susceptibility to rainfall-induced landslides. Causative factors for the analysis in this study include rock types, soil types, land use, elevation, slope, aspect, precipitation, topographic wetness index (TWI), normalized difference vegetation index (NDVI), and leaf area index (LAI). These layers were prepared using GIS. Based on the logistic regression, the most statistically significant variables were aspect, elevation, and leaf area index (LAI). The model considered with the combination of the causative variables resulted with an R squared value of 86% which indicates good variability for the conditioning factors used for the mapping procedure. Results indicate that 69% of Benguet is highly susceptible to landslides, 7% area is moderately susceptible to landslides, and 24% area is low susceptible to landslides.