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http://www.gmfs.info26/10/2011, Nairobi, Kenya
Global Monitoring for Food Security 3
ESA’s Crop MonitoringAnd
Early Warning Service
http://www.gmfs.infoNairobi 26/10/2011
Outline
This presentation
• Background• Partners• Services
• Early Warning• Agricultural Monitoring• CFSAM
http://www.gmfs.infoNairobi 26/10/2011
ESA Stage 3 Background
• GMFS started in 2003• New contract GMFS3 -> 2013• Continuation of services to Stage 2 users• Focus is on Sustainability
http://www.gmfs.infoNairobi 26/10/2011
4
Partners
Nr. Company Country General competences, tasks & responsibilities
01 VITO Belgium Project management, early warning, Medium/(high) resolution optical and training , information packaging, promotionSecondment to FAO through visiting scientists programme
02 Consorzio ITA
Italy Agriculture Mapping with optical remote sensing and ground statistics - Validation methodology definition – Training: MALAWI
03 EARS Netherlands FAST Meteosat based Early Warning Services for African regions. Generation of rainfall and evapotranspiration data fields. Production of crop yield forecasts. Provision of dedicated user software (Imageshow 2) and training.
04 EFTAS Germany Agriculture Mapping with optical remote sensing and ground statistics, with radar data - Validation data collection - Service network support: SUDAN
05 SARMAP Switzerland Agriculture mapping with radar – Software programming – Training package: RCMRD
06 ULg Belgium Early warning support and Service portfolio evolution for Early Warning Services: AGRHYMET
07 GeoVILLE
Austria Soil Moisture Indicator products
http://www.gmfs.infoNairobi 26/10/2011
Early Warning
Crop Yield and Vegetation Monitoring Service
FAST Service
Soil Moisture Monitoring Service
Agricultural Monitoring
Support to the Optimization of the National Agricultural Survey Service
Agricultural Mapping Service
SAR Knowledge Transfer Service
CFSAM Support
Support to Crop and Food Supply Assessment Mission Service
ZimbabweMoA
Senegal CSE
Overall ManagementVITO
Southern AfricaRegional coordinator
ESATechnical officer
User Board Scientific Board
West AfricaRegional coordinator
AGRHYMET(CRA)
East AfricaRegional coordinator
RCMRD
West Africa region East Africa region Southern Africa region
MozambiqueINAM
EthiopiaMoARD
SudanFMoAF
MalawiMoAFS
MaliLaboSEP
Service Groups 7 GMFS3 Services
http://www.gmfs.infoNairobi 26/10/2011
Early Warning Service
• Crop Yield and Vegetation Monitoring• Soil Moisture Analysis• FAST
http://www.gmfs.infoNairobi 26/10/2011
MERISVegetation
ASCATSoil moisture
MSGRainfall
Radiation
SUPPORT EW ANALYSIS
TRAIN HOW TO USE THE DATA
Early Warning Service
3 independant sources of information Convergence of evidence analysis
http://www.gmfs.infoNairobi 26/10/2011
Context: In Sahel Region the start of Season is a first indicator of crop development success or failure (Approach published in the CILLS bulletin)
Qualitative Analysis: Early or Late Start of the Growing Season
Profile Matching Approach: - Compare the fAPAR profile for the 3 first months of the 2011 season with the average 1999-2010
- Display the shift that have the best fit with the Long Term Average
- Give an overview of the anticipated or delayed area at pixel level
Crop Yield and Vegetation Monitoring
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Decades
ND
VI
current year historic year
Phase/shift
http://www.gmfs.infoNairobi 26/10/2011
Crop Yield and Vegetation Monitoring
Qualitative Analysis of ongoing Growing Season for Agricultural Monitoring based on low Resolution DataContext: Standard Anomaly Maps based on comparison with LTA give a good spatial overview of anomalies, this approach adds duration and intensity to the maps
Cluster analysis : - Iso data classification based on the relativedifference between 10 day VI and - Display classified map with the corresponding classes profiles
http://www.gmfs.infoNairobi 26/10/2011
Crop Yield and Vegetation Monitoring
Yield Estimation based on Low Resolution Monitoring
Context: Non Parametric Yield ForecastSimilar Years to Yield estimate
Similarty Analysis : - CROP MAP !- for each pixel the most similar year is found- Display classified map - Per ADMIN percentage
