Satellite remote sensing for landslide hazard analysis

Satellite remote sensing for landslide hazard analysis Dr. Sigrid Roessner Helmholtz Center Potsdam GFZ German Research Centre for Geosciences Department 1 Geodesy and Remote Sensing Section 1.4 Remote Sensing E-mail: roessner@gfz-potsdam.de

Overview Short background on landslides Landslide situation in Southern Kyrgyzstan Remote sensing based multi-temporal landslide inventory Automated landslide identification InSAR based land-slide analysis

Albert Einstein Science Park at Telegrafenberg GFZ Potsdam Department 1 Einstein Tower Historical observatory Hosts German Research Centre for Geosciences (GFZ), Astrophysical Institute Potsdam (API) Alfred Wegener Institute for Polar and Marine Research (AWI), Potsdam Institute for Climate Impact Research (PIK)

Main reseach topics of GFZ Remote sensing section Energie Reflektierte Sonnenstrahlung Thermalstrahlung Sonnenenergie (6000 K) Erdenergie (300 K) 0.3 0.6 1.0 2.0 4.0 6.0 10 20 40 60 0.1mm 0.2 0.5 1cm 1m 10 100 Mikrowellen Wellenlänge Methodological developments: Imaging spectroscopy - Sensor development (EnMap) - Calibration / validation - Material identification - Object recognition - Change detection InSAR- Interferometry - Deformation modelling - Time series analysis Applications: Natural hazards Soil / vegetation studies Dry land degradation Mineral exploration Mine waste Analysis Urban development

Fatal Landslides in the Period 2002-2012 Global Hotspots: - Central to SE Asia: (China, India, Sri Lanka, southern edge of Himalayan Arc) - Indonesia, Phillipines - Central Carabian Islands - Mountain chains along the western coast of America Source: The landslide blog http://blogs.agu.org/landslideblog/

Badakhshan Landslide - Afghanistan May 02, 2014 450 m x 1100m Fatalities: 350-2700? 1000 buildings affected; 300 destroyed Source: theatlantic.com

Badakhshan Landslide Rapid Response with Remote Sensing WorldView 1: 50cm resolution May 03, 2014 Overview of destroyed structures Help for coordination of rescue Source: reliefweb.int

Badakhshan Landslide Prior Precipitation Haevy rainfall in the days before the landslide event Several hundred mm rain Estimated by Satellite based measurements (TRMM) Source: reliefweb.int

Fall Slide Flow Sources: USGS: Multilingual Landslide Glossary The landslide handbook Rockfall Yosemite Park California, July 1996 Landslide La Conchita (St. Barbara) California, 2005 Debris Flow Sierra Nevada California, 1996/97

Description of mass movements Further parameters for description: State of activity: active; suspended; re-activated; inactive Crown cracks Distribution of activity: advancing; retrogressive;enlarging; diminishing; confined; moving; widening Style of activity complex; composite; successive; single; multiple Velocity of movement (see next slide) Radial cracks Transverse cracks Transverse ridges Longitudinal fault zone main body D Top VC* Tip Toe of surface of rupture *HC/VC: ratio of horizontal to vertical distance from the toe to the crown of a landslide Sources: Varnes (1978), Multilingual Landslide Glossary (1993) Schematic view of rotational landslide that has evolved into an earthflow

Landslide Hazard Assessment Predisposing factors - Lithology - Structural and neotectonic setting - Relief - Human interference Landslide inventory Hazard assessment Spatial probability after Guzzetti et al. 2005 Triggering factors -Precipitation/ seasonal snowmelt and related infiltration - Seismic activity/ earthquakes Temporal probability Risk assessment Magnitude probability Risk elements - Infrastructure - Census data Landslides are a highly dynamic phenomenon in space and time -> importance of detailed spatio-temporal assessment of landslide activity Multi-temporal landslide inventory main prerequisite for objective landslide hazard assessment -> investigation of potential contributions of satellite remote sensing and GIS

