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

Lband Synthetic aperture radar

Cband and Xband SAR

LIDAR

Global forest cover change datasets

Groundbased observations

National forest inventories

Auxiliary data

Guiding principles – Remote sensing and groundbased observations

Activity data

Methods for estimating activity data

Maps of forest/nonforest, 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
Actions


5.1.5 Estimating uncertainty of area and change in area
The IPCC definition of good practice requires that emissions inventories should satisfy two criteria: (1) neither over nor underestimates so far as can be judged, and (2) uncertainties reduced as far as is practicable (IPCC, 2003; preface).
In statistical terms, the first criterion is closely related to the statistical concept of bias. Bias is a property of a statistical formula called an estimator which, when applied to sample data, produces an estimate. An estimator is characterized as unbiased if the average of all estimates calculated using data for all possible samples acquired using the sampling design equals the true value of the parameter of interest; otherwise, an estimator is characterized as biased. In practice, application of the estimator to all possible samples is impossible, so that bias can only be estimated, and an estimate obtained using an unbiased estimator may still deviate substantially from the true value; hence, the concept of confidence interval. A confidence interval expresses the uncertainty of a samplebased estimate and is formulated as a samplebased estimate of the parameter plus/minus the samplebased estimate of the standard error of the parameter estimate, multiplied by the confidence level. Confidence intervals at the 95%level are interpreted as meaning that 95% of such intervals, one for each set of sample data, include the true value of the parameter. The width of a confidence interval is closely related to precision, a measure of the uncertainty addressed by the second IPCC criterion. Confidence intervals constructed using unbiased estimators therefore satisfy both IPCC good practice criteria specified above. This section provides advice on how to use such estimators to infer central values and confidence intervals for activity data.
Methods that produce estimates of activity data as sums of areas of map units assigned to map classes are characterized as pixel counting and generally make no provision for accommodating the effects of map classification errors. Further, although confusion or error matrices and map accuracy indices can inform issues of systematic errors and precision, they do not directly produce the information necessary to construct confidence intervals. Therefore, pixelcounting methods provide no assurance that estimates are “neither over nor underestimates” or that “uncertainties are reduced as far as practicable”. The role of reference data, also characterized as accuracy assessment data, is to provide such assurance by adjusting for estimated systematic classification errors and estimating uncertainty, thereby providing the information necessary for construction of confidence intervals for compliance with IPCC good practice guidance.
Direct observations of ground conditions by field crews are often considered the most reliable source of reference data, but interpretations of aerial photography and satellite data are also used. When the source of reference data is not direct ground observations, the reference data must be of at least the same and preferably of greater quality with respect to both resolution and accuracy than remote sensingbased map data. For accuracy assessment and estimation to be valid for an area of interest using the familiar design or probabilitybased framework (McRoberts, 2014), the reference data must be collected using a probability sampling design, regardless of how the training data used to classify for example a satellite image are collected. Probability sampling designs to consider are simple random (SRS), systematic (SYS), stratified random (simple random sampling within strata) or systematic (systematic sampling within strata) (STR), and twostage and cluster sampling. A key issue when selecting a sampling design is that the sample size for each activity must be large enough to produce sufficiently precise estimates of the area of the activity, given the policy requirement and the costs involved. SRS and SYS designs produce sample sizes for individual activities that are approximately proportional to their occurrence. If a very large overall sample is obtained, then SRS or SYS may produce large enough sample sizes for individual activities to produce estimates of sufficient precision. However, unless the overall sample size is large, sample sizes for activities representing small proportions of the total area may be too small to satisfy the precision criterion. Thus, given the likely rarity of some activities and the potentially large costs associated with large samples, serious consideration should be given to stratified sampling (STR) for which the strata correspond to map activity classes. With twostage sampling, initial primary sampling locations are chosen, then several secondary sample units are selected within the primary sampling units. The motivation is often to reduce sampling costs but several factors must be considered when planning a twostage sampling design. If distances between pairs of secondstage sampling units are less than the geographic range of spatial correlation, then observations will tend to be similar and the sampling will be less efficient. Further, the analysis of the sample is often more complex than if analysing a sample selected by SRS, SYS or STR designs. When dealing with continuous observations (such as proportion of forest) rather than classifying forest into classes or categories, modelassisted estimators may be more efficient. Typically, these estimators use the map predictions as the model predictions and then use a reference sample selected using an appropriate sampling design to correct for estimated bias resulting from systematic classification or prediction error.
Once a sample of reference observations has been collected, the activity area and the associated confidence interval are estimated using a statistical estimator corresponding to the sampling design.