Module developers: Sandra Brown, Winrock International

Module 2.3 Estimating emission factors for forest cover change: Deforestation and forest degradation
Module developers: Sandra Brown, Winrock International Lara Murray, Winrock International Felipe Casarim, Winrock International After the course the participants should be able to: Describe the procedures and methods to develop estimates of emission factors for deforestation and for forest degradation by selective logging within a national forest monitoring system at a Tier 2–3 level. Estimate emission factors including all pools for deforestation for a selected forest region within a country and estimate emission factors for forest degradation by selective logging. V1, May 2015 Creative Commons License

Background material GOFC-GOLD. 2014. Sourcebook. Section 2.3.
UNFCCC Decisions: Decisions of the Conference of the Parties (COP). UNFCCC Methodological guidance for REDD+. UNFCCC Decision 12/CP17. UNFCCC Decision 1/CP18. GFOI Integrating Remote-sensing and Ground-based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observation Initiative (MGD).

Outline of lecture Changes in C stocks from deforestation and forest degradation by selective logging IPCC Tiers for estimating emission factors (EFs) Emission factors using stock-difference and gain-loss methods Estimating emission factors for deforestation Strategies Stratification of lands Carbon pools Collecting data for estimating C stocks: field measurements Estimating C stocks and developing EFs Sources of error and quality assurance/quality control Estimating emission factors for forest degradation by selective logging Collecting data for estimating the EFs: field measurements Estimating EFs and total emissions How to estimate gains through regrowth of logged areas

Outline of lecture Changes in C stocks from deforestation and forest degradation by selective logging IPCC Tiers for estimating emission factors (EFs) Emission factors using stock-difference and gain-loss methods Estimating emission factors for deforestation Strategies Stratification of lands Carbon pools Collecting data for estimating C stocks – field measurements Estimating C stocks and developing EFs Sources of error and Quality assurance/Quality control Estimating emission factors for forest degradation by selective logging Collecting data for estimating the EFs – field measurements Estimating EFs and total emissions How to estimate gains through regrowth of logged areas

Changes in C stocks from deforestation (1/2)
Potential C storage in wood products Pre-deforestation C stocks Future land use C stocks Deforestation Forest land replaced with nonforest vegetation leads to a decrease in C stocks. Deforestation/degradation can lead to increased dead wood (decomposition emissions), forest products (storage), or immediate emissions from combustion. CO2 and non-CO2 emissions from combustion and decomposition of dead biomass and soil

Changes in C stocks from deforestation (2/2)
Magnitude of change varies by pool Need to know initial C stocks of all selected pools of forest and postdeforestation land cover/use Changes occur over different time frames for different pools Magnitude of C in each pool varies depending on climate and geography. Carbon stock pools will be discussed in more depth later in this presentation.

C emissions in selective logging (degradation)
Selective logging is diffuse across the forest area—only a few trees are felled. Difficult to capture C stock changes with biomass assessments It can cause significant emissions because it covers large areas. Emissions from selective logging are considerable because vegetation must be cleared to create roads, skid trails, and landing decks. There is also incidental damage from felled trees (vegetation damaged from felling) because the carbon stored in that vegetation is eventually emitted into the atmosphere through decomposition. Furthermore, CO2 in the extracted timber will eventually be emitted to the atmosphere through turnover and retirement of wood products—a small fraction will likely end up in long-term storage (considered to be 100 years or more). Long-term storage includes wooden products that last for a long time, like tables, chairs, houses, etc.

IPCC Tiers for estimating emission factors (EFs)
The Intergovernmental Panel on Climate Change (IPCC) refers to three general tiers for estimating emissions/removals of GHGs. The tiers represent different levels of methodological complexity: Tier 1 is the basic method Tier 2 uses country-specific data Tier 3 is the most demanding in terms of complexity and data requirements Nations are encouraged to use higher tiers for the measurement of significant C sinks / emission sources: Assess significance of selective logging activities Typically, it is expected that the largest pools for deforestation will be measured applying Tier 2–3 methodology and data. If there are significant selective logging activities within a nation, it is necessary to assess whether they should be included in national greenhouse gas (GHG) monitoring and accounting frameworks.

