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National Forest Monitoring System México GFOI Capacity Building Summit

National Forest Monitoring System México GFOI Capacity Building Summit. Sept 2014. Emission and removals from forests. Context. IPCC basic method. Activity Data. Emission Factors. Emission estimates GHG emissions and removals. Elements IPCC. X =.

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National Forest Monitoring System México GFOI Capacity Building Summit

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  1. National Forest Monitoring System MéxicoGFOI Capacity Building Summit Sept 2014

  2. Emission and removals from forests Context IPCC basic method Activity Data Emission Factors Emission estimates GHG emissions and removals Elements IPCC X = land representation C stock changes Satellite Forest National Forest National GHGs Elements of NFMS system Inventory Inventory Monitoring system Series USV INEGI 1:250,000 Landsat 1:100,000 Rapid Eye 1:20,000 FRA 2015 (BUR, 2014) 6th National Comunication (2016) NFI (2004-2007 y 2009-2013) IEF (2013-2014) 350 Allometric models Specifications

  3. Remote Sensing Operational System CALIBRATION DESIGN LANDSAT 2000 ACURACY ASEESMENT ADJUSTMENT

  4. Landsat RapidEye Scale 1:20.000 Scale 1:100.000 2011 2012 2013 2003 1997 2008 2015 2020 1993 1995 2000 2005 2010

  5. Forest Land Grassland Matorral xerófilo Bosques COHERENT REPRESENTATION OF LAND Pastizal Especial otros tipos Selvas Vegetación hidrófila Agriculture Wetland Agricultura anual Acuícola Agricultura permanente Agua Settlement Other Land Otras tierras Asentamientos

  6. Change matrix

  7. NATIONAL FOREST INVENTORY( INFyS ) • Spacial • Systematic • Temporal UMP = 26, 220 Sitos = 81, 665 Grid of km | 5x5 | 10x10 | 20x20 | INFyS 2004-2007 1’ 171, 506 trees (2004-2007) Muestreo: 2004-2007 Re muestreo: 2009 – 2013 (2014) 976,738 trees (2009-2012)

  8. Standarized protcol Biomass / SMU (plot) QA/QC taxonomic and dasometric of NFI Assignation of the best allometric model Biomass Standardized data Biomass / tree Stratification based on satellite images Ppulation parameters t2-t1 Population parameters t1 and t2 National forest carbon and change per strata Stratification based on uncertainty EF / Carbon per strata

  9. Start A INEGI vegetation Model for sp Geografic proximity (Ecorregión) Sample size (n) Similar DBH range Higher determination coefficient (R2) Model for genus A Densidad de Madera Model for vegetation types A B B B Final C C

  10. Criteria to asign EF

  11. Challenges NFI, State forest inventory … Allometric equations Different data bases (Basic- dinamic etc.) Remote sensing, forest cover, change maps, activity map… Emission Factors Carbon assessment (plot) Land Cover map EF / Class (reporting) Processing unit Compilation and distribution Maps Reports

  12. Challenges • Asses carbon from other pools (DOM, Soil, etc.) from NFI • Integration of information from disturbances (degradation) • Degradation (forest cover) • Using MAD-Mex information (including forest cover change). • Labeling changes (deforestation, degradation, phenollogical)

  13. Cooperation • Workshops to share progress and challenges (let countries to speak) • Identify specific needs in each country • Technical visit for exchange and feedback with experienced persons. Ongoing • Natural ResourcesCanada

  14. Inmediate needs • To use and spread information and methodological framework used • Advise from third parties (external) • Improvement in the integration of components (institutionalization) • Analytic capacities more than technological • Management of big databases (dynamic) • Developing robust tools useful for governmental institutions

  15. Future • Based in country gaps • Based on a master plan • Including activities and budget • Implementing bilateral cooperation with international support

  16. josemaria.michel@fao.org jmichel@conafor.gob.mx www.mrv.mx

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