Time to enter the era of 1

Earth-Observation based landslide warning system 2

Keren Dai1,2,5, Zhenhong Li3, 4, 5 *, Qiang Xu1, Roland Burgmann6, 7, David G 3

Milledge4, Roberto Tomás8, Xuanmei Fan1, Chaoying Zhao5, Xiaojie Liu5, Jianbing 4

Peng5, Qin Zhang5, Zheng Wang3, 4, Tengteng Qu4,9, Chaoyang He1, Deren Li10, 5

Jingnan Liu11 6

1State Key Laboratory of Geohazard Prevention and Geoenviroment Protection, 7

Chengdu University of Technology, Chengdu 610059, China. 8

2College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, 9

China. 10

3COMET, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 11

7RU, UK. 12

4School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK. 13

5College of Geological Engineering and Geomatics, Chang'an University, Xi'an 14

710054, China 15

6Berkeley Seismological Laboratory, University of California, Berkeley, CA 94720, 16

USA. 17

7Department of Earth and Planetary Science, University of California, Berkeley, CA 18

94720, USA. 19

8Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de 20

Alicante, P.O. Box 99, E-03080 Alicante, Spain. 21

9College of Engineering, Peking University, Beijing, 100871, China. 22

10State Key Laboratory of Information Engineering in Surveying, Mapping and Remote 23

