MODUS A Multi-sensOr approach to characterize ground … · 2018-09-17 · Antoine Dille1,...

Antoine Dille1, François Kervyn1, Dominique Derauw2, Jean-Philippe Malet3 and Olivier Dewitte1

1 Royal Museum for Central Africa2 Centre Spatial de Liège

1 Ecole et Observatoire des Sciences de la Terre, Strasbourg

FEDERAL SCIENCE POLICY

MODUS

A Multi-sensOr approach to characterize ground Displacements in Urban Sprawling contexts

ECGS

Context

Population density (source: Afripop)

• Landslide-prone region

• Natural predisposition

factors

• Combination of triggering

factors

• Human pressure

• Data poor

InSAR in tropical conditions?

2002 Nyiragongo InSAR signal?

Can we improve GVO Monitoring capacity ?



More SAR data neededNeed ground control


Geological study

ERS

GORISK = User needsGVO, UGR, IPS

Passive degassing




Tiltmetry GPS


Levelling

Goma map

Geological study

ERS

ENVISAT, RADARSAT

SRTM, IKONOSLANDSAT, SPOT

Volcano. map


Impact on health

Need for decisionsupporting tools

Need for regional approach

Need for Holistic approach

2002 eruptionmodeledAtmospheric

artifacts

Mazuku

GPS Net./Proc.development

GORISKData

transmission

Technical limitations

Poor/No data

Poor/No data

Data transmission

Passive degassing




Tiltmetry GPS


Levelling

Goma map

Geological study

ERS

ENVISAT, RADARSAT

TERRASAR


Volcano. map


Impact on health




GeoRisCA = Assessing the Risk

Vulnerability assessment

Poor/No data

Eruptive signalsdetected

InSAR Time series

RS + FieldPopulation data

Distribution maps

Vulnerability maps Risk maps

Hazards assessment

Susceptibility maps

Landslidesinventory

Historical seismicdata catalogue

Volcano structural analysis


artifacts

Mazuku

Multi hazards

DRR institutionmapping


GVO seismicNetw. weak

MSBASdevelopment

DRR institutionnetworking

GeoRisCA

GORISKTechnical limitations

Poor/No data

Poor/No data

Passive degassing




Tiltmetry GPS


Levelling

Goma map

Geological study

ERS

ENVISAT, RADARSAT

TERRASAR


Volcano. map


Impact on health






Poor/No data

Eeruptive signalsdetected

InSAR Time series

SENTINEL-1

(SBInSAR)MSBAS

Time SeriesRS + Field

Population data

Distribution maps


Hazards assessment

Susceptibility maps

Landslidesinventory



2002 eruptionmodeled

Mazuku

Multi hazards




MSBASdevelopment


Assessingeruptive signals

Assessing landslidetriggers/rain threshold

Gas flux fromSentinel-5P

LS CharacterizationKivu-G-Net

Kivu-S-Net

Rain fall Modelvs field

HR optic RS

Volc. activitycharacterization

COSMOSKYMEDRESISTGeoRisCA

GORISKData

transmission

Atmosphericartifacts


Poor/No data


Poor/No data

Data transmission

Passive degassing




Tiltmetry GPS


Levelling

Goma map

Geological study

ERS

ENVISAT, RADARSAT

TERRASAR


Volcano. map


Impact on health


Poor/No data

Eruptive signalsdetected

InSAR Time series

SENTINEL-1

(SBInSAR)MSBAS


Population data

Distribution maps

Hazards assessment

Landslidesinventory




artifacts

Mazuku

Multi hazards




MSBASdevelopment






Kivu-S-Net


HR optic RS


COSMOSKYMEDRESIST

GORISK





Susceptibility maps

GeoRisCA

Poor/No data

Passive degassing




Tiltmetry GPS


Levelling

Goma map

Geological study

ERS

ENVISAT, RADARSAT

TERRASAR


Volcano. map


Impact on health






Poor/No data

Eeruptive signalsdetected

InSAR Time series

SENTINEL-1

(SBInSAR)MSBAS


Population data

Distribution maps


Hazards assessment

Susceptibility maps

Landslidesinventory



2002 eruptionmodeled

Mazuku

Multi hazards




MSBASdevelopment






Kivu-S-Net


HR optic RS


COSMOSKYMEDRESISTGeoRisCA

GORISKData

transmission

Atmosphericartifacts


Poor/No data

Techniquesdevelopments

Societalimpacts

Geodynamic Mechanisms

SENTINEL-1

(SBInSAR)MSBAS

Time Series

Landslidesinventory




Landslidecharacterization Kivu-G-Net

Kivu-S-Net


HR optic RS


COSMOSKYMEDRESIST

BEODAY – Beersel 30th January 2018

From ground-based close-range to space borne remote sensing techniques:

• REMOTE SENSING• Multi-temporal InSAR• Image Correlation

• HR & VHR optical• SAR Amplitude• UAV

• DSM Point cloud Comparison

• FIELD-BASED• Geomorphological mapping• DGPS measures • Time-Lapse Cameras• Terrestrial Laser Scanning (LIDAR)• UAV

Cross validation

http://resist.africamuseum.be

http://resist.africamuseum.be

From ground-based close-range to space borne remote sensing techniques:

• REMOTE SENSING• Multi-temporal InSAR• Image Correlation

• HR & VHR optical• SAR Amplitude• UAV

• DSM Point cloud Comparison

• FIELD-BASED• Geomorphological mapping• DGPS measures • Time-Lapse Cameras• Terrestrial Laser Scanning (LIDAR)• UAV

Cross validation


Landslide processes characterisation

Improve landslide hazard forecasts in

space and time

Explore parameters controlling dynamics of

landslides in such environment

Evaluate the relevance of techniques for other

applications on sites with similar characteristics

MODUS = Spin off of RESIST

From RESIST to MODUS

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RESIST

MODUS

RESIST: Regional approach

• Regional landslide inventory

• Characterisation of leading processes

• Exploration effects of rainfall on landslide

initiation

MODUS: Local approach

Detailed landslide processes characterisation

Focus on city of Bukavu

• 30% of city territory identified as landslide

• Unique Spatio-Temporal landslide inventory build at RMCA

• City facing problematics (slope instabilities, urban sprawl, poverty …) representative for many cities in the tropics

Preliminary results: Multi-temporal InSARFunu LandslideBukavu (DR Congo)

141 year time series

2 years' time series

Seasonality?

Preliminary results: Multi-temporal InSAR

• From RESIST to MODUS Expand RESIST deformation time series

Funu Landslide


Preliminary results: UAV 3D mapping

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Funu Landslide



17

Funu Landslide



Targeted study areas

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- Two scales:- Grand Bukavu

- InSAR, Image Correlation

- Detailed study of 3 LS- InSAR, Image

Correlation- Field-based measures:

UAV, DGPS, TLC, TLS

Funu/Bagira/Ikoma• Varying anthropogenic activity

and landcover• Movements with different

spatio-temporal scale

UAV – Tool for characterisation

20

• 0.67 km²

• UAV acquisition in Sep. 2017

Ikoma Landslide

Here are some ideas I got during a rainy week-end …

MODUS logo: the competition is now open!

Regional Context

23

4 August 2015 19 February 2016+ 6 months

Example of fractures in houses and road observed in the field over Funu landslide

Kivu Rift: Landslide prone region

• Landslide dynamics?

• Triggering factors?

• Controlling factors?

• Landslide Hazard?

• From RESIST to MODUS Expand RESIST deformation time series

Confront those to additional measures obtained from other space- and ground-based techniques

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Multi-temporal InSAR

UAV – Tool for characterisation

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• 0.67 km²

• UAV acquisition in Sep. 2017

Ikoma Landslide

Funding: BELSPO-SDD / 2012 – 2016

Partners: RMCA-ULB-VUB-ECGS (Lux.)

local (DRC,BDI, Rw)

Geo-risk in Central Africa: integrating multi-hazards and vulnerability to support risk management.

Funding: BELSPO-BRAIN / 2014 – 2018

Partners: KUL – VUB – RMCA – ULB

Landslides in Equatorial Africa: Identifying culturally, technically and economically feasible resilience strategies

REmote Sensing and In Situ detection and Tracking of geohazards

Funding: BELSPO-STEREO / 2014 – 2019

Partners: RMCA – ECGS (Lux.)

Centre Spatial de Liège (ULg) - IASB/BIRA – NASA

http://georisca.africamuseum.be/

http://afreslide.africamuseum.be/

http://resist.africamuseum.be/

A Multi-sensOr approach to characterize ground Displacements in Urban Sprawling contexts

Funding: BELSPO-STEREO / 2017 – 2020

Partners: RMCA (Bel.) – Centre Spatial de Liège (ULg) – Ecole et Observatoire des Sciences de la Terre (Fr.)

26

MRAC research Project in Central Africa

http://georisca.africamuseum.be/

http://afreslide.africamuseum.be/

http://resist.africamuseum.be/

MODUS A Multi-sensOr approach to characterize ground … · 2018-09-17 · Antoine Dille1,...

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