10th Asian Regional Conference of IAEG (2015)

Landslide run-out derived from LiDAR micro-topography and numericalmodeling: Route 9, Dakeng bridge watershed
Wei-Kai Huang(1), Ching-Fang Lee(1), Lun-Wei Wei(1), Yu-Lin Chang(1),Shu-Yeong Chi (1)(1) Disaster Prevention Technology Research Center, Sinotech Engineering Consultants, INC.
(Taipei 115, Taiwan, R.O.C.)
E-mail: wuangwk@sinotech.org.tw
Abstract
High resolution digital elevation data acquired from LiDAR scanning isused to interpret the micro-topography of the Dakeng bridge watershed, an areacrossed by Route 9 in Taiwan that is frequented by natural disasters. In
addition to identifying the extent of potentially unstable areas within thewatershed, an empirical model based on disaster records of landslide volume
and area is employed to estimate resultant landslide volume. That volume isthen input into Particle Flow Code (PFC3DTM) to model the landslide failure
process and run-out.
Results reveal that the Dakeng bridge watershed is marked by several
clear tension cracks, head scarps and gravitationally deformed areas. The total
gravitationally deformed area is 5.9 ha. The landslide volume resulting fromthe gravitationally deformed area is 324,490 m3 and the landslide run-out
includes the confluence of the watershed where deposits may be as much as 13
m and cause the formation of a landslide dam. In the future, rapid stream
aggradation or scour effect may occur in the area downstream of this deposit.
Keywords: Micro-topography, LiDAR, Discrete Element Method, Landslide
run-out

between Suao and Chongde into 15 uniquegeologic zones. In addition to performing field
experiments and noting outcrop orientation,
exposed rock was also evaluated using a rockmass rating (RMR) and the Q system andphysical experiments were carried out on theslope material in order to assess the stability andthe failure mode of the slope. Results revealed
that the primary failure mode is toppling andwedge failure. Chu et al. (2013) used Midas
GTS finite element analysis software to create amodel of the Su-Hua highway 115.9k landslide.
The model was calibrated using field data andresults clarified characteristics about the extent
and the depth of the landslide. Lo et al. (2014)
examined the landslide occurring in the Dakeng
bridge watershed at 115.9k of the Su-Hua
highway and used PFC3DTM to model slope
failure processes. From that model, the debris
slide
failurehistory
anddeposition
characteristics of the slide were discussed.
Between Suao and Nanao on Route 9, thearea most frequented by landslides is the Dakeng
bridge watershed. Consequently, that portion of
the highway is also frequently severed.
Moreover, on October 21st, 2010, during

1.

Introduction
Route 9, also known as the Su-Hua highway,
is the primary transportation route along the east
coast of Taiwan. As a result of heavy typhoonrains during recent years, the highway has
frequently been severed by debris flows andlandslides. According to the Ministry ofTransportation and Communication (MOTC)
Highway
Disaster
Information
System,regarding disasters along Route 9 between Suaoand Dongao between 2009 to 2013, slope-landdisasters were most frequent during 2010
Typhoon Megi. Additionally, according toCentral Weather Bureau precipitation records,
Typhoon Megi affected the Suao area between
October 19th and October 21st, 2010 dropping a
total of 939 mm of rain with rainfall intensities
was as high as 121 mm/hr(10/21, 13:00-14:00).
In addition to setting precipitation records,
Typhoon Megi also triggered several landslides
between Suao and Nanao that severed both lanesof Route 9 and ensnared a tour bus in debris,pushing it off the highway and into the ocean(Central Geologic Bureau, 2011; Lee, 2010).
Lai (1986) divided the stretch of Route 91

10th Asian Regional Conference of IAEG (2015)

