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ESA Alcantara No. 5: InSAR and Landslides in Peru

Contribution of Earth Observation to

landslide early warning

White Paper

University of Zurich / Gamma Remote Sensing / National Water Authority, Peru / Czech Academy of

Sciences

Alcantara Study Reference No.: 15/P25

Study Type: Pilot Study

Contract Number: 4000117655/16/F/MOS

This contract was carried out within ESA’s General Studies Programme and funded by the European

Space Agency. The view expressed herein can in no way be taken to reflect the official opinion of the

European Space Agency

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Authors:

Christian Huggel1, Holger Frey1, Jan Klimeš2, Tazio Strozzi3, Rafael Caduff3, Alejo Cochachin4

1Department of Geography, University of Zurich, Winterthurerstrasse 190,

8057 Zurich, Switzerland 2Institute of Rock Structure and Mechanics, Academy of Sciences, V Holešovičkách 41,

Prague 8 182 09, Czech Republic 3Gamma Remote Sensing, Worbstrasse 225, 3073 Gümligen, Switzerland 4Unidad de Glaciologia y Recursos Hidricos (UGRH), National Water Authority (ANA), Av. Confraternidad

Internacional Oeste 167, Independencia – Huaraz, Peru

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Table of Contents

0 Scope and context of this document .......................................................................................................... 4

1 Early warning systems ................................................................................................................................ 5

1.1 International EWS standards ............................................................................................................... 5

1.2 Landslide monitoring and landslide EWS – state of knowledge ......................................................... 5

2 Available and potentially feasible EO systems ......................................................................................... 10

3 Understanding of systems and risks ......................................................................................................... 14

3.1 Landslide hazard analysis .................................................................................................................. 14

3.2 Exposure and vulnerability analysis .................................................................................................. 17

3.3 Past events and impacts .................................................................................................................... 18

3.4 Generation of landslide hazard and risk maps .................................................................................. 20

4 Monitoring and warning service .............................................................................................................. 22

4.1 Landslide precursor processes and parameters ................................................................................ 22

4.2 Landslide trigger and flow processes and parameters ...................................................................... 24

5 Landslide EWS design and operation ....................................................................................................... 25

5.1 Local landslide EWS ........................................................................................................................... 25

5.2 Regional landslide EWS ..................................................................................................................... 28

6 Perspectives and recommendations ........................................................................................................ 33

References ................................................................................................................................................... 34

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0 Scope and context of this document

This White Paper has been developed within the framework of the Alcantara initiative of the European

Space Agency (ESA) as part of the project ‘Integrating EO information for cascading landslide and flood

hazard monitoring and early warning in the Cordillera Blanca, Peru’ (Contract No. 4000117655/16/F/

MOS). This Alcantara project was preceded by S:GLA:MO (Service for Glacial Lake Monitoring, 2014-

2016), a service for the assessment of GLOF-related hazards based on EO derived products, in-situ data

and information, and modelling results, developed in the framework of an ESA project (ECO-PROJ-EOPS-

MM-16-0033). The service was developed in close collaboration with local users and investigated critical

glacial lakes in Greenland, Tajikistan, Switzerland, and Peru. The analyses of the InSAR products for the

Peruvian cases revealed a series of interesting potential applications in the region, such as different

landslide processes at lower elevation bands (cf. Frey et al. 2016). This project demonstrated the basic

potential of EO information, in particular InSAR products, for the detection and analysis of different

types of terrain motions in support of first-order GLOF hazard assessments and as input information for

mass movement modeling for the generation of GLOF hazard maps. These achievements of the

predecessor project have been applied and put in value in the present project.

The aim of the present Alcantara project is to develop and test the potential of Earth Observation (EO) –

mainly InSAR – in landslide monitoring, landslide and flood hazard mapping, and Early Warning Systems

(EWS). First, the potential of InSAR for the detection and interpretation of different types of terrain

motions was evaluated by comparing InSAR data products to landslide inventories based on optical EO

information and geomorphological mapping. Then, InSAR derived slope deformations were compared to

geotechnical model results and ground-based survey data. For a high mountain test site, different

aspects of a landslide monitoring system have been evaluated and tested, combining EO and in-situ

information, including advanced Structure from Motion (SfM) analyses. Information from InSAR analysis,

geotechnical modeling, and on-site investigations have been used for supporting numerical mass flow

modelling in view of GLOF hazard assessments and mapping. Finally, an evaluation of the potential of the

integration of EO information in landslide EWS is a major goal of this projects.

Over the course of the S:GLA:MO and Alcantara project activities and other studies and long-term

expertise of the project consortium it has become evident that (i) much research and efforts have been

dedicated to developing EO based methods to monitor landslide processes, and (ii) a very limited

amount of experience and expertise exists with respect to integration of EO related information into

operational landslide early warning systems (EWS) although many studies emphasize the potential for it.

