Methodology and Architecture
Workpackage 1. Studies 1& 2
Measurements, Standard Setting and Monitoring
DRAFT 1. October 2007
Submitted by the World Resources Institute and Winrock International
1
Fred Stolle, Florence Daviet, Susan Minnemeyer, and Lauriane Boisrobert, World Resources Institute
Sandra Brown, Nancy Harris, and Silvia Petrova, Winrock International
In collaboration with Badan Planalogi (BAPLAN)
Ministry of Forestry, Indonesia
And with contributions from:
1
Anne Casson and Ketut Deddy, Sekala
Matthew Hansen and Peter Potapov, South DakotaStateUniversity
1
Methodology for Monitoring and Setting Indonesia’s Baselines for Deforestation and Forest Degradation
1. Objective of Study
The report presented here will cover the following two topics:
•Study 1: Methods for measuring and monitoring the state and change of forest carbon stocks
•Study 2: Quantification of past (reference scenario) and future (projected business as usual) baseline carbon emissions, with a focus on CO2 emissions only
2. IPCC Guidance on Emission Inventories
2.1 Approaches and Tiers
The 2006 IPCC Guidelines for National Greenhouse Gas Inventories for Agriculture, Forestry and Other Land Uses (AFOLU) and the 2003 IPCC Good Practice Guidance for Land Use, Land Use Change and Forestry (GPG-LULUCF) refers to specific sources of emissions/removals of greenhouse gases. For the purposes of this report for Indonesia, the following categories of land cover/land use conversions are considered:
- Forest Land Converted to CropLand, Forest Land Converted to GrassLand, Forest Land Converted to Settlements, and Forest Land Converted to Other Land are equated to deforestation.
- ForestLandRemainingForestLand is equated to forestdegradation
The IPCC Guidelines refer to two basic inputs with which to calculate greenhouse gas inventories: activity data and emissions factors. Activity data refer to the extent of an emission/removal category, and in the case of deforestation and forest degradation refers to the areal extent of those categories, presented in hectares—referred to here as area change data. Emission factors refer to emissions or removals of greenhouse gases per unit activity, e.g. tons carbon dioxide emitted per hectare of deforestation. Emissions and removals resulting from land conversion are manifested in changes in ecosystem carbon stocks, and for consistency with the IPCC Guidelines, we use units of metric tonnes of carbon per hectare (t C ha-1), to express emission factors for deforestation and forest degradation.
The AFOLU guidelines define a methodology for assessing the activity data or the change in area of different land categories. The guidelines describe three different approaches for the area change component:
- Approach 1 identifies the total net area change for each land category, but does not provide information on the nature and area of conversions between land uses;
- Approach 2 involves tracking of land conversions between categories. Both approaches 1 and 2 provide “net” area changes.
- Approach 3 extends Approach 2 by using spatially explicit land conversion information; thus allowing for an estimation of both “gross” and “net” changes. Because the global interest is on reducing emissions from deforestation, Approach 3 that gives gross deforestation is the only practical approach that can be used for REDD implementation.
The emission factors are derived from assessments of the changes in carbon stocks in the various carbon pools of a forest. Carbon stock information can be obtained at different Tier levels (1-3):
- Tier 1 uses IPCC default values (i.e. biomass in different forest biomes, carbon fraction etc.);
- Tier 2 requires some country-specific carbon data (i.e. from field inventories, permanent plots), and
- Tier 3 national inventory-type data of carbon stocks in different pools and assessment of any change in pools through repeated measurements.
Tier 1 requires no new data collection to generate estimates of forest biomass and is unlikely to deliver the accurate and precise measures of performance. Default values for forest biomass and forest biomass mean annual increment (MAI) are obtained from the IPCC Emission Factor Data Base (EFDB), corresponding to broad continental forest types. Tier 1 estimates thus provide limited resolution of how forest biomass varies sub-nationally and have a large error range (~ +/- 30-50%). The lack of limited spatial resolution is important because deforestation and degradation tend to be localized and may affect subsets of forest that differ consistently from a larger scale average. Tier 1 also uses simplified assumptions to calculate emissions. For deforestation, Tier 1 uses the simplified assumption of instantaneous emissions from woody vegetation, litter and dead wood. To estimate emissions from degradation, Tier 1 applies the gain-loss method using a default MAI combined with losses reported from wood removals and disturbances, with transfers of biomass to dead organic matter estimated using default equations.