Characteristics of different IPCC Tiers
Data granularity Default values for broad continental forest types Country-specific Region/forest specific Data Sources IPCC Emission Factor Data Base (EFDB) Country-specific data for key factors (e.g. from field measurements) Comprehensive field sampling repeated at regular time intervals, soils data, and use of locally calibrated models Cost & Uncertainty Low cost and High uncertainty Medium to low cost and uncertainty High cost and Low uncertainty Fate of pools post deforestation Assume immediate emissions at time of event—i.e. committed emissions Can use disturbance matrices to model retention, transfers, and releases Model transfers and releases among pools to reflect emissions through time Tier 3 is largely distinguished by the ‘repeated measurements’ part of it – either through actual repeated measurements or through process-based modeling approaches. Repeated measurements does not mean permanent sample plots even though these are the preferred approach, but as long as a well-designed field sampling plan is implemented temporary field plots can be used just as well. Tiers should be selected on the basis of goals (e.g. accurate and precise estimates of emissions reductions in the context of a performance-based incentives framework; conservative estimate subject to deductions), the significance of the target source/sink, available data, and analytical capability. The IPCC recommends that it is good practice to use higher Tiers for the measurement of significant sources/sinks. To more clearly specify levels of data collection and analytical rigor among sources/sinks of emissions/removals, the IPCC Guidelines provide guidance on the identification of “Key Categories”. Key categories are sources/sinks of emissions/removals that contribute substantially to the overall national inventory and/or national inventory trends, and/or are key sources of uncertainty in quantifying overall inventory amounts or trends. Due to the balance of costs and the requirement for accuracy/precision in the carbon component of emission inventories, a Tier 2 methodology for carbon stock monitoring will likely be the most widely used in both for setting the reference level and for future reporting of net emissions from deforestation and degradation. Although it is suggested that a Tier 3 methodology be the level to aim for key categories and pools, in practice Tier 3 may be too costly to be widely used, at least in the near term. And, a statistically well designed system for Tier 2 data collection for estimating emission factors could practically be as good as a Tier 3 level. On the other hand, Tier 1 will not deliver the accurate and precise estimates needed for key categories/pools by any mechanism in which economic incentives are foreseen. However, the principle of conservativeness (See Module 2.7 on estimation of uncertainties) will likely represent a fundamental instrument to ensure environmental integrity of REDD+ estimates. In that case, a tier lower than required could be used – or a carbon pool could be ignored - if it can be soundly demonstrated that the overall estimate of reduced emissions are underestimated. Different tiers can be applied to different pools where they have a lower importance. For example, where preliminary observations demonstrate that emissions from the litter or dead wood or soil carbon pool constitute less than 20% of emissions from deforestation, the Tier 1 approach using default transfers and decomposition rates would be justified for application to that pool.

Estimating emission factors: stock-difference
 Most commonly applied for estimating emissions from deforestation 𝐸𝐹= 𝐶 𝑏𝑖𝑜,𝑝𝑟𝑒 − 𝐶 𝑏𝑖𝑜,𝑝𝑜𝑠𝑡 − 𝐶 𝑤𝑝 + {[𝐶𝑆 0 − 𝐶𝑆 𝐷 ]/𝐷} × 𝐸 𝑂𝑡ℎ Where: EF = Emission factor, t CO2-e ha-1 Cbio,pre = C stock in biomass prior to forest change, t C ha-1 Cbio,post = C stock in biomass post-deforestation, t C ha-1 CSO = Initial or reference soil organic carbon, CSD = Soil organic carbon at default time D, t C ha-1 D =Default time period to transition to a new equilibrium value (20 year) Cwp = Carbon stored in long term wood products, t C ha-1 44/12 = Conversion factor for C to CO2 Eoth = Emissions of non-CO2 gases, such as CH4 & N2O released during burning, t CO2-e ha-1 This is the most commonly applied method. This method is used when there are national inventories available: measure at time 1, go back and measure at time 2, and the overall stock change is calculated as the difference in total stocks between the two measurements. This is the best method to use when estimating emissions for deforestation. But remember: you are interested only in the anthropogenic changes—those caused by humans, such as deforestation caused by conversion to agricultural land, where the time 2 stocks are very low. Subtracting the C stocks at time 2 from time 1 for forests-remaining-forests with no human disturbance is not the value you want—this is not a change caused by humans but rather what can be considered the background rate of change due to natural events This method is good for estimating changes in land cover types—it can be used to estimate smaller differences within a particular land use.