In line with this, virtually no official documents nor research papers exist to guide the integration of EO

information into operational landslide EWS. Such guiding documents would, however, be extremely

useful and important to set EO information into specific value for landslide EWS, in particular also in view

of the international policy agenda, e.g. the Sendai Framework for Disaster Risk Reduction (UN, 2015) and

the high priority of EWS in these strategies and policies.

Therefore, within the framework of the Alcantara initiative, we address this major gap and undertake an

effort to develop a first White Paper that lays out basic aspects of EO based information for landslide

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EWS. By clarifying terms and concepts, and structuring techniques, methods and processes it provides a

first basis to guide future initiatives towards designing landslide EWS that implement EO information. To

allow for a relatively wide application the document has a rather generic character but repeatedly makes

reference to specific cases for illustration.

1 Early warning systems

1.1 International EWS standards

Early warning systems have become an increasingly important tool for disaster risk reduction. They have

been developed for a number of different hazards such as tsunamis, volcanoes, floods, avalanches, or

landslides.

The United Nations International Strategy for Disaster Reduction (ISDR) has defined international EWS

standards (UN 2006). Accordingly four different components of an EWS are distinguished:

1) Understanding of system and risks

2) Monitoring and warning service

3) Communication

4) Response

An appropriate EWS needs to include all four components, and is therefore a highly complex system.

It may be useful to further distinguish technical, institutional and social aspects of an EWS. EO primarily

contributes to technical aspects, however, EO may contribute to all four components of an EWS even

though its greatest potential is for components 1) and 2).

We do not distinguish here between warning, alarm and alert systems as it is done in some other studies

(e.g. Sättele et al. 2016, Stähli et al. 2015).

1.2 Landslide monitoring and landslide EWS – state of knowledge

Landslide terminology

The term landslide encompasses a rather broad range of slope deformation and mass movement

processes of the Earth’s surface. Many different disciplines are involved in landslide research and

practice and therefore the definition of landslide can vary quite substantially. A landslide can be defined

as a downslope movement of rock, soil or both, along a rupture which can have different characteristics

(e.g. rotational or translational slides) (Highland and Brobowsky, 2008). Several movement types can be

distinguished: fall, topple, slide, spread or flow. Landslide processes may include more than one type of

movement, e.g. slides that evolve into flows. Classical landslide type classifications can be found in

Varnes (1978), Cruden and Varnes (1996) and more recent updates such as from Hungr et al. (2014).

Here we use the term landslide in a broad sense that encompasses various types of slope deformation

and mass flow processes.

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Landslide monitoring

There exists an extensive literature on landslide monitoring methods and systems, and the scientific and

practitioner communities are generally well aware of these. We broadly categorize them here as follows:

Topographic and geodetic methods

Techniques focusing on monitoring surface and subsurface characteristics

Methods monitoring meteorological parameters.

In addition there are specific methods for detecting landslide flow (slow or fast flowing) such as

geophones, seismometers, radar, video cameras or other sensors detecting landslide flow (see also Table

1).

Several key aspects need to be considered and defined for landslide monitoring:

Scale: landslide monitoring ranges from local to regional scale, i.e. from single landslides to

multiple landslides. It may be point based or may integrate information of larger areas.

Surface/sub-surface: monitoring techniques can be dedicated to measuring parameters at the

Earth surface (e.g. slope deformation at the surface, precipitations, vegetation effects on water

infiltration) or at the sub-surface (e.g. slope deformation at depth, water saturation).

Measured parameters: the range of measured parameters is wide and includes surface and sub-

surface deformation, properties of the material (such as rock, soil, ice), meteorological

parameters (rainfall, temperature, etc), hydrological or hydrogeological parameters.

Table 1 provides an overview of some landslide relevant parameters and corresponding techniques to

measure them.

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Table 1: Landslide relevant parameters observed in current landslide monitoring and early warning

systems, and corresponding technologies applied (adapted from Stähli et al., 2015).

Observed parameter Technology

Precipitation Sum, intensity Rain gauge

Precipitation radar

Snow cover Depth

Wetness

Soil moisture Water content TDR

Water suction / pressure Tensiometer

Groundwater table Piezometer

Rock /Soil surface Precursor of failure Acoustic sensors

Displacement Trigger line

Extensometer, total stations

Inclinometer

Ground-based radar interferometry

Satellite-based radar interferometry

Triggered mass movement Vibration Geophone

Seismometer

Flow surface height Radar

Flow characteristics Video

New technologies and model integration

Terrestrial laser scanning (TLS) has seen an impressive development over the past years and has found

widespread application in landslide monitoring. The potential and limitations of TLS has been reviewed

and assessed in a number of scientific studies (Jaboyedeff et al. 2010). The high precision and repeat

monitoring capabilities represent important advantages while geometry, range and access can be

limitations depending on the landslide to be monitored.

