Landslide Susceptibility Assessment of Forest Roads*

Year 2016, Volume: 2 Issue: 2, 54 - 60, 18.11.2016

Remzi Eker , Abdurrahim Aydin

Abstract

In last few decades, there has been an increasing
interest in using Landslide Susceptibility Maps (LSMs) especially in planning
and decision making stages of landslide prevention and mitigation activities,
as well as in landslide related studies. In forested areas, inappropriately
located roads potentially cause slope instability problems such as landslides
which then result in serious destructions on road networks and deformations on
road platforms. Thus, one of the further usages of LSM may involve overlapping
analysis with forest roads in order to obtain information about how road networks
should be planned and located considering land sliding potential. Statistical
approaches such as Logistic Regression (LR) method are well integrated with GIS
based evaluation of landslide probability of slopes in larger regions. In this
study, LSMs of two forest districts (Gölyaka and Kardüz) in Gölyaka Forest
Directorate (Düzce, Turkey) was generated by using LR method based on an
inventory of 52 landslides and eight conditioning parameters. These parameters
include elevation, slope, land-use, lithology, aspect, distance to faults,
distance to streams, and distance to roads. For overlapping analysis, forest
road layer was obtained from Bolu Regional Directorate of Forestry (RDF) in
vector data format. It was found that landslide susceptibilities obtained in
study area were between 0 and 0.57 with 0.85 AUC (Area Under the Curve) value.
The results indicated that all of the selected parameters had positive effects
on landslide occurrences. After normalization of generated susceptibility
values between 0 and 1, LSM was classified into following five classes: very
low (0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high
(0.8-1.0). Then, classified LSM was overlapped with forest road layer which
includes the total of 380.8 km road. According to classified susceptibility
map, more than 95% of total area is located in very low and low susceptibility
classes, 3% of the area has moderate landslide susceptibility, while remains
have high and very high susceptibilities. According to overlapping analysis,
1.3 km of roads is located within very high susceptibility and 5.1 km of roads
is located within high susceptibility classes. The rest of the roads (i.e. more
than 95%) are located in other susceptibility classes.

Keywords

Forest roads, Landslide Susceptibility Maps, Logistic Regression

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