Uncertainty [MGD Sections]

UNFCCC decisions and requirements
IPCC good practice guidance
Relationship to UNFCCC
GHGI coverage, approaches, methods and tiers
Design decisions relevant to national forest monitoring systems
Land cover, land use and stratification
Forest reference emission levels and forest reference levels
Quality assurance and quality control
Guiding principles – Requirements and design decisions
Estimation methods for REDD+ activities
Integration frameworks for estimating emission and removals
Selecting an integration framework
Activity data x emission/removal factor tools
Fully integrated tools
Practical considerations in choosing an integration tool
Guiding principles – Methods and approaches
Remote sensing observations
Coarse resolution optical data
Medium resolution optical data
High resolution optical data
L-band Synthetic aperture radar
C-band and X-band SAR
LIDAR
Global forest cover change datasets
Ground-based observations
National forest inventories
Auxiliary data
Guiding principles – Remote sensing and ground-based observations
Activity data
Methods for estimating activity data
Maps of forest/non-forest, land use, or forest stratification
Detecting areas of change
Additional map products from remote sensing
Estimating uncertainty of area and change in area
Estimating total emissions/removals and its uncertainty
REDD+ requirements and procedures
Reporting forest reference emission levels and forest reference levels
Technical assessment of forest reference emission levels and forest reference levels
Reporting results of REDD+ activities
Technical analysis of the REDD+ annex to the BUR
Additional advice on REDD+ reporting and verification
Guiding Principles – Reporting and verification of emissions and removals
Financial considerations
Country examples – Tier 3 integration
Use of global forest change map data
Relative efficiencies
Developing and using allometric models to estimate biomass

Record Keeping [MGD Sections]

Integration + Estimation [MGD Sections]

Ground Based Observations [MGD Sections]

3.1   Estimation methods for REDD+ activities Previous topic Parent topic Child topic Next topic

Since IPCC guidance does not refer to REDD+ activities specifically, MGD advice makes the necessary links between IPCC guidance and REDD+ activities. The MGD does not reproduce IPCC guidance, but cross-references it where necessary. The GPG2003 provides guidance on data sources which need to be used in conjunction with the remote sensing and ground-based data, e.g. on carbon densities for non-forest land uses or emissions and removals factors associated with non-CO2 greenhouse gases.
The MGD assumes that there should be methodological consistency between the estimates, and that double-counting of emissions and removals is to be avoided. The advice provided below achieves consistency by suggesting the same forest stratification and estimation methods across the range of REDD+ activities. Potential double counting is avoided by providing advice on the circumstances under which forest degradation and the other REDD+ activities should be estimated together. Remote sensing methods can also have rules to ensure that any pixel or mapping unit is not double counted between REDD+ activities.
The method for combining changes in area and carbon density changes will depend on the sampling or modelling approach adopted by the NFMS. In the gain-loss methods described below, the area of land affected by REDD+ activities is multiplied by the change in carbon per unit area (the carbon density change) in the various pools to estimate the total net carbon emissions or removals. The methods described in this chapter are to be used with Chapter 4 and Chapter 5, which describe the acquisition of area and carbon density data, and associated uncertainties, and includes correction of area data for bias. The methods assume that annual estimates will be made, including the correction for estimated bias, although in principle other periodicities could be used.
Where NFIs or other design-based sampling approaches (including model-assisted inference) are used, the mean carbon densities can be estimated from the sample, which may be stratified by forest type or disturbance regime to increase sampling efficiency. Where model-based inferential approaches are used, carbon densities for the areas in question are inferred from the model being used. and change in carbon density is modelled for each type of forest to non-forest conversion. The method assumes that NFIs, where they exist, will be used as a source of plot data rather than extended to estimate REDD+ activities directly. Appendix B contains a discussion on sampling.
It is most likely that countries will use medium resolution optical data to implement MGD advice. Other types of data, including high resolution optical data and radar are likely to be used increasingly as availability improves and processing techniques are further developed(1). Advice on methods based on transitions and trends between strata and within strata is included in Section 3.1; Section 3.2 includes advice on methods that can track individual changes in pixels or mapping units over time.

 (1)
There is no generally agreed definition of the terms coarse, medium and high (also called fine) resolution, and therefore for complete clarity it is better to specify resolution numerically. Where these terms are used in the MGD, coarse refers to spatial resolutions above 250 meters, medium to 10 to 80 metres and high to better than 10 metres. These ranges are determined by the methodologies described in the MGD, and the remote sensing data available via the SDCG core data streams Opens in new window. Intermediate resolutions between 80 and 250 would by default be categorised as coarse.