The use of unmanned aerieal vehicles (UAV), or drones, has seen an enormous rise over the past few

years in geoscience and other disciplines. The number of applications to landslide monitoring are still

somewhat limited but on the rise as well. Typically, a Structure for Motion (SfM) procedure is applied to

generate digital terrain models. Single overflights can provide important high-resolution details on

landslide surface characteristics. Repeat overflights and DEMs are able to provide information on

landslide motion (Tanteri et al. 2017). First experiences reported in the literature are largely positive,

indicating that UAV may be a more cost-effective tool than TSL to regularly monitor landslide areas

(Rossi et al. 2016), but still involves a substantial amount of human resources and field logistics. The

combination of different techniques and data is a promising approach that deserves further

development.

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In addition to satellite based SAR, ground-based SAR has also been increasingly applied for landslide

monitoring (Montserrat et al. 2014; Caduff et al. 2015). A fair number of ground based SAR systems are

available on the market and are regularly applied in landslide monitoring as an effective and high

precision (up to sub-millimeter) technique to detect slope deformation rates from safe distance.

Valuable experiences are also available from combining satellite and ground based SAR systems (Strozzi

et al. 2014, Frodella et al. 2016). Ground based SAR is potentially feasible and effective for landslide

EWS, in particular for progressive slope deformation processes eventually leading to failure (Atzeni et al.

2015). Experiences in operational EWS are nevertheless still limited or poorly documented.

The integration of slope stability, hydrological or combined types of models into landslide monitoring

and EWS activities is an important development (Thiebes and Glade 2016). Actually, only in very few

cases numerical models have been integrated into operational landslide EWS, mostly due to the

challenge and limited experience to run such models in an operational mode, limited confidence in

model results or limited utility of model results for EWS tasks. An example is the integration of a

deterministic hydrological and slope stability model (Combined Hydrology and Slope Stability Model,

CHASM) in a landslide EWS in southwestern Germany (Thiebes 2012). Another example is the La Saxe

landslide in Courmayeur in the western Alps of Italy where a rockslide of ca. 8 million m3 is unstable.

Monitored displacement rates are directly integrated into an inverse velocity model allowing for

estimating timing of potential final slope failure (Manconi and Giordan 2015). It resulted successful in

predicting partial slope failures about 10 hours before failure.

Landslide EWS

Landslide EWS have been reported from several locations around the world, especially in the western

US, central Europe and south-east Asia (Table 1, Figure 1). Probably the first operational landslide EWS

was developed and deployed in the San Francisco Bay Region as early as the 1980’s, however it has been

discontinued later. If the reported EWS are analyzed in more detail it can be recognized that many of

these systems do not fully integrate all four components of an EWS (as they were specified above), or

are not fully operational, or have been operational only for a limited amount of time (e.g. Bulmer and

Farquhar 2010). Maintaining an operational landslide EWS over extended periods of time is a challenging

task. Especially challenging are the (quasi-) real-time monitoring capabilities that are typically required

for operational EWS (Casagli et al. 2010). Experiences that incorporate EO information in operational

landslide EWS are particularly rare to date.

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Table 2: Experiences of worldwide landslide EWS (from Tiebes and Glade, 2016).

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Figure 1: Map of selected landslide EWS sites worldwide, as reported in the literature (from Stähli et al.,

2015). Note that this list is not comprehensive nor complete, see e.g. Fathani et al. 2016 or Yin et al. 2010

for additional sites of landslide EWS.

2 Available and potentially feasible EO systems

We make a basic distinction between ground-, air- and space based systems. Here we focus mainly on

space-based remote sensing systems.

We further distinguish between optical and radar systems.

The following characteristics of EO systems are relevant for landslide EWS:

Spatial resolution

Temporal resolution, i.e. revisiting time

Spectral bands/wavelength

Spatial coverage / swath

Method and time of data processing

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A recent publication (Casagli et al. 2017) compiled a list of EO system for potential use for landslide EWS,

as given in Figure 2.

Figure 2: Available EO system and their characteristics (from Casagli et al., 2017).

In the following we analyse potentially feasible EO systems and sensors for landslide EWS. This list is not

intended to be complete nor comprehensive but should provide some guidance and overview.

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Table 3: ESA EO systems feasible for landslide monitoring and EWS.

System Spatial resol. Revisiting

frequency

Wavelengths Spatial

coverage

(swath)

Operational period Observed landslide parameter

ERS-1/2 ~20m 35 days 2.8 cm 100 km 1991-2011 Slope/surface deformation

Vegetation

Land-cover/surface changes

Landslide occurrence/impacts

Envisat ~20m 35 days 2.8 cm 100 km 2002-2012

Sentinel-1 ~20m 12 days 2.8 cm 250 km 2014-

Sentinel-2 MSI 10-20m 6 days 0.46-2.3 µm 290 km 2016-

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Table 4: ESA Third-Party Missions with capacities for landslide monitoring and EWS.