Typhoon Megi, the largest landslide on record
for the watershed occurred. The volume of thelandslide exceeded 13,333 m3 and caused the
Suao to Dongao section of the Su-Hua Highway
to be shut down for 25 days. Precipitation
associated with storms following Typhoon Megi
re-mobilized the landslide debris and caused
additional deposition and aggradation of thestream channel. The stream bed aggradated tothe point that it nearly crested the bridge. The
elevated stream channel also allowed debris
flows to flow against and scour the channel
walls and in turn caused additional undercutting
and failure of the adjacent slopes.
Due to the high risk slope instabilities at
115.9k of Route 9 posed to motorists, the MOTC
decided to construct a new, larger bridge toincrease the cross section for flood flows andreduce the risk associated with the debris flows
and landslides. Image interpretation of the
Dakeng bridge watershed has revealed fresh
tension cracks along the edges of the watershed
and the landslide bodies associated with thosecracks are becoming increasingly unstable,
indicating that the watershed will continue to beaffected by debris slides. Resultantly, this study
aims to clarify the future evolution of these
unstable or gravitational deformed areas and thepossible effects of continued debris slides on
Route 9. Using 1 m resolution LiDAR datacollected via a Reigal LMS-Q680i scanner, themicro-topography of the landslide is interpreted
and the extent of the gravitationally deformed
areas associated with instability are identified.
Then using an empirical formula for landslidevolume based on the area, a volume
representative of the volume that might result by
failure of the gravitationally deformed areas is
estimated. Finally, adopting PFC3DTM, thefailure process is modeled and the extent
affected by the landslide, or run-out, isestimated.

an area of 30.5 ha and is divided by a dense
network of streams. The average slope of thewatershed is 41 degrees, with the highest slope
being 63 degrees. The highest elevation in thewatershed is 800m which is 900 meters
horizontally from the junction of the highway
and the watershed, which has an elevation of 270
m. The steep topography promotes highly
erosive stream flow that during heavy rains,often cause landslides. In extreme cases thelandslides transition into highly destructive
debris flows, which in turn threaten Route 9.
According to a 1/50,000 Central Geologic
Agency map of the watershed, the watershed iscomprised of an exposed section of Amphibolite
within the Tungao Schist. The rock has a strikeof N80W and a dip of 80 degrees to the south.
The rock has a consistent orientation within thewatershed. Although the strength of theAmphibolite is relatively high, the rock isheavily jointed and highly fractured.
The study area lies in a south Asia tropicalmonsoon climate which is characterized byheavy rainfall. Between 1971 and 2009, theaverage annual rainfall was greater than 4,480
mm. According to Central Weather Bureau
records between 1971 and 2009, the hottest
temperature occurs during July with an averageof 32.0 degrees and the coldest temperature
occurs in January, with an average of 12.9
degrees. The average number of rainy days is210. Rainfall is heaviest during October toDecember. Every year, October to January is thenortheast monsoon period. During that period,
rainfall intensity is relatively low but duration islong. During the summer and fall, rainfall is
intense and primarily due to typhoons.

2.

Study area
The Dakeng bridge watershed is located in
Suau township of Yilan County (indicated by the
red square in Fig. 1). The west side of the area is
the Central Mountain Range while the east sideis the Pacific Ocean. Su-Hua highway is locatedat the foot of the mountains that boarder the
Pacific Ocean. The topography is incredibly
steep and the location of the road snakes throughthe coastline with the natural variations in
topography. Much of the slopes are south,
south-east facing. The study area extends from115.8k to 116.4k of Route 9 and includes all of
the Dakeng bridge watershed. The watershed has

Fig. 1 Study location and geologic conditions(based on Central Geologic Survey 1/50,000
Taiwan geologic map for the Su-Hua area)2

10th Asian Regional Conference of IAEG (2015)

Table 1 Suao and Dongaoling weather station
monthly average rainfall (CWB)

compressive strength values from the MOTC
survey, which were obtained from solid,
un-weathered cores, are reduced using the Hoek
and Brown failure criterion (Hoek et al., 2002).
In that method, the compressive strength of thecores sample, Geologic Strength Index (GSI),
rock characteristics and degree of disturbanceare taken into account to estimate the reduced
strength of the exposed bedrock. Based on field
investigation, the mean GSI value in the study
area was 50. Sample locations, bedrock
conditions and the strength reduction parameterare shown in Fig. 4 and Table 3.

