Growing population and the resultant pressure on environment has forced the occupation of dangerous
and unstable mountainous areas. The Bhatwari area along the Bhagirathi river in between greater and
middle Himalayas in Uttarakhand state of India is a typical example. It is part of a zone that is highly
prone to landslides, where debris slides and rock slides are a major threat to people. In recent time
land-use and land cover changes have taken place following various development activities that have
changed the slope conditions and have resulted in a frequent occurrence of landslides. In the present
study an attempt is made to assess the vulnerability to landslides in a stochastic way and to model the
dynamic movement different vulnerable element with the help of remote sensing images and on the
basis of field base data from the study area. The main focus is to assess the stochastic vulnerability to
landslides in an area on the basis of a dynamic modelling of different elements at risk and to assess the
vulnerability of dynamic elements at risk. Different scenarios of day-time, night-time vulnerability
have been generated for the optimal assessment of landslide vulnerability. An effort has also been
made to monitor dynamic land cover changes using satellite images from different dates to quantify
the changes that occurred in the area and to analyze the effect of these changes on landslide vulnerability.
For the present work, LISS-III and Ortho-rectified Cartosat-I images have been used along
with ancillary information collected from the field. The study quantifies changes in land cover (forest,
agricultural land, barren land, scrubs) in a historical prospective and analyzes their impact on vulnerability
to landslides. A change detection technique is being used to identify changes from two different
years from satellite data by monitoring the differences in the state of a land-cover object. Results
showed that from 1998 to 2006 a large portion of barren land, scrub land and forest land has been
converted into agriculture land. It was also observed that around one fourth area under agriculture has
changed to barren land resulting in decreased land values. Accuracy assessment of both classified images
was carried out and overall classification accuracies of 84% and 83% were obtained with kappa
statistics equal to 0.79 and 0.785, respectively. To assess the vulnerability of such an object to landslides,
both property vulnerability and population vulnerability are considered. Vulnerability of each
object depends on its relative position with respect to a particular hazard or event in a given time. Vulnerability
of a vehicle on the road is assessed by the expected number of vehicles at any time on a road
section. A working methodology has been established by assuming 1 km of road length as unit road
stretch and the average speed of vehicle as 35km/h. Results indicate that the vulnerability of vehicles
to landslide events on road sections varies throughout the day, depending on the number of vehicles.
Population vulnerability was assumed to depend upon population density. Results indicate that vulnerability
of land-cover class has changed between 1998 and 2006. Vulnerability of these classes has increased
at several places, whereas in other places it has decreased. Therefore the vulnerability of an
element is very much dependent upon the spatial location of the exposed element at risk at a given
time and varies greatly within the space and time.