System Spatial resol. Revisiting

frequency

Wavelengths Spatial

coverage

(swath)

Operational period Observed landslide parameter

JERS-1 ~20m 44 days 11.8 cm 70 km 1992-1998

Slope/surface deformation

Vegetation

Land-cover/surface changes

Landslide occurrence/impacts

ALOS-1 PALSAR-

1

~10m 46 days 11.8 cm 70 km 2007-2011

ALOS-2 PALSAR-

2

~10m 14 days 11.8 cm 70 km 2014-

Radarsat-1 ~20m 24 days 2.8 cm 100 km 1995-2013

Radarsat-2 ~5m 24 days 2.8 cm 100 km 2008-

SPOT4-7 1.5-20m 3 days 0.51-1.75

µm

60 km 1998-

Pleiades 0.5-2m 1 day 0.48-0.95

µm

20 km 2011-

IKONOS 2 0.8/3.3m 3-4 days 0.45-0.93

µm

11 km 1999-2015

QuickBird 0.66/244m 1-4 days 0.45-0.90

µm

16.5 km 2000-2015

RapidEye 6.5m 1 day 0.44-0.85

µm

77 km 2008-

WorldView-

1/2/3

0.5m 2-3 days 16.4 km 2007-

Terra SAR-X ~3m 11 days 2.8 cm 10-100 km 2008-

TanDEM-X ~3m 11 days 2.8 cm 10-100 km 2010-

Cosmo-SkyMed

1/2/3/4

~3m 1 to 16 days 2.8 cm 10-50 km 2008-

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3 Understanding of systems and risks

Developing an understanding of a landslide system is essential for any warning activity. In general, the

better a landslide system is understood the better an EWS can be designed and operated. An

appropriate understanding typically implies the analysis of observations of the past, and the EO record

can represent an essential source for this purpose. Yet, any past events and processes need to be

analysed carefully because conditioning and trigger characteristics may change over time, in particular in

highly dynamic high-mountain regions.

There are several technical aspects that are relevant and to which EO can contribute (see below sub-

sections).

A landslide EWS not only requires understanding of the landslide system but also of the associated risks.

Risk is thereby defined as a function of hazard, exposure and vulnerability (e.g. IPCC 2014). Hazard is

typically a function of the magnitude and probability of occurrence of a landslide. Exposure involves any

type of assets, from people to structures or non-economic material. Vulnerability refers to different

dimensions of the assets of objects at risk, such as their physical resistance against landslide processes,

or their degree of preparedness, capacity to recover etc. for people. EO typically contributes to

understanding of the hazard component of risk but may also be highly valuable to assess exposure.

3.1 Landslide hazard analysis

Landslide inventories

A primary purpose of landslide inventories is the identification of landslide occurrence in space

and time. Ideally, the inventory also provides information on landslide type and processes.

EO has been extensively used for, and contributed to compiling landslide inventories. Both

optical and radar technologies are relevant.

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Figure 3: An InSAR-based slope-instability inventory on the eastern slope of the Santa River near Carhuaz,

Peru (for location see insert in a). a) ALOS-1 PALSAR-1 PSI for the time period 2007-2011, b) ALOS-1

PALSAR-1 DinSAR of the time period 2007.07.12-2007.08.27, c) ALOS-1 PALSAR-1 DinSAR of the time

period 2009.07.17-2009.09.01, d) ALOS-2 PALSAR-2 DinSAR of the time period 2016.02.21-2016.10.02.

From Stozzi et al., (in prep.).

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Identification of stable slopes

The identification of stable slopes is not necessarily a typical task for a landslide EWS but it is

considered here important because it allows the responsible authorities to identify reasonably

safe locations.

Figure 4: Persistent Scatter Interferometry (PSI) in the surrounding of Lake 513 (red arrow), Cordillera

Blanca, Peru, based on 19 ALOS PALSAR scenes from January 2007 until March 2011. No terrain

deformations are observed in the surrounding of the lake outside the glacierized area. Courtesy of the

S:GLA:MO Project (ESRIN/RFQ/3-13731/12/I-BG).

Surface deformation and landslide process understanding

The magnitude and rate of surface deformation is an important parameter for characterization

and understanding of landslide types and processes, and hazard. Information on slope

deformation in 2.5 to 3 dimensions can be particularly useful to identify and understand

processes and extent of landslides.

Repeat EO data analysis, both optical and radar, can be applied to this purpose.

This information provides an important link to causal factors of landslide activity, i.e. EO data

crossing with hydro-meteorological data, other possibly anthropogenic factors (e.g. undercutting

of slopes).

Glacier outlines RGI

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This type of information can also be used to support landslide dynamic models (including mass

flow models). EO derived digital elevation data (DEM) can be of important value for numerical

models that help assessing the landslide hazard, including also timing. As such they can be

integrated into EWS frameworks as specified above.

