GEO-XIII Plenary, 8/10/2016 St Petersburg, Russian Federation Capacity Development for Stimulating Innovation in Global Agricultural Monitoring GEO-XIII Plenary, 8/10/2016 St Petersburg, Russian Federation Sven Gilliams, VITO-TAP SIGMA Consortium

SIGMA - Facts http://www.geoglam-sigma.info/ Funded By The European Commission Start 1 November 2013 Agriculture 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 A Major European contribution to GEOGLAM-> Supporting JECAM 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 time Characterize changes in agricultural production levels Assess environmental impact of agriculture over time

SIGMA Activities Land cover & crop land assessment Agricultural Productivity Env. Impact Assessment of Land use change Sites: IKI RAN, SRI, RADI, CIRAD, INTA, VITO, UCL, GEOSAS, AGHRYMET Data Management Capacity Building

SIGMA: Data Management SIGMA distribution facility SIGMA Analysis facility (VEGA) SIGMA Validation facility(GeoWiki) Agricultural database (STAC) In Situ Data Storage

Cross Site Initiatives; EO indicators in crop yield forecasting How do (EO based) indicators from publically available, global data sets perform in crop yield forecasting across different agro-environmental landscapes (JECAM sites)? Potential for agricultural intensification Assess intensity of agricultural production via crop yield gaps and thus identifying areas that might have or will have environmental problems

HR CROPLAND mapping in South Africa Upscaling of the method presented in Waldner et al, 2015 in ISPRS Landsat-based statistical metrics Local supervized classification with Support Vector Machines Uncertainty map Unsupervised local selection of reliable calibration pixels with self-organizing maps mean 2013 0-10% ranked NDVI percentile 2012-2014 90-100 % ranked NDVI percentile 2012-2014 Cropland map Existing land cover map Validation data National Land Cover 2000

Global Validation Effort Core reference data set (~ 4000 samples) Object-based validation samples of high quality SIGMA project partners & invited experts Geowiki crowd sourcing campaign Large number of point validation samples of unknown quality (~ 50 000 samples) Expert validation tool Crowd validation tool

Capacity Development GOAL: to generate learning and resource materials and make available as a global public good. APPROACH: Develop three curricula (modules) consist of a series of training-units. training/e-learning materials must build on data, materials and experiences used/gained during WP 2-5; On the job training -> internal SIGMA partners (cross site experiment) The e-learning modules / ppt’s will be made available freely via the Internet Additional tools, open source software for geo-spatial analysis, methodological guides will be made available

Curriculum Design Based on the assessment and the results of the sigma WP (2-5), three training modules are proposed : Methodological aspects of the use of geospatial technology for agriculture statistics; Agricultural monitoring using hyper- temporal remote sensing; Geospatial data for Monitoring Agricultural changes and environmental impacts SIGMA MODULE 1: Agricultural statistics and monitoring; MODULE 2: Agricultural monitoring using hyper- temporal remote sensing; MODULE 3: Geospatial data for Monitoring Agricultural changes and environmental impacts;

Module 1: Methodological aspects of the use of geospatial technology for agriculture statistics The Lessons of the Module 1 are being developed by FAO; Main topics: Objectives, definitions and ground data collection Basic types of agricultural surveys: main concepts Agricultural probability sample surveys for exploiting geospatial information Estimation methods using geospatial information Data processing and analysis of survey results Improving the precision of estimates with geospatial auxiliary variables Resources required

Module II: Agricultural Monitoring using Multi temporal Remote Sensing Module: Lesson 2.1: The Hyper-temporal time-space domain    Lesson 2.2: Vegetation and Remote Sensing·        Lesson 2.3: Acquisition and pre-processing of hyper-temporal vegetation (NDVI) data sets·        Lesson 2.4:  Classification hyperspectral data·         Lesson 2.5: Visualizing, interpreting and analyzing·      

Module 3: Geospatial data and tools for monitoring global trends, changes and environmental impacts of agriculture: three e-learning lessons are in production: Lesson 1: Remote Sensing for monitoring and change detection in agriculture Lesson 2: Geospatial data and analysis tools for global environmental trends and changes related to agriculture Lesson 3: Geospatial data and tools for analyzing large-scale Environmental Impacts of Agriculture

Dissemination of CD material SIGMA training material ready by end 2016 Through SIGMA workshops: Internal project workshops (training event connected to SIGMA meetings) Regional Workshops Through Web E-learning and ppt’s will be made available through SIGMA Portal

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