2.2. UN Framework Convention on Climate Change
2.3. Disaggregation and Ecosystem Classification Systems
2.4. Completeness and Intergovernmental Reporting Guidelines
- The UNFCCC also allows governments to follow the IPCC 2006 Guidelines . This can have two contrasting effects:
- Greater completeness. Transfers of land between the various uses listed in the IPCC 2006 Guidelines represent a more comprehensive matrix model of land use and land cover change than in the UNFCCC model (Table 3) and include the main types of forest area change fluxes. Changes in carbon stocks without any land-use change, e.g., forest degradation and its reversal, are shown in the diagonal cells in the matrix (Figure 2). Forest and grassland are not combined, as in the UNFCCC model, so the deforestation flux is now clearly specified.
- Less completeness. The IPCC 2006 Guidelines only have five main reporting categories: (a) Energy; (b) Industrial Processes and Product Use; (c) Agriculture, Forestry and Other Land Use (AFOLU); (d) Waste; and (e) Miscellaneous. Combining the UNFCCC Agriculture and LUCF categories in the AFOLU category underspecifies forest carbon fluxes .
- Governments may not provide estimates in all possible reporting categories. While the IPCC  specifies “completeness” as one of four good reporting practices, a Tier System described in the IPCC 2006 Guidelines gives governments the flexibility to decide how complete their reports of carbon fluxes are, and how much they disaggregate forest types and fluxes , as their monitoring capacity may be limited . These tiers involve using:
- Global default values of two types of parameters when national empirical data are lacking: (i) “activities”, such as deforestation rates; and (ii) “emission factors”, which convert activities into carbon fluxes (Tier 1).
- Combinations of global default values of parameters and nationally specific values where these are available (Tier 2).
- Nationally specific values of parameters (Tier 3).
- Governments do not provide estimates in all possible emissions and removals categories in the historical baselines which they submit.
- Governments may structure emissions categories in their reports in different ways. The guidelines for Forest Reference Emission Level reports (and for reports on how emissions are reduced relative to these levels) are less detailed than the UNFCCC 2011 Guidelines and appear to give countries more flexibility in determining their structure and content . However, reports are supposed to meet certain criteria, e.g., by being “transparent” and “consistent” with UNFCCC and IPCC reporting guidelines, and describing the methodologies used to construct estimates. Complying with these criteria can lead to the inclusion of far more detail and disaggregation than in NGGIs.
2.5. Assessing Uncertainties
3. Literature Review
4. Methodology and Methods
4.1. Uncertainty Assessment Framework
- The features of a phenomenon, which determine the extent of complete knowledge about it and the overall scope for uncertainty. The larger and more complex a phenomenon is, the more knowledge is required to understand it properly, the more opportunities there are for information gaps to emerge, and the greater its inherent uncertainty. So a global phenomenon is generally more uncertain than a national phenomenon; and forest carbon change, which has two biophysical attributes (forest area and carbon density), is more uncertain than forest area change, which has just one attribute (forest area). Uncertainty also arises from randomness in the distribution of a phenomenon and through the diversity of human-environment interactions which it involves.
- Insufficient capacity to conceptualize the phenomenon. This leads to: (a) terminological difficulties, in which the use of unclear, poorly defined or group-specific terms to name and represent a phenomenon and its attributes leads to confusion or ambiguity; (b) underspecificity, involving a lack of detail in statements describing the multiple attributes of a phenomenon, which limits the completeness of these statements; (c) understructuralization, in which classifications of these attributes are insufficiently detailed to fully represent its complexity and disaggregation; and (d) the use of proxies, which may represent attributes by indicators only loosely related to the variables most directly linked to these attributes, or entire phenomena by models constructed with easily quantified variables.
- Insufficient capacity to measure the phenomenon. This leads to: (a) random errors in measured data; (b) systematic errors in measured data, resulting from, for example, the use of equipment with insufficient resolution to observe a phenomenon properly, or of sampling designs that allow data to be misinterpreted; (c) scalar deficiencies in measurement, which can involve short-cuts or approximations in measuring large phenomena, as in the trade-offs mentioned in Section 1; and (d) the use of subjective judgment in making estimates, in response to the other measurement difficulties .
4.2. Uncertainties in the Three Dimensions of Earth Observation
- Spatial uncertainty is concerned with the spatial distribution of ecosystems and particularly their area attribute. It relates to a norm of using optimum resolution optical sensors to map changes in the areas of different features on the ground according to the spectral reflectances of their vegetation canopies .