Figure 5: InSAR slope deformation profiles and glacier ice height changes at the Moosfluh slope above the

terminus of Aletsch Glacier (Switzerland). Annual displacements based on satellite InSAR ERS1/2, JERS,

Envisat, and TerraSAR-X (TSX) for the period 1992 to 2012. Glacier height change for the years 1926,

1957, 1980, 1999, 2005, 2011, and 2015 from digital elevation data. Rock slope failure events at the

landslide toe are shown on top, with respect to the number of events and their estimated volume (from

Kos et al. 2016).

3.2 Exposure and vulnerability analysis

The exposure of people and assets is a key element to determine existing landslide risks.

In rapidly developing and changing areas (e.g. urban areas in developing countries) EO can be

the only means to achieve an up-to-date assessment of exposed assets.

Optical satellite imagery, in particular high-resolution data, is particularly well suited for this

purpose.

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The first-order analysis of physical vulnerability of structures based on high-resolution EO data.

Figure 6: Tsunami exposure map of the Khuek Kak Tambon in the in the Phang-Nga province, Thailand,

based on IKONOS imagery. Römer et al. (2012).

3.3 Past events and impacts

Past landslide events and related impacts represent important evidence to advance the

corresponding understanding.

Numerous landslide studies have made use of EO data to analyse past events and impacts of

landslides. EO has become an invaluable source of information both for research and risk

management practice.

Both radar and optical EO can be very useful to analyse past events and impacts, depending on

the characteristics of the site, weather conditions and purpose of the study.

An important recent case study where past event analysis and early warning has been combined

is described in Figure 7.

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Figure 7: In 2016 two exceptional landslide events occurred in Tibet. Two glaciers collapsed in the Aru Co

region, one in July 2016, the other one in September 2016. The glacier collapses produced enormous

avalanches on the order of 60-80 million m3 that killed 9 herders and hundreds of animals. These events

have then been studied in detail by various types of EO data, including Sentinel-1 and Sentinel-2 data by a

international team of experts under the umbrella of GAPHAZ (the Glacier and Permafros t Hazards in

Mountains), a Scientific Standing Group of the International Association of Cryospheric Sciences (IACS)

and the International Permafrost Association (IPA). The four figure panels show the situation before, after

the first and after the second avalanche, plus a simulation of an avalanche model. While analyzing the

first glacier collapse and avalanche the international team recognized similar patterns of instability at the

neighbouring glacier and alerted them about a possible upcoming second glacier collapse. In an ad-hoc

coordinated way, facilitated through GAPHAZ, the information was used to alert Chinese colleagues, who

then informed the local government. The fact that remote observations based on a large number of very

different satellite data could be carried out with a delay of only few hours or a day between data

acquisition from space and analyses on the ground, involving scientists and satellite teams from several

nations, in fact is substantial progress for early warning capabilities related to natural hazards in remote

regions (modified from GAPHAZ 2016).

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3.4 Generation of landslide hazard and risk maps

The identification and selection of EWS sites should rely on knowledge of hazard and risks.

Accordingly, the first EWS component consists of the understanding of hazard and risks, and

ideally should produce hazard and risks maps. However, this is not yet common standard in

many regions of the world.

Generation of hazard and risk maps typically relies on numerical landslide (mass flow) models, in

combination with field work and EO data.

The above aspects all contribute to hazard and risk maps. Depending on the national guideline (if

existing) a hazard map may imply the definition of different hazard scenarios and EO may

contribute to define these scenarios which are then typically modeled to assess the impacts of a

landslide.

As a next step, the EWS component on understanding the system and risks could be used to

update hazard and risk maps if already existing.

Figure 8: Geomorphodynamic analysis of glacier changes and dynamics in the south face of Mount

Hualcán, Cordillera Blanca, Peru, based on a high-resolution Pléiades (optical) scene. This analysis was

used for the definition of ice avalanche scenarios and subsequent modeling of the process chain involved

in outburst scenarios of Lake 513, located below this face (cf. lower left corner of the figure). Schaub et al.

(2016).

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Figure 9: Flow heights of the ice avalanche and glacier lake outburst flow (GLOF) of the 2010 outburst of

Lake 513, Peru, as modelled with the avalanche model RAMMS. For such modelling, the quality of the

DEM is fundamental, in this case a DEM of 8m spatial resolution (shown in the background) was derived

from 2012 (optical) WorldView imagery. Based on such model results, the final hazard map for the

catchment and the city of Carhuaz (located at the left margin of the figure) was produced (cf. Schneider

et al. 2014).

Figure 10: Hazard map for the city of Carhuaz, Peru, visualized over a high-resolution satellite image. The

map relates to hazards from ice and rock avalanches and glacier lake outburst flows (GLOF) from Lake

513 (as above) and distinguishes five levels of hazards. For more details refer to Schneider et al. (2014)

(Glaciares project / University of Zurich).