Sensing, Wuhan University, Wuhan 430079, China. 24

11GNSS Research Center, Wuhan University, Wuhan 430079, China. 25

* Corresponding author. Email: zhenhong.li@newcastle.ac.uk 26

27

Abstract. Landslide early warning 28

remains a grand challenge due to the high 29

human cost of catastrophic landslides 30

globally and the difficulty of identifying 31

a diverse range of landslide triggering 32

factors. There have been only a very 33

limited number of success stories to date. 34

However, recent advances in earth 35

observation (EO) from ground, aircraft 36

and space have dramatically improved 37

our ability to detect and monitor active 38

landslides and a growing body of 1

geotechnical theory suggests that pre-2

failure behavior can provide clues to the 3

location and timing of impending 4

catastrophic failures. In this paper, we 5

use two recent landslides in China as case 6

studies, to demonstrate that (i) satellite 7

radar observations can be used to detect 8

deformation precursors to catastrophic 9

landslide occurrence, and (ii) early 10

warning can be achieved with real-time 11

in-situ observations. A novel and exciting 12

framework is then proposed to employ 13

EO technologies to build an operational 14

landslide early warning system. 15

16

INTRODUCTION 17

Landslides (where soil or rock moves 18

down a slope) have been shaping 19

mountainous regions for millennia, but 20

today they pose a destructive hazard to 21

people and infrastructure resulting in 22

hundreds of deaths and billions of dollars 23

of damage every year [1]. The 24

combination of a rapidly increasing 25

global population and intensifying 26

weather extremes associated with recent 27

climate change suggests that landslide 28

risk will dramatically increase over the 29

next decade. Landslide deformation can 30

be extremely slow (few mm per year) or 31

involve sudden extremely rapid failure 32

[2], and thus their hazards include both 33

enduring damage to manmade structures 34

and catastrophic destructive events. 35

While small landslides make up the vast 36

majority of landslide ‘events’ in any 37

given year, it is large landslides that tend 38

to be responsible for most of the damage 39

and loss of life [3]. Current landslide risk 40

mitigation strategies tend to reduce 41

exposure - the likelihood that someone or 42

something is impacted by a landslide - 43

primarily by moving to, or locating 44

infrastructure in, less hazardous 45

locations; but for many people and assets 46

relocation is not feasible. In these 47

situations, short-term evacuation is often 48

the most attractive or only option. 49

Therefore, improved landslide 50

forecasting and the development of early 51

warning capabilities are expected to play 52

crucial roles in managing landslide risk 53

for many individuals and communities. 54

The major landslide triggering factors 55

(e.g., rainfall and seismic shaking) and 56

the basic physics governing landslide 57

initiation are well known. Yet predicting 58

where and when landslides will occur 59

remains a grand challenge primarily due 60

to the difficulty in forecasting the 61

triggering factors themselves, and the 62

spatial variations in earth materials and 63

slope conditions. Existing forecasting 64

methods generally involve functional 65

relationships between trigger-factor 66

intensity (e.g. precipitation history and 67

peak seismic ground acceleration) and 68

landslide probability. However, the 69

connection between triggers and 70

landslides is complex, with some 71

landslides occurring in the absence of an 72

identifiable trigger and others occurring 73

with significant delay. For example, the 1

2006 Leyte landslide that killed over 2

1100 people in the Philippines, occurred 3

five days after a large rainstorm, so that 4

although the population were initially 5

evacuated they had returned to their 6

homes [4]. Displacements recorded over 7

time could provide critical additional 8

information for predicting the possible 9

timing of impending slope failure [5]. 10

Based on conventional in-situ survey 11

methods, the concept of ‘landslide early 12

warning systems’ has been proposed for 13

several years, e.g. [6-12]. The outcomes 14

of these works are often suggested 15

warning criteria for specific locations. 16

Successful early warning cases, where a 17

clear warning was given prior to 18

catastrophic slope failure, have been very 19

limited due to the inadequate temporal 20

and spatial precision of ground 21

observations [13]. Building trustworthy 22

real-time early warning systems (capable 23

of identifying the ‘very high-risk time’ to 24

prompt short-term evacuation) with 25

suitable spatial and temporal precision is 26

an important but difficult challenge. 27

Spaceborne Synthetic Aperture 28

Radar (SAR) sensors emit radar signals 29

and record the amplitude of the 30

backscattered signal as well as the phase 31

(from which the changes in range 32

between satellite and Earth’s surface can 33

be inferred) [14]. Interferometric SAR 34

(InSAR) is a powerful tool for measuring 35

the Earth’s surface motion over large 36

regions (e.g. [15-17]) in all weather 37