Month
1
2
3
4
5
6
Accumulated
Suao
364.9
339.7
211.2
192.8
267.4
248.8
precipitation
Dongaoling
687.8
659.3
328.0
215.0
797.0
363.5
[mm]
Month
7
8
9
10
11
12
Accumulated
Suauo
178.7
280.7
536.9
719.6
696.6
422.7
precipitation
Donguoling
256.8
558.0
412.3
618.8
846.0
815.8
[mm]
註：Suao station： 1971/1 to 2009/12 Data；Dongaoling station：2012/1to2013/12data。

3. Methods
A flow chart describing the methodology thelocations of unstable landslide bodies, predicting
landslide volume and estimating the extent
affected by the landslide is shown in Fig. 2.
Micro-topography interpretation of LiDAR
generated, high accuracy Digital Elevation
Models (DEM) were used to identify the extent
of the unstable, gravitationally deformed
portions of the watershed. An empirical formulabased on historic disaster records for landslidearea and volume is then used to estimate thevolume of material that may be generated by thegravitationally deformed areas. That volume isentered into a discrete element method modelthat is used to identify the area affected by thelandslide.

Table 2 Bore sample parameters (MOTC, 2012)
Core ID

Lithlogy

X(TWD97)

BT-07-02
Amphibolite
Peak strength

Y(TWD97)

Depth(m)3346402714397
Residual strength

96.12-96.25
Uniaxial
compressive
strength
(Mpa)
68.6

c(kg/cm )Ø(degree)c(kg/cm )Ø(degree)

-

-

0.00

25.5

2

2

Unit
weight3(t/m )3ν
0.1

Elastic modulus(Mpa)
33486.8

Fig. 3 Study area and location of borehole(MOTC, 2012)

Fig. 2 Analysis flow chart of the study
3.1 Field investigation and modeling
Regarding numerical modeling, inaddition to being heavily dependent on themodel type and mesh, geotechnical parameters
are also very important. Parameters that reflect
the bedrock strength and deformability include
the rock type, stratification, single compressive
strength and deformation modulus. Usually,
bedrock model parameters are obtained using
physical experiments and published data and as aresult are not necessarily representative.
However, this study uses values recorded fromthe study area collected by the MOTC (Table 2,
Figure 3) to select the parameter values for themodel. Additionally, in order to account for themoderate to high degree of weathering andjointing observed in the exposed bedrock along
the entire length of Suhua highway, theFig. 4 The locations of GSI evaluation near
Dakeng bridge
Table 3 Dakeng bridge watershed bedrock
reduction parameters
Mean
GSI

Rock
type

Elastic
modulus of
intact rock
(Mpa)

50

Amphibolite

33,486.8

Revisedelastic
Angle
modulus
Uniaxial
Rock
Unit
of
from
compressive
lithology
weight internal
Hoek&Brown
strengthparameter(t/m3) friction
failure(MPa)miø
criteria
E (Mpa)2,233.7
68.6
3.00
25.5
26

DisturbancefactorD

1.0

3.2 Historical sediment disaster records
To define a relationship between
landslide area and volume, this study uses datareported by the MOTC Highway Disaster
Information System, Fourth Maintenance Officeof the Directorate General of Highways, Central
Geologic Survey and Chou et al. (2012).

3

10th Asian Regional Conference of IAEG (2015)

Additionally, the area of the unstable landslide
bodies are identified using Suauo to Donao
historical sediment disaster records and 2004
high resolution topographic data compared to2011 high resolution LiDAR data collected bythis study. We choice five areas of exposed bed
rock by field investigation, which didn’t hadcolluvium accumulation body on the slope. To
ensure regression result is correct, and beside
add more samples. The lost volume between
2004 to 2011 were selected (Fig. 5) to develop alandslide area and volume relationship. That
relationship was then used to estimate thevolume of sediment produced by unstable areasof the Dakeng bridge watershed (Table 4).
To ensure numerical model results are
representative of field conditions, an empiricalformula was developed based on historiclandslide area and volume records and was used
to estimate landslide volume. Chen et al. (2010)
constructed landslide area and volume regression
equations using remote sensing data of the
Shihmen reservoir watershed, Also, Guzetti et al.
(2009) and Korup (2005) developed power law
regression equations based on landslide area andvolume using landslides in Collazzone, Italy andthe Southern Alps of New Zealand, respectively;
the volume-area relationships were generally inagreement. In this study, a power law regression
equation is developed using the landslide areaand volume of 17 landslides that occurred near
Route 9 to develop an empirical equation forlandslide area and volume for landslides that
occur between Suao and Donao along Route 9.

Fig. 5 Difference in 2004 and 2011 DEM inupper reach of the Dakeng bridge watershed andfield photos. Positive signifies deposition;negative signifies reduced surface elevation.