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4 Monitoring and warning service

The EO contribution to the central EWS component of monitoring and warning service depends on the

landslide type and process. In terms of deformation and movement speed the following main landslide

types are distinguished for the purpose of defining EO contributions:

Slow-moving landslides (mm to cm per year). These landslide types often imply deep-seated

slope deformation (meters to decameters deep), but also shallow landslide types can be of slow-

moving nature (e.g. in bedrock or soil, including in frozen state (permafrost), or glacier ice). The

landslide can originate in different types of material, including bedrock, soil, or any type of

sedimentary material. In high mountains landslides involving glacier ice are common but it needs

to be recognized that glaciers per nature are moving according to the material properties of ice

and surface inclination. Glacier speed can vary across orders of magnitude but typically their

range is in the order of 100 to 102 m/year. In sedimentary and bedrock material causes for

landslide motion are often related to geologic structure, variations in water content, or

topography changes such as when glacier recede and stress fields change within the affected

landslide mass, erosional processes, or seismic shaking. EO is ideally suited to monitor slow-

moving landslides.

Fast-moving landslides and mass flows (meters per second). Such landslides include a range of

different processes with different terminology, including debris flows, debris, rock or ice

avalanches, etc. Fast-moving landslides typically involve varying degrees of solid and liquid

contents. The liquid content and the material properties of the solid content (lithology, grain size

distribution) determine the flow behavior. Due to the rapid nature of this type of landslides

there is limited possibility for EO to detect the landslide flow. However, such processes are

typically preceded by slow-moving deformation and/or precursory events such as smaller rock

fall processes. EO may thus well support the early recognition of these processes.

Combinations of slow and fast-moving landslides. It is not uncommon that combinations of

slow and fast-moving landslides are observed, e.g. slow-moving landslides that at some point

involve complete failure and disintegration of the landslide mass, thus resulting in rock or debris

avalanches.

4.1 Landslide precursor processes and parameters

Monitoring of landslide precursor processes and parameters provides an important opportunity for EO,

essentially because the relevant time periods involved (weeks to months or years) correspond to typical

revisit frequency of most EO systems. We distinguish here the following main processes and parameters:

Slope deformation rate

The monitoring and measurement of slope deformation over time is one of the central elements of a

landslide EWS. In fact, a transient history of landslide and slope deformation processes is especially

useful and important to better understand the dynamics of the landslide beyond categories of slow and

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fast moving landslides (see also section 3.1). For instance, correlation of slope deformation rates with

potential driving factors such as rainfall, snow melt or mechanical redistribution of stress due to glacier

downwasting, provides important insights to forecast and alert when landslide processes are increasing

and may become hazardous.

Figure 11: Acceleration of slope deformations at the Moosfluh landslide above Aletsch Glacier,

Switzerland, as observed by InSAR from different Sensors, dGPS and Aerial Digital Photogrammetry (ADP)

(Strozzi et al. 2010).

Antecedent precipitation and soil moisture

Antecedent precipitation belongs to the important landslide trigger parameters that are often monitored

for different types of EWS. Typically information is derived from meteorological stations in the vicinity of

the site of interest (Huggel et al. 2010). In high mountain areas the density of meteorological stations is

low, and the complex topography additionally introduces challenges and uncertainties (Salzmann et al.

2014). EO based monitoring of precipitation has the potential to support pre-event analysis of

cumulative precipitation. There exist a good number of studies that analyzed data from the Tropical

Rainfall Measurement Mission (TRMM) that later was transformed into the NASA Global Precipitation

Measurement (GPM) Missions. The scale of analysis is usually regional, continental or global (Hong and

Adler 2007, Kirschbaum et al, 2010). In Java Island, Indonesia, a prototype landslide EWS has been

developed that integrates TRMM precipitation data. This prototype system is furthermore based on

landslide inventory information, weather forecasts and a physically based rainfall induced landslide

model (SLIDE) (Liao et al. 2010).

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Soil moisture is a similarly important parameter for landslide monitoring and early warning as it

determines, or relates to several slope stability parameters. It is typically monitored using ground based

instrumentation. Soil moisture monitoring from EO is challenging, in particular in mountain terrain with

complex topography. The ESA SMOS satellite (Soil Moisture and Ocean Salinity) is a targeted instrument

for this purpose but its potential for landslide application is very limited due to the coarse spatial

resolution and additional topography related issues.

4.2 Landslide trigger and flow processes and parameters

In contrast to landslide precursor processes landslide triggers pose a much higher challenge to EO based

monitoring. An important reason are the short time scales involved. For instance, a typical landslide

triggering rainstorm may take a couple of hours and will thus go unnoticed by virtually any EO system.

Earthquakes are acting on even shorter time scales. Relevant time scales thus may range from seconds

to minutes and hours. In addition to the times scales the detection of the relevant physical processes

may equally pose important challenges to EO system, such as reasonably accurate (in time, space and

magnitude) rainfall detection.

