Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. Mapping rice in Africa and
assessing the potential for
development
Sander Zwart
Researcher Remote Sensing & GIS
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

2. Short CV – Sander Zwart
Born in 1976 in the Netherlands
Wageningen:
• 1994-2000 MSc Irrigation and Water Engineering
• 2000-2002 MSc Geoinformation Science
• 2002-2010 WaterWatch company (water resources /
remote sensing, ET mapping)
(Delft:)
• 2003-2010 PhD Mapping and modelling of water
productivity
Cotonou:
• 2010-present Africa Rice Center
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

3. Africa Rice Center - Introduction
• Started as 40 years ago as the West-African
Rice Development Association
(WARDA/ADRAO)
• Pan-African organization with member states
• Goals: reduce poverty and reduce imports
through increasing rice production in Africa
• Member of the CGIAR group of international
agricultural research organizations
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

4. West Africa Rice
Development Association
(WARDA)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

5. Africa Rice Center
(AfricaRice)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

6. Future
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

7. Africa Rice Center - Introduction
4 pillars:
• Genetic Diversity and Improvement (rice
breeding) – major achievement: NERICA
• Sustainable Productivity Enhancement
(rice agronomy)
• Policy, Innovation Systems and Impact
Assessment (economy, sociology & impact)
• RiceTIME: Training, Information Management
and Extension linkages (extension)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

8. Africa Rice Center – Modus Operandi
1. Projects are always in collaboration with
National Agricultural Research Systems
(NARS) + capacity building
2. Taskforces (Gender, Rice Breeding, Policy,
Agronomy)
3. Rice Sector Development Hubs
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

9. Africa Rice Center – Introduction
Rice Sector Development Hubs:
• Regions where research and development are
concentrated along the entire rice value chain
• Participatory on-farm / real-life research
• Hubs are operated by NARS; locations are
appointed by NARS
• Efficient impact pathway: research answers to
demands and is tested in real conditions,
adopted by development sector for scaling out
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

10. Africa Rice Center – Spatial analysis activities
Unit is operational again since 4 years
• Researcher
• Postdoctoral Fellow
• Three research assistant
• Two PhD students
Strong collaboration between IRRI and AfricaRice
through CRP GRiSP – exchange of data and
development of approaches
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

11. Africa Rice Center – Spatial analysis activities
1. Mapping rice and rice ecologies
(upland/lowland/mangrove/deep water)
2. Mapping the potential for rice development
3. Mapping biotic and abiotic stresses in rice
production systems
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

12. Spatial analysis – Mapping rice
Justification
Rice statistics are very unreliable in Africa
Rice is spatially highly dynamic compared to Asia
Rice is booming in Africa
Impact assessment AfricaRice
Figure
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

13. Spatial analysis – Mapping rice
AfricaRice and IRRI co-organized an expert
meeting in Cotonou (June 2012)
Goal: discuss the options for mapping rice using
remote sensing (optical/radar) and develop a
strategy for operational monitoring
Question: what methodologies exist and can they
they be applied for African rice environments?
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

14. Spatial analysis – Mapping rice
Differences between Asian and African rice
environemnts
Asia
Africa
Irrigated rice (80%)
upland rainfed
lowland rainfed
lowland irrigated (~10%)
Stable area
Dynamic & expanding
30% of arable land
4% of arable land
Contiguous rice areas
Fragmented
Paddy land preparation
Dry land preparation
High fertilizer inputs
Low fertilizer inputs
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

15. Spatial analysis – Mapping rice
Recommendations/findings:
- Radar remote sensing is best bet
- Alternative method needs to be adopted
- Sentinel program will likely provide high spatial
and temporal resolution imagery
- Focus on monitoring rice area in Rice Sector
Development Hubs
- Mapping of inland valleys and lowland to
distinguish upland from lowland
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

16. Spatial analysis – Mapping rice
Pilot testing of radar remote sensing in two hubs:
Cosmo-SkyMed imagery is acquired every 16
days during rice season
Spatial resolution of 3m
Senegal: irrigated rice conditions (July-December)
Benin: upland and lowland rice (June-december)
Goals: mapping rice and assessing crop
phenology dates (SoS and harvest)
Field validation collected (500 points)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

17. Spatial analysis – Mapping rice
Preliminary results December 2013
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

18. Spatial analysis – Mapping inland valleys
Inland valley
Areas suitable for rice production due to favorable
hydrological conditions
Important for current and future rice production
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

19. Spatial analysis – Mapping inland valleys
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

20. Spatial analysis – Mapping inland valleys
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

21. Spatial analysis – Mapping inland valleys
stream
Digital Elevation Model
(2-dimensional)
30m
25
24
24
25m
23
23
21
20 20 21
Selected inland valley bottom
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
altitude
(m)

22. Spatial analysis – Mapping inland valleys
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

23. Spatial analysis – Mapping inland valleys
Benin: IMPETUS project (Germany): +/- 100
digitized inland valleys from Benin (accomplished)
Togo: SMART-IV project: student collecting field
data with GPS, 50 in Benin and 50 in Togo
Burkina Faso: existing data set from Min of
Agriculture
Mali: RAP-IV project, 40 inland valleys
Sierra Leone & Liberia: RAP-IV project (planned)
GOAL: entire West-Africa mapped and validated
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

24. Spatial analysis – Mapping inland valleys
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

25. Spatial analysis – Mapping inland valleys
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

26. Spatial analysis – Mapping potential
Question what is the potential for development?
Currently only 10% cultivated
Goal: provide maps that indicate the potential for
development of rice-based systems in an IV.
Users: NGO’s, government bodies (inland valley
development cell, national IV development
projects, etc.)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

27. Spatial analysis – Mapping potential
Suitability mapping is usually done with a selection
of indicators that are given a value of importance
based on expert knowledge
Disadvantage: not objective, biased
Random Forest is a statistical analysis tool that
allows explaining the presence or non-presence
without prior knowledge.
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

28. Spatial analysis – Mapping potential
Methodology has been applied to map the
potential for irrigated rice development in Laos
(IRRI / Laborte et al., 2012)
Use of data sets on roads, travel distance, villages,
markets, population density, soil suitability, water
availability, rainfall, precipitation, etc., etc.
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

29. Spatial analysis – Mapping potential
• On-going activity in two pilot sites in Benin.
• Collection of data on inland valleys and
presence or non-presence of rice or agriculture
• Building a spatial data base containing roads,
markets, travel distance, population density,
villages, inland valleys, soil types, water
availability, rainfall (remote sensing), etc.
Outlook: application at national level for westAfrican states. Implementation and validation with
national partners and users.
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City

30. Thank you! Merci!
Center of Excellence for Rice Research