The Application of TransdisciplinaryTechniques on Slope Disaster
Monitoring in Taiwan
Chung-Ray Chu

Chiao-Yin Lu Che-Hsin, Liu Chih-Hsin, Chang
Slopeland and Hydrology Division

National Science and Technology Center for Disaster Reduction

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Field investigation and monitoring⚫
⚫
⚫

Very important work for awareness
Deeply and truly understand the environmental
condition and creeping behavior, BEFORE sliding.
How many sites we need to concern?

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Seismology can help⚫

Seismology becomes a promising way to monitor slope disasters remotely and continuously.
➢Far field: monitor deep-seated landslide by national seismic network (A>0.2 km2 or V>106 m3)➢

Near field: monitor shallow landslide, rockslide, debris flow by local network (more necessary)⚫

Challenge: the auto-processing technique is needed and should be trustworthy and efficient.

⚫

Challenge: small scale movement generates seismic wave with higher frequency.

⚫

Challenge: local network deployed near village or road where lots of random noise exists.

⚫

The technical gap is how to deal with Noise.
Far field monitoring by physical model

Ekström and Stark (2013)

W. Chao, et al (2017)

Far field monitoring by machine learning

C. Hibert, et al (2019)

Lin, et al (2020)

Near field monitoring

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Lu-Hu landslide, Miaoli, Taiwan⚫ Rockslide prone area
since a significant
landslide occurred in2018

Geophone
Broadband
R ShakeNN⚫ Seismic network with 4geophones have been
deployed across thefailure slope in 2019
⚫ Seismic events of 4
witnessed rockslides
have been recorded

2019/04/01

2019/05/24

2020/02/21

2021/08/11

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Different seismic events⚫ Unique patterns in timeseries and spectrogram
reveal different sources⚫ Wave attenuation between
sensors implies source
location⚫ More types: farming, piping,villager activity, wind, rainfall,
and thunder
Car passing

Earthquake and aftershocks
Rockslide

Machine operation and human activity

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Automatic monitoring algorithm⚫
⚫
⚫

Detection: short term average/long term average (STA/LTA) and Kurtosis picker
Noise elimination (de-Noise): signal-to-noise ratio (SNR) and cross-correlation (CC) analysis
Classification: machine learning (ML) model7

Noise-influenced database⚫ de-Noise: path 1➢ Test period: 2019/5/13-2019/5/21 (9 days)➢ Diverse noises dominate the sample pool so that machine learningmodel get lower sensitivity to RS and EQ

EQNORS

SEN

EQ

138

93059.7%

NO131584

798.8%

RS

19323

19.7%

ACC

90.8%

89.5%

76.7%

89.4%

manualTotal calculation: 51,840
STA/LTA triggering: 7,008 (13%)
Qualified sample: 1,952

AutomaticRandom
Forest

ACC: Accuracy; SEN: sensitivity
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Noise-free database⚫ de-Noise: path 2➢ Test period: 2019/5/13-2020/6/13 (1+ years)➢ The performance of ML model is enhanced significantly because ofdecreased noises➢ AI-based seismic sensor is possible as next generation device

CAEQNORSSENCA

13001

1788%

EQ

44082

63299%

NO

026

421650%RS51357519

78%

ACC

93%

96%

76%

89%

95%

manualTotal calculation: 588,960
STA/LTA triggering: 17,447 (3%)
Qualified sample: 5,022

Automatic

The 2ndattributes

ACC: Accuracy; SEN: sensitivity

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Aerophotography can help
⚫ Drone is getting more popular in various
application (filming, recreation, military usage)⚫ Provide broad viewpoint in geographic
feature investigation⚫ Surveying with high resolution and efficient
operationFeatured with• Optic Cam• LiDAR• Multispectral Cam

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Particle image velocimetry (PIV)⚫ Methodology– Capture orthophotos of the study site
– Identify appropriate markers on the ground as
reference points– Obtain the coordinates of the reference points by
high-precision GNSS
– Create the orthomosaic map and Digital Surface
Model (DSM) with high resolution coordinate position

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Particle image velocimetry (PIV)⚫
⚫
⚫
⚫

PIV estimates pixel velocity by each orthomosaic pair.
The stretch series corresponded with in-situ extensometer data.
PIV provides spatial evolution of landslide.
Time-lapse ortho-photography is more perceptible for observation.

8/5 ~ 8/11

2021/02/17~2021/06/11

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Survey History of Large-scale Landslide⚫

General survey by airborne LiDAR and recognize 9848 places of large-scale landslide potential areas since 2009.

⚫

Identify 238 places containing protected targets with medium to high risk.

⚫

Officially select 34 places with probable activity by InSAR, and increase to 48 places this year.

984823848

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Space geodesy can help

Sentinel-1A&1B satellites
Launch date：2014.4.3 (1A) / 2016.4.25 (1B)
Repeat Cycle：12 days / 6 days
Band/Wave length：C band/5.5cm
Interferometric Wide Swath: 250km Swath
5x20m spatial resolution

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Persistent scatterers pixel, PS pixel⚫

Main idea: Find stable and strong reflective objects of a pixel⚫

From such strong scatterer, the surface information signal can be extracted from thebackground noise.

(Modified from Hopper et al., 2007)

Distributed scatterer pixel

Persistent scatterer pixel

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Multitemporal InSAR, MTInSARMTInSARDInSAR
SAR
imagesInterferograms

Phase stability
estimation andPS selection

Velocity
field map

Displacement timeseriesSentinel Application Platform,
snap2stamps
SNAP

Stanford Method for Persistent Scatterers,
StaMPS
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The activity of large-scale landslides

Time series interpretation
Shiding - T001
Wulai - D006
Jianshi - D097
Heping - T003
Renai - D066
Renai - D057
Meishan - T001

Yuli - D018

Wutai - D038

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Landslide in Huhulun, 2022⚫

2019-2021 PSInSAR result (descending)• Seismic wave recorded by WFSB Broadband Station
10/17 01:25

AB
10/17 21:22

10/18 11:00

AB

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Landslide in Huhulun, 2022⚫

Orthophotograph and LiDAR scaning in 10/21 and 10/27, 2022
UAV光達點雲產製
的高精度DSM主要崩塌區及上方保全對象

坡頂崩崖的地表裂縫

2022.10.21

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Landslide in Huhulun, 2022
DEM
2
1

3-26.1 m

2

1

3

2022.10.21
舊林道

正射影像
Lanslide area(2022/10/17)

2
1+22.4 m

3⚫ The geographic features, suchas landslide area, ancient scarp,
and forest trail could be
discovered on DEM (by LiDAR).
⚫ The elevation changes ofcollapse and deposition areascan be estimated bycomparison of dual DEM pairs.

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Thank you for listening.

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