Potential of optical satellite multi

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Potential of optical satellite multi-sensor time series
data for landslide mapping covering large areas
Robert Behling, Sigrid Roessner, Hermann Kaufmann and
Birgit Kleinschmit1
GFZ Potsdam, Section 1.4 – Remote Sensing
1TU Berlin, Department of Geoinformation in Environmental Planning
Behling et al. Potential of optical satellite multi-sensor time series data for landslide mapping
Overview
• Landslide situation in
Southern Kyrgyzstan
• Remote sensing based
multi-temporal landslide
inventory
• Automated landslide
identification using
- RapidEye data
(recent activity)
- Multi-sensor data
(backdated activity)
• Conclusions
Kyrgyzstan
Study Area
Region of high landslide activity in Southern Kyrgyzstan
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
Landslide inventory – main prerequisite for hazard assessment
Landslide inventory
Predisposing factors
- Lithology
- Structural and
neotectonic setting
- Relief
- Human interference
Triggering factors
-Precipitation/ seasonal
snowmelt and related
infiltration
- Seismic activity/
earthquakes
Hazard assessment
after Guzzetti et al. 2005
Spatial
probability
Temporal
probability
Magnitude
probability
Risk elements
Risk assessment
- Infrastructure
- Census data
Development of landslide prone slope - Kainama
Field picture of Kainama landslide taken on 18th of June 2004
Rapid landslide failure on 26th of April 2004 – landslide runout
crossed river and buried parts of settlement (33 casualities)
16. Juni 2004
26. Mai 2002
R‐G‐B: Red‐NIR‐Green
14. Juni 2003
Development of Kainama landslide between 2002 und 2004 – interpreted from multi-temporal ASTER data
Remote Sensing Data for Landslide Inventory
1980
LANDSAT
multi/pan
30m/15m
SPOT
multi/pan
20-10m/10-2,5m
IRS-1C/D
multi/pan
23,5m/5,8m
ASTER
multi
90-15m
ALOS
multi/pan
10/2,5m
RapidEye
multi
6,5m
1990
2000
2010
TM
ETM+
1
2
3
4
5
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
Challenges: Seasonal Variability of Landslides
R-G-B: color infrared
RapidEye: 09.05.2009, 5m
Foto June
ALOS: 02.07.2009, 10m
RapidEye: 29.09.2009, 5m
Foto Sept
Challenges: Long-Term Variability of Landslides
Landsat TM R-G-B: 7-4-1
TM: Sept 1989, 30m
RapidEye: Sept 2011, 5m
TM: Juni 1993, 30m
TM: Aug 1998, 30m
Foto 2012/09/13
R-G-B: true color
Overall approach for automated landslide detection
Challenges
 High seasonal and long-term surface variability
 Large area of ~12 000 km²
 High amount of multi-sensor satellite data
 Automated, efficient and robust techniques suitable for multi-sensor time-series-analysis

Pre-processing, Change Detection, Segmentation, etc.
Pre-processing
Multi-temporal
Optical data
Cloud masks
Automated
co-registration
Derivation of landslide objects
Spatio-temporal landslide inventoy
-> bi- & multi-temporal change detection
-> Segmentation
-> Quantitative and qualitative
Characterization of landslide objects
Thematic data
Pixel and object-based
change detection
Multi-temporal NDVI data
NDVI – Normalized Difference Vegetation Index
ASTER: June 2004
RE: May 2009
RE: Sept 2011
NDVI [-0.8 , 0.5]
ASTER: June 2003
2 km
NDVI 
NIR RED
NIR  RED
[-1;1]

Analysis of vegetation cover
Index derivable in all sensors
Reducing radiometric differences

Low NDVI -> absence of vegetation -> landslide?


Fieldfoto: June 2004
Landslide from April 2004
Landslide Identification – Temporal NDVI Trajectories
NDVI [-0.8 , 0.5]
ASTER: June 2003
1
ASTER: June 2004
RE: May 2009
RE: Sept 2011
3
2
4
2 km
NDVI [-1,1]
1.0
1
2
3
4
Fieldfoto: June 2004
0.5
0.0
-0.5
-1.0

landslide characteristic variations in
vegetation cover over time
Landslide from April 2004
Landslide Identification – Temporal NDVI Trajectories
Multitemporal:
Variation of vegetation cover in time
6
3
1.0
NDVI [-1,1]
Landslide objects between 2003-04
0.5
4
0.0
-0.5
1.0
0.5
1 landslide
2 vegetation
3 field
2
4 water
5 outcrop
6 urban
1
0.0
-0.5
5
2 km
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, 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, 2572-2600.
RapidEye - High Resolution Satellite Database
Data acquisition within RESA
RapidEyeScience Archive
Program since 2010
• 6,5m spatial resolution
• 5 spectral bands (440 – 850nm)
• Acquisition of data in pre-defined
time periods of high process activity
• Orthorectified Data in 21 25x25km
tiles
• Best temporal and spatial resolution
for regional scale
• ~ 600 datasets until 2013
• Status of RESA priority area since
2014
Identified Landslides – Exemplary Results
2012/06/18 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
2009/05/26 – 2011/05/02
2012/05/24 – 2012/06/18
A
A
field foto taken: 2012/09/10
2009/05/26 – 2011/05/02
2012/05/24 – 2012/06/18
C
B
B
C
2012/09/10
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
27 years of data coverage: 1986 – 2013; since 1998 nearly annual coverage
~ 670 orthorectified datasets of 4 different multispectral sensors
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
5m
592 datasets
2009-2013
Example Uzgen – Landslide Situation 2012
B
A
C
Field Photo Sept. 2012
A
B
C
Perspective View: RapidEye True color Sept. 2012
Multi-Temporal Landslide Identification
2000
2010
Data
Identified
landslides
C
C
B
B
A
A
2 km
32 datasets starting in 1986
2000
2010
Data
Identified
landslides
2 km
2000
2010
Data
Identified
landslides
2 km
Conclusions and outlook
Developed automated approach for landslide identification enables:
• Identification of landslides of different sizes, shapes, types and in
different stages of development in an object-based form
• Multi-temporal dynamic landslide inventory, not only event-based
inventory
• Spatial and temporal explicit input for hazard and risk analysis
• Update of existing inventories -> Monitoring of landslide activity
• Analysis at regional scale
• Identification of landslide events for variable time span covering the
whole time period of multispectral remote sensing data availability
• Transfer of methodology to other global hot-spots of landslide activity
THANK YOU FOR YOUR ATTENTION
Contact: [email protected] [email protected]

Potential of optical satellite multi

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