swaths of terrain under land cover. Previous landslide suscep-
tibility mapping investigations have focused on geotechnical
mapping evaluations over large landslides (e.g., Van Den Eeck-
haut
et al.
, 2005; Booth
et al.
, 2009; Ballabio and Sterlacchini,
2012; Tien Bui
et al.
, 2012). Our study presents a new opportu-
nity to map small failures utilizing airborne lidar-derived
DEM
s.
This proposed algorithm provides a means to evaluate each
cell in the
DEM
to identify patterns of slope instability over the
study area, which covers an area of approximately 3.0 square
kilometers. The outputs of the algorithm were tested and
compared to an independently compiled landslide inventory
map to assess the classification performance. Assuming, that
the landslide inventory is complete and accurate, our algo-
rithm was able to identify 84 percent of the landslides in the
study area. The findings of this study demonstrate that various
types, scales, and deformations of landslide surface features
(such as hummocky terrain, scarps and displaced blocks of
material) can be extracted through the proposed approach and
a surface model generated from sub-meter spatial resolution.
Although, the local topographic roughness can be exploited
through the geomorphological features described, an adequate
sample representative of the study area is necessary to train
the supervised classification algorithm. It is not foreseen for
new landslide susceptibility mapping techniques to replace
traditional mapping methods; however, new opportunities can
improve the efficiency of landslide susceptibility mapping.
Future studies may include; water tables or water entering the
landslide area, the angle of internal friction of the landslide
material and the configuration of the landslide itself.
In order to quantify the amount of activity observed be-
tween landslides, careful monitoring is necessary. It is clear
that there are different scales and degrees of surface deforma-
tion observed within the landslides throughout the study
site. To monitor and quantify the temporal changes, further
research is necessary to investigate more quantitative patterns
of the surface deformation observed, between the different
landslides. However, this task is not time and cost-effective,
although, it can be highly effective; it is dependent on the
needs to monitor mass movement. The proposed approach
allows for a semi-automated, fast, objective surface feature
extraction of small landslide topography.
Acknowledgments
The authors wish to acknowledge the support of Kirk Beach
from the Office of Geotechnical Engineering of the Ohio Depart-
ment of Transportation. Also, the authors wish to acknowledge
Dr. Tien Wu for his insight and feedback throughout this study.
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