conditions, at metre-resolution and offers 38

the capability to remotely monitor 39

unstable slopes, e.g. [18-21]. Recent 40

studies have demonstrated that 41

conventional InSAR and related time 42

series techniques (e.g. Persistent 43

Scatterer InSAR and small baseline 44

InSAR) can identify, map and monitor 45

active landslides [22-26] and even to 46

detect precursory deformation signals 47

prior to their eventual failure, e.g. [27-48

29]. Note that spaceborne InSAR 49

currently has a minimum repeat cycle of 50

6 days for Sentinel-1, 1 day for COSMO-51

SkyMed [30], 11 days for TerraSAR-X 52

and longer for other satellites, which 53

represents a major limitation of 54

spaceborne InSAR for early warning 55

systems. 56

In-situ global navigation satellite 57

system (GNSS) monitoring is capable of 58

measuring three-dimensional landslide 59

motion at very high temporal frequency 60

(e.g. 20 Hz) and spatial accuracy (2-4 mm 61

in plan and 4-8 mm in vertical) [31]. 62

Other in-situ monitoring methods include 63

extensometers, inclinometers, and pore 64

water pressure sensors. However, these 65

methods only provide point-based 66

measurements at sensors that are costly to 67

install and maintain. Thus in-situ 68

observations are limited by the number of 69

sensors that can be deployed at the key 70

locations and may not capture the spatial 71

variations in landslide motion prior to 72

failure. There are two obvious hurdles to 73

the deployment of ground-based 74

monitoring techniques: (i) the sites with 1

potential landslides should be detected 2

prior to their failure; and (ii) the key 3

monitoring locations in the landslide 4

bodies should be identified. 5

Spaceborne InSAR and in-situ 6

sensors are complementary tools to 7

monitor surface displacements given 8

InSAR’s high spatial resolution (metres 9

to 10s metres) over a wide region (e.g. 10

250 km x 250 km for Sentinel-1) but 11

limited temporal resolution (constrained 12

by the frequency of satellite overpasses) 13

and in-situ sensors’ fine temporal 14

resolution at their locations. We suggest 15

that it is now both feasible and timely to 16

combine these EO technologies to build 17

an integrated landslide early warning 18

system. In this paper, the 2017 Xinmo 19

(Sichuan, China) landslide is used to 20

demonstrate the ability of spaceborne 21

InSAR to identify precursory landslide 22

deformation, while the 2017 Dangchuan 23

#4 landslide in Heifangtai (Gansu, China) 24

is used to demonstrate the successful 25

application of timely early warning for 26

landslides by in-situ measurements [32]. 27

Based on the advantages, limitations and 28

complementarity of different EO 29

methods, a landslide early warning 30

framework is proposed to increase the 31

resilience of local communities to 32

landslide hazards by informing short-33

term evacuations. 34

Our paper makes the case that 35

landslide early warning from earth 36

observation (EO) is now within our 37

grasp. We believe that this is a message 38

that is both important and timely. It is 39

important because landslides kill 40

thousands of people every year, 41

predominantly in those parts of the world 42

that are poorest and thus least able to 43

protect themselves. It is timely because, 44

though early warning has long been 45

touted as a ‘golden bullet’ in landslide 46

risk mitigation, it requires accurate 47

predictions that have generally been out 48

of reach until now. 49

50

METHODOLOGY 51

The InSAR dataset for the time series 52

displacement extraction of Xinmo 53

landslides includes 29 descending SAR 54

images acquired by Sentinel-1A/1B 55

satellites from 09 November 2015 to 19 56

June 2017 SAR on every 6-24 days. 57

ESA’s Sentinel-1A/1B satellites operate 58

day and night performing C-band 59

microwave SAR imaging, providing 60

radar imagery with a wide coverage (e.g. 61

250 × 250 km) and a short repeat cycle 62

(6-24 days). The SAR data in this study 63

were interferometrically processed with 64

GAMMA software. Shuttle Radar 65

Topography Mission (SRTM) with 30 m 66

horizontal resolution was used to 67

simulate and eliminate the topographic 68

phase. Interferograms were filtered by 69

the adaptive filtering method to reduce 70

the noise. Coherent pixels were detected 71

using the full-rank matrix approach 72

demonstrated in [33] and their time series 1

analysis was performed following the 2

InSAR time series integrated 3

atmospheric estimation model (InSAR 4

TS+AEM) described in [34]. Both the 5

coherent pixel detection approach and the 6

InSAR TS+AEM method have been 7

successfully used in previous InSAR 8

studies. The mean velocity map and time 9

series displacements results were finally 10

geocoded into WGS84 coordinate 11

system. 12

The Heifangtai area has been monitored 13

with a range of in-situ sensors including 14

7 GNSS receivers, 34 crackmeters, 2 15

range gauges and 13 piezometers since 16

2017 by researchers from the State Key 17

Laboratory of Geohazard Prevention and 18

Geoenviroment Protection (SKLGP) at 19

Chengdu University of Technology. The 20

data collected by all the sensors was 21

transmitted to SKLGP in real time with 22