Estimating emission factors: gain-loss
 Particularly useful for estimating emissions from forest degradation 𝐸𝐹= ∆ 𝐶 𝐺 −∆ 𝐶 𝐿 × 𝐸 𝑂𝑡ℎ Where: EF = Emission factor (t CO2-e ha-1 ) ΔCG = Carbon stock gains in all pools (t C ha-1) ΔCL = Carbon stock losses in all pools (t C ha-1) 44/12 = Conversion factor for C to CO2 Eoth = Emissions of non-CO2 gases, such as CH4 & N2O released during burning, t CO2-e ha-1 This equation estimates the net balance of additions and removals from a pool. For example, after continuous monitoring of tree C stocks in a given forest stratum, the gains from forest growth or expansion are calculated and the losses from forest removal are calculated to produce a net value of C stocks. This approach is useful for degradation, as one can estimate the losses due to removals of, for example, timber or fuel wood and then the gains from regrowth after the disturbance when trees and other plants will grow into the gaps. As forest degradation involves smaller changes in C stock over time, it is more appropriate to apply the gain-loss method for EF development. Emissions from selective logging may be estimated by summing all the gains (regrowth) and losses in forest carbon stocks resulting from the selective logging activity.

Comparison of stock-difference and gain-loss
Stock-Difference Gain-Loss Description Difference in C stocks in a particular pool in pre- and post–forest cover change Net balance of additions to and removals from a carbon pool Data requirements Data needed on forest carbon stocks in key pools before and after conversion Annual data needed on C losses and gains, e.g., annual tree harvest volume and annual rates of forest growth post–tree removals Applications Appropriate for deforestation and afforestation and for reforestation Appropriate for forest degradation caused by tree harvest and the regrowth of carbon stocks postdisturbance

Collecting data for emission factor development…
Which carbon pools to include in the monitoring plan? Collecting data for emission factor development… How to stratify C stocks? How to measure the carbon pools?

Strategies for estimating emission factors
Strategies for estimating stocks in forest carbon pools must be planned based on an assessment of the quality, quantity, and availability of existing data and whether new data need to be collected Criteria that existing data need to meet are: The data are less than 10 years old The data are derived from multiple measurement plots All species must be included in inventories Minimum diameter at breast height (DBH) is 20 cm or less Data are sampled from a good coverage of strata Questions that should be asked might include: Do national forest inventories already exist? Have studies on biomass already been conducted by academia, government institutions, or international bodies like the FAO? What methods were used? Were the data collected from the forest type of interest and with enough plots to produce precise estimates (e.g., 95% confidence interval of 10–15% of the mean)? Who conducted the inventory? Which C pools were assessed in the inventory? What kind of budget and resources are available to collect new data? How difficult would it be to collect new data? Are there large remote/inaccessible areas?

Stratification and sampling approach
Plans should be designed to stratify the forest lands to ensure sampling is cost-effective while producing results with low uncertainty. One needs to select pools to include and sampling methods. Sampling plans should be designed to meet a targeted or required level of certainty for newly collected data and to meet international standards. Common sampling designs are: Cluster sampling—can reduce sampling costs Stratified sampling—allows for specification of sample size for specific strata Systematic sampling—long history of use in forestry Consult a biometrician with experience in forestry. Stratifying should make sampling more efficient. The bottom line is that it doesn’t really matter how you stratify as long as you achieve your targeted accuracy/precision within each stratum.

Purpose of stratification
The purpose of stratification is to organize a heterogeneous area into subpopulations or “strata” that form relatively homogenous units. If stratum sample sizes are chosen properly, the estimated mean is more precise than simple random sample. By stratifying, the sampling effort should be reduced, because in theory more homogeneous strata mean that fewer samples are needed to achieve a given target for accuracy/precision.

Stratification plan Develop initial stratification plan based on biophysical or human factors that influence the distribution of carbon stocks, such as: Land use Vegetation/forest Type Elevation/slope Drainage Proximity to human infrastructure Collect preliminary data on carbon stocks. (~10 plots per stratum) Generally, the stratification approach should be selected based on an assessment of how complex the landscape and land-use dynamics are. The more complex, the more strata necessary to accurately and precisely portray C stocks. For example, if resources are limited and deforestation is significant, stratifying by proximity to human infrastructure may be appropriate as deforestation is most likely to occur in those areas (like areas close to roads and urban centers).