Precipitation

Precipitation, typically rainstorms, is among the most common triggers of landslides, especially fast-

moving landslides such as debris flows. Precipitation intensity and duration are key parameters that are

monitored and used for early warning. Rainfall is commonly monitored by ground based meteorological

stations, i.e. rain gauges, and/or ground based radar instruments. Possibilities for (quasi-) real-time EO

based monitoring are limited at the current state of technology but ex-post EO based analyses of rainfall

duration and intensity (e.g. using TRMM or GPM) can be very helpful to determine critical landslide

triggering rainfall thresholds.

Earthquakes

Earthquakes, depending on magnitude and energy direction and dissipation, have triggered hundreds to

thousands of landslides in single events. The potential of EO for detecting earthquake triggers for the

purpose of early warning is not feasible. However, as for precipitation mentioned above, ex-post analysis

of earthquake impacts on landslide processes, distribution and characteristics may be very useful and

able to inform early warning research as demonstrated in several studies (e.g. Kargel et al. 2016).

Landslide flow processes

For slow-moving landslides EO is able to provide important process related information serving early

warning purposes. For fast-moving landslides involved time scales are too short but EO based analysis of

past landslides can be important to support early warning (see also section 3.3). There exists a large

25

number of studies that used EO data to analyze landslide flow parameters such as landslide flow

geometries (width, length, runout, drop height to runout ratio, etc.), landslide flow depth, landslide

speed related parameters (e.g. curve superelevations), material properties and others. Both high-

resolution optical EO data as well as SAR data can be valuable, depending on the context and objectives.

5 Landslide EWS design and operation

5.1 Local landslide EWS

Landslide EWS termed local here are typically concerned with single (or some few) slopes, with single

catchments with clearly defined sites and structures potentially affected. The following aspects should

be considered:

The design of a local landslide EWS involves the identification/monitoring of:

1. Precursor activity: permanent or repeated monitoring (slope deformation)

2. Triggers: meteo data, landslides impacting water bodies (camera, geophone,

extensometers, trigger line, level/pressure sensor)

3. Ongoing mass flows (in case of fast landslides): cable based sensors, level/pressure

sensors, camera, geophone, rip cords)

For local level EWS it is particularly important that the design and implementation is done in

concert with all actors, from responsible authorities and institutions to affected people. Cultural

and social aspects, depending on the country/region of the EWS, can be important.

All EWS components need to be designed in an integrative manner.

For the technical components initial warning thresholds can be defined based on past events,

experiences and physical/mathematical process understanding. A calibration and validation

phase of the warning thresholds, and the EWS in general, is indispensable and may take a year or

more. This needs to be clearly discussed with local authorities and affected people.

26

Figure 12: Basic concept of a design of a landslide EWS, and more specifically the development of

warning levels and thresholds. The potential contribution of EO is highlighted.

For the communication and warning component of an EWS clear structures, processes and procedures

are crucial. In practice a protocol is commonly developed which indicates the chain of responsibilities

and decisions on an escalating scale across different levels of warning. The role and responsibilities of

different bodies and institutions needs to be clearly defined and responsible people train on it. Issues of

fluctuations of personnel and institutional instability need to be considered and addressed in order to

ensure long-term sustainability and operability of the EWS. Figure 13 shows an example of a protocol for

a glacier lake outburst flood EWS in Carhuaz, Peru.

27

Figure 13: Example of a EWS protocol for a glacier lake outburst flood EWS in Carhuaz, Peru (developed

by Crealp and University of Zurich within Proyecto Glaciares).

Furthermore, it is worth mentioning that the experiences of an increasing number of EWS now have

started to result in more standardized applications. Specifically, the wide application of EWS at the

community scale in SW Asia resulted in an effort for standardization of the methodology and approach

for community-based EWS. The prepared standard contains the concept of people-centered EWS as

defined by UN-ISDR and will be developed by ISO/TC 292 Security and Resilience, with the participation

28

of 43 countries in the committee’s work and another 14 countries as observers (Fathani et al. 2017). The

standard contains seven subsystems:

1. Risk assessment;

2. Dissemination and communication;

3. Establishment of disaster preparedness and response teams;

4. Development of evacuation routes and maps;

5. Development of standard operating procedures;

6. Monitoring, early warning, and evacuation drills;

7. Commitment of the local government and community to the operation and maintenance of the whole

system.

The last point is crucial to ensure long-term sustainability and operation of the EWS as the community

understanding and acceptance will not only have direct effect on the system functionality but will affect

community response to issued warnings thus efficiency of the system.

