The REDD+ satellite based land cover monitoring system for Mexico
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The REDD+ satellite based land cover monitoring system for 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)

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The REDD+ satellite based land cover monitoring system for Mexico The REDD+ satellite based land cover monitoring system for Mexico Presentation Transcript

  • The REDD+ satellite based land cover monitoring system for Mexico CIMMYT Remote Sensing Workshop, 14./15.12.2013, Mexico City, Mexico Steffen Gebhardt, CONABIO, steffen.gebhardt@conabio.gob.mx
  • Objectives Activity Data (AD) monitoring within REDD+ is primarily based on wall-to-wall land cover and land cover change information. Automatic satellite image classification is required to assure timely product generation in a standardized and cost-beneficial manner especially for a country the size of Mexico. Operative satellite forest monitoring system implemented by CONABIO within the Mexican-Norwegian Project Reinforcing REDD+ Readiness in Mexico and enabling South-South cooperation.
  • REDD+ Measuring, Reporting and Verification (MRV) system Context IPCC elements MRV system elements System Specifications Emission and removals from forests IPCC basic method Activity Data land representation Satellite Forest Monitoring system Operational wall-to-wall system based on satellite remote sensing data, with a sampling approach to assess historical deforestation and degradation rates. Changes in forest area to be assessed in order to fulfil the IPCC Tier 3 reporting requirements X Emission Factors C stock changes = Emission estimates GHG emissions and removals National Forest Inventory National GHGs Inventory INFyS implemented in 2004. Consistent and comparable over time, revision in 5 year interval. Data on carbon stock for all forest carbon pools for the main forest types at IPCC Tier 2 and Tier 3 reporting requirements. National inventory for the LULUCF sector developed following the reporting requirements of the Annex-I Parties under the UNFCCC. Following the IPCC default methods: ‘gain-loss’ or ‘stock difference’, but it could also be developed to implement a Tier 3 model.
  • Operational Processing System • “The Measuring, Reporting and Verification - Activity Data (MRV-AD) Monitoring System within the Mexican REDD+ program” = MAD-Mex • Products at 1:100,000 and 1:20,000 • Land Cover (LC), Land Cover Change (LCC) • Forest / Non-Forest, Forest Change (FC) • Cover density • Automatic classification by MAD-Mex and subsequent visual interpretation to 60 classes in agreement with INEGI • Base Line starting 1990-2020 (Landsat 5,7,8) and operational yearly monitoring 2011-2020 (RapidEye)
  • Operational Processing System Storage Software MAD-Mex Processing Workflows / Processes
  • Remote Sensing Data for AD Monitoring Landsat 135 distinct tiles
  • Remote Sensing Data for AD Monitoring RapidEye 4000 distinct tiles
  • MAD-Mex Landsat LCC method
  • MAD-Mex Landsat LCC products
  • MAD-Mex Landsat LCC products
  • MAD-Mex Landsat LCC accuracies Run 1 Run 2 Run 3 Run 4 Run 5 Temperate forest 82.1 80.5 79.3 81.2 78.8 Tropical forest 77.3 76.9 76.2 77.5 77.0 Scrubland 80.7 80.7 80.7 80.7 80.7 Wetland vegetation 66.7 64.8 66.7 64.8 68.5 Agriculture 77.0 76.9 75.4 78.5 76.0 Grassland 62.2 61.6 62.2 62.2 62.5 Water body 68.9 66.2 59.5 64.9 64.9 Barren land 72.0 88.0 80.0 80.0 84.0 Urban area 67.2 73.4 67.2 67.2 64.1 1993 76.2 75.8 76.1 76.1 76.1 1995 75.7 75.7 76.3 77.1 76.7 2000 74.8 76.2 75.7 75.8 75.3
  • MAD-Mex RapidEye LCC method
  • Escalas 1:250,000 vs. 1:100,000 vs. 1:20,000
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex Landsat vs. RapidEye
  • MAD-Mex RapidEye Change Detection 2011-03-10 2010-01-24 Change Intensities Strong negative Medium negative Light negative No change Light positive Medium positive Strong positive
  • MAD-Mex RapidEye Change Detection
  • MAD-Mex RapidEye Change Detection
  • MAD-Mex RapidEye Change Detection
  • MAD-Mex RapidEye Change Detection
  • Highlights • The Measuring, Reporting and Verification - Activity Data (MRV-AD) Monitoring System within the Mexican REDD+ program (MAD-Mex) enables automatic wall-to-wall land cover classification. • Using Landsat data seven national land cover maps at a scale of 1:100,000 between 1993 and 2008 have been generated yielding in overall accuracies up to 76% over 9 land cover classes. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. • A first and preliminary national land cover product at a scale of 1:20,000 using RapidEye data of 2011 is expected by the end of the year.
  • • Thank you • steffen.gebhardt rainer.ressl michael.schmidt @conabio.gob.mx