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Global Monitoring for Food Security Stage II

Global Monitoring for Food Security Stage II. WMO/FAO/SADC Workshop, 14-18 November 2005. Food Security Information services in Africa. Paolo Ragni Paolo.ragni@snamprogetti.eni.it. GMFS Project - Context. GMES G lobal M onitoring for E nvironment and S ecurity

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Global Monitoring for Food Security Stage II

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  1. Global Monitoring for Food SecurityStage II WMO/FAO/SADC Workshop, 14-18 November 2005 Food Security Information services in Africa Paolo Ragni Paolo.ragni@snamprogetti.eni.it

  2. GMFS Project - Context • GMES • Global Monitoring for Environment and Security • Joint EC and ESA initiative • Stage II: Scaling Up Consolidated GMES Services • GMFS • Global Monitoring for Food Security • Operational delivery of user-driven services • 2 phases • 2003-2004: Startup, consolidation & definition • 2005: transition year • 2005-2008: Implementation

  3. Summary • Call ESRIN/AO/1-4704/05/I-LG came out early February • GMFS II Proposal submitted end march 29th. Negotiation since 14/6 • Project Kick-off meeting 6/10 (Baseline). Partners KO meeting 14/10 • Current proposed activities (Baseline): • Continental level: LR indicators (METEOSAT, SPOT-VGT, MSG) • National level: MR-HR crop status, area & dates, AMM, ground reference data • WA: Senegal, • EA: Sudan, Ethiopia, • SA: Malawi, Zimbabwe (2005-06), • Support to FAO/WFP Crop & Food Suppy Assessment missions (CFSAM) • Extensions (KO unknown) • 2 more countries (Kenia, Mauritania, Burkina-Faso, Zambia) • Pasture/rangeland monitoring • Rainfall estimates, seasonal forecasts

  4. Users / Partners (SADC area) • SADC-Regional Remote Sensing Unit (RRSU) • MoAIFS, Malawi • WFP • FAO • EC JRC MARS-FOOD • FEWS Net • CIMMYT • ….

  5. WP A/B/C.2000 Service network coordination. WP A/B/C. 3000 Service Provision & qualification SP1 SP1 SP1, SP2, SP3, SP6, SP7, SP9, SP10, SP11, SP12, SP13 WP A/B/C.1100 User requirements – continental WP A/B/C.1200 National/regional User liaison SP1, SP5 WP A/B/C.3130 Crop state indicators: MSG WP A/B/C.3140 Crop state indicators: MERIS WP A/B/C.3110 Crop state indicators: SPOT-VGT SP3, SP9, SP6 SP1 WP A/B/C.1300 Training SP1 SP1 SP1 SP3, SP2, SP5, SP1, SP11 WP A/B/C.322X Fieldwork organization & execution WP A/B/C.1400 Promotion & sustainability SP3 SP1 WP A/B/C.3120 FAST service SP3, SP9, SP11 WP A.321X GIS ancillary data collection SP13 SP6 WP A/B/C.5000 Project Management SP3, SP9, SP6 WP A.2100 Common Service infrastructure SP1 WP A/B/C.326X Product Integration-validation SP4 SP3 WP A.2200 Geonetwork setup SP3, SP9, SP6 WP A/B/C.3300 CFSAM support & geonetwork database population SP5 SP13, SP5, SP1 WP A/B/C.323X Yield estimates WP A/B/C.324X SAR agricultural monitoring products WP A/B/C.325X Opt. agricultural monitoring products SP11 WP A/B/C.3400 User monitoring & evaluation SP11, SP3, SP12, SP9 SP1 SP2 SP7, SP8 WP A/B/C.4000-08 Service portfolio evolution SP1, SP2, SP3, SP5, SP13 WP A.2300 SLA’s and common access conditions SP1 Continental, regional and national services Work package A/B/C.1000 User Federation & strategic planning WP A/B/C.3100 Continental scale WP A/B/C.320X Regional & national scale

  6. Responsibilities by regional and national service (EFTAS) EAST • Local EFTAS • MR VITO • HR SARMAP Regional & national (FMA) WEST • Local FMA Synoptics, ULG • MR VITO • HR SARMAP (ITA) SOUTH • Local ITA (Synoptics*) • MR VITO • HR SARMAP * AMM first year