Stratification of lands
Stratification by carbon stocks: Stratifying by carbon stock reduces sampling effort required to achieve targeted precision level Stratification by threat: Stratifying by threat focuses on measuring and monitoring in areas where changes are most likely to occur Stratification by potential threat is a good initial step, as this helps to identify where change is likely to occur. There are various models available to help generate zones that are more likely to undergo human disturbance. These are presented later

Stratification by C stocks (1/2)
C stocks vary by land use and forest type. Differences in C stocks are affected by elevation, climate, soil type, and disturbance.

Stratification by C stocks (2/2)
Grouping forest and other land covers with similar carbon stocks is a cost- effective and good practice to estimate emission factors.

Stratification by threat (1/4)
Not all forests are under immediate threat of conversion. Sumatra, Indonesia Madre de Dios, Peru Nonforest Forest Roads Rivers Nonforest Forest Stratification by threat attempts to lower sampling effort by first grouping areas that are most likely to be affected by change. Then, C stocks within those areas under higher threat are stratified and sampled.

Can be a practical, more cost-effective option. Provides advantages if forest accessibility is limited in regions and thus likely the forest is likely under no threat. Can serve as the first phase in the creation of a national forest monitoring system. Uses information such as: Roads, rivers, towns/settlements, proximity to cleared areas Logging concessions, protected areas Post land-use change Patterns of historical deforestation Stratification by threat may be a good initial step in designing a plan to sample for C stocks.

Ecological zone Roads Stratified forest A simple stratification by threat combines data on ecological zones and forest type with data on human infrastructure to more accurately pinpoint areas where sampling should be focused.

Use spatially explicit land-use change model Identify key factors impacting historical deforestation patterns Identify areas with high suitability for deforestation Create deforestation threat map This slide demonstrates a sophisticated approach to stratification by threat. A model is developed to describe spatially explicit deforestation dynamics, which are then mapped. However, areas can be stratified with less sophisticated threat analyses that are just as valid in terms of an acceptable sampling efficiency. This is an example of the application of GEOMOD in the Idrisi software package and is explained more in the country case study (Guyana).

Five forest carbon pools
Aboveground live biomass Dead wood Litter This diagram depicts the five main carbon pools: litter, aboveground live biomass, belowground live biomass (roots), soil organic matter, and dead wood. The magnitude of C in each of these pools varies according to forest type, climate, and geography. While aboveground live biomass normally constitutes the majority of C stocks in an ecosystem, there are areas where soil organic matter makes up the majority of C stocks (e.g., the peat forests in South East Asia). Belowground live biomass Soil organic matter

Which carbon pools to monitor (1/2)
Choosing which C pools to monitor depends on: What EF you are developing (e.g., deforestation or degradation) Magnitude of pool Rate of change of pools in response to human disturbance Expected direction of change Costs to measure Methods available to measure Attainable accuracy and precision Choosing C pools to monitor also depends on the type of EF you are estimating. For example, soil organic carbon estimates are developed for a deforestation EF but not for a degradation EF (selective logging EF).

Which Carbon Pools to monitor (2/2)
Which C pools should be selected? In general, all those pools representing 5% or more of total should be included: The aboveground and below ground tree carbon pool Standing and lying dead wood, because it can represent up to 10% of the total carbon pool Understory in a mature forest is often negligible—less than 2–3% of the total; in a secondary forest this can be a larger proportion—up to 5% of the total The soil carbon pool should be included if the forest conversion is to agriculture or roads or other land uses with high soil disturbance (e.g., mining); if converted to pasture soil, C pool can be ignored The soil C pool should be included if deforestation occurs on peat swamp forests In all cases, trees are measured because their measurement is relatively easy and they represent a large portion of the total carbon stock. The belowground (roots) carbon pool should be included as robust models/estimates are provided in IPCC, 2006, AFOLU GL. In general all pools that represent 5% or more of total C stock should be included.

Traditional field measurements
Measure different carbon pools within strata. Repeat measurements in many sample plots across landscape, using to a stratification strategy. A sampling strategy is developed by a field crew that travels to randomly selected locations in a stratified landscape and collect data that describe the C content in each of the pools selected (e.g., diameter of trees, soil samples, litter samples).

Methods for field measurements
Methods for field measurements must be developed and documented prior to sampling. Methods must be standardized to ensure measurements are implemented consistently between field crews and inventories. For example, Winrock Standard Operating Procedures (SOPs) for Terrestrial Carbon Measurements can be used to measure carbon stocks of forests and other land cover types. More info on importance of SOPs, see Module 3.1. Standard methods for field data collection need to be used and documented and there are many applicable manuals. The slide shows one example from Winrock that has been developed over multiple years, peer reviewed, and tested in many countries.

