PE&RS July 2016 Public - page 547

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
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
reflex camera) image sequence to detect pre-failure events,
while a combined analysis tool used the surface velocity fields
and deformation areas based on fast
(high-speed stereo-
camera 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.
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-
et al.
, 2011). A systematic study of landslide mechanisms
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
combined with modeling techniques have the potential to de-
scribe 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
in situ
extensometers, global navigation satellite systems
s), and robotic-theodolite-based systems (Castagnetti
., 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 applica-
tions. To overcome this drawback, point-based observations
can be integrated with remote-sensing observations, includ-
ing terrestrial photogrammetric images, airborne and satellite
images, laser scanning, and interferometric synthetic aperture
radar (
) from ground-based and spaceborne platforms
(Kääb, 2002; Ventura
et al
., 2011; Abellán
et al
., 2014; Lu
., 2011, 2014; Monserrat
et al
., 2014). These observations
can be used to monitor changes in the slope surface at dif-
ferent angles 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
images (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
morphological terrain modeling in the geosciences (Zanutta
., 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, geomechani-
cal properties and hydrologic characteristics, is made based
on the field surveys. The model test allows for a replicable
system experimentation before its implementation into the
field practice, which is a benefit to sensitivity analysis of criti-
cal 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-
Informatics, Tongji University,1239 Siping Road, Shanghai
200092, China (
Photogrammetric Engineering & Remote Sensing
Vol. 82, No. 7, July 2016, pp. 547–557.
© 2016 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.82.7.547
July 2016
447...,537,538,539,540,541,542,543,544,545,546 548,549,550,551,552,553,554,555,556,557,...582
Powered by FlippingBook