Measurement of Surface Changes in a
Scaled-Down Landslide Model Using
High-Speed Stereo Image Sequences
Tiantian Feng, Huan Mi, Marco Scaioni, Gang Qiao, Ping Lu, Weian Wang, Xiaohua Tong and Rongxing Li
Abstract
Construction of scaled-down landslide models is an important
means for landslide study. The objective of this study is to
develop an innovative non-contact photogrammetric system to
meet the challenge for monitoring the fast surface deformation
of a laboratory-simulated landslide, which can detect pre-fail-
ure events and the final failure, provide sectional and overall
surface deformation patterns, and generate speed maps during
the rapid slope failure. We proposed an event detector based
on altered surface features in the slower
SLRC
(single-lens-re-
flex camera) image sequence to detect pre-failure events, while
a combined analysis tool used the surface velocity fields and
deformation areas based on fast
HSCS
(high-speed stereo-cam-
era system) stereo image sequences to reveal fast-changing
landslide behavior during the short final failure. The intro-
duced surface change detector uses the percentage of sliding
block areas, percentage of changed features, and average
speeds. It successfully detected four pre-failure local collapse
events and the final slope failure; the extent of surface changes
reached its maximum to accumulate energy 1.5 seconds
before the failure when the average speed of changed features
achieved its peak of 0.8 m/s. The developed system achieved a
position accuracy of 3.8 mm and a speed accuracy of 0.11 m/s.
The analysis result demonstrated a time period of 66 minutes
before the failure which is confirmed by significant signals
from both imaging and contact sensors and is important for
landslide early warning. A field implementation scheme in
western China will be designed and realized in the near future.
Introduction
A landslide is the movement of a mass of rocks, debris, or
earth down a slope, and may be triggered by earthquakes,
heavy rains, volcanic eruptions, erosion by rivers, and
anthropogenic factors. Landslides represent one of the most
common and destructive geo-hazards; therefore, research and
operations that improve their monitoring and prediction are
highly valuable for disaster management and emergency re-
sponse (Guzzetti
et al.
, 1999; Dell’Acqua and Polli, 2011; Hus-
sain
et al.
, 2011). A systematic study of landslide mechanisms
(Arbanas
et al.
, 2014) requires a suitable model that relates
landslide dynamics to external inputs and boundary condi-
tions. A detailed and timely observational framework coupled
with a reliable model is critical for predicting the timing of
landslide failures with an adequate alerting time to perform
necessary mitigation actions. Unfortunately, these predictions
are highly challenging and currently nearly impossible to
achieve because of the complexity of landslide processes and
the insufficient level of reality represented in models.
Observations of surface displacement are an important
factor in modeling landslide behavior, and observations com-
bined with modeling techniques have the potential to describe
the detailed reality of a failing slope, particularly during the
very short period of the collapse of the slope. In recent de-
cades, several types of sensors have been proposed and imple-
mented to record geotechnical and hydrogeological parameters
and to measure deformation in soil or rock masses (Tu
et al
.,
2009). Traditionally, such measurements have been obtained
using
in situ
extensometers, global navigation satellite systems
(
GNSS
s), and robotic-theodolite-based systems (Castagnetti
et
al
., 2013; Cina and Piras, 2014). These techniques may provide
precise observations of 3D point displacements with sub-cen-
timeter precision. However, these point-based measurements
have insufficient spatial coverage and detail for many appli-
cations. To overcome this drawback, point-based observations
can be integrated with remote-sensing observations, including
terrestrial photogrammetric images, airborne and satellite
images, laser scanning, and interferometric synthetic aperture
radar (
InSAR
) from ground-based and spaceborne platforms
(Kääb, 2002; Ventura
et al
., 2011; Abellán
et al
., 2014; Lu
et al
.,
2011, 2014; Monserrat
et al
., 2014). These observations can be
used to monitor changes in the slope surface at different an-
gles and scales (Dewitte
et al
., 2008). For example, volumetric
changes may be estimated from photogrammetric stereo-image
sequences or continuous laser scanning data sets. Moreover,
deformation features may be tracked using optical-tracking
techniques or by analyzing “persistent scatters” in
InSAR
imag-
es (Farina
et al
., 2006; Lu
et al
. 2012). Among the techniques
used in the reduction and integration of multi-sensor data,
the photogrammetric processing of a series of stereo images
is recognized as a state-of-the-art technique for morphologi-
cal terrain modeling in the geosciences (Zanutta
et al
., 2006;
Cardenal
et al
., 2008; Forlani
et al
., 2013).
Although a variety of techniques have been used to observe
landslide behavior, complete observations for the study of
landslide mechanisms are difficult to be obtained due to the
rapid processes of landslides and the uncontrolled environ-
mental conditions. As a result, construction of scaled-down
landslide models has been an important supplemental means
for landslide study. Usually, the design of a scaled-down land-
slide model, such as geological characteristics, geomechanical
properties and hydrologic characteristics, is made based on
the field surveys. The model test allows for a replicable sys-
tem experimentation before its implementation into the field
practice, which is a benefit to sensitivity analysis of critical
parameters and the decision of the observation system. After
laboratory experiments, the observation system can be further
improved and deployed in the field (Lu
et al.
, 2015).
The Center for Spatial Information Science and Sustainable
Development Applications, College of Surveying and Geo-In-
formatics, Tongji University,1239 Siping Road, Shanghai
200092, China (
).
Photogrammetric Engineering & Remote Sensing
Vol. 82, No. 7, July 2016, pp. 547–557.
0099-1112/16/547–557
© 2016 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.82.7.547
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2016
547
