Sven Gilliams, VITO-TAP SIGMA Consortium

This project has received funding from the European Union’s Seventh Programme for research,technological development and demonstration under grant agreement No 603719.Sven Gilliams, VITO‐TAP

SIGMA Consortium

Introduction

2050 – 70% increase in agricultural productivity? Sustainable intensification of agriculture:

• Agricultural Expansion• Agricultural Intensification

This project has received funding from the European Union’s Seventh Programme for research,technological development and demonstration under grant agreement No 603719.

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SIGMA ‐ Facts Funded By The European Commission Start 1 November 2013 – 30 March 2017 Agriculture AND EnvironmentAgriculture AND Environment 22 partners, 17 countries

• VITO, CIRAD, JRC, IIASA, Alterra, RADI, NMSC, DEIMOS, GeoSAS, RCMRD, Aghrymet, RCMRD, Sarvision, Sarmap, INTA, Geoville , UCL, EFTAS, FAO, ITC, GISAT, IKI, SRI

Argentina, Ukraine, China, Russia, Burkina Faso, Ethiopia USA, Brazil, Vietnam, Belgium, …11 2 M EUR 11,2 M EUR

A Major European contribution to GEOGLAM

Coordinated by VITO Coordinated by VITO http://www.geoglam‐sigma.info/


SIGMA ‐ Goal

Improve Remote Sensing based methods and indicators to

monitor and assess progress towards “sustainable agriculture”,

Inventory of Crop land distribution and its changes over timeCh t i h i i lt l d ti l l Characterize changes in agricultural production levels

Assess environmental impact of agriculture over time


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SIGMA Activities

Land cover & crop Agricultural E I tLand cover & cropland assessment

Agricultural Productivity

Env. Impact Assessment of Land use change

Sites: IKI RAN, SRI, RADI, CIRAD, INTA, VITO, UCL, GEOSAS, AGHRYMET


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Data Management

Capacity Building

SIGMA: Data Management• SIGMA distribution facility• SIGMA Analysis facility (VEGA)• SIGMA Validation facility(GeoWiki)• Agricultural database (STAC)

Expert validation campaignCore reference data set (~ 4000 samples)o e e e e ce da a se ( 000 sa p es)

Object‐based validation samples of high qualitySIGMA project partners & invited experts

Geowiki crowd sourcing campaignLarge number of point validation samples of unknown


Large number of point validation samples of unknownquality (~ 50 000 samples)

Cross‐site experiments & Global Validation Effort

htt // t b / t h? PR3 MPP https://www.youtube.com/watch?v=PR3xMPPyp‐I&feature=youtu.be


SIGMA: Global Cropland Priority map for land cover mapping + Global cropland map

Spatial aggregation of country levelcropland maps that best satisfy 4criteria:1) timeliness, 2) confidence, 3)thematic definition and 4) spatialresolution adequation.

Waldner, F.; Fritz, S.; Di Gregorio, A.; Plotnikov, D.; Bartalev, S.; Kussul, N.; Gong, P.; Thenkabail, P.;


Waldner, F.; Fritz, S.; Di Gregorio, A.; Plotnikov, D.; Bartalev, S.; Kussul, N.; Gong, P.; Thenkabail, P.; Hazeu, G.; Klein, I.; Löw, F.; Miettinen, J.; Dadhwal, V.K.; Lamarche, C.; Bontemps, S.; Defourny, P. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3.

SIGMA: Global AE StratificationGlobal SIGMA geodatabase – 1 kmAgricultural landscape character as a functional

hierarchy of abiotic, biotic and cultural phenomena


SIGMA next: Global Cropland

A fully automated cropland classification frameworkCombining SIGMA’s validation stratification and mapping achievementsThe method relies on baseline data sets for training and handles high dimensional input data and is trained specifically for different agro‐ecological strata.

Combining SIGMA’s validation, stratification and mapping achievements

Waldner F., Sepulcre Canto G., Defourny P., “Automated Annual Cropland Mapping using Knowledge-Based Temporal Features” 2015 ISPRS Journal of Photogrammetry and Remote Sensing Volume 110 December 2015 Pages 1 13


Features , 2015, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 110, December 2015, Pages 1–13

Production; Potential for agricultural intensificationintensificationConcept of yield gaps Use of crop models

to assess potential yield level Use of EO & models & statistics

to assess actual yield levels

Developed procedure to select sites using an agro-climatic zonation (CZ) and crop masks

Crop mask millet

Locations for crop calibration

CZ representativeness Burkina Faso


Production; Trends of environmental parametersparameters Provision of time‐series of EO data:

• LR vegetation indices & spectral inter‐calibration of sensors to replace pmissing values & to extend time series

• Soil moisture• Evapotranspiration

HR data simulation (data fusion) Trend analysis & change detection

• Yearly phenology (SoS, EoS, length)• Number of growing seasons per year

Crop monitoring & yield estimation protocols


SIGMA: take away for UFA JECAM SUPPORT

• Expand African network!??S d d d B i• Standards and Best practices

• Cropland definition,…• Field data collection• Validation• Validation• Cross site experiments

SIGMA TRAINING SESSIONS• 2 regional sessions in Africa (organised by RCMRD)• Topics

o Agricultural Statisticso EO based Agricultural Monitoringo Crop modellingo Environmental Impact Assessment of Agricultural land use change


Thank you!VITO, CIRAD, JRC, IIASA, Alterra, RADI,

NMSC, DEIMOS, GeoSAS, RCMRD, A h t RCMRD S i i SAghrymet, RCMRD, Sarvision, Sarmap, INTA, Geoville , UCL, EFTAS, FAO, ITC,

GISAT, IKI, SRI


Documents

Sven Gilliams, VITO-TAP SIGMA Consortium