Global Monitoring for Food Security Stage II

Global Global Monitoring for Monitoring for Food SecurityFood SecurityStage IIStage II

WMO/FAO/SADC Workshop, 14-18 November 2005

Food Security Information services in Africa

Paolo [email protected]

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http://www.gmfs.info

• GMESGMES – GGlobal lobal MMonitoring for onitoring for EEnvironment and nvironment and SSecurityecurity– Joint EC and ESA initiativeJoint EC and ESA initiative– Stage II: Scaling Up Consolidated GMES ServicesStage II: Scaling Up Consolidated GMES Services

• GMFSGMFS– GGlobal lobal MMonitoring for onitoring for FFood ood SSecurityecurity– Operational delivery of user-driven servicesOperational delivery of user-driven services– 2 phases 2 phases

• 2003-2004: Startup, consolidation & definition2003-2004: Startup, consolidation & definition• 2005: transition year2005: transition year• 2005-2008: Implementation2005-2008: Implementation

GMFS Project - ContextGMFS Project - Context

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SummarySummary

• 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

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Users / Partners (SADC area)Users / Partners (SADC area)

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

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Continental, regional and national services

Work package A/B/C.1000User Federation & strategic planning

WP A/B/C.4000-08Service portfolio evolutionSP1, SP2, SP3, SP5, SP13

WP A/B/C.1100User requirements

– continental

SP1

WP A/B/C.1200National/regional

User liaison

SP3, SP9, SP6

WP A/B/C.1400Promotion & sustainability

SP1

WP A/B/C.1300Training

SP3, SP2, SP5, SP1, SP11

WP A.2200

Geonetwork setup

SP5

WP A.2300SLA’s and common access conditions

SP1

WP A/B/C.2000Service network coordination.

SP1

SP1, SP5

WP A.2100Common Service infrastructure

SP4

WP A/B/C.3100Continental scale

WP A/B/C.3110Crop state indicators:

SPOT-VGTSP1

WP A/B/C.3120FAST service

SP13

WP A/B/C.323XYield estimates

SP11SP11, SP3, SP12, SP9

WP A/B/C.324XSAR agricultural

monitoring products

SP2

WP A/B/C.325XOpt. agricultural

monitoring products

SP1

WP A.321XGIS ancillary data collection

SP6SP3, SP9, SP6

WP A/B/C.322XFieldwork organization & execution

SP3SP3, SP9, SP11

WP A/B/C.326XProduct Integration-validation

SP3SP3, SP9, SP6

WP A/B/C.3400User monitoring & evaluation

SP7, SP8

WP A/B/C.3300CFSAM support & geonetwork database population

SP13, SP5, SP1

WP A/B/C. 3000Service Provision & qualification

SP1

SP1, SP2, SP3, SP6, SP7, SP9, SP10, SP11, SP12, SP13

WP A/B/C.5000Project Management

SP1

WP A/B/C.320XRegional & national scale


MSGSP1


MERISSP1

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Regional & national

EAST

WEST

SOUTH

(EFTAS)

(FMA)

(ITA)

• Local EFTAS• MR VITO• HR SARMAP

• Local FMA Synoptics, ULG• MR VITO• HR SARMAP

• Local ITA (Synoptics*)• MR VITO• HR SARMAP

* AMM first year

Responsibilities by regional and national service

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WP A/B/C.3234Yield estimates

SP3

WP A/B/C.3244SAR HR agricultural monitoring products

SP2

WP A/B/C.3254Opt. agricultural

monitoring products

SP1

WP A.3214GIS ancillary data collection

SP3WP A/B/C.3224

Fieldwork organization & executionSP3

WP A/B/C.3264Product validation

SP3

SOUTHERN AFRICA: MALAWI

WP A/B/C.1204National/regional user liaison: MoAIFS, SADC-RRSU

SP3

WP A.3245SAR MR agricultural monitoring products

SP2

WP A.3255Opt. agricultural

monitoring products

SP1

WP A.3215GIS ancillary data collection

SP3

WP A.3265Product validation

SP3

WP A.1205National/regional user liaison: FAO/WFP, SADC-RRSU

SOUTHERN AFRICA: ZIMBABWE

SP3

Specific tasks: national services

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GMFS - ProductsGMFS - ProductsProducts Scale Data ResolutionRainfall continental MSG 5 km, AU

Relative Evapotranspiration continental MSG 5 km, AUCrop Yield Forecast continental MSG 5 km, AU

Vegetation Productivity Indicator continental SPOT VGT 1 km, AU

Dry Matter Productivity national MERIS FR(or MODIS)

250-300mAU

Crop Extent at Emergence and Harvest Time national

MERIS FR(or MODIS)ASAR WSFieldwork

100 m250-300m

AU

Crop Acreage at Emergence and Harvest Time sub-national ASAR AP

Fieldwork20mAU

Crop Yield sub-national to national

Field WorkMeteoAMM

GeodatabaseAU

CFSAM reports 20 countries combined

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Meteosat 7 Meteosat 8-9

30 Minutes 15 Minutes 3 Channels 12 Channels 2500 x 2500 pixels 5 km

3712 x 3712 pixels 3 km

UK MET OFFICE

New capabilities with MSGNew capabilities with MSG

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LoRes-INDICATORS: LoRes-INDICATORS: Vegetation Productivity IndexVegetation Productivity Index

- Every 10 days- Difference of vegetation growth with Historical year (Sannier et al.)

