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. 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

2. 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.

3. 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.

4. 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)

5. Operational Processing System
Storage
Software
MAD-Mex
Processing
Workflows / Processes

6. Remote Sensing Data for AD Monitoring
Landsat 135 distinct tiles

7. Remote Sensing Data for AD Monitoring
RapidEye 4000 distinct tiles

8. MAD-Mex Landsat LCC method

9. MAD-Mex Landsat LCC products

10. MAD-Mex Landsat LCC products

11. 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

12. MAD-Mex RapidEye LCC method

13. Escalas 1:250,000 vs. 1:100,000 vs.
1:20,000

14. MAD-Mex Landsat vs. RapidEye

15. MAD-Mex Landsat vs. RapidEye

16. MAD-Mex Landsat vs. RapidEye

17. MAD-Mex Landsat vs. RapidEye

18. MAD-Mex Landsat vs. RapidEye

19. MAD-Mex Landsat vs. RapidEye

20. MAD-Mex Landsat vs. RapidEye

21. MAD-Mex Landsat vs. RapidEye

22. MAD-Mex Landsat vs. RapidEye

23. 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

24. MAD-Mex RapidEye Change Detection

25. MAD-Mex RapidEye Change Detection

26. MAD-Mex RapidEye Change Detection

27. MAD-Mex RapidEye Change Detection

28. 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.

29. • Thank you
• steffen.gebhardt rainer.ressl michael.schmidt @conabio.gob.mx