5.2 Regional landslide EWS

Regional landslide EWS are understood as systems that cover larger areas, .e.g. multiple catchments,

provinces, i.e. operate on a national or sub-national scale (exceptionally also over several countries). The

following aspects need to be considered for landslide EWS over regional scales:

For regional landslide EWS all four components of the EWS are designed, and operated in a

different way than local level EWS. The level of detail, accuracy and precision is typically less

than for a local landslide EWS.

The first EWS component of understanding the risks may be addressed by analyzing GIS based

landslide susceptibility, combined with population and infrastructure data. EO data, both optical

and SAR can substantially contribute to this analysis by providing area-wide information on slope

deformation, landslide reconnaissance, vegetation, soil and surface parameters and exposed

assets.

The second EWS component (monitoring and warning service) can, for instance, be developed by

combining GIS service providing basic landslide susceptibility information with a network of real-

time meteorological rainfall information, potentially also soil moisture information, and possibly

weather forecasts. Additionally, so-called climate services, i.e. easily accessible weather data and

weather forecasts, seasonal forecasts, warnings of severe weather, agrometeorological

information, are currently fostered and provided by different international organizations and

have a potential for being integrated in regional landslide EWS. As for local landslide EWS

warning thresholds needs to be carefully defined and calibrated and validated during a test

phase. EO information can be an important source for establishing the thresholds and for (ex-

post) validation (e.g. with InSAR analysis). Integrating EO information into the operational

29

aspects of threshold definition and changes is challenging and depends, among other, on the

duration of the respective warning levels and the time for EO data availability and processing.

Corresponding experiences are hardly available so far. This important potential, not yet

sufficiently exploited is emphasized by the value of integrating EO information into landslide

EWS to provide additional robustness to the EWS (not only for regional, also for local EWS). EO

technology is capable of supplying necessary input data regardless of ground conditions which

may possibly destroy or temporarily disable field monitoring and management systems e.g. due

to energy shortage, damage caused by earthquakes or human interference. The fact that the EO

data may be processed elsewhere without necessity to access local or regional monitoring or

EWS networks can in some cases represent a significant advantage as well.

The third and fourth EWS components (communication and response) for a regional landslide

EWS operate more on an institutional level involving, for instance, authorities and responsible

institution (such as Civil Defense) for the respective scale of the EWS (e.g. national or provincial

scale). These institutions are then responsible to take the necessary actions on a more local level

by communicating the warning level to local disaster prevention and emergency bodies.

At the current state of technology and research existing regional landslide EWS have the

character of forecasting systems rather than fully developed EWS.

In the following we present a few examples of existing regional landslide EWS, specifically from Colombia

and Japan, both countries that are heavily affected by landslide processes and disasters (Figures 14-16).

30

Figure 14: Example of a landslide alert and forecast bulletin for Colombia, based on a regional scale type

EWS operated by the national meteorological and hydrological service (IDEAM).

31

Figure 15: Example of an online, real-time, landslide alert and forecast for Colombia, based on a regional

scale type EWS operated by the national meteorological and hydrological service (IDEAM). Colors refer to

different warning levels. See also http://www.ideam.gov.co/web/pronosticos-y-alertas/alertas.

32

Figure 16: An example of landslide EWS providing warning levels based on 60-min cumulative rainfall and

calculated soil-water index operational from 2007 (Osani et al. 2010, see also

https://www.jma.go.jp/en/doshamesh/).

33

6 Perspectives and recommendations

The potential of EO for (operational) landslide EWS is currently not sufficiently exploited.

The great majority of research focuses on monitoring aspects of EO for landslides, and very little

has so far actually been concerned with early warning issues. Similarly, there are limited

experiences yet when it comes to integration of EO into operational landslide EWS. One of the

reasons includes constraints concerning delayed availability of EO information and complex and

time-consuming processing procedures necessary to analyze the EO data for their use in the EWS

applications.

A co-design of the EWS with local people and authorities is essential for the longer term success

of the system.

Repeat periods, data availability (time lag and costs), preprocessing, and characteristics of EO

data are crucial parameters for an operational integration into EWS.

Design, implementation and calibration of the EWS needs time. In particular the calibration

phase is critical and needs to be well discussed with responsible authorities and affected people

to avoid false expectations.

Continuous maintenance of the EWS needs to be considered and planned (including in terms of

budget) from the beginning. An important advantage of EO based landslide EWS is that it is

largely independent from structures and instruments operating on the ground which are

susceptible to malfunction or damage.

In-depth training of the local operating people is an important element of success of an EWS.

Overall, in view of the high potential of EO information for landslide EWS, contrasted with the

currently limited use in operational EWS, it is highly recommended to strengthen the efforts

beyond EO based landslide monitoring to the integration in operational EWS.

Pilot studies and cases can be a feasible means to test EO information in operational EWS, gain

further real-world experience, and demonstrate the EO capabilities to a wide range of

stakeholders, authorities and institutions.

This White Paper is the first of its kind and is expected to support and guide future initiatives on

EO integration into landslide EWS.

34

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