  7. Specific tasks: national services SOUTHERN AFRICA: ZIMBABWE SOUTHERN AFRICA: MALAWI WP A/B/C.1204 National/regional user liaison: MoAIFS, SADC-RRSU WP A.1205 National/regional user liaison: FAO/WFP, SADC-RRSU SP3 SP3 WP A.3214 GIS ancillary data collection WP A.3215 GIS ancillary data collection SP3 SP3 WP A/B/C.3224 Fieldwork organization & execution SP3 WP A.3245 SAR MR agricultural monitoring products WP A.3255 Opt. agricultural monitoring products WP A/B/C.3234 Yield estimates WP A/B/C.3244 SAR HR agricultural monitoring products WP A/B/C.3254 Opt. agricultural monitoring products SP3 SP1 SP2 SP1 SP2 WP A/B/C.3264 Product validation WP A.3265 Product validation SP3 SP3

  8. GMFS - Products

  9. UK MET OFFICE New capabilities with MSG Meteosat7 Meteosat8-9 • 30 Minutes • 15 Minutes • 3 Channels • 12 Channels • 2500 x 2500 pixels • 3712 x 3712 pixels • 5 km • 3 km

  10. LoRes-INDICATORS: Vegetation Productivity Index • Every 10 days • Difference of vegetation growth with Historical year (Sannier et al.)

  11. Envisat MERIS MERIS Medium Resolution Imaging Spectrometer Agency European Space Agency Resolution 300 m Bands 15 bands Swath 1150 km Envisat ASAR SAR Synthetic Aperture Radar Agency European Space Agency Resolution 150 m Swath 400 Km MODIS MODIS Moderate Resolution Imaging Spectroradiometer AgencyNASA Swath 2230 Km Resolution 250 m Medium Resolution:Input data

  12. MeRes-INDICATORS: Dry Matter Productivity • Every 10 days (MERIS) / 16 days (MODIS) • ≈ Net Primary Productivity (measure of standing biomass)

  13. Classification MeRes: Classification • Fieldwork • Crop ‘probability’ map / hard classification • Classes fairly well distinguishable Green = agriculture

  14. MR-optical / MR-ASAR integrated products MODIS DMP coarse discrimination of cropped area ASAR WS better spatial resolution sensitivity to early crop stages Total crop extent/acreage at emergence Multitemporal ASAR WS σ° product MODIS-ASAR Crop extent/acreage Multitemporal MODIS 16-days DMP

  15. Malawi, Temporal changes in planted areas 10 December 26 December ASAR AP High spatial resolution. High sentivity to roughness changes. Planting/emergence/harvest Cropped area 27 January

  16. Area Estimates: Input data • ENVISAT ASAR • 15m, 150m resolution (AP & WS) • ENVISAT MERIS FR • 15 bands • 300 m • MODIS • 250 m bands • Landsat TM / other • Fieldwork

  17. Crop Calendar Land practices Agro-ecological zones Ground data, Meteo, Agro-meteorological Model High resolution Acreage and Yield at ar sub-national level AreaEstimates

  18. Medium/Low resolution Agro-ecological zones Yield estimation at national level Trends Analysis AreaEstimates Acreage and Yield at 3rd level End member identification

  19. Yield Forecast:Agro-meteorological model

  20. YieldForecast:Agro-meteorologicalmodel Millet yield forecasts 2003 relative to 10-year average Senegal - results 2003 Average = + 5% Peanut yield forecasts 2003 relative to 10-year average Average = - 6%

  21. FAO – WFPGMFS Support to Crop and Food Supply Assessment missions • Provide remote sensing based maps to • cross-check information provide by the gov. departments • assess crop status • Provide bulletins / reports on • Crop forecasts • Evapotranspiration • Estimation of cropped areas

  22. FAO – WFPGMFS Support to Crop and Food Supply Assessment missions • Relative evapotranspiration maps & statistics • measure of crop water availability and crop growth rate. • Regional statistics & graphs • comparison of current, last years and 5 yr average

  23. FAO – WFPGMFS Support to Crop and Food Supply Assessment missions • Yield forecasts • Sorghum • Millet • Overall assessment & sub national analysis

  24. Collection of data, or useful information? • LR, MR, HR, Optical, SAR data, AMM, ground data,historical statistics, • Areall of them coherent? • Interpretation of data requires time, knowledge, experience and can bring to misleading conclusions. • END Users = Decision Makers • End Users need: • Simple, straightforward products. No matter how complex is the technology behind them. • A good assessment of how much products are reliable

  25. Ground reference data Goals Calibration/validation of GMFS products Provide reference data to GMFS users for other applications Requirements • Statistically sounded sampling methodology. Observe a well spatially distributed representative sample. Reduce as much as possible subjective choice of sampling units. • All land covers included in a balanced proportion (cropland, pasture, shrubs, forest, artificial) • Clear separation between training and validation datasets. Both in terms of selection and field survey methodology. • Land cover/use nomenclature coherent with EO processing requirements, but also with national/international standards