Fig 6. Power regression model constructed from
historical landslide area and volume records of
17 landslides.
This study adopts a regression equation toestimate landslide volume based on landslidearea (Fig.6). The R-squared value for theequation used is 81.9%, which demonstrates asignificant exponential relationship. The optimal
regression equation was found to be:

Table 4 17 landslides used to construct landslidearea-volume regression equation
Landslide
nameSourceSlopearea(m2)

Landslidevolume, (m3,loose material)

Landslidevolume, in situ
(m3)

Mean
depth(m)0.1751 A .
(1)3
where, VL = landslide volume(m ) and AL is thelandslide area(m2). Equation (1) is used toconvert that landslide area to a volume. This
study uses micro-topography interpretation to
identify the extent of gravitational deformation
areas, which is assumed equal to the landslidearea. Once the landslide volume has been
determined, the numerical model is then run
based on that value.

Hwy 9, 115.9k,
6,572.9
44,326.6
7.40(Area 1)
Hwy 9, 115.9k
2,010.0
13,063.1
6.50(Area 2)Comparison of two
Hwy 9, 115.9k
2004 and 2011
2,668.1
20,275.9
7.60(Area 3)DTM
Hwy 9, 115.9k
545.9
3,170.4
5.81(Area 4)Hwy 9, 115.9k
773.0
5,169.5
6.69(Area 5)Hwy 9, 115.9k
Chou et al., 2012
126,795.2
1,786,000.0
14.09
Hwy 9, 111.18k
1,495.6
714.0
0.48
CGB
Hwy 9, 124.27k
496.3
255.2
0.51
Hwy 9, 104.87k
1,822.2
1400.01,120.0
0.96
Hwy 9, 105k
495.3
1400.01,120.0
3.53
Hwy 9, 106.6k Directorate General
1,834.96480.05,184.0
4.41
Hwy 9, 106.8k
2,548.6
1960.01,568.0
0.96
of Highways,
Hwy 9, 107.69k
154.2
50.4
40.3
0.41
FourthMaintenance
Hwy 9, 109.1k
52.0
45.0
36.0
1.08
OfficeHwy 9, 110.4k
635.7
3240.02,592.0
6.37
Hwy 9, 116.8k
346.5
2580.02,064.0
9.31
Hwy 9, 121.7k
644.2
2520.02,016.0
4.89※The volumes reported by the Fourth Maintenancec Office of the Director General of Transporations areloose material. According to the New Taipei Agricultural Department and Technology analysis
manual(2011), 1 m3 of in situ material is equal to 1.25 m3 of loose material.

3.3

LiDAR

Landslidemicro-topographyinterpretation
The stability of a landslide body and thedevelopment of a potential failure surface areinterrelated. If the landslide body is comprisedof disturbed and loose material, the failuresurface may develop within the body itself.
There is an existing failure surface within thehillslope and deformation and displacement are4

10th Asian Regional Conference of IAEG (2015)

likely to occur. If the failure surface exists along
a free surface outside of the landslide body, oncethe shear strength along the surface is smaller
than the driving forces resulting from gravity
acting on the landslide body, shear failure occurs(Baker, 1981; Sharma et al., 1995). Tension
cracks, scarps and other linear features markthese types of failure processes and can beutilized to identify the extent of the failuresurface (Agliardi, 2001; Chigira, 2009; Razak etal., 2009, 2011; Lopez Saez et al., 2012).
The gravitation deformation area is alsosurrounded by the linear landslide features. In thepast, such linear landslide features were
unidentifiable on slopes covered with dense
vegetation. However, modern high resolution
surveying techniques can now work around thevegetation. Moreover, while high resolution
survey images are critical to successful landslide
identification, the methodology used identify thelinear landslide features in the images is also
important. This study references featuresassociated with gravitation deformation areas
described in Soeters & van Westen (1996) andChigira (2011)(Table 5) to identify the boundaryof the gravitation deformation area in the Dakeng
bridge watershed. The identified area is thenentered into the landslide area-volume empiricalequation to compute the probable landslidevolume. That volume is then input into thenumerical model to estimate the zone affected by
the landslide. Interpretation of the gravitational
deformation areas is shown in Fig. 7.