GPRS (General Packet Radio Service). 23

Note that the crackmeter was a real-time 24

adaptive one developed by SKLGP [35], 25

which acquired one sampling per hour in 26

normal conditions but automatically 27

increased its samples when a 28

displacement acceleration was detected. 29

30

RESULTS 31

Pre-failure movement signals revealed 32

with spaceborne InSAR 33

On 24 June 2017, a landslide of 13 34

million cubed meters suddenly buried 35

Xinmo village, Sichuan province, China, 36

causing 10 deaths, with 73 persons still 37

missing. Xinmo village is located on the 38

left bank of the Songping River, a first-39

order tributary of the upper reaches of 40

Minjiang River [36]. The surrounding 41

steep slopes are prone to rock falls, 42

landslides, and debris flows [37]. The 43

region is tectonically active with several 44

active faults nearby that have generated 45

three Mw >=6.7 earthquakes since the 46

1930s (Fig. 1A). Xinmo village itself was 47

built on the deposits of an old landslide 48

triggered by the 1933 Mw 7.3 Diexi 49

earthquake [36, 38] (Fig. 1A). 50

To explore the pre-failure 51

displacement history of the Xinmo 52

landslide, InSAR analysis was performed 53

on Sentinel-1 data to determine a mean 54

velocity map and a time series of 55

landslide motion for a ~1.5-year period 56

prior to failure (Fig. 2). The accumulative 57

displacement map during the period from 58

November 2015 to June 2017 (Fig. 2A) 59

shows that the area near the head scarp of 60

the landslide exhibited clearly detectable 61

displacements with a maximum of 3 cm 62

preceding failure. Figs 2C, 2D and 2E, 63

show the displacement times series 64

results for three selected points P1, P2 65

and P3 whose locations are shown in Fig. 66

2B. The last three acquisition dates are 26 67

May 2017, 07 June 2017 and 19 June 68

2017 (5 days before the failure), 69

respectively. A dramatic acceleration can 70

be observed during the period from 07 71

June 2017 to 19 June 2017 (from 17 days 72

before the failure). It should also be noted 73

that all the interferograms were carefully 1

checked to avoid phase unwrapping 2

errors and the InSAR time series was 3

performed pixel by pixel. We did NOT 4

apply strong spatial filtering, and hence 5

our InSAR mean velocity map is not as 6

smooth as those in previous studies. 7

However, the overall pattern of our 8

InSAR mean velocity map is consistent 9

with those in previous results (e.g. [28], 10

[29]). 11

This clearly demonstrates that 12

quantitative time series analysis from 13

satellite radar observations can detect 14

accelerated movements prior to 15

catastrophic failure, occurring 5-17 days 16

before the landslide. It should be noted 17

that the source area of the Xinmo 18

landslide is located on a steep slope at an 19

altitude of ~3400 m a.s.l. where in-situ 20

sensors would be difficult to install. This 21

highlights one notable advantage of 22

InSAR over in-situ monitoring sensors. 23

Fig. 1. The location, pre-event and post-event photos of the 24 June 2017 Xinmo 1

landslide. (A) Location of the Xinmo landslide and the epicenters of three large 2

historical earthquakes. (B) Unmanned aerial vehicle (UAV) aerial photo of the Xinmo 3

landslide with an inset photo of Xinmo village taken before the event. (C) Post-failure 4

photo of the Xinmo landslide (the whole village was buried under the accumulated 5

debris). 6

Fig. 2. Pre-failure movement signals and source area revealed by InSAR. (A)

Cumulative displacements for coherent pixels from time series InSAR analysis. (B)

Enlarged active displacement area and the location of points P1, P2 and P3; (C)(D)(E)

Displacement time series for points P1, P2 and P3, respectively.

Early warning for the Dangchuan 4# 1

landslide using in-situ sensors 2

The Heifangtai loess terrace, located 3

in Yongjing County, Gansu Province, 4

China (Fig. 3B) with an area of 13.7 5

squared km, is formed from a terrace of 6

Quaternary aeolian loess deposits [39]. 7

Since the Yellow River pumping 8

irrigation project was kicked-off in 1966, 9

frequent landslides have occurred on the 10

terrace margins. The Dangchuan 4# 11

landslide lies in southwest-central 12

Heifangtai near Guoxia town, Yongjing 13

County. Among all the in-situ sensors, a 14

crackmeter installed across the trailing 15

head scarp edge of Dangchuan 4# (Fig. 16

3A) provided critical displacement 17

measurements in real time which were 18

used in a successful 8-hour early warning 19

in 2017. 20

Fig. 3. landslide warning at Dangchuan 4# landslide in Heifangtai. (A) The location

of Dangchuan 4# landslide with various in-situ sensors; (B) cumulative displacement

and displacement rates from a crackmeter installed across the trailing head scarp edge

during the period from 1 August 2017 to 1 October 2017; (C) On 23 September 2017 a

photo of Heifangtai landslide warning announcement which was posted on a pillar in

Guoxia town by the local government; (D) At 20:55 on 30 September 2017, a red

warning message was delivered to the local government through WeChat app; (E) The

post-failure photo of the Heifangtai landslide (Dangchuan 4# slope) which failed at

05:00 on 1 October 2017.