  26. Ground reference data Spatial sampling frame definitionsampling units: POINTSSystematic grid: representative and well distributed samples for any kind of application Clustering:reduced travelling time and costs Parameters : - distance between clusters - number of points per cluster - distance between points within a clusterSurvey coveragefull country / selected districts (HR products)

  27. Ground reference data Optimisation by photointerpretation Photointerpretation of HR imagery (e.g: Landsat) allows to classify most of not agricultural points Ground survey limited to cropped/mixed areas Cropland -> field survey Mixed/dubious -> field survey Other land covers (Forest) -> no field survey

  28. Ground reference data Selection criteria for training & validation are different. “A representative point is chosen typically at the corner of an agricultural field and ideally surrounded by other agricultural fields. The point should be at least 200m away from houses, roads, trees and other obstacles……. It is important that the non crop points are a bit away from any agricultural fields, when possible within a radius of 500-1000m there should be no agricultural activities” This approach of acquisition field data is optimised for the training of RS classifications, but it gives an optimistic assessment of the classification accuracy. It gives: the classification accuracy of fields that can be easily classified. If training dataset has specific requisites, they should not be applied to the validation dataset

  29. RS single service production-validation line Improve product integration/validation Data acquisition Preprocessing products Analysis & validation Definitive product & Reporting ordering planning modes … atm., geo, Correction acreages veg. indicators … Feedback • ground reference data • statistical/trend analysis • - Convergence of evidence

  30. Improve product integration/validation Multiple service production-validation line Data acquisition Preprocessing products Analysis, validation integration Definitive product & Reporting Single line products Assessment of products coherence and reliability 1 Integrated product ? • ground reference data • statistical/trend analysis • tematic coherence • Spatial coherence • - Convergence of evidence

  31. GMFS / Geonetwork Products Catalogue • Allows to catalogue data according to ISO standards • GMFS products not yet catalogued, to be implemented

  32. Role of regional centresfor data sharing • receive GMFS data products for food security • collect GIS base data for the region, created by themselves and other organizations => further share/disseminate all data to other relevant organizations in their region and to GMFS partners => need for a catalog to realize this data dissemination function

  33. Why using a Catalog? • Spatial data management • Share Common Base Maps • Facilitate Access • Share Information Quicker among Agencies • Know the Data Source, Maintainer, Owner

  34. Why using a Catalog? • Provide Information on Quality, Validity etc… • Maintain Institutional Memory (Archive) • Save Money and Time • Make Better Decisions

  35. GeoNetwork - described • standardised and decentralised spatial information management environment => web based Geographic Metadata Catalog System • developed by FAO, WFP, UNEP, WHO, OCHA and CGIAR (GeoNetwork consortium) • purpose to easily share geographically referenced thematic information between different FAO Units, other UN Agencies, NGO's and other institutions • Open Source project

  36. GeoNetwork regional nodes - benefits for the centres • have a catalog at hand they can use for three purposes: • to manage the GMFS products, to search for particular products and access them in an easy way • to catalog in a systematic way the ancillary (GIS) data sets • to make own products available to other users within or outside their own organisation => being a GeoNetwork Node will allow the Regional Centres a better management of data resources and offer an improved facility for data sharing • have a direct node to WFP-VAM, FAO and GMFS

  37. GeoNetwork regional nodes - implementation plan • Set up jointly with WFP-VAM • development, installation, training and support by GMFS and VAM • financial and manpower contributions by all implementation partners • close collaboration with the centres

  38. GeoNetwork regional nodes - implementation plan • Schedule • identification of (external) local/regional GIS expert for the centre (due end of 2005) • close involvement of the centres’ leading staff to define installation and training schedule (due end of 2005) • purchase of HW/SW (due early 2006) • installation and training foreseen in first half of 2006, carried out jointly by GMFS and VAM • IT staff and GIS expert by VAM, GIS expert by GMFS • share installation and training sessions • total duration of 10 days at each centre

  39. MeRes: Classification Statistics MODIS compared to Historical Stats (1990 – 1999) • Higher figures for MODIS compared to official statistics • - Similar trends

  40. Consortium composition Advisory panel (user exec body, service strategy group) Project management (VITO) Services Supporting tasks Geonetwork & infrastructure (GIM, TRASYS) Service delivery (VITO, EARS, UK Met, Sarmap, ITA, EFTAS, FMA, Synoptics, ULG) Promotion (VITO, ITA, EFTAS, FMA) Service validation (ITA, EFTAS, FMA, users) Information packaging (EARS, VITO, GIM) Service evaluation (AVIA-GIS, ESYS, users)

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