Shaded relief generated from 2011 highresolution LiDAR data as well as aerial photos
and a map shaded according to slope were usedtoperform
topographyinterpretation.
Identifiable linear features were recorded. Fromthe map shaded according to slope, the locationof linear, mobile areas is clear. From the twodimensional profile A-A' point 2 and 4 andprofile B-B' point 6 and 8 show the oversteepening of slopes. Combined with aerial
photos and the shaded relief topography, tension
crack features at the head of the landslide are
evident. These clues signify that the scar andhead of the landslide are still mobile and within
the gravity deformation area. The shaded relief
map reveals mounds of material deposited at thefoot of the hillslope that may have resulted fromlandslides at the top of the slope. The gravity
deformation area is 59,560 m2 and is shown in
Fig. 8.

Table 5 Irregularities associated with gravitation
deformation areas (From Soeters and vanWesten, 1996; Chigira, 2011)
Irregularity

Step-likemorphology

SemicircularnichesBack tilting
of slope
faces

HummockyreliefFormationof cracks

Steeping ofslopesDescription
Step-like morphology usuallyforms after the landslide bodyhas mobilized. Material upslopeof the landslide scar moves
downslope into the scar,
forming
the
step
like
topography at the upper edge ofthe landslide

Illustration

Fig. 7 Indications of gravitation deformation
area. The number 1~8 is the location of slope
map topographic profile.

Semicircular niches form in thehead of the landslide.
The occurrence of back tilting
slope faces indicates rotational
failure. This feature is often
oval or square in the horizontal
surface and generally occurs inpoorly drained material.
Irregular
topography
thatgenerally indicates instability
and past movement and may berelated to shallow failures orsmall failures moving towards
an existing landslide scar.
Fresh, open cracks that areevidence of recent movement
and indicate the landslide bodyis sliding or tipping. Often thecracks parallel the edge of a
scar.
Once
the
landslidehasoccurred, an over-steepened
slope, or scar remains, severing
the head of the landslide.

Fig. 8 Results of gravitation deformationinterpretation

5

10th Asian Regional Conference of IAEG (2015)

Numerical modelTo estimate the area affected by failure of thegravitation deformation in the Dakeng bridge
watershed, this study utilizes three dimensional
discrete element method software, PFC3DTM (Loet al., 2011；Chou et al., 2012；Lo et al., 2014).
This software was developed by Itasca company
in 1999 (Itasca, 2002) and primarily uses walland ball shaped elements and the discreteelement method to compute the change inlocation of each ball shaped element for eachtime step. In this way, the ball shaped elements
are used to model the landslide body and thewall is used to model the failure surface. For
each time step, the location and overlap amountor relative displacement is computed. Using theforce-displacement law to estimate contact
forces, Newton's second law of motion is applied
using a model coefficient of friction. Once theforce between the ball shaped element and thewall element exceeds the resistance force due tofriction, the ball shaped elements begin moving,and in this way the velocity and position of eachelement are computed and the dynamic behaviorof the mobilized landslide body as well as thearea affected by the landslide are estimated.
PFC3DTM utilizes the forces resulting at eachcontact point between elements to model themechanics involved in the deforming material.
Three types of contacts were used including stiff,
smooth and bonded. Stiffness is computed
assuming that the stiffness coefficient of the twoneighboring particles is normal or in shear andinteracts like a series of springs. Stiffness in thenormal direction (kn) and the shear direction (ks)are described by equation (2)The volume of the landslide was derived fromthe gravitational deformation area and thearea-volume empirical relationship. The volumeof the landslide body was determined to be324,490 m3. To model this volume, 4,960 ballshaped elements, each having a radius of 2.5
meters were used as the landslide material. The
topography of the model is based on the 2011
LiDAR DEM which was represented in themodel by a total of 76,951 wall elements. Modelconditions and parameters are shown in Fig. 9.
PFC3DTM model results reveal that mostof the landslide material moves along the stream
upstream of the Dakeng bridge (Fig. 10). The
highest velocity attained during failure is 10 m/s
and occurs as the landslide body drops from the
top of the slope. As a result of the relatively
large assumed volume of the landslide body,multiple zones of deposition form at the stream
confluence with deposition depths as high as 13
m. Consequently, these results suggest that alandslide dam could possibly form near the
confluence.

k [ A]k [ B ]k [ A]  k [ B ]Fig. 9 Dakeng bridge watershed landslide model
initial conditions

3.4

k

(2)
where, [A] and [B] are the two elements incontact, k is the normal stiffness(kn) or the shear
stiffness(ks). The numerical model uses theaforementioned ball shaped elements in the
PFC3DTM software to model the movement of
the landslide body and the wall elements tomodel the intact, stationary bedrock boundary.
Characteristics of the landslide material in thefield are reflected in the model parameters using
methods described in Potyondy and Cundall(2004):
kn , ks  4 RE
(3)Fig. 10 PFC3DTM model results

where, kn is the normal stiffness, ks is the shear
stiffness, R is the radius of the model ball shaped
elements (unit: m), E is the Young's modulus.
In consideration of computation time, a
5m x 5m DEM of the intact slope andsurrounding topography were used for modeling.

