mapping of cropland areas over africa combining various land cover/use datasets
DESCRIPTION
Mapping of cropland areas over Africa combining various land cover/use datasets Food Security (FOODSEC) Action Monitoring Agricultural ResourceS (MARS) Unit Institute for Environment and Sustainability (IES) Joint Research Centre (JRC) – European Commission - PowerPoint PPT PresentationTRANSCRIPT
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Mapping of cropland areas Mapping of cropland areas over over Africa combining various land Africa combining various land
cover/use datasetscover/use datasets
Food Security (FOODSEC) ActionMonitoring Agricultural ResourceS (MARS) Unit Institute for Environment and Sustainability (IES)
Joint Research Centre (JRC) – European Commission
Christelle Vancutsem, Francois Kayitakire, Jean-Francois Pekel, Eduardo Marinho
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Pasture & Crop
Masks
RS time series
Agriculture
monitoring and early
warning
NDVI anomalies
LegendVery poor
Poor
Normal
Good
Very Good
Water
Vegetation Index profile
extraction
Context
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Objective
Map cropland areas at 250m
• « STATIC »Expert-based combination of existing datasets At the global scale (17)with emphasizing on Africa (10)
• « DYNAMIC » Every year Sub-saharien african
countries Identify potential cropland
areas Analyse the inter-annual
variabilityFrom MODIS time series
20092008
…
…
multi-annual mask
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10 sources
Landsat-based:- SADC 1990-1995 (CSIR)- CUI 1988 (USGS)- LULC 2000 (USGS)- Woody Biomass 2002 (World Bank)- Africover 2000 (FAO)- LC Senegal 2005 (GLCN, FAO)- LC Mozambique 2008 (DNTF)- MODIS-derived Crop mask 2009
(JRC, MARS)
Low/medium resolution:- Globcover 2005-2006 (ESA)- RDC LC 2000 (UCL)
Crop mask
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• 10 sources
• Data preparation
• Selection of cropland classes
• Combination of datasets
• Regularly updated
Static crop mask
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Validation
JRC contributes to the improvement of the tool:-as beta-tester (7 experts)-providing SPOT VGT NDVI profiles
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Validation
With agriculture.geo-wiki.org
Comparison with two existing crop masks: Fritz et al. (2011) and Pittman et al. (2010)
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ValidationAgreement (%) between experts for each category of crops taking into account the category concerned only (% of agreement 1cl) and the neighbouring classes (% of agreement 3cl)
1
130 points
Niger-Nigeria
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Validation
Comparison between the 3 crop masks and two validation datasets
MARS IIASA Pittman et al. MARS IIASA Pittman et al.>50% 65.15% 30.26% 21.3% 69.6% 49.8% 17.3%
Africa Niger-Nigeria window
JRC
IIASA
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• Combination of the best existing datasets available (static mask)- half of the African countries covered by high and medium resolution-derived products
- validation shows that the product better agrees with the validation dataset than other existing crop masks- need of up-to-date information and feedback from users !- in continuous improvement (global)
• Training and validation datasets with agriculture.geo-wiki.org - Reliable and user-friendly collaborative tool
- Allows sharing data and expertise between experts in a win/win approach- As powerful as the number of user is growing - Allows a high productivity of the interpreter
Conclusion
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Thank you Global Cropland Map (JRC-MARS, 2011)
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10 sources
Data preparation From feature to Raster Reprojection Resampling at 250m Translation in the LCCS legend (5cl)-Cultivated and managed areas
- Post-flooding or irrigated croplands
- Rainfed croplands-Mosaic cropland (50-70%)/vgt-Mosaic vgt / cropland (20-50%)
Static crop mask
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10 sourcesData preparation
Selection of cropland classes– By default, crops >50%– IF crops <50%
Selection by experts based on
comparison with HR imagery (GE)
- Globcover 20-50% in equatorial countries- CUI 30-50%
Static crop mask
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10 sourcesData preparationSelection of cropland classes
Data combinationWhen different sources:1) Comparison with high resolution imagery (GE) & Analysis by experts 2) Rules:
1st priority to the highest resolution
2nd priority to the most recent
Static crop mask
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10 sourcesData preparationSelection of cropland classesCombination of datasets
Possible issues Out-dated Global LC data (Globcover) Spatial inconsistencies Spatial resolution 250m not
“real”
Static crop mask