Selected landslide parameters commonly assessed in inventories

Contribution of remote sensing and GIS for landslide mapping Attribut autom. erfassbar ID TYPE? segments LOCATION coordinates administrative river MOVEMENT DATE GEOMETRY area volume? DEM depth? DEM width length DAMAGE victims --- injured --- infrastructure DEVELOPMENT Relocation Reactivation GEOLOGY LITHOLOGY Attribut TOPOGRAPHY slope slope position aspect elevation CAUSE/TRIGGER? LANDCOVER autom. yp erfassbar

Kyrgyzstan at the western rim of High Asia Source: Wikimedia Commons

Study Area Southern Kyrgyzstan Ongoing active mountain building in Central Asia Frequent occurrence of extreme natural processes Landslides one of the major hazards in Kyrgyzstan Highest concentration at eastern rim of Fergana basin Study area: ~100x100km² Kyrgyzstan Study Area

Main natural hazards in Kyrgyzstan MODIS RGB bands 4 3 1 (32 days composite: 7 th of July through 8 th of August 2001) Earthquakes Deep seated landslides Glacial lake outburst floods (GLOF) Background and needs: Kyrgyz Republic is faced with a high number of natural disaster affecting large parts of the country (e.g., 1994: about 1,000 landslides failed and 115 people were killed; 2008: Nura earthquake M=6.6, 75 people were killed and 150 injured, 90 glacial lakes endangered for regularly occurring ouburst floods) Human living space is limited (94% of country above 1000 m NN, only 7% arable land) Need for efficient and spatially differentiated hazard assessment Improved understanding of natural processes forms basis for objective and spatially differentiated hazard assessment. Remote sensing and GIS techniques are required for efficient multi-temporal analysis of process characteristics for large areas with limited ground data availability.

Landslides reported by Ministry of Emergency Situations in Kyrgyzstan 120 100 100 80 74 60 47 53 64 61 40 20 0 32 25 21 22 21 15 17 10 11 8 2 2 4 3 5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 More than 5000 known landslides in Kyrgyzstan Cyclic activation recent peaks in 1994, 2003/2004, 2009/2010 2002-19 victims 2003 47 victims 2004 53 victims Source: MELESHKO, Report Kyrgyz Ministry of Emergency Situations, 2010

Area of high landslide activity along Eastern rim of Fergana Basin Landslides represent most severe natural hazard in Southern Kyrgyzstan Several thousand known landslide events since 1950 More than 300 victims since 1990 Frequent destruction of houses and infrastructure Big need for systematic inventory of landslide events in space and time Landslides reported by Ministry of Emergency Situations of Kyrgyzstan between 2005 and 2010 Areas of known landslide activity Study area for landslide analysis at regjonal scale

Dominant process type deep seated landslides Rapid displacement of quaternary loess during 15 minutes period in March 1994 (50 victims) Displacement of clay-rich tertiary sediments during period of several days in June 1998 High number of complex rotational/translational slides often reactivation and repetitive failure Regional spatial distribution determined by lithology and neotectonic structures Landslide initiation by complex interplay between predisposing and triggering factors Investigation of landslide activity since 1950-ies, mostly in areas close to settlements Need for systematic inventory of landslide events and quantitative process understanding

Destroyed building Rotational landslide complex of Sary Bulak failure of Tertiary sediments (sandstone, clay) in June 1998 of more than one million cubic meters, destroyed house indicates ongoing activity of landslide (field pictures taken in August 1998)

Data Sources for Landslide Inventory Field investigations Data of Kyrgyz authorities 1970s - 2011 Field campaigns 1998-2012 Landslide inventory Visual interpretation of remote sensing data undated, selected data sets 1986 2002 Automated analysis of remote sensing data time series 1990 2012 Satellite remote sensing

Investigations by Kyrgyz Ministry of Emergency Situations 1) Verbal data: report by Ibatulin (2011) describes selected landslide failures in 1980's - 2004 A B C F D E 2004 landslide on the left bank of the Budalyk river on the northern edge of the Kainama village A, B, C, D, E or F? 2) Tabular data with point coordinates: selective field investigations by Ministry of Emergency Situations in 2002-2011 RapidEye image 2011 Landslides mapped based on report by Ibatulin (2011) Landslides reported by Ministry of Emergency Situations in 2002-2011 Landslides mapped in field in 2012