The crackmeter observations showed 1

a clear acceleration in the displacement 2

rate at Dangchuan 4# on 23 August 2017 3

(Fig. 3B), and hence a yellow warning 4

was issued to the village head and local 5

government by text message, informing 6

them to: ‘pay close attention to this slope 7

and prepare for disaster prevention’. 8

After a detailed field investigation, the 9

local government confirmed the warning 10

and released an official landslide warning 11

announcement to local communities on 12

23 September 2017 with several alert 13

boards posted around the landslide area 14

(Fig. 3C). On 27 September 2017 the 15

yellow warning was upgraded to an 16

orange warning due to the accelerating 17

displacement rate measured at the 18

crackmeter. At 17:50 on 30 September 19

2017, a red warning was released 20

automatically by the system (Geohazard 21

Real-time Monitoring and Early Warning 22

System [40]) developed by SKLGP, 23

which was confirmed by a panel of 24

experts. Three hours later (at 20:55 on 30 25

September 2017), an official red warning 26

was issued to the local government (Fig. 27

3D), prompting a government led 28

emergency response and evacuation. The 29

local government immediately started 30

their emergency response, and more than 31

20 villagers in the landslide hazard zone 32

were evacuated. At 05:00 on 1 October 33

2017, a landslide occurred (Fig. 3E), 34

damaging several buildings but with no 35

casualties thanks to the early warning 36

[32]. 37

This successful case clearly 38

demonstrates the potential importance of 39

real-time displacement measurements 40

and the role that in-situ sensors could 41

play in early warning systems. A 42

preliminary retrospective InSAR study 43

showed that InSAR with L-band ALOS-44

2 images was able to capture the 45

accelerated movements prior to failure, 46

occurring 15 days before the landslide 47

(Fig. 4). 48

49

Fig. 4. Pre-event displacements of the 50

Dangchuan 4# landslide revealed by L-51

band observations. (A) The mean 52

velocity map from time series InSAR 53

analysis. (B) Enlarged active 54

displacement area and the location of 55

points P1, P2 and P3; (C)(D)(E) 56

Displacement time series for points P1, 57

P2 and P3, respectively. 58

59

DISCUSSION 60

The feasibility and complementarity of 61

EO for landslide early warning 62

A range of laboratory, field and 63

theoretical studies have identified pre-64

failure creep acceleration of landslides 65

and suggest that it can be divided into 66

three phases [41-44]: (i) Primary creep, 67

(ii) Secondary creep, and (iii) Tertiary 68

creep (Fig.A). Primary creep is 69

characterised by a decreasing strain rate 70

over time, which often lasts for a short 71

period or can be even absent in some 72

cases [42]. Secondary creep is 73

characterised by slow movement at near 74

constant rate (but with fluctuations in real 75

slopes due to the influence of external 76

factors, such as rainfall). The duration of 77

the secondary creep is difficult to 78

estimate and can last for months, years or 79

even decades [42, 45], despite continuous 80

displacement during this phase. Tertiary 81

creep is characterized by a rapid 82

acceleration of displacement until final 83

failure [46]. Although such speed-ups 84

may be common prior to catastrophic 85

failure events [45], the number of actual 86

observations of such speed-up behavior 87

remains limited due to the absence of the 88

right EO technologies in the right 89

locations at the right times. Therefore, 90

there are two primary challenges for 91

landslide early warning: (i) monitoring 92

surface displacements over a wide region 93

with sufficient resolution and accuracy to 94

identify areas undergoing secondary 95

creep; and (ii) identifying when or under 96

what circumstances a slow-moving 97

landslide (i.e. in secondary creep phase) 98

enters the accelerated displacement 99

tertiary creep phase leading to rapid 100

failure. 101

Advances in EO offer the potential to 102

address these two challenges. In the 103

primary and secondary phases, weekly to 104

monthly observations would be sufficient 105

to distinguish areas undergoing more 106

rapid creep. In the tertiary creep phase, 107

sub-daily sampling intervals are needed 108

to capture the acceleration in creep (Fig. 109

5B). InSAR currently has a shortest 110

repeat cycle of 1-11 days while GNSS 111

and some other in-situ sensors can 112

provide high-rate (e.g. 1-20 Hz) 113

measurements. Only slow tertiary creep 114

displacements (e.g. <0.012 m/day over a 115

distance of 100 m for Sentinel-1 [47]) 116

could potentially be captured by InSAR 117

because its measuring capability is 118

limited by the spatial displacement 119

gradients. This limitation can be 120

overcome by SAR pixel offset tracking 121

(e.g. [19]) and/or Range Split Spectrum 122

Interferometry assisted Phase 123

Unwrapping (R-SSIaPU) method [47]; 124

in-situ sensors generally do not have such 125

limitations (Fig. 5C). On the other hand, 126

InSAR offers extensive spatial coverage 127

enabling detection of potential landslides 128

in the primary and secondary creep 129

phases. To monitor a single slope in its 130

tertiary phase InSAR and in-situ sensors 131

can provide complementary coverage in 132

space and time. 133

Fig. 5. EO feasibility analysis on the three stages of landslide. (A) Idealized

displacement-time curves for the three stages of creep [6, 41, 42]. (B)-(C) Typical

sampling intervals and velocity scale analysis for satellite InSAR and in-situ sensors in

three creep phases. The landslide speeds in (c) are defined according to [48, 49], i.e.

extremely slow (<16 mm/year), very slow (1.6 m/year), slow (13 m/month) and

moderate (1.8 m/h).

