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

Pasture & Crop

Masks

RS time series

Agriculture

monitoring and early

warning

NDVI anomalies

Legend

Very poor

Poor

Normal

Good

Very Good

Water

Vegetation Index profile

extraction

Context

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

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

• 10 sources

• Data preparation

• Selection of cropland classes

• Combination of datasets

• Regularly updated

Static crop mask

Validation

JRC contributes to the improvement of the tool:-as beta-tester (7 experts)-providing SPOT VGT NDVI profiles

Validation

With agriculture.geo-wiki.org

Comparison with two existing crop masks: Fritz et al. (2011) and Pittman et al. (2010)

Validation

Agreement (%) 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

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

• 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

Thank you Global Cropland Map (JRC-MARS, 2011)

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

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

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

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