Small Landslide Susceptibility and Hazard
Assessment Based on Airborne Lidar Data
Omar E. Mora, Jung-kuan Liu, M. Gabriela Lenzano, Charles K. Toth, and Dorota A. Grejner-Brzezinska
Abstract
Landslides are natural disasters that cause environmental
and infrastructure damage worldwide. To prevent future risk
posed by such events, effective methods to detect and map
their hazards are needed. Traditional landslide susceptibility
mapping techniques, based on field inspection, aerial pho-
tograph interpretation, and contour map analysis are often
subjective, tedious, difficult to implement, and may not have
the spatial resolution and temporal frequency necessary to
map small slides, which is the focus of this investigation.
We present a methodology that is based on a Support Vector
Machine (
SVM
) that utilizes a lidar-derived Digital Elevation
Model (
DEM
) to quantify and map the topographic signatures
of landslides. The algorithm employs several geomorpholog-
ical features to calibrate the model and delineate between
landslide and stable terrain. To evaluate the performance
of the proposed algorithm, a road corridor in Zanesville,
Ohio, was used for testing. The resulting landslide suscep-
tibility map was validated to correctly identify 67 of the 80
mapped landslides in the independently compiled land-
slide inventory map of the area. These results suggest that
the proposed landslide surface feature extraction method
and airborne lidar data can be used as efficient tools for
small landslide susceptibility and hazard mapping.
Introduction
The hazards of natural disasters occur from processes of the
earth and cause damage, devastations, loss of life, and envi-
ronmental change. One particular natural hazard of interest
known to cause economic, human and environmental damage
worldwide are landslides (Glenn
et al.
, 2006). Landslides
have consistently damaged human infrastructure and have
impeded the daily lives of many. They have a broad range of
geologic processes that cause the downward movement of
mass over spatial and temporal scales (McKean and Roering,
2004). In addition, their effects have a strong dependability
on their spatial pattern of incident, rate of recurrence, and
amount of movement (McKean and Roering, 2004). Their haz-
ards are well-understood, yet current methods of identifying
and assessing their conditions are inefficient, and are difficult
to predict. Existing techniques are typically based on field
inspection, aerial photograph interpretation, and contour map
analysis (Booth
et al.
, 2009). However, these methods have
limitations that reduce the accuracy, completeness and reli-
ability necessary to map landslides with high probability, es-
pecially, small failures where mass movement rates are slower
(Booth
et al.
, 2009; Galli
et al.
, 2008). Additionally, many sites
are not easily accessed for field inspections. Highly vegetat-
ed areas present difficulties for both on-site inspections and
aerial photographic interpretation. Historical contour maps
do not have the resolution necessary to map small landslides
in highly vegetated areas where conventional remote-sensing
methods cannot penetrate the land cover (Van Den Eeckhaut
et al.
, 2005; Booth
et al.
, 2009; James
et al.
, 2012). For these
reasons, traditional methods are not cost-effective and a new
approach to landslide susceptibility and hazard mapping is
necessary.
Remote-sensing technology has seen large advances in
the past decade, in cost, accuracy, and accessibility. One of
the major improvements has been the spatial resolution of
Light Detection and Ranging (lidar) technology. In earlier
stages only coarse nominal point spacing (>10 meters) was
available. Improvement of this technology has allowed for
higher spatial resolutions (<1 meter). The increase made in
spatial resolution provides mapping opportunities at remark-
able scales. This tool provides the accuracy necessary to map
surface models precisely (Shan and Toth, 2008; Jaboyedoff
et
al.
, 2012). Furthermore, it has the potential to overcome many
challenges faced in landslide susceptibility mapping, for
example, the spatial resolution, broad terrain coverage and ac-
curacy necessary to map precise surface models. A particular
lidar technology capable of overcoming the aforementioned
challenges is airborne lidar. This instrument is capable of pen-
etrating vegetation, mapping areas up to thousands of square
kilometers (Shan and Toth, 2008; Guzzetti
et al.
, 2012), and
providing sub-meter spatial resolutions. For these reasons, it
is a prime consideration.
Previous landslide susceptibility mapping techniques
revealed the potential that remote-sensing technology pre-
sented to identify and map the geomorphic features related to
landslide morphology (McKean and Roering, 2004; Glenn
et
al.
, 2006; Booth
et al.
, 2009). However, their focus has been
on mapping large landslides in hilly terrain and mountainous
regions, along coastal bluffs, and river basins (e.g., Van Den
Eeckhaut
et al.
, 2005; Booth
et al.
, 2009; Ballabio and Ster-
lacchini, 2012; Tien Bui
et al.
, 2012). Less attention has been
paid to map small failures, which impact our transportation
networks. Small failures have been overlooked in previous
Omar E. Mora, Charles K. Toth, and Dorota A. Grejner-
Brzezinska are with the Department of Civil, Environmen-
tal and Geodetic Engineering, The Ohio State University,
470 Hitchcock Hall, 2070 Neil Ave., Columbus, OH 43210
(
).
Jung-kuan Liu was with the Department of Civil, Envi-
ron-mental and Geodetic Engineering, The Ohio State
University, Columbus, OH 43210, and currently with the
CSS-Dynamac/National Operations Center, Bureau of Land
Management, Denver Federal Center, Building 50, Denver, CO
80225.
M. Gabriela Lenzano is with the International Center of Earth
Sciences (ICES), UNCuyo-CONICET, Ciudad Universitaria,
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 3, March 2015, pp. 239–247.
0099-1112/15/813–239
© 2014 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.3.239
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
March 2015
239