1

EO based landslide early warning 2

system 3

Fig. 5 illustrates that EO can provide us 4

with unprecedented and encouraging 5

opportunities for pre-failure creep 6

monitoring. However, the different 7

technologies have their own advantages 8

and limitations as illustrated by the 9

Xinmo and Dangchuan case studies. A 10

single EO method is insufficient to 11

capture all the signals in the different 12

creep stages, and hence multiple EO 13

technologies should be combined to 14

develop landslide EWS. Fig. 6 shows 15

the framework of an operational 16

landslide early warning system relying 17

on an optimal combination of these EO 18

technologies.19

Fig. 6. EO based landslide early warning system. (A) Field investigation to

determine geomechanical response properties. (B) Simulation and assessment of

potential impact. (C) Real-time monitoring on displacement, precipitation etc. (D)

Long-term displacement rate monitoring and analysis.

Step 1. Spaceborne InSAR is 1

employed to comprehensively detect 2

active slopes (i.e. clusters of points that 3

exhibit certain deformational activity 4

[50]) to find potential landslides at a 5

regional scale. The archived and newly 6

acquired SAR images (e.g. ESA’s 7

Sentinel-1) are interferometrically 8

processed and then analysed in time 9

series. An automatic feature detection 10

algorithm (possibly relying on machine 11

learning approaches, e.g. [51, 52]) should 12

be developed to detect potential 13

landslides based on the regional 14

deformation rate maps and displacement 1

time series. Time series analysis can be 2

used to determine the sensitivity of 3

landslide motion to external factors such 4

as seasonal precipitation and seismic 5

shaking (e.g. [23, 53]). First-order 6

geomechanical modeling of landslide 7

behavior based on critical-state soil 8

mechanics or rate-and-state friction can 9

provide important insights on the 10

stability conditions of landslides (e.g. 11

[54-56]). Eventually, such 12

geomechanical analysis may allow us to 13

anticipate failure conditions prior to the 14

pronounced accelerations of the tertiary 15

phase (e.g. [57]). 16

Step 2. Assessment of potential 17

impacts of the active landslides at a local 18

scale. After the potential landslide 19

initiation hazard is identified for specific 20

locations, field investigations help assess 21

the geological setting of the landslide. A 22

landslide dynamics model (e.g. [58, 59]) 23

can be applied to predict the speed and 24

run-out extent of potential landslide 25

events. Potential landslide sites identified 26

in Step 1 can be simulated to determine 27

the likely impact on human settlements 28

for each landslide. Topographic and 29

socio-spatial data can be collated for 30

landslide modelling and impact 31

assessment. A detailed local land 32

property map, including key 33

infrastructures such as buildings, roads, 34

power lines, and a population-35

distribution map could be generated 36

based on existing open source data and 37

community participation. These will 38

support the impact assessment as well as 39

early warning communication with the 40

local community. This step also identifies 41

the sites for which real-time landslide 42

monitoring (RTLM) is required. 43

Step 3. A multi-sensor integrated 44

system is installed combining remote 45

sensing methods and in-situ sensors for 46

the specific sites where the RTLM is 47

needed. In-situ sensors can be carefully 48

located according to the landslide motion 49

information provided by InSAR so that 50

an accurate continuous monitoring in 51

time and space for all hazardous 52

landslides in a region can be achieved by 53

integrating these two systems whilst 54

minimizing the associated costs by 55

limiting the number of in-situ sensors. 56

High-rate (e.g. 1 Hz) raw in-situ 57

observations (e.g. GNSS and 58

crackmeters) can be transmitted to a data 59

centre via wireless communication 60

infrastructure, and real-time processed 61

with short baselines in a kinematic mode. 62

Recent experiments with GNSS suggest 63

~2-4 mm horizontal and 4-8 mm vertical 64

accuracy are possible at 1 Hz [60, 61]. 65

Real-time monitoring is particularly 66

important since existing observations on 67

tertiary creep suggest that the timescale 68

for this phase ranges from minutes to 69

months [44, 62, 63]. Thus the data should 70

be transmitted back to the data centre in 71

real time and processed automatically. 72

However, these in-situ observations are 73

not only useful for identifying the onset 74

of tertiary creep but can be used in the 1

secondary phase to determine the 2

sensitivity of landslide motion to external 3

factors at a higher resolution and 4

precision than was possible in stage 1 5

[23, 53]. The mechanical models 6

introduced at stage 1 can be refined and 7

calibrated through monitoring of 8

environmental factors and geological-9

geotechnical parameters such as the pore 10

pressure in soils (Table 1) [13, 64]. 11

Table 1. Commonly used technologies for landslides monitoring. Note that UAV

and TDR represent unmanned aerial vehicle and time domain reflectometry,

respectively.