At the other extreme, Tier 3 is the most rigorous approach with the highest level of effort, using actual inventories with repeated measures of permanent plots to directly measure changes in forest biomass. Tier 3 often focuses on measurements of trees only, and uses region/forest specific default data for the other pools. Unlike Tier 1, Tier 3 does not assume immediate emissions from deforestation, instead modeling transfers and releases among pools that more accurately reflect how emissions are realized over time. To estimate emissions from degradation, in contrast to Tier 1, Tier 3 uses the stock difference approach where change in forest biomass stocks is directly estimated from repeated measures. The Tier 3 method requires long-term commitments of resources and personnel, generally involving the establishment of a permanent organization to house the program. The Tier 3 approach can thus be prohibitively expensive in the developing country context, particularly where only a single objective (estimating emissions of greenhouse gases) supports the implementation costs.
Tier 2 is akin to Tier 1 in employing static forest biomass information, but improves on that approach by using country-specific data (i.e. collected within the national boundary), and by resolving forest biomass at finer scales through the delineation of more detailed strata. Also, like Tier 3, Tier 2 can modify the Tier 1 assumption that carbon stocks in woody vegetation, litter and deadwood are immediately emitted following deforestation (i.e. that stocks after conversion are zero), and instead develop disturbance matrices that model retention, transfers (e.g. from woody biomass to dead wood/litter) and releases (e.g. through decomposition and burning) among pools. For degradation, Tier 2, absent repeated measures from a representative inventory, uses the gain-loss method using locally-derived data on mean annual increment. Done well, a Tier 2 approach can yield significant improvements over Tier 1 in precision achieved, and though not as precise as repeated measures using permanent plots that can focus directly on stock change and increment, Tier 2 does not require the sustained institutional backing and more intensive logistics (detailed permanent plot locations) and data archiving demanded by Tier 3.
A summary of which approach can be used for the activity data and which Tier for the emission factors for estimating gross emissions of CO2 from deforestation and degradation is shown in the shaded boxes of the following table.
Approach for activity data: Area change / Tiers for emission factors: change in C stocks1. Non-spatial country statistics (e.g. FAO )—generally gives net change in forest area / 1. IPCC default values
2. Based on maps, surveys, and other national statistical data / 2. Country specific data for key factors
3.Spatially specific data
- From interpretation of remote sensing data
- b. National inventory of permanent plots with repeated measurements
Although a country could use a Tier 1 method for obtaining data on forest carbon stocks, the uncertainty level would be very high and thus any estimate of CO2 emissions would also be highly uncertain. Tier 3 method for obtaining emissions factors practically calls for a national inventory of forest lands ,including repeated measurements of permanent plots, with the number and placement of the plots statistically derived to achieve a certain confidence level. To achieve a high confidence level for a country like Indonesia would be extremely costly. For example, in the USA, there are more than 175,000 permanent plots to monitor about 256 million ha of forest, measured on a 5-year cycle with a total budget of about $60 million per year. This USA example is designed to address other issues related to forests (volume growing stock, annual growth, etc.) and only measures the carbon pool in trees with high confidence.
In these methods, the focus will be on obtaining data with low uncertainty for both area change and the emissions factors—in practice the methodology here will focus on methods for a high level Tier 2 for emissions factors.
2.2 Carbon Pools
The IPCC AFOLU recognizes five forest pools that store carbon: aboveground biomass, below ground biomass, litter, dead wood, and soil. In all cases the carbon pool that changes the most when forests are cleared or degraded is aboveground biomass and there are three destinations for the stored carbon – dead wood, wood products or the atmosphere.
The key emission factor will be related to change in carbon stock of aboveground biomass and if this pool is estimated to a high level of certainty, then the overall certainty in emissions estimates would be high.