- Vertical uncertainty refers to the height, depth and complexity of ecosystems and their carbon attribute in layers above and below the ground. It relates to a norm of using (especially) non-optical sensors supported by ground data, both at optimum resolution, to measure changes in the biomass densities of the various components of ecosystems.
- Temporal uncertainty is linked to rates of change in the distribution of ecosystems and their carbon attribute over time. It relates to a norm of using sensors whose operational frequencies of data collection match the temporal variation of ecosystem features and utilizing data at frequencies that fully benefit from the operational frequencies of sensors.
- Spatial uncertainty should decline as the spatial resolution of sensors employed for monitoring increases since this enables: (a) more accurate measurement of deforestation and forestation for different forest types; and (b) more accurate measurement of spatial forest degradation, which reduces tree density.
- Vertical uncertainty should decline as the design of monitoring and deployment of individual and multiple data collection methods allow better vertical resolution, e.g., by: (a) greater use of non-optical satellite data, to track vertical degradation linked to changes in vegetation structure and canopy height; (b) greater disaggregation of natural and modified ecosystems with different carbon densities; and (c) better collection of ground data, to provide more accurate information on the sizes of various carbon pools, and on other vegetation features needed to ground-truth remote sensing data.
- Temporal uncertainty should decline as a rise in the temporal resolution of sensor and ground data, and their use, distinguishes better between: (a) deforestation and forestation; and (b) forest degradation and subsequent regeneration.
4.3. Coding the Uncertainty Fingerprint of an Estimate
- Estimates of forest carbon fluxes are grouped into those for:
- Carbon dioxide (CO2) emissions resulting from: (i) deforestation, and the subsequent burning of vegetation and soil; and (ii) logging and other types of forest degradation.
- The estimate which is the focus of a government report or scientific paper is identified so the uncertainty assessment can refer to it.
- Information provided with each estimate is used to assess the spatial, vertical and temporal dimensions of its uncertainty (see Supplementary Material).
- The presence of the different types of conceptualization and measurement uncertainties (Table 4) in each dimension is identified and coded as follows:
- Spatial dimension: terminological difficulties (tsp); underspecification uncertainties linked to area change fluxes, e.g., not referring to deforestation, reforestation, or afforestation (uspspa), and not covering spatial forest degradation (uspspdg); systematic errors linked to measuring forest area change (sysp) and visually analysing photographic versions of satellite images (syvis); scalar deficiencies (sc); and subjective judgment (su).
- Vertical dimension: terminological difficulties (tv); underspecification uncertainties linked to the coverage of fluxes that reduce or increase carbon pools, e.g., vertical degradation and (re)growth (uspv); understructuralization uncertainties linked to the number of carbon pools reported (ustpool), the number of ecosystem types included (ustecol), and whether land cover changes in carbon densities (ustlc) are reported; systematic errors linked to the measurement of forest biomass change (syv); and the use of proxies, e.g., basing estimates of logging degradation on wood production volumes (prlog).
- Temporal dimension: systematic errors linked to the frequency of spatial (Tsp) and vertical (Tv) mapping and reporting.
- The fingerprint is assembled for each dimension in turn by combining the codes shown above in shorthand or graphical forms. In the shorthand form groups of conceptualization uncertainties are prefixed by the letter C and measurement uncertainties by the letter M.
- The total number of uncertainties in a fingerprint gives its Uncertainty Score.
4.4. Ranking Completeness and Disaggregation
- The number of carbon pools in the six fluxes. This includes Zero pools; Low (1): above- and/or below-ground biomass; Medium: above- and below-ground biomass plus one of the following: deadwood, litter and soil (2); and High: all the pools (3).
- The number of ecosystem types into which forest is divided for carbon accounting. By referring to the full range of ecosystem types shown by global classifications e.g., , ecosystem stratification is ranked and scored as Zero, Low (1), Medium (2) and High (3).
- Whether estimates of emissions following land cover change include the biomass of the new land cover (net) or exclude it (gross). Gross estimates are scored 0 and net estimates 1.
- National Greenhouse Gas Inventories (NGGIs) contained in National Communications submitted between 2011 and 2016 by the governments of Brazil , Cambodia , Democratic Republic of the Congo , Costa Rica , Ghana , Indonesia , Laos , Malaysia , Mexico , Nigeria , Peru  and Tanzania  (Supplementary Tables S1–S2). These should be typical of reports to the Global Stocktake.
- Forest Reference Emission Level (FREL) reports submitted from 2016 to 2019 by the governments of Brazil , Cambodia , Democratic Republic of the Congo , Costa Rica , Ghana , Indonesia , Laos , Malaysia , Mexico , Nigeria , Peru  and Tanzania  (Tables S3 and S4). These should typify progress reports to REDD+.
