1. Introduction
The adverse impacts of landslides on the socio-economic environment are
increasing rapidly on a local and global scale. Due to complex
physiography and increasing anthropological activities, Himachal Pradesh
is recognised for frequent landslide occurrences, especially during the
monsoon season. Rainfall induced landslide of Kotrupi on 13 August 2017
was one such devastating incidence. At least 46 people lost their lives
as two state transportation busses got buried under a massive landslide
along National Highway-154. The highway construction in mountainous
regions significantly influences landslide occurrences (Dugonji, 2014).
As many highways are under construction and many are in the planning
phase in Himachal Pradesh which continuously interfere and destabilise
the natural bed slope. The deforestation along the highway alignment
also enhances the risk of landslides in Himachal Pradesh.
To quantify and manage the risk due to landslides, the landslide
susceptibility analysis and mapping is of utmost importance. Landslide
susceptibility of a region can be described as the probability with
which a landslide can occur in a region based on that region’s
topographical and geographical conditions (Brabb, 1984) and the previous
occurrences of landslides in the region (Nohani et al., 2019).
Generally, landslide susceptibility is shown on maps that display
multi-layered spatial and temporal distribution and the probability with
which landslides can occur (Nagarajan et al., 1998). The susceptibility
analysis and zonation of landslide hazard is considered as a complex
process. It is a readily perceived research area in recent times, and
experts have proposed various techniques and methodologies in different
geological and meteorological settings (Nayak, 2010; Reichenbach et al.,
2018). The multiple stages of landslide risk analysis and management, as
compiled by (Fell, 1993), are widely accepted in evaluating and
analysing landslide hazard. Various studies have shown the suitability
of remote sensing and geomatics-based approaches for susceptibility
analysis on a regional scale due to availability of spatially and
spectrally varied temporal data (Prakash and Nagarajan., 2018). The
remote sensing and GIS-based approaches using high/medium-resolution
satellite imagery and Digital Elevation model allows a cost effective
and rapid extraction of geological and topographical data on the
regional scale (Prakash and Nagarajan., 2017). The first stage in most
of the landslide susceptibility mapping methods is to develop a
comprehensive landslides inventory map by compiling landslide event data
from the various sources for the region. (Pradhan et al., 2014).
Landslide hazard assessment and susceptibility analysis are highly
dependent on the accuracy of landslides data acquisition and mapping
(Henriques et al., 2009; Guzzetti, 2005). Landslide inventory map can be
prepared from satellite image by visual interpretation and computer
processing of the imagery, field inspections, aerial photographs, high
resolution DEM and available data from various published reports
(Remondo et al., 2003).
The next phase in landslide susceptibility analysis is to formulate
layers of thematic variables of landslide causative factors leading to
slope instability. The factors affecting land surface include regional
geology, slope curvature, aspect, soil, distance to road, elevation,
lineament density, drainage network, NDVI etc. These factors can be
extracted and mapped using high resolution remote sensing images, arial
photographs, published maps and digital elevation models (DEM) using
geospatial tools and techniques. Remote sensing data has been accepted
as the most accurate and authentic source of Earth’s surface data
(Banshtu and Prakash, 2014). Remote sensing provides the benefit of
mapping landside areas according to research demand using updated
satellite images (Jaiswal, 2009). These satellite images and aerial
photographs being stereoscopic provides three dimensional perspectives
for the characterisation of landslides based on their spatial and
temporal features of the region (Mantovani et al., 1996; Chakraborthy,
2008). This spatial and temporal thematic dataset needs to be integrated
with ground based information (Nagarajan et al., 1998). For this
purpose, GIS is a widely accepted tool to store extensive data (Rengers
et al., 1992; Soeters et al., 1991). GIS can manipulate and analyse
remotely sensed data for assessing landslide hazard (Carrara et al.,
1991; McKean et al., 1991). The factors can then be ranked according to
available codes, expert opinions, statistical modelling and
multicriteria evaluation based techniques (Reichenbach et al., 2018).
Hence using GIS in analysing remotely sensed images and DEM’s can be
considered as a highly efficient tool for landslide susceptibility
mapping (Van Westen, 2000; Jebur et al., 2014b).
Geo-physical laws generally control landslides occurrence, and
statistical, empirical and deterministic methods can be used for their
analysis (Crozier, 1989; Hutchinson, 1988; Dietrich et al., 1995). The
key to predicting landslides of the future is to analyse the past and
present scenarios (Varnes, 1984). Along with this, various quantitative
and qualitative approaches have been developed, helping determine the
frequency and probability of a landslide event (Frangov et al., 2017).
Some of the most accurate statistical techniques in landslide
susceptibility modelling include logistic regression analysis, data
overlay analysis, multi criteria analysis, the weight of overlay
analysis, bivariate analysis and entropy based analysis etc. (Huabin et
al., 2005; Kanungo et al., 2009; Reichenbach et al., 2018).
The present study is perceived with the motivation that road
construction activities significantly influence the study area’s
landslide susceptibility. Despite ever increasing landslide incidences
along the highways, there is still a significant research gap in
comprehensive studies regarding road construction’s impact on landslide
susceptibility of a mountainous region like the Mandi District of
Himachal Pradesh. Hence this study aims to compare the change in
Landslide Susceptibility of Mandi district due to construction of roads
in the district using Frequency Ratio (FR), Certainty Factor (CF) and
Shanon Entropy (SE) models.