2.3 Experience with Monitoring Emissions and Developing Baselines
All Annex 1 countries are required to submit on a regular basis a national inventory of GHG emissions from all sectors. The AFOLU sector has methods and reporting requirements for deforestation and forest degradation (mostly from timber harvesting) and thus theoretically there is experience by Annex 1 countries in doing such emissions estimates. However, in many industrialized countries who have ground-based national forest inventories in place, they tend to analyze the inventory data for net emissions and report as such for the forestry sector.
Technical assistance has been provided to many developing countries from organizations such as UNDP, US EPA and US DOE to perform a GHG emissions inventory for the AFOLU sector often using IPCC Approach 1 combined with Tier 1 data for the most part.
Some experience has been gained for developing baseline projections of carbon emissions at the project scale in several countries Latin America[1]. Of note is The Nature Conservancy’s Noel Kempff project in Bolivia that developed and implemented baseline methodologies for both stopping logging and deforestation—these methodologies have since been certified by SGS,[2]
3. Definitions of Relevant Terms
3.1 Forest
The estimation of deforestation is affected by the definitions of ‘forest’ versus ‘non-forest’ area that vary widely in terms of tree size, area, and canopy density. Forest definitions are myriad, however, common to most definitions are threshold parameters including minimum area, minimum height and minimum level of crown cover. In its forest resource assessment of 2005, the FAO uses a minimum cover of 10%, height of 5m and area of 0.5ha. However, the FAO approach of a single worldwide value excludes variability in ecological conditions and differing perceptions of forests.
For the purpose of the Kyoto Protocol , it was determined that Parties should select a single value of crown area, tree height and area to define forests within their national boundaries. Selection must be from within the following ranges, with the understanding that young stands that have not yet reached the necessary cover or height are included as forest:
- Forest area: 0.05 to 1 ha
- Potential to reach a minimum height at maturity in situ of 2-5 m.
- Tree crown cover (or equivalent stocking level): 10 to 30 %
Under this definition a forest can contain anything from 10% to 100% tree cover; it is only when cover falls below the minimum crown cover as designated by a given country that land is classified as non-forest. To date, Indonesia defines forests with a minimum crown cover of 30%. However, consistency in forest classifications through time for all REDD activities is critical for integrating different types of information including remote sensing analysis.
3.1.1 Stratification of Indonesian forests
Forests should to be stratified into classes that have significance for carbon measurements (e.g., forest type, forest status). These classes might be different than the current classification that the Ministry is using (see Table 2).
Classes that are needed for carbon measurements need to be stratified according to function, elevation, species type, or other factors. Therefore two different typologies are proposed here.
- In Table 3 (proposed by Rizaldi Boer) Indonesia forests are classified mainly according to function, disturbance, and hydrology. There are four main functional types (production, conservation, protection, and conversion), within which are two disturbance classes (primary [propose use term intact rather than primary] and secondary), and last three classes based on hydrology (up land or “dry land”, swamp forests including peatswamps, and coastal mangroves), as described below.
- In Table 4, a different classification is shown that is based on elevation and rainfall data.
Table 2: Ministry of Forestry classes
Forested area:Primary dry land forest
Secondary dry land forest
Primary swampy forest
Secondary swampy forest
Primary mangrove forest
Secondary mangrove forest
Plantation forest
Non Forested area:
Shrub/bush
Swampy bush
Savannah
Estate
Dry land agriculture
Dry land agriculture and shrub
Transmigration
Wet land
Brackish water
Opened land
Mining area
Settlement area
Water body
Swamp
Airport
Unavailable Data:
Cloudy
None
Table 3: Proposed classification of forests by Rizaldi Boer.
