Kranz-Earth Observation for Conflict Mitigation and

Transcription

Earth observation for conflict mitigation and peacekeeping – from
humanitarian relief to supporting peace and conflict studies
Olaf Kranz1,2, Elisabeth Schoepfer3, Kristin Spröhnle3 & Stefan Lang1
1
Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg/Austria
2
Helmholtz Association Head Office, Research Section, Berlin/Germany
3
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen/Germany
EARseL, 21 June 2016
65.3 million forcibly displaced worldwide
21 million refugees
approx. 41 million internally displaced persons
approx. 3 million asylum-seekers
approx.
Quelle: THW
Causes of flight and migration
Wars and armed conflicts
Violations
Natural or man-made disasters
Climate change / environmental change
Population pressure
Poverty
etc.
A complete picture of the in-field situation
is needed (short-, mid- and long-term)!
How can Earth Observation provide reliable information
for supporting humanitarian relief action or decision
making within the context of conflict mitigation,
prevention activities and peace-building?
Quelle: THW
Example Refugee-/IDP-camps: What is needed?
OBJECTIVE: sustainable camp management,
sustainable management of food and natural
resources (e.g. fire wood, building timber and
water)
Information about:
• Infrastructure
• Tents, Dwellings, Huts
• Facilities
• Population Estimation & Density
• Monitoring of Development over
time
• LULC status quo and change
• Availability of Natural Resources (e.g.
fire wood, building timber, water)
IDP Population in Zalingei
Chart 7
Data & Methods: The Approach
Object-Based Image Analysis (OBIA) on Very High Resolution Satellite data
for the Estimation of Population & Availability of Resources
WorldView
GeoEye
Quickbird
IKONOS
Impact of Refugees on the Environment
Quelle: THW
Multi-scale satellite image analysis approach
• Medium resolution (MR)
• monitoring of land cover
changes
• High resolution (HR)
• detection of broad
vegetation categories
• Very high resolution (VHR)
• separation of woody
vegetation features
Chart 10
Multi-scale satellite image analysis approach (MR)
• Data:
•
•
•
•
Earth observation data, from MODIS VI (MOD13Q1), over one decade, 237 data sets
Precipitation data, from Hydrological Data and Information System (HyDIS)
Temperature data, from Berkeley Earth (2014)
Population data, from HIC, UN OCHA, IRIN, UNEP, Sudan Tribune
• Methodology:
• Time series analysis, based on Seasonal Kendall (SK) test
• Results:
• Trend pattern showing trends in vegetation cover => significant land-cover changes
• Positive and negative trends => increase or decrease in vegetation
• Hotspots in areas with specific characteristics, i.e. near wadi banks or within the vicinity of
settlements and IDP camps
• Indication for the influence of increasing population on natural resources
=> Correlation of these trend patterns with precipitation, temperature, and population data
 KRANZ, O., SACHS, A. & LANG, S. (2015), Assessment of environmental changes induced by internally displaced person (IDP) camps in the Darfur region, Sudan, based on
multi-temporal MODIS data. Int. Journal of Remote Sensing, Vol. 36, No. 1, 190–210
Chart 11
Multi-scale satellite image analysis approach (MR)
Comparison of trend pattern
resulting from the time series
analysis with VHR satellite
Imagery
Red indicate negative change
Green highlights increasing vegetation cover
 KRANZ, O., SACHS, A. & LANG, S. (2015), Assessment of environmental changes induced by internally displaced person (IDP) camps in the Darfur region, Sudan, based on
multi-temporal MODIS data. Int. Journal of Remote Sensing, Vol. 36, No. 1, 190–210
Chart 12
Multi-scale satellite image analysis approach (HR)
• Data:
• Earth observation data, from SPOT between 2003 and 2007
• Precipitation data, from Hydrological Data and Information System (HyDIS)
• Population data, from UN OCHA, IRIN, UNEP, Sudan Tribune
• Methodology:
• Object-based image analysis
• Results:
• Trend pattern suggests negative and positive changes in woody vegetation
• Influence of the camps is not that obvious
• Precipitation data reveal that the change detection results are influenced by
different precipitation and consequent phenological conditions at the time of data
acquisition
 SPRÖHNLE, K., KRANZ, O., SCHÖPFER, S., VOIGT, S. & MÖLLER, M. (2015), Earth observation based multi-scale impact assessment of refugee camps on wood resources in Zalingei,
Darfur. Geocarto International.
 SPRÖHNLE, K., KRANZ, O., & SCHÖPFER, S. (2010), Abschätzung der Umweltauswirkungen von Flüchtlingslagern mit Hilfe multitemporaler Fernerkundungsdaten am Beispiel des
Camps Zalingei in Westdarfur. In: STROBL, J., BLASCHKE, T. & GRIESEBNER, G. (Eds.), Angewandte Geoinformatik 2010. Beiträge zum 22. AGIT Symposium, 697–702.
 SPRÖHNLE, K. (2010): Earth Observation for Environmental Impact Assessment in the surroundings of Refugee Camps in Darfur. Unveröffentlichte Magisterarbeit, Bamberg, 105 S.
Chart 13
Multi-scale satellite image analysis approach (HR)
Chart 14
Multi-scale satellite image analysis approach (VHR)
• Data:
• Earth observation data, from IKONOS and QUICKBIRD from 2004 and 2008
• Precipitation data, from Hydrological Data and Information System (HyDIS)
• Population data, from UN OCHA, IRIN, UNEP, Sudan Tribune
• Methodology:
• Object-based image analysis
• Results:
• Land degradation becomes evident when comparing the VHR data of 2004 and 2008
• Decline of woody vegetation in the surroundings of the IDP camps becomes obvious
 SPRÖHNLE, K., KRANZ, O., SCHÖPFER, S., VOIGT, S. & MÖLLER, M. (2015), Earth observation based multi-scale impact assessment of refugee camps on wood resources in Zalingei,
Darfur. Geocarto International.
Chart 15
Multi-scale satellite image analysis approach (VHR)
Chart 16
Approach for Estimating Water Availability
Free of charge data only!
Landsat-data
First step:
Analysis according to
hydrogeological methods
to get a broad overview of
potential aquiferous
regions
Second step:
More detailed analysis adapted
for the specific conditions of
each study site to cut down the
detected area of aquiferous
regions
Overall methodological approach for the detection of water resources including the
analysis according to hydrogeology as well as more specific spatial analyses.
Chart 17
Approach for Estimating Water Availability
Aquiferous regions have been
extracted taking under
consideration the following initial
parameters:
 climatic
 geological
 hydrogeological
 vegetation cover
The IDP camp is displayed in red; aquiferous regions are highlighted from dark blue (which
indicates high water probability) to light blue (indicating low water probability); regions with
permanent vegetation cover over the year are displayed in yellow and green
Chart 18
Conclusions
OBJECTIVE: sustainable camp management, sustainable management of food
and natural resources (e.g. fire wood, building timber and water)
• EO derived infromation together with further information coming from reports and
statistics provide a more complete picture in highly dynamic regions.
• The analysis and quantification of environmental impact of mass migration might be
an important indicator for mitigation and prevention strategies and in the context of
setting up strategies for sustainable use of natural resources, economic development
and peace-building.
Thank you for your attention !
Chart 19

Kranz-Earth Observation for Conflict Mitigation and

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