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]

3.2.1   Selecting an integration framework Previous topic Parent topic Child topic Next topic

Selecting an integration framework for MRV requires consideration of practical and scientific issues including:
  • national and international reporting requirements;
  • data availability;
  • technical means and capacity;
  • standards by which the system and its outputs will be assessed;
  • availability of integration frameworks (also referred to as integration tools) and the expertise to implement these within the country;
  • cost effectiveness.
There are two main methods for integration remote sensing and ground-based observations:
  1. The activity data x emission/removal factor frameworks (representative of Tier 1 or Tier 2 methods).
  2. Fully Integrated frameworks, with two sub-cases:
    1. Spatially-referenced models (representative of Tier 3, Approach 2/3 methods)
    2. The spatially-explicit methods (representative of Tier 3, Approach 3 methods) which track individual units of land (polygons or pixels).
All these methods have been used by countries in developing land sector GHG estimates and, when applied correctly, all comply with UNFCCC rules and IPCC guidelines. However, the accuracy of estimates obtained can vary greatly. Tier 3 approaches may be more accurate or precise because they do not have to deploy simplifying assumptions inherent in emission/removal factor based approaches, and because they may be able to accommodate more refined stratification of forest conditions (forest types, ecological and climate conditions, age classes, disturbance and management history, etc.) although the complexity may increase and transparency decrease as a consequence.
Methods of integration are not mutually exclusive. Most countries currently use a combination of integration methods depending on the nature of forest land use, and availability of data. It is sensible to implement a national system progressively within a single integration framework. This makes it possible initially to implement simpler methods to meet short-term needs, without sacrificing the longer-term goals. For example, integration framework can initially represent only a small number of forest strata with the associated small number of growth curves. As more data become available through implementation of improvement plans for identified significant or key categories (Chapter 2, Section 2.2.3) the spatial scope of the integration framework can be expanded. Well-designed frameworks should be able to accommodate increase in estimation complexity and data richness.