A / Production forestA1 / NaturalForest
A1.1 / Non Disturbed
Primary dry land forest
Primary swampy forest
Primary mangrove forest
A1.2 / DisturbedForest
Secondary dry land forest
Secondary swampy forest
Secondary mangrove forest
A2 / PlantationForest
A2.1 / Non Disturbed
A2.2 / Disturbed
B / ConservationForest
B1 / NaturalForest
B1.1 / Non Disturbed
Primary dry land forest
Primary swampy forest
Primary mangrove forest
B1.2 / DisturbedForest
Secondary dry land forest
Secondary swampy forest
Secondary mangrove forest
C / ProtectionForest
C1 / NaturalForest
C1.1 / Non Disturbed
Primary dry land forest
Primary swampy forest)
Primary mangrove forest
C1.2 / DisturbedForest
Secondary dry land forest
Secondary swampy forest
Secondary mangrove forest
D / ConversionForest
D1 / NaturalForest
D1.1 / Non Disturbed
Primary dry land forest
Primary swamp forest
Primary mangrove forest
D1.2 / DisturbedForest
Secondary dry land forest
Secondary swamp forest
Secondary mangrove forest
D1.3 / Non Forest Cover
E / Forested APL (Area Penggunaan Lain)
E1 / NaturalForest
E1.1 / Non Disturbed
Primary dry land forest
Primary swamp forest
Primary mangrove forest
E1.2 / DisturbedForest
Secondary dry land forest
Secondary swampy forest
Secondary mangrove forest
Table 4: Proposed classification based on elevation and rainfall (by WRI).
Forest Type / Peat / Elevation (m) / Rainfall, cm/yrPrimary forest / No / 0-600 / <200
0-600 / 200-300
0-600 / 300-400
0-600 / >400
600-1000 / <200
600-1000 / 200-300
600-1000 / 300-400
600-1000 / >400
1000-2000 / <200
1000-2000 / 200-300
1000-2000 / 300-400
1000-2000 / >400
>2000 / <200
>2000 / 200-300
>2000 / 300-400
>2000 / >400
Primary forest / Yes / 0-600 / <200
0-600 / 200-300
0-600 / 300-400
0-600 / >400
SecondaryForest / No / 0-600 / <200
0-600 / 200-300
0-600 / 300-400
0-600 / >400
600-1000 / <200
600-1000 / 200-300
600-1000 / 300-400
600-1000 / >400
1000-2000 / <200
1000-2000 / 200-300
1000-2000 / 300-400
1000-2000 / >400
>2000 / <200
>2000 / 200-300
>2000 / 300-400
>2000 / >400
SecondaryForest / Yes / 0-600 / <200
0-600 / 200-300
0-600 / 300-400
0-600 / >400
3.2 Deforestation
Most definitions characterize deforestation as the long-term or permanent conversion of land from forested to non-forested. Under Decision 11/CP.7 the UNFCCC defined deforestation as follows:
‘Deforestationis the direct, human-induced conversion of forested land to non-forested land’.
Effectively this means a reduction in crown cover from above the threshold for forest definition to below this threshold. For example, if a country defines a forest as having a crown cover greater than 30%, then deforestation would not be recorded until the crown cover was reduced below this limit. Yet other countries may define a forest as one with a crown cover of 20% or even 10% and thus deforestation would not be recorded until the crown cover was reduced below these limits.
Deforestation causes a change in land cover and in land use. Common e.g conversion of forests to annual cropland, conversion to perennial plants (oil palm, shrubs), conversion to slash-and-burn (shifting cultivation) lands, and conversion to urban lands or other human infrastructure.
3.3 Degradation
Where there are emissions from forests caused by a decrease in canopy cover that does not qualify as deforestation, it is termed as degradation. Therefore, estimations of degraded areas will be affected by the definition of a “degraded forest”, which is not standardized.
The IPCC special report on ‘Definitions and Methodological Options to Inventory Emissions from Direct Human-Induced Degradation of Forests and Devegetation of Other Vegetation Types’ (2003) suggested the following characterization for degradation:
‘A direct, human-induced, long-term loss (persisting for X years or more) or at least Y% of forest carbon stocks [and forest values] since time T and not qualifying as deforestation’.
where X and Y are undefined.
In terms of changes in carbon stocks, degradation therefore would represent a measurable, sustained, human-induced decrease in canopy cover, with measured cover remaining above the threshold for definition of forest. The question to resolve is what are the values of X and Y.
Degradation presents a much broader land cover change than deforestation. Technically, a land cover change would be termed ‘degradation’ if canopy cover dropped from e.g., 100% to 85%, or 50% to 40%, or 90% to 35%. In reality, monitoring of degradation will be limited by the technical capacity to sense and record the change in canopy cover, so that small changes will likely not be apparent unless they produce a systematic pattern in the imagery.