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Remote sensing products in support of crop subsidy in Mexico

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Remote sensing –Beyond images …

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)

Published in: Education, Technology

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  • 1. Remote sensing products in support of crop subsidy in Mexico Carlos Dobler cdobler@siap.gob.mx
  • 2. • Two projects are presented • Retrieve useful information in support of crop subsidy and/or insurance using RS techniques • 1. Evaluation of sowing conditions • 2. Agricultural Drought Index
  • 3. Evaluation of sowing conditions • PROCAMPO Productivo: – SAGARPA’s program that subsidies agricultural activities. – Eligible areas: those planted as accorded between producer – program (ha. per field).
  • 4. Evaluation of sowing conditions • Traditional way of verifying sowing conditions: – On-field work. – Cost: $MXN p/year – ~ 20,000 fields verified. …a lot
  • 5. Evaluation of sowing conditions • Using satellite images and advanced remote sensing techniques, this process can be optimized: considerable increase in the number of verified fields. • Classification: – Object based – Decision trees
  • 6. Evaluation of sowing conditions 23 CADERs (partials) in 15 states. Spring-summer season. Imagery cover
  • 7. Imagery schedule N Image useful? Y Atmospheric correction Image segmentation Object aggregation Image Pre-processing Sampling design Unsupervised classification Sampling points selection On-field sampling Data gathering: planted/non-planted Auxiliar info: croptype, phenology, % cover, crop height, pictures Decision trees (classification) 6 info layers: -4 SPOT bands -NDVI -Texture (std. dev.) Results
  • 8. Object-based 4 spectral bands Segmentation NDVI Layer aggregation into objects = 6 object-based data layers Texture
  • 9. Sampling design Classes (unsupervised) Canatlán, Dgo. Sep 2013. Sampling points
  • 10. On-field sampling González, Tamps. Sep. 2013.
  • 11. Decision trees Sampled points: N:96 Y: 4 Sampled points: N: 11 Y:117 0.96 confidence 0.91 confidence LOW HIGH probability sowed probability sowed Sampled points: N:20 Y:15 0.57 confidence UNCERTAIN!
  • 12. Classification Miguel Auza, Zac. Aug. 2013.
  • 13. Area cuantification Folio HIGH PROB. 702901605-1 81% UNCERTAIN 0% LOW PROB. 19%
  • 14. Results State CADER Verified Fields Zacatecas Miguel Auza 19,409 San Luis Potosí Villa De Ramos 26,715 Guerrero Acapulco 5,860 Durango Canatlán 2,363 Chihuahua El Terrero 2,733 Tamaulipas González 4,793 Puebla Libres 36,520 Tlaxcala Huamantla-Cuapiaxtla 30,198 Oaxaca Pinotepa 5,279 Chihuahua Anahuac, Cusihuiriachi 3,350 Michoacán Venustiano Carranza 4,708 Jalisco La Barca, Ocotlán, Atotonilco El Alto 8,332 Jalisco Lagos de Moreno, Teocaltiche 6,364 Aguascalientes Aguascalientes 2,476 Sinaloa Mazatlán 4,034 Coahuila Monclova, San Buenaventura 1,020 Colima Armería 4,348 15 23 168,502 in 4 months
  • 15. Agricultural Drought Index • Some effects: – – – – – – Lower yields Late planting season Early harvest Crop re-conversion Interruption in cycle $$$ • Developed with assessment of National Drought Mitigation Center (UNL; USA) & Servicio Meteorológico Nacional (MX).
  • 16. Agricultural Drought Index • Conditions: • 1. Rain-fed agriculture • 2. Monthly delivered • 3. 1km2 resolution
  • 17. Agricultural Drought Index • 4. National: in-season areas
  • 18. Agricultural Drought Index • 4. National: in-season areas Rain-fed agr. (out-of-season) In-season Feb Apr Jun Aug Oct Dec
  • 19. Variables • 1. SPI (anomaly in precipitation) +400 stations (MX + US Border) Parameter tuning (for interpolations)
  • 20. Variables • 1. SPI (anomaly in precipitation)
  • 21. Variables • 2. VCI (anomaly in NDVI)
  • 22. Variables • 3. TCI (anomaly in LST)
  • 23. Variables • 4. VHI (VCI & TCI) Ensenada, BC Camargo, CHIH Silao, GTO
  • 24. Variables • 4. VHI (VCI & TCI)
  • 25. Variables integration 1 Valores fuzzy Valores fuzzy 1 0.5 0.5 0 0 -2 -1 0 Valores originales (SPI) 1 2 0 25 50 Valores originales (VHI) [%] SPI + VHI normalization 75 100
  • 26. Variables integration Drought level Extreme Very high High Med Low
  • 27. Results August 2011 August 2013
  • 28. Results
  • 29. Thank you! Carlos Dobler cdobler@siap.gob.mx