Outline of lecture Changes in C stocks from deforestation and forest degradation by selective logging IPCC Tiers for estimating emission factors (EFs) Emission factors using stock-difference and gain-loss methods Estimating emission factors for deforestation Strategies Stratification of lands Carbon pools Collecting data for estimating C stocks Estimating C stocks and developing EFs Sources of error and Quality assurance/Quality control Estimating emission factors for forest degradation by selective logging Collecting data for estimating the EFs – field measurements Estimating EFs and total emissions How to estimate gains through regrowth of logged areas

Developing EFs at Tier 1 Biomass C stock maps can be useful for an IPCC Tier 1–type emission factor for deforestation, although Biomass C stock maps are constantly improving. (The map shown here is an improvement over the IPCC Tier 1 values.) EFs can be developed using biomass C stock maps, which provide estimates of carbon stocks by each strata (described in this module’s section 4.ii) and used with the stock-difference method. Biomass C stock maps can be overlain in GIS with a previously developed strata map. Then an average C stock per strata can be obtained and used with the stock-difference method (slide 12) as the C stock in biomass prior to forest change in t C ha-1. The map depicted above is an improvement over the IPCC Tier 1 values. A digital version of this map can be obtained from Global or regional default values of EFs (mostly for Tier 1) based on the best science when guidelines were compiled can be found in IPCC Emission Factor Database (EFDB): . Developed by S. Saatchi 2013, in collaboration with Winrock and Applied GeoSolutions; map at 250 m resolution.

Procedure for calculating average forest carbon stocks
Measurements of carbon pools are recorded in the field. Models and conversion factors are used to estimate carbon stocks in each major pool based on field measurements. Statistical analysis is used to calculate average forest carbon stocks based on plot data. Biomass (kg C per tree) Estimating C stocks involves collecting field measurements and analyzing the data to accurately and precisely describe the average forest carbon stocks per stratum. DBH (cm)

Data analysis Trees often have predictable relationships among their height, diameter, and roots. Mathematical formulas are available or can be developed to express these relationships. Forest field data can be applied to these mathematical formulas to indirectly estimate biomass. Biomass estimates can be converted to carbon using the conversion factor of 1 unit biomass = 0.5 unit of C. 44/12 = Conversion factor for C to CO2. To calculate tree carbon content, the tree must be felled, dried, and weighed. As felling every tree in a population is not an appropriate or realistic approach, tree C stocks are estimated using mathematical formulas developed through destructive sampling (felling, drying, and weighing a representative sample of the tree population). Thus, tree C content can be estimated by measuring, for example, the diameter of a tree without the need to fell it.

Estimate aboveground biomass carbon using allometric equations
An allometric equation describes the relationship between a readily measureable variable and mass (biomass) of tree. There are many equations published for forests worldwide based on DBH or a combination of DBH, species wood density, and height. Local regression equations may exist in literature but would need to be verified with local data or through destructive sampling. The most commonly applied and readily measurable variables include diameter at breast height (1.3 m) or tree basal diameter species wood density (need a tree identification person on the field team). Using total tree height also improves the accuracy of the allometric equation, but measuring the heights of trees in plots is very time consuming and it can be difficult to see the tree top in many cases. An alternative is to develop a local relationship between DBH and height and then use estimated height in the allometric equation.

Validating existing allometric equations
An example of validating existing equations with destructive sampling of local trees Chave et al. (2005) equation based on DBH and wood density—includes large data set Chave et al. (2005) provide a comprehensive review of equations. It is a good starting point if local equations do not exist (as is often the case for the tropical mixed species humid forests). No matter which equation is selected, it should first be validated by destructively harvesting some large diameter trees (about five or more of different species) to see if the equation is suitable for the forest in question. This could be done for a selection of equations to ensure the most appropriate equation is selected. If none of the available equations appear suitable then one will have to be developed locally. This tends to be the case for drier forest types where few equations exist. The biomass of such drier forests tends to be very sensitive to tree height.

Estimating belowground biomass carbon
Belowground tree biomass (roots) is rarely measured. A root-to-shoot ratio can be applied instead, such as those from IPCC. In mature tropical rainforests or humid rainforests with aboveground biomass >125 t/ha, the aboveground biomass is multiplied by 0.24 to estimate the belowground tree biomass.

