Mapping rice and rice growing environments in West-Africa using remote sensing and spatial modelling tools
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The document details a study on rice production mapping in West Africa using remote sensing and spatial modeling tools, highlighting the significance of rice for food security in the region. It discusses various rice production environments, the strategies employed for mapping, first results from pilot testing in Senegal and other countries, and the challenges faced in accurately identifying rice cultivation areas. Climate change impacts, unreliable rice statistics, and the need for technological interventions to improve food security are emphasized throughout the document.
Mapping rice and rice growing environments in West-Africa using remote sensing and spatial modelling tools
- 1. Mapping rice and rice growing environments in West-Africa using remote sensing and spatial modelling tools Sander Zwart AS El-Vilaly, JF Djagba, S Steinbach, F Holecz 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 2. 1. Importance of rice production for food security in West-Africa 2. Rice production environments 3. Strategy for rice mapping in West-Africa 4. First results 5. Challenges for rice mapping 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 3. Rice and food security in West-Africa Rice production and consumption in Africa (1970-2010) Source: FAOSTAT, AfricaRice 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 4. Rice and food security in West-Africa Contribution of various staple crops in diets in West-Africa (1961-2010) Source: FAOSTAT, AfricaRice 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 5. Rice and food security in West-Africa Challenges for food security: (West-)Africa is by far not self-sufficient and depends on international markets. Climate change is impacting W-Africa strongly; less rainfall and more erratic and intense rainfall events, lower river discharges, floods. 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 6. Rice and food security in West-Africa Why do we need creating maps and statistics of rice? • (sub-)national rice statistics are very unreliable or absent in Africa • Understanding where rice is for efficient targeting of technologies, interventions and actions 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 7. Rice production environments Rainfed upland and lowland Smallholder fields Intercropping Fragmented landscape Inland valleys / wetlands Very dynamic One rice crop Irrigated rice Large-scale systems Gradual expansion Two seasons Mangrove rice Cleared lands in forested areas Stable systems One season only 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 8. Rice production environments Differences between Asian and African rice landscapes 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 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 9. Strategy for rice mapping in West-Africa Rainfed upland and lowland Radar RS Spatial modelling Random Forest Irrigated rice Optical RS Radar RS Mangrove rice off-season Landsat GoogleEarth interpretation 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 10. Strategy for rice mapping in West-Africa An assessment of the rice growing areas in planned using data no older than 5 years. Irrigated rice: Landsat 8 imagery, supervised classification Use of radar imagery planned Mangrove rice: Landsat 8 imagery (off-season) GoogleEarth interpretation Rainfed systems: spatial modelling Random Forest Radar imagery 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 11. First results – irrigated 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) 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 12. First results – irrigated rice Preliminary results December 2013 (green is rice) 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 13. First results – mangrove rice 1. Visual interpretation and digitization in GoogleEarth (2010-2014 high resolution satellite images) 2. Remove water and mangrove forest patches using off-season NDVI maps derived from Landsat 8 imagery Implemented in Senegal, The Gambia, Guinea- Bissau, Guinea-Conakry, Sierra Leone, Liberia Total of 11 Landsat scenes 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 14. First results – mangrove rice 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 15. First results – mangrove rice 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 16. First results – mangrove rice 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 17. First results – mapping inland valleys / wetlands Inland valley or wetlands (irrigated and rainfed lowland) • Areas suitable for rice production due to favorable hydrological conditions • Important for current and future rice production 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 18. First results – mapping inland valleys / wetlands stream Digital Elevation Model (2-dimensional) 25 25m 23 23 21 21 20 20 24 24 altitude (m) 30m Selected inland valley bottom 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014 1 m threshold
- 19. Spatial analysis – Mapping inland valleys Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
- 20. First results – mapping inland valleys / wetlands Validation Omission/comission errors, accuracy and area estimation/comparison 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 21. First results – mapping inland valleys / wetlands Currently only 10% cultivated (official stats) Mapping rice in the inland valleys using remote sensing classification is (currently) impossible: • valley size • heterogenous agricultural landscape • image resolution • extent • dynamics 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 22. First results – mapping inland valleys / wetlands Random Forest Machine learning technique based on the construction of decision trees that can be used for regression or classification purposes Predict the presence of rice cultivation in the inland valleys. 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 23. First results – mapping inland valleys / wetlands • Collection of data on inland valleys and presence or non-presence of rice or agriculture • Building geo-spatial data bases containing: Road networks, villages, travel distance, markets (inputs and outputs), population density, inland valleys, soil types, water availability, rainfall (remote sensing), etc. • Implementation in Sierra Leone, Liberia, Benin and Mali 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 24. Challenges for rice mapping • Rainfed upland agriculture might be too fragmented, too small scall-scale, too dynamic, to be able to identify. • Skilled person-power - Very few young people are educated in GIS/RS - (Almost) no experience with radar remote sensing. 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014
- 25. Thank you! Merci! Center of Excellence for Rice Research 4th International Rice Congress Bangkok, Thailand, Oct 27 – Nov 1, 2014