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]

5.4   Guiding principles – Estimation and uncertainty Previous topic Parent topic Child topic Next topic

  • Image classification can be by human interpretation, or it can be automated with human interpreters checking the results. The latter can be less resource intensive and may increase consistency.
  • The results of using a classification algorithm can be improved by an iterative process involving human interpretation, choice of training data, and use of auxiliary information usually obtained via the NFMS on forest conditions on the ground.
  • Reference data should be used with map data to correct for estimated bias and estimate confidence intervals.
  • The stratification used for activity data and for estimating emissions and removals factors should be consistent.
  • In addition to the general principles of consistent representation of land when using remote sensing for representing land or tracking units of land using a pixel approach, MGD advice is that:
    • Once a pixel is included, then it should continue to be tracked for all time. This will prevent the double counting of activities in the inventory and will also make emissions estimates more accurate.
    • Stocks may be attributed to pixels, but only change in stocks and consequent emissions and removals are reported with attention to continuity to prevent the risk of estimating large false emissions and removals as land moves between categories.
    • Tracking needs to be able to distinguish both land cover changes that are land-use changes, and land cover changes that lead to emissions within a land-use category. This prevents incorrect allocation of lands and incorrect emissions or removals factors or models being applied that could bias results.
    • Rules are needed to ensure consistent classification by eliminating oscillation of pixels between land uses when close to the definition limits.
  • In addition to classification errors, uncertainties arise from biomass and other sampling used to establish emissions and removals factors and other parameters, and use of default data.
  • Combining activity data and emission factor uncertainties to estimate overall uncertainty estimates is possible using repeated application of straightforward rules, or (in the case of more complex modelling approaches) by Monte-Carlo analysis.