Estimating soil carbon
What about soil C? IPCC guidelines for the different Tiers for estimating soil carbon changes in deforested areas are:

Tier 2 approach for soil carbon
Tier 2 approach for soil carbon stocks and change: Reference or initial soil carbon stock to 30 cm obtained from field soil sampling—need samples for estimating both bulk density (in g/cm3) and carbon content (in %) Change in soil carbon stock = [𝐶𝑆 0 − 𝐶𝑆 𝐷 ]/𝐷, where: CSO = Initial or reference soil organic carbon, t C ha-1 CSD = Soil organic carbon at default time D, t C ha-1 D = Default time period to transition to a new equilibrium value (20 year) CSD = CSO*FLU*FMG*FI, where the F parameters are stock change factors related to land use system, soil management regime, and organic matter inputs (from IPCC, 2006, AFOLU GL) It is not enough just to have measures of the carbon concentration in soil; it is also important to have a measure of the soil bulk density too. The IPCC method described here assumes that the loss in soil carbon is a result of three factors: (i) how the land is used, (ii) how the soil is managed, and (iii) the quantity of organic inputs. The details and values for these factors are given in IPCC, 2006, AFOLU GL.

F stock change factors (future land use)
Example values of F stock change factors (all are dimensionless): FLU= factor for land use; FMG= factor for management regime; and FI= factor for input of organic matter When forests are converted to agricultural uses or unpaved roads, factors must be applied to account for the expected emissions from future land use, the management regime, and agricultural inputs. These are called F stock change factors. Factors for conversion to permanent agriculture are different from shifting cultivation (also called swidden agriculture). The soil emission factor for converting forest to agriculture or unpaved roads is calculated according to the equation in the previous slide and the relevant soil factor. An example is given in slide 47 and in the tutorial.

From sample plots to estimating total biomass C
Biomass estimates from sample plots are scaled up to calculate carbon in each biomass pool: Estimate biomass stocks for each pool Scale each sample to per hectare level Convert biomass values to carbon values Calculate average and 95% confidence interval of carbon stock in each pool within each stratum Sum mean stock per pool This order is important to maintain methodological consistency

Example of EF development for deforestation
Example of EF for conversion of forest to annual cropland where no wood was extracted for products  Assume all emissions occur at time of event EF for biomass components: =(Cpre-Cpost-Cwp )×44/12 =( ) x 44/12 = 954 t CO2/ha EF for soil (slides 44 & 45): = (CSo - CSO*FLU*FMG*FI) =( x 0.48 x 1 x 1)x44/12 = 194 t CO2/ha Total EF = 1,148 t CO2/ha For this example, all emissions are assumed to occur at time of the event. However, in the real world this is not the case, as all the live biomass is converted to dead mass that would decompose over time. Rates of decomposition for the various components of the now dead mass are not well known. But if one assumes a decomposition rate of 10% per year and uses a simple linear rate of decomposition (about 95 t CO2/ha emitted per year from all biomass components and for soil, where it is assumed decomposition reaches a new steady state after 20 years) the annual emissions from soil would be 8.7 t CO2/ha, or an amount almost an order of magnitude lower than from the now dead biomass pool.

Sources of error There are three main sources of error: Sampling error
Measurement error Model or Regression error Total error is the sum of these sources. See Module 2.7 for details on how to estimate total error. All these sources can and should be quantified to assess overall uncertainty.

Sampling error Sampling error reflects the variability in the estimate due to measuring only a subset of the population of interest. A large sampling error can result from incorrect distribution or number of plots used for sampling C stocks. Plot size and distribution must adequately and efficiently capture spatial variability.

Measurement error There are many opportunities to make measurement and recording mistakes during field inventory!

Model or regression error
Regression equations are developed specifically for a specific set of tree species within a specific DBH range. A regression line generally does not go through all the data: approximating the data using the regression line entails some error. Large regression errors can occur if field inventory DBH values are applied to an inappropriate regression formula for the DBH and species range.

Quality assurance / Quality control (1/2)
To minimize error, data collection and analysis should include quality assurance/quality control (QA/QC) measures for: Collecting reliable field measurements Verifying methods used to collect field data Verifying data entry and analysis techniques Maintaining and archiving data See Module 3.1 on national data organization and management for implementing QA/QC and the importance of sound data management.