Observation

Types

Technology Precision Examples

Displacement Spaceborne InSAR mm-cm [65] [21, 66, 67]

Airborne InSAR mm-cm [68] [68, 69]

Ground-based InSAR mm-cm [70] [63, 70, 71]

UAV photogrammetry ~ 6cm [72] [72, 73]

GNSS mm-cm [74] [80, 81]

Optical image matching cm-m [75] [75, 76]

Crackmeter mm-cm [77] [78, 79]

Extensometer ~3 mm [80] [81, 82]

In-place inclinometer ~8 mm [65] [10, 83, 84]

Tiltmeter ~0.1°[13] [13, 79, 87]

Total station ~±1 ppm [77] [77, 85]

Terrestrial Lidar ~0.2-0.5 m [80] [80, 86]

Shape acceleration array ±1.5 mm/30 m

[87]

[13, 81, 87]

Active waveguides Mm [88] [13, 88]

Seismometer \ [89, 90]

Pore pressure Piezometer \ [13, 91, 92]

TDR \ [93, 94]

Tensiometer (Soil

hygrometer)

\ [54, 94]

Precipitation Rain gauge \ [79, 95]

1

Step 4. Communication with local 2

communities. Providing timely and 3

useful warnings to people exposed to 4

landslide hazard is the ultimate objective 5

of an early warning system. Thus 6

engagement and communication with 1

local communities should be a key 2

feature of an effective landslide EWS. A 3

large body of work already exists on the 4

social science of early warning, 5

providing useful insights, explanations 6

for unexpected EWS failure, potential 7

secondary disasters and examples of 8

good practice. Experience from past 9

disasters worldwide suggests that 10

emergency preparedness, planning and 11

response are some of the weakest 12

elements in many existing EWSs [96]. In 13

particular, the link between the technical 14

capacity to issue a warning and the 15

public’s capacity and commitment to 16

respond effectively to the warning is 17

often weak, limiting the capacity of the 18

warning to trigger an appropriate and 19

effective response from the community. 20

Warning systems that mainly focus on 21

technical aspects and ignore social 22

factors generally do not work effectively 23

because the warnings do not prompt 24

effective action due to lack of community 25

buy-in, which results in poor engagement 26

and operation. There appears to be fairly 27

widespread consensus among both 28

academics and practitioners that EWSs 29

are most effective when they are built in 30

collaboration with those at risk rather 31

than imposed from outside. 32

33

OUTLOOK 34

The remaining three Big Questions for 35

landslide forecasting and early warning 36

are as follows: (Big Question 1) where 37

are potential landslides, (Big Question 2) 38

when will landslides occur, and (Big 39

Question 3) how to best reduce landslide 40

disaster risk. 41

Big Question 1 - where are potential 42

landslides: We are entering an exciting 43

new era of Earth Observation data, and 44

recent advances in satellite radar and in-45

situ sensors (e.g. GNSS) have allowed us 46

to collect high-quality measurements to 47

quantify the Earth's surface 48

displacements and then address Big 49

Question 1 over entire mountain ranges at 50

space and time scales that are finer than 51

ever before and at relatively low cost. In 52

the EO based landslide early warning 53

system, the relatively short repeat cycles 54

of current SAR missions still represent a 55

limitation of InSAR to detect potential 56

landslides, but the Geosynchronous - 57

Continental Land-Atmosphere Sensing 58

System (G-CLASS), one of the three 59

Earth Explorer ideas that have been 60

accepted by ESA’s Programme Board for 61

Earth Observation to compete as the tenth 62

Earth Explorer mission, might provide a 63

solution. Considerable work has been 64

done to interferometrically process 65

massive SAR data sets in an automatic 66

way (e.g. [97]), but more should be done 67

to investigate how to detect potential 68

landslides from big SAR data in a 69

consistent, reliable and smart manner. 70

Machine learning technologies have been 71

widely implemented in the field of 72

computer science and remote sensing 73

[98-99], where statistical techniques are 1

employed to learn specific and complex 2

tasks from given data. Recent studies 3

report that machine learning has the 4

capability to identify signals associated 5

with geohazards from large data sets (e.g. 6

[100]), suggesting that the integration of 7

machine learning with EO technologies 8

might be one encouraging solution to 9

automatic landslide detection. To address 10