5.1. Overview of Estimates
5.2. Sources of Spatial Uncertainty
5.2.1. Terminological Difficulties
5.2.2. Underspecification Uncertainties
5.2.3. Systematic Errors Resulting from Using Data with Insufficient Spatial Resolution
- Insufficient sensor resolution to separate forest from non-forest. Only the NGGIs for Brazil, Costa Rica and Peru state that their estimates of forest change are based on satellite data with at least the medium (20–100 m) resolution needed to identify the full range of sizes of forest clearances observed in the tropics (i.e., ≥ 1 ha). For other countries, the highest quality of source data cited are “maps” or “statistics”, with no measurements mentioned (Table S7). All the FREL reports are based on at least medium resolution satellite data (Table S7).
- Insufficient resolution to distinguish between deforestation and forestation when classifying remotely sensed data. Most of the NGGI and FREL estimates which are based on satellite data use visual analysis of photographic image products (Table S7). This can underestimate both deforestation and forestation, as the minimum patch size it can distinguish is below sensor resolution, e.g., 6 ha for 30 m Landsat images.
5.3. Sources of Vertical Uncertainty
5.3.1. Terminological Difficulties
5.3.2. Understructuralization Uncertainties
5.3.3. Systematic Errors Resulting from Using Data with Insufficient Vertical Resolution
5.3.4. Underspecification Uncertainties
5.4. Sources of Temporal Uncertainty
5.5. Fingerprinting Uncertainties in National Submissions to the UNFCCC
5.5.1. Constructing the Uncertainty Fingerprints
5.5.2. Comparing Uncertainty Fingerprints
5.5.3. Qualitative Uncertainties Versus Quantitative Uncertainties
5.5.4. Completeness and Disaggregation
5.6. Uncertainty Assessments in the NGGIs and FREL Reports
6. Implications for Reducing Uncertainty in National Reports to the UNFCCC
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|Area||Carbon Stock||Carbon Density|
|106 ha||103 Mt C||t C ha−1|
|Tropical rain forest||1700||359.6||211.50|
|Tropical seasonal forest||750||122.2||162.93|
|Temperate evergreen forest||500||82.3||164.50|
|Temperate deciduous forest||700||98.7||141.00|
|Woodland and shrubland||800||22.6||28.20|
|Tundra and alpine||800||2.4||2.94|
|Swamp and marsh||200||14.1||70.50|
|Lake and stream||250||0.0||0.09|
|1.||Tropical rain forest||1.||Tropical rain forest|
|2.||Tropical moist deciduous forest||2.||Tropical semi-evergreen/deciduous forest|
|3.||Tropical dry forest||3.||Monsoon forest|
|4.||Tropical shrub land||4.||Tropical deciduous forest|
|5.||Tropical desert||5.||Tropical montane forest|
|6.||Tropical mountain systems||6.||Broadleaved tree savanna|
|8.||Thorn tree tall grass savanna|
|9.||Thorn tree desert grass savanna|
|11.||Cactus scrub with desert grass|
|UNFCCC LUCF Categories||IPCC AFOLU Categories|
|1.||Changes in forest and other woody biomass stocks||1.||Forestland|
|2.||Forest and grassland conversion||2.||Cropland|
|3.||Abandonment of managed lands||3.||Grassland|
|4.||CO2 emissions and removals from soils||4.||Wetlands|
|7.||Harvested wood products|
|2.||Systematic errors linked to technical constraints|
|3.||Scalar deficiencies in measurement|
|Estimates in NGGIs||LUCF Emissions||All Emissions||LUCF/ All Emissions (%)||Number of Countries|
|a.||Net CO2 Emissions (Mt CO2 a−1)||1003||1959||45||7|
|Estimates in NGGIs and FREL Reports||NGGIs||FREL Reports||FREL Reports/NGGIs (%)||Number of Countries|
|b.||Gross Deforestation CO2 Emissions (Mt CO2 a−1)||1302||585||45||8|
|All 12 Countries||11.6||6.9|
|Group of 8 countries (Table 5b)||8.6||5.3|
|Completeness Ranking||Disaggregation Ranking|
|Vertical temporal uncertainties||6|
|Understructuralization by carbon pools||5|
|Spatial degradation underspecification||5|
|Understructuralization by land cover change||4|
|Spatial temporal uncertainties||4|
|Vertical terminological difficulties||3|
|Proxies for logging||3|
|Systematic errors due to use of visual image analysis||3|
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