Quality assurance / Quality control (2/2)
Data collection should follow a set of standard operating procedures (SOPs) (See Module 3.1). Field measurements should be complemented by regular checks by supervisors to verify measurement. Entered data should be reviewed and examined for outliers and mistakes. All data should be stored in a secure location and backed up routinely. Copies of data should be stored separately in fire-proof facilities. A QA/QC plan must be developed for all steps in the data collection and analyses and should be documented and implemented.

Strategies for estimating EFs
Forest lands undergoing selective logging activities must be identified. Sampling must take place very soon after felling timber trees—if possible while timber tree is still in the forest Hard to assess damage if not sampled quickly Avoids miscounting from regrowth Reduces uncertainty from key parameters (DBH, Llog) Sampling plans should be designed to meet a targeted or required level of certainty and to meet international standard of representative, unbiased, consistent, transparent, and verifiable. Field measurements must be conducted to estimate the impact of selective logging on forest carbon stocks. Immediate sampling is important to accurately assess the forest carbon stocks before and after log extraction.

Estimating EF for selective logging (1/2)
Carbon losses—or EF—due to selective logging are estimated as: EF (t C/m3) = ELE + LDF + LIF Where: ELE = extracted log emissions (t C/m3 extracted) LDF = logging damage factor: dead biomass carbon left behind in gap from felled tree and incidental damage (t C/m3 extracted) LIF = logging infrastructure factor: dead biomass carbon caused by construction of infrastructure (t C/m3) Field data are collected from multiple logging gaps (>100 plots) to quantify each of these factors—the steps to collect data are given next. (See Pearson, Brown, and Casarim 2014) This is the standard equation used to estimate the carbon losses side of the gain-loss equation given in slide 13. These emissions from selective logging and quantifying each factor determines the types of field data that need to be collected.

Estimating EF for selective logging (2/2)
Relate total selective logging carbon emissions to easily measurable parameters: Area of canopy gaps (m2) Volume of timber extracted (m3) –> easier to track Logging components that impact C emissions: Extracted volume Amount of timber going into long-lived (>100 year) wood products Portions of timber tree left in forest (crown, stump) Incidental damage to surrounding trees (the increase in dead wood resulting from felling and extraction) Logging infrastructure (skids, log landings, and roads) Total CO2 emitted per unit of timber extracted allows estimates to be scaled up. Because timber extracted is usually reported in volume (m3 ) it is recommended that one develop an EF that can estimate the CO2 emissions associated with every cubic meter of wood extracted through selective logging.

Field measurements for selective logging EF development (1/2)
Timber tree—cut up into the extracted log and other noncommercial pieces Length of the log (lLog) DBH (and diameter of top of the piece – dPiece-T) Diameter at the top cut of the log (dTop) Diameter of the stump (DStump) Height of the stump (HStump) Length of the noncommercial piece or pieces (lPiece) Diameter of the bottom of the noncommercial piece (dPiece-B) Diameter of the top of the noncommercial piece (dPiece-T) The intact felled tree may not be present for field measurement, but its biomass can be approximated by measuring remaining parts as shown in this diagram. When a tree is felled, the log is extracted, often leaving noncommercial pieces of wood near the base—this might be part of the buttress or deemed to not be useful—but one needs to have measurements taken on these pieces.

Field measurements for selective logging EF development (2/2)
Timber tree: It is very important to know the DBH of the felled tree and its species because these two pieces of data are used to estimate the biomass of the felled tree. If felled tree still present: Measure DBH and Llog directly If felled tree has already been extracted: Measure Llog Estimate DBH through taper factor Llog It is important to know the diameter at breast height of the felled tree and its species so that the appropriate allometric equation is correctly applied to estimate the aboveground biomass of the tree before log extraction. In many cases, the log is no longer present as it has already been extracted and transported. Therefore, the DBH must be estimated using a taper factor through measurable parameters of remaining tree pieces like the stump and the diameter of the remaining tree part.

Estimating DBH using taper factor
Applying the taper factor equation to estimate DBH Field Measurements: Dstump = 70.8 cm Dtop = 49.7 cm Length = 19.9 m Hstump = 80 cm Tree taper = 70.8 – = cm/m 19.9 This is an example of how the taper factor is calculated and then applied to estimate the DBH of the logged tree. Change in length = 130 – 80 = 50 cm

Extracted log emissions (ELE)
Apply the appropriate allometric equation to the felled tree parameters to estimate aboveground biomass. Biomass left in forest = biomass of felled tree – biomass of volume extracted [=volume X species-specific wood density]. In general for selective logging, only one allometric equation is used. The allometric equation selected should include wood density as one of the parameters—for example, the Chave et al. (2005) equation commonly used for forests in the humid tropics. The use of species specific wood density takes into consideration the differences in species. The differences in the biomass of different species is commonly reflected by the differences in wood density.