Big Question 1, there is an urgent need to 11

answer the following: (i) at what 12

percentage are the detected landslides 13

true positives? (ii) what is the percentage 14

of the missing landslides (false 15

negatives)? and (iii) in which scenarios 16

are the landslides more likely be 17

successfully detected? 18

Big Question 2 - when will landslides 19

occur: A range of state-of-the-art 20

landslide initiation and runout models 21

have enabled us not only to estimate the 22

location and geometry of potential 23

landslides, but also to assess their 24

potential impacts. 25

It remains a grand challenge to 26

predict when landslides will occur. There 27

have been a limited number of successful 28

case studies including the 2017 29

Heifangtai landslide. In these cases, 30

deformation anomalies (acceleration 31

and/or change in pattern) observed prior 32

to failure have been recongnised as 33

‘precursors’. However, accurate EWSs 34

require the identification of a diagnostic 35

signature that can be somewhat uniquely 36

related to impending failure. The degree 37

to which this signature is unique, defines 38

the confidence with which a warning can 39

be issued, which represents a much 40

stricter definition of ‘precursor’. Further 41

research is required to constrain the 42

relationship between accelerated 43

displacement and landslide failure and 44

thus to establish these diagnostic 45

signatures with more confidence. We 46

suggest that widespread and long-term 47

deformation monitoring combined with 48

landslide observations will enable 49

considerable progress on this problem. 50

Big Question 3 - how to best reduce 51

landslide disaster risk: The experience of 52

the cooperation between experts and 53

local communities in Dangchuan 4# 54

landslide has improved our 55

understanding of best practices for 56

Community-Based Disaster Risk 57

Management (CBDRM). How to best 58

coproduce a site specific warning system 59

with both local experts and with members 60

of at-risk communities to reduce 61

landslide disaster risk remains an open 62

challenge for the whole community. 63

64

Acknowledgments - Funding: This 65

work was supported by National Natural 66

Science Foundation of China (NO. 67

41801391), the National Science Fund 68

for Outstanding Young Scholars of China 69

(Grant No. 41622206), the Fund for 70

International Cooperation (NSFC-71

RCUK_NERC), Resilience to 72

Earthquake-induced landslide risk in 73

China (Grant No. 41661134010), the 1

open fund of State Key Laboratory of 2

Geodesy and Earth’s Dynamics 3

(SKLGED2018-5-3-E), the Spanish 4

Ministry of Economy and 5

Competitiveness (MINECO), the State 6

Agency of Research (AEI) and the 7

European Funds for Regional 8

Development (FEDER) under projects 9

TEC2017-85244-C2-1-P and TIN2014-10

55413-C2-2-P, and the Spanish Ministry 11

of Education, Culture and Sport under 12

project PRX17/00439. This work was 13

also partially supported by the UK NERC 14

through the Centre for the Observation 15

and Modelling of Earthquakes, 16

Volcanoes and Tectonics (COMET, ref.: 17

come30001) and the LICS and 18

CEDRRIC projects (ref. NE/K010794/1 19

and NE/N012151/1, respectively), and by 20

European Space Agency through the 21

ESA-MOST DRAGON-4 project (ref. 22

32244). RB acknowledges support by the 23

NASA Earth Surface and Interior focus 24

area (ESI). 25

Author Contributions: Keren Dai, 26

Zhenhong Li, Roland Burgmann, David 27

Milledge and Roberto Tomás contributed 28

to the writing of the paper. Keren Dai, 29

Zhenhong Li, Zheng Wang, Tengteng 30

Qu, Chaoying Zhao and Xiaojie Liu 31

carried out the SAR data processing, 32

related experiment and analysis. Qiang 33

Xu, Xuanmei Fan and Roberto Tomás 34

contributed to the geological analysis and 35

result interpretation. Chaoyang He and 36

Qiang Xu collected and analysed the 37

crackmeter data for landslide warning in 38

Heifangtai loess landslide. Zhenhong Li, 39

Qiang Xu, Keren Dai, and Jianbing Peng 40

came up with the concept of the EO-41

based landslide EWS proposed in this 42

paper with contributions from David 43

Milledge, Roland Burgmann, Qin Zhang, 44

Deren Li and Jingnan Liu. 45

Data and materials availability: All 46

relevant data are available from the 47

authors upon reasonable request. 48

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