Landslide hazard zonation mapping using bivariate statistical model-a satellite prospective
Student name: Ms Swati Goyal
Guide: Dr P K Joshi
Year of completion: 2014
Host Organisation: Intergraph SG & I India Pvt. Ltd., New Delhi
Supervisor (Host Organisation): Mr Peeyush Gupta
Abstract: Landslides are one of the most widespread and damaging natural disasters in hilly regions. The
study of landslides has drawn global attention due to its immense socioeconomic impact as well
as increasing pressure of urbanization on mountain environment. Recently few landslide
disasters in Himalaya have made tremendous impact on society. It is very severe in Uttarakhand
due to the complex geology and tectonic set up of Himalaya supplemented by heavy rainfall and
earthquake. The problem is more intense in the vicinity of Main Central Thrust (MCT). The
already weak geological formations are severely eroded by numerous streams producing huge
debris which mostly travel downstream as debris flow.
In this study, a part of Kumaoon Himalaya region (Nainital and around area) has been chosen to
identify landslide susceptible areas. Geographical Information System (GIS) has been used for
preparation of database, analysis, bivariate statistical modeling and Landslide Hazard Zonation
(LHZ) Map.Landslide hazard zonation refers to the division of a land surface into homogeneous
areas or domains and their ranking according to degrees of actual /potential hazard caused by
mass movement.
Bivariate statistical model has been used to calculate the weights of landslide influence
parameters. The input parameters used in this study area are World view image, quick bird
image, cartosat 1, topographic maps, geological maps, and field visits. ERDAS imagine 14 and
Arc GIS 10.2 software have been used for integration of input layers. All the thematic layers
(geology, slope, aspect, structure, drainage, lineament, geomorphology, road network and land
use/land cover) have been correlated for assessment of landslide hazard zonation mapping on
the basis of observations made therein. After assigning them suitable weights, ILWIS Software is
used for bivariate statistical modeling. The resultant landslide susceptible map has been
classified into three categories low, medium and high. The validation was done by field data and
World view2 high resolution imagery.
Keywords: MCT, LHZ, ILWIS, BIVARIATE STATISTICAL MODEL, GIS