Logging damage factor (LDF)
Incidental damage: Measure DBH of all surrounding trees fatally damaged due to timber tree falling: Classify damaged trees into: Snapped Uprooted Measure broken branches from surrounding trees. Estimate damaged biomass using appropriate allometric equations for damaged trees. Incidental damage is almost impossible to avoid during selective logging. All trees fatally damaged as a result of felling operations will also decompose and emit CO2. Estimate total incidental damage as tons of C damaged in incidental trees per m3 extracted from the gap.

Logging infrastructure factor (LIF)
Roads, skid trails, and decks: Length Width Infrastructure emission factor (Skid trails+decks+roads) Estimate C impact for constructing each Skids likely have lower impact than larger roads or tracks as they deviate around larger trees Total logging infrastructure impact is estimated as tons of C per m3 extracted Infrastructure must be calculated as area, therefore both width and length of skid trails, decks, and roads must be known or estimated. Multiply area by average forest carbon stocks prelogging to estimate carbon impact of logging infrastructure.

(cone with top sliced off)
Estimating ELE (t C/m3) Estimate log volume: What shape best approximates a tree log? Conical Frustum (cone with top sliced off) Biomass = Density x Log Volume Density = species-specific wood density (t/m3) Repeat for all logging gaps to estimate mean ELE The ELE estimates C emissions associated with the extracted logs over their lifetime.

Estimating LDF Sum biomass: Top + stump + pieces left in forest
(biomass of timber tree – biomass of log) Incidentally damaged trees (snapped + uprooted + broken branches) Repeat for all plots and estimate mean LDF The logging damage factor is the sum of the dead biomass left in the forest resulting from felling (tree stump and crown) and the dead biomass resulting from incidentally damaged trees. This total sum of dead biomass is expressed as tons of C per cubic meter extracted in the logging gap, and the mean LDF is estimated from it. This dead wood could decompose over time, but the rates of wood decomposition in tropical forests are variable, poorly known, and vary depending on species and size of the dead wood. In any given forest, sizes of dead wood vary from small twigs, which will decompose rapidly, to large branches and stumps, which decompose very slowly. Thus guidance on what value to use for dead wood decomposition is not available. Instead, the concept of committed emissions is used—it is assumed all dead wood will eventually decompose, so it is modeled as being emitted at the time of the event.

Estimating LIF Sum C impact of: skids + roads + decks
The C impact is estimated as the product of C stock estimates of unlogged forest and area of infrastructure. Divide by total harvested volume in m3. The LIF estimates the C emissions from the infrastructure built to support selective logging activities.

Estimating total emissions
C emissions, t C/yr = [vol x ELE x (1-LTP)]+[vol x LDF]+[vol x LIF] Where: Vol = volume timber extracted over bark per logging block (m3) ELE = extracted log emissions (t C/m3) LTP = proportion of extracted wood in long term products still in use after 100 yr (dimensionless) LDF = logging damage factor (t C/m3)—dead wood left behind in gap LIF = logging infrastructure factor (t C/m3)—dead wood produced by construction Combining data on volume extracted, logging infrastrucutre, and logging incidental damage will provide a total figure of total emissions from selective logging activities. For more information on this topic, see Brown, Casarim, Grimland, and Pearson (2011).

Example of estimating total emissions
Concession TBD Inc. constructs 15 km of skid trails and roads to harvest 10 m3/ha on 500 ha in No decks are built as logs are piled alongside wide roads. Five percent of the harvested wood went into long-term wood product storage. Assumed factors: ELE= 0.36 t C/m3 LTP= 0.05 LDF= 1.05 t C/m3 LIF= 1.49 t C/m3 C emissions, t C/yr = [vol x ELE x (1-LTP)]+[vol x LDF]+[vol x LIF] C = [(500*10) x 0.36 x (1-0.05)] + [(500*10) x 1.05] + [(500*10) x 1.49] C = 1, , ,450 C = 14,410 t C ~ 52,837 tCO2 This is an example of how information from concession practice is combined with the emission factors to estimate total emissions from logging.

How to estimate gains through regrowth