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Medium Resolution:Medium Resolution:Input dataInput data

Envisat ASARSAR Synthetic Aperture RadarAgency European Space AgencyResolution 150 mSwath 400 Km

Envisat MERISMERIS Medium Resolution Imaging Spectrometer Agency European Space AgencyResolution 300 m Bands 15 bandsSwath 1150 km

MODISMODIS Moderate Resolution Imaging Spectroradiometer Agency NASASwath 2230 KmResolution 250 m

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

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MeRes: Classification

Green = agriculture

• Fieldwork• Crop ‘probability’ map / hard classification• Classes fairly well distinguishable

Classification

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MR-optical / MR-ASAR MR-optical / MR-ASAR integrated productsintegrated products

MultitemporalMODIS16-days DMP

MultitemporalASAR WSσ° product

MODIS-ASARCrop extent/acreage

MODIS DMPcoarse discrimination of cropped areaASAR WSbetter spatial resolutionsensitivity to early crop stagesTotal crop extent/acreageat emergence

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Malawi, Temporal changes in planted areas

Cropped area

10 December

27 January

26 December

ASAR APHigh spatial resolution.High sentivity to roughness changes.Planting/emergence/harvest

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Area Estimates: Input dataArea 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

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AreaArea EstimatesEstimates

Crop Calendar

Land practices

High resolution

Ground data, Meteo,Agro-meteorologicalModel

Acreage and Yield at ar sub-national level

Agro-ecological zones

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Acreage and Yield at 3rd level

End member identification

Agro-ecological zones

Yield estimation at national level

Trends Analysis

Medium/Low resolution

AreaArea EstimatesEstimates

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Yield Forecast:Yield Forecast:Agro-meteorological modelAgro-meteorological model

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YieldYield ForecastForecast::

Agro-meteorologicalAgro-meteorological modelmodelMillet yield forecasts 2003relative to 10-year average

Peanut yield forecasts 2003relative to 10-year average

Average = + 5%

Average = - 6%

Senegal - results 2003

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FAO – WFPFAO – WFPGMFS Support to Crop and GMFS Support to Crop and Food Supply Assessment Food Supply Assessment missionsmissions

- 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

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- 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

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- Yield forecasts - Sorghum- Millet

- Overall assessment & sub national analysis

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Collection of data, or useful Collection of data, or useful information?information?

LR, MR, HR, Optical, SAR data, AMM, ground data, historical statistics, Are all of them coherent? Interpretation of data requires time, knowledge, experience and can bring to misleading conclusions.

END Users = Decision MakersEND Users = Decision MakersEnd Users need:- Simple, straightforward products. No

matter how complex is the technology behind them.

- A good assessment of how much products are reliable

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Ground reference dataGround reference data

GoalsCalibration/validation of GMFS productsProvide 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

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Spatial sampling frame definition

sampling units: POINTS

Systematic 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 cluster

Survey coverage

full country / selected districts (HR products)


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Cropland -> field survey

Mixed/dubious -> field survey

Other land covers (Forest) -> no field survey

Photointerpretation of HR imagery (e.g: Landsat) allows to classify most of not agricultural points Ground survey limited to cropped/mixed areas

Optimisation by photointerpretation

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“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

Ground reference dataGround reference dataSelection criteria for training & validation are different.

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Data acquisition Preprocessing products Analysis &validation

- ground reference data- statistical/trend analysis- Convergence of evidence

Definitive product & Reporting

ordering planning modes …

atm., geo, Correction

acreages veg. indicators …

Feedback

RS single service production-validation lineRS single service production-validation line

Improve product Improve product integration/validationintegration/validation

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Data acquisition Preprocessing products Analysis, validationintegration

- ground reference data- statistical/trend analysis- tematic coherence- Spatial coherence- Convergence of evidence

Single line products

Assessment of products coherence and reliability

1 Integrated product ?

Definitive product & Reporting

Multiple service production-validation lineMultiple service production-validation line

Improve product Improve product integration/validationintegration/validation

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GMFS / GeonetworkGMFS / GeonetworkProducts CatalogueProducts Catalogue

-Allows to catalogue data according to ISO standards- GMFS products not yet catalogued, to be implemented

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• 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

Role of regional centresRole of regional centresfor data sharingfor data sharing

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• Spatial data management

• Share Common Base Maps

• Facilitate Access

• Share Information Quicker among Agencies

• Know the Data Source, Maintainer, Owner

Why using a Catalog?Why using a Catalog?

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• Provide Information on Quality, Validity etc…

• Maintain Institutional Memory (Archive)

• Save Money and Time

• Make Better Decisions

Why using a Catalog?Why using a Catalog?

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• 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

GeoNetwork - describedGeoNetwork - described

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• 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

GeoNetwork regional nodes - GeoNetwork regional nodes - benefits for the centresbenefits for the centres

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• 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

GeoNetwork regional nodes - GeoNetwork regional nodes - implementation planimplementation plan

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• 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

GeoNetwork regional nodes - GeoNetwork regional nodes - implementation planimplementation plan

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MeRes: ClassificationStatistics

MODIS compared to Historical Stats (1990 – 1999)

- Higher figures for MODIS compared to official statistics- Similar trends

Growing Season 04-05

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MODIS

HIST AV

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Project management(VITO)

Services Supporting tasks

Service delivery(VITO, EARS, UK Met, Sarmap, ITA,

EFTAS, FMA, Synoptics, ULG)

Service validation(ITA, EFTAS, FMA, users)

Service evaluation(AVIA-GIS, ESYS, users)

Geonetwork & infrastructure(GIM, TRASYS)

Promotion(VITO, ITA, EFTAS, FMA)

Information packaging(EARS, VITO, GIM)

Advisory panel(user exec body, service strategy group)

Consortium compositionConsortium composition

Global Monitoring for Food Security Stage II

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