TABLE1: Percentage of similar year to 2009 per administrative area
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Addis Ababa ETH001 10% 9% 0% 8% 14% 8% 6% 10% 10% 23% 100%Amhara ETH003 7% 2% 1% 12% 23% 19% 21% 2% 6% 8% 100%Harari ETH007 36% 3% 9% 2% 25% 4% 0% 8% 12% 2% 100%Oromiya ETH008 9% 4% 8% 17% 11% 13% 9% 13% 7% 9% 100%Somali ETH009 6% 4% 8% 9% 9% 10% 4% 15% 4% 31% 100%Southern ETH010 10% 5% 7% 20% 7% 10% 8% 12% 6% 15% 100%Tigray ETH011 4% 2% 1% 11% 20% 17% 17% 6% 15% 6% 100%
TABLE2: WHEAT yield statistics from CFSA
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Addis Ababa 11.97 11.69 11.815 11.94 12.45 18.71 17.32 13.87 17.32 13.87Amhara 7.38 9.4 10.36 8.87 12.93 14.5 15.24 15.94 15.24 15.94Harari 6.08 6.37 12.43 6.37 12.43 12.43 12.43 11.16 12.43 11.16Oromiya 13.23 13.27 14.12 12.04 18.82 17.02 18.17 17.64 18.17 17.64Somali 7.73 4.9 5.2 7.98 16.27 9.55 7.32 4.46 7.32 4.46South Gonder 5.59 6.28 7.31 8.91 8.78 7.54 8.48 10.41 8.48 10.41Tigray 10.3 8.95 9.78 6.56 13.15 9.79 13.32 14.79 13.32 14.79
TABLE3 = TABLE1 X TABLE2: Test of calculation of the estimated WHEAT yield for 2009
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Calculated yield
for 2009Addis Ababa ETH001 1.25 1.07 0.00 0.98 1.73 1.48 1.10 1.45 1.81 3.20 14.08Amhara ETH003 0.49 0.15 0.08 1.10 2.91 2.71 3.25 0.32 0.98 1.22 13.21Harari ETH007 2.20 0.18 1.09 0.14 3.07 0.48 0.00 0.92 1.43 0.18 9.70Oromiya ETH008 1.13 0.57 1.12 2.07 2.12 2.14 1.57 2.27 1.33 1.65 15.97Somali ETH009 0.46 0.22 0.40 0.72 1.51 0.91 0.31 0.68 0.26 1.39 6.85Southern ETH010 0.53 0.34 0.52 1.80 0.65 0.77 0.65 1.21 0.49 1.56 8.53Tigray ETH011 0.45 0.18 0.09 0.71 2.65 1.71 2.24 0.90 2.04 0.91 11.87
http://www.gmfs.infoNairobi 26/10/2011
Crop Yield and Vegetation Monitoring
USERS
Participatory Development
Yield Forecasting
http://www.gmfs.infoNairobi 26/10/2011
Agricultural Monitoring
• Support to the Optimization of the National Agricultural Survey Service
• Agricultural Mapping• SAR Knowledge Transfer
http://www.gmfs.infoNairobi 26/10/2011
“ASO” service: “support to the optimisation of national surveys”. In collaboration with JRC this service is focussed to consultancy on the introduction of area frame sampling approaches , making use of EO data which i stechnically sound and sustainable in the Malawian context
ASO in Malawi
1. EO data are used for the design of the sampling frame and its realisation: location of sample points, interpretation and classification, masking and stratification. Points vs. segments
2. EO data are used with GPS for survey optimisation and execution, e.g. maps for identifying the sampled points, planning of itineraries, control (synergies with field area measurements for APES, etc. see also proposals by MoAFS on the use of IT , FAO)
3. EO data are also used as auxiliary variables (land cover/land use classified images) to improve the accuracy in the estimation of the crop acreage by means of ad hoc statistical procedures.
Point frame: 500 m spacing , - approx. 4 points per km2
to be interpreted and classifiedBased on a simple LCLU legend
Up to 58.000 points are visited on the ground (sampling rate around 15 %)
http://www.gmfs.infoNairobi 26/10/2011
2 Scales, Medium Resolution, High Resolution
LCCS support
• High Resolution Satellite Image coverage NKOR• Multi resolution segmentation of NKOR• Harmonized field work
• OutputsÞ Data & processing support for current LCCS mappingÞ recent HR Satellite image mosaic on state levelÞ Segmentation layerÞ Field work
ASO + AM North Sudan
http://www.gmfs.infoNairobi 26/10/2011
GMFS fAPAR-EoG North Sudan 2010
ASO + AM in North-Sudan
GMFS fAPAR-EoG North Sudan 2005
Change maps:Context: there is a huge variability in the extent of growth (EoG) in North Sudan, based on MERIS-FR fAPAR images and analysis is made on this difference. Maps can be used in support of the Agricultural Survey
• Indication upon differences in growth activities
• Training on use of change maps is currently ongoing (September 2011)
http://www.gmfs.info
CFSAM support to FAO/WFP
FAO publication explaining the importance of Environmental Remote Sensing indicators for monitoring, using amongst others ESA-MERIS RR fAPAR (data processing + methodology = GMFS)
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