4. Conclusion
Field examination of the Dakeng bridge
watershed reveals that bedrock is generally highin strength but heavily fractured (GSI =50).

6

10th Asian Regional Conference of IAEG (2015)

From interpretation of high resolution remotesensing data, it is evident that the north-westridge boundary of the Dakeng bridge watershed
is not only marked by many tension cracks but
also has many areas of gravitational deformation.
The area defined by these features is 59,560 m2.
Historical hazard records were used to develop alandslide area to volume empirical model. Using
this model, the volume of landslide material was
found to be 324,490 m3, which was then input
into PFC3DTM. Overall, the study area is asteep, convergent drainage. Stream flow is rapidand during high flows, colluvium and otherlandslide materials are entrained by the waterand the erosive force of the stream is rapidly
enhanced. In the future, in addition tomonitoring the area downstream of thedeposition zone (Route 9 115.9k to 116.1k), thebanks of the stream that are actively eroded by
the stream in the sinuous portion of the stream(approximately 115.8k - 116.4k) also need to be
frequently examined. During typhoon rainfall,hazard response plans will need to be initiated.

Bureau.(In Chinese)
Chigira, M. (2011): The Potential Area of
Large-Scale Landslides (In Chinese), Scientific& Technical Publishing Co., Ltd: Taipei,
Taiwan.
Chu, H.K., Lo, C.M., Chang, Y.L. (2013):
Numerical analysis of slope stability at the115.9k point of the Su-Hua Highway. Journalof Chinese Soil and Water Conservation, Vol.
44(2), pp. 97-104.
Chou, H.T., Lee, C.F., Lo, C.M., and Lin, Z.P.
(2012): Landslide and alluvial fan caused by anextreme rainfall in Suao, Taiwan, 11th
International Symposium on Landslides (ISL)and the 2nd North American Symposium on
Landslides, Banff, Alberta, Canada.
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report of geological exploration services along
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Chinese)
Guzzetti, F., Ardizzone, F., Cardinali, M., Rossi,M., and Valigi, D. (2009): Landslide volumes
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(2002): Hoek-Brown failure criterion-2002
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pp. 267-273.
Itasca Consulting Group Inc. (2002): PFC3D
Particle Flow Code in 3 Dimensions. User's
Guide. Minneapolis, USA.
Korup, O. (2005): Distribution of landslides insouthwest New Zealand, Landslides, Vol. 2, pp.
43-51.
Lai, T. C. (1986): Study on the geotechnicalproperty of rock masses along the Suhua
Highway, and their engineering significance,
Bulletin of Central Geological Survey, Vol. 4,
pp. 55-102. (In Chinese)
Lee, K.T. (2010): Analysis of disaster during
Typhoon Megi along Route 9 between Mileage
about 104K to 120K. (In Chinese)
Lo, C. M., Lee, C. F., Chou, H. T. and Lin, M. L.
(2014): Landslide at Su-Hua Highway 115.9k
triggered by Typhoon Megi in Taiwan,
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Lo, C. M., Lin, M. L., Tang, C. L. and Hu, J. C.
(2011): A kinematic model of the Hsiaolin
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Lopez Saez, J., Corona, C., Stoffel, M., Astrade,L., Berger, F., and Malet, J.P. (2012):
Dendrogeomorphic reconstruction of pastlandslide reactivation with seasonal precision:
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5. Acknowledgements
This research is indebted for assistance anddiscussion on natural disaster investigation
provided by C. M. Lo at Department of Civil
Engineering, Chien-Kuo Technology Unversity,and M. L. Lin at Department of Civil
Engineering, National Taiwan University.
Additionally, the authors thank Strong
Engineering Consulting Co., Ltd. for providing
the airborne LiDAR data (2011).
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