Remote Sensing in support of spatially explicit landslide mapping Field campaigns by GFZ Potsdam in cooperation with Ministry of Emergency Situations in 1998 2012: GPS way points and field photos 18.06.2004 26.05.2008 RapidEye image 2011 Landslides mapped in field in 2012 Landslides mapped after Ibatulin (2011) 12.09.2012 Way points: 2012 2008 2002 2011 2004 1999

Remote Sensing Data for Landslide Inventory 1980 1990 2000 2010 LANDSAT multi/pan 30m/15m TM ETM+ SPOT multi/pan 20-10m/10-2,5m 1 2 3 4 5 IRS-1C/D multi/pan 23,5m/5,8m ASTER multi 90-15m ALOS RapidEye multi/pan 10/2,5m multi 6,5m General Availability Limited Quality Available for study area 1986-2013: ~690 datasets, around 80 acquisition dates between April and September nearly annual coverage since 1989

NDVI Index for Vegetation Analysis Normalized Difference Vegetation Index NDVI NIR RED [-1;1] NIR RED 100 80 R 60 Green 1 Red NIR 2 3 ASTER, 15m Colorinfrared: R-G-B: NIR-R-G NDVI [-0.8, 0.5] ASTER 40 20 0 0.4 0.6 0.8 1.0 Wavelength µm - Analysis of vegetation cover - Index useful for reducing radiometric differences - Low NDVI -> absence of vegetation -> landslide?

Multitemporal Analysis NDVI Time-Series ASTER: June 2003 ASTER: June 2004 RE: May 2009 RE: Sept 2011 1.0 NDVI [-1,1] NDVI [-0.8, 0.5] 0.5 0.0-0.5 landslide specific variations in vegetation cover over time -1.0

NDVI based automated time series analysis Multitemporal: Variation of vegetation cover over time NDVI [-1,1] 1.0 0.5 1 landslide ASTER 2003 3 6 4 0.0-0.5 1.0 0.5 2 vegetation 3 field 4 water 5 outcrop 6 urban 2 1 0.0-0.5 5 2 km

RapidEye - High Resolution Satellite Database Data acquisition within RESA (RapidEye Science Archive): 5 m spatial resolution 5 spectral bands (440 850nm) Data grant since 2010 Priority area since 2014 Acquisition of data in pre-defined time periods of high process activity - Until 2013: High temporal resolution: April to June - Low from July to September - Priority acquisition from April to August (2 weeks repeat rate) Orthorectified data for 21 25x25km tiles

Automated Landslide Identification approach Analysis of landslide occurrence for all subsequent images (time period) Combined pixel- and object-based approach for each time period: 3 main steps: Bi-temporal vegetation change analysis with multiple thresholds (Castilla et al. 2009) followed by a segmentation Multi-temporal analysis of revegetation rates Relief oriented analysis (slope, slope orientation) Behling, R.; Roessner, S.; Kaufmann, H.; Kleinschmit, B. Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data. Remote Sensing, 2014, 6, 9, 8026-8055. Behling, R.; Roessner, S.; Segl, K.; Kleinschmit, B.; Kaufmann, H. Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection. Remote Sensing, 2014, 6, 3, 2572-2600.

Automated Landslide Identification Exemplary Results 2012/06/18 Multitemproal satellite remote sensing data base: Multispectral RapidEye data Time period of occurrence: 2009/05/26 2011/05/02 2012/05/24 2012/06/18 Approach identifies landslides of varying: Size Shape Lithology Stage of development: fresh failures reactivation relocation

Automated Landslide Identification Field Check 2012/06/18 Time period of occurrence: 2009/05/26 2011/05/02 2012/05/24 2012/06/18 B B C C Field fotos taken 2012/09/10

Results of Automated Identification at Regional Scale Study area: 12,000 km² Analyzed time period: 2009-2013 600 RapidEye datasets (5 m resolution) SRTM-DEM (30 m resolution) 612 identified landslides Size 125 775,000 m² A total of 7.3 million m² affected 2009-2013 no major triggering event Shows the need for regular multi-temporal landslide inventories Identification of landslides with different sizes, shapes, types and stages of development in objects form Multi-temporal dynamic landslide inventory as Input for Hazard and Risk analysis Continuation of existing inventories -> Potential for continuous monitoring of landslide activity

Optical Remote Sensing Database Study area: 12,000 km² ~ 670 orthorectified datasets of 4 different multispectral sensors 27 years of data coverage: 1986 2013; since 1998 nearly annual coverage Landsat (E)TM 30 m 49 datasets 1990-1999 & 2009-2013 ASTER 15 m 30 datasets 2000-2008 SPOT 10-20 m 12 datasets 1986 & 2006-2010 RapidEye 5 m 592 datasets 2009-2013

Uzgen slope Multi-Temporal Landslide Occurrences B A C Field Photo Sept. 2012 A B C Perspective View: RapidEye True color Sept. 2012 Dr. S. Roessner W - Day GFZ Potsdam 15. 06. 2015 Satellite Remote Sensing for Landslides

Results Multi-Temporal Landslide Identification field photo C I II 2004: 3 Landslides 166,000 m² 2011: 3 Landslides 205,000 m² Fresh failures, reactivations B II I A A 2 km field photo taken in September 2012

Results of Automated Identification at Regional Scale Study area: 12,000 km² Analyzed time period: 2009-2013 600 RapidEye datasets (5 m resolution) SRTM-DEM (30 m resolution) 612 identified landslides Size 125 775,000 m² A total of 7.3 million m² affected 2009-2013 no major triggering event Shows the need for regular multi-temporal landslide inventories Identification of landslides with different sizes, shapes, types and stages of development in objects form Multi-temporal dynamic landslide inventory as Input for Hazard and Risk analysis Continuation of existing inventories -> Potential for continuous monitoring of landslide activity

Landslide Detection using Radar Interferometry (InSAR) Data: May-October 2009 Radar (active microwave) Independent of weather conditions (clouds) Resolution of up to 1m Short revisit time of 11 day X-band (wavelength 31 mm) InSAR: Phase differences between two SAR images Detection of surface deformations (mm relative to the sensor: parrallel to line of sight) Identification of moving areas before landslide failure Not suitable for fast ground movements and collapse: Temporal decorrelation

Characteristics TerraSAR-X DLR/Astrium (Germany) ALOS/PALSAR Jaxa (Japan) Launch date June 15, 2007 Jan. 24, 2006 Wavelength X-band (3.1cm) L-band (23.6cm) Repeat cycle 11 days 46 days Resolution up to 2m (SpotLight) up to 3m (StripMap) up to 18m (ScanSAR) 7m to 44m (FBS Mode) 14 to 88m (FBD Mode) 100m (ScanSAR) InSAR processing has been performed using DORIS and SarScape software for: (1) TerraSAR-X data of one frame (30m x 50km) for ascending and descending modes (~20 interferograms each) between May 2008 and October 2009 (2) ALOS/PALSAR data of one frame (70 x 60 km) for ascending mode (28 interferograms) between February 2007 and October 2010

e N InSAR time-series analysis Terra SAR X 6 may 9 nov 2009 d c b g Rapid Eye aq. date: 3-5-aug2012 Dr. S. Roessner W - Day GFZ Potsdam 15. 06. 2015 Satellite Remote Sensing for Landslides

Radar Interferometry (InSAR) TerraSAR-X time series InSAR timeseries analysis 6 may 9 nov 2009 Terra SAR X Motagh, Wetzel, Roessner & Kaufmann (2013): Remote Sensing Letters, 4, 7, p. 657-666

Contribution of multi-temporal remote sensing Optical Satellite Remote Sensing Automated identification of landslide activity at regional scale Potential for analysis of past process activity (last 25 years) Potential for regular monitoring of landslide activity Radar Satellite Remote Sensing Identification of deformation related to active landslide prone slopes Identification of landslide activization -> potential for early warning system GIS-based Hazard Assessment Development of dynamic landslide inventory (combination of all available sources with potential for regular updates) Dynamic hazard and risk assessment Improved process understanding (correlation with triggering and predisposing factors; magnitude frequency analysis)

Thank you very much for your attention! Contact: Dr. Sigrid Roessner GFZ German Research Centre for Geosciences Section 1.4 Remote Sensing E-mail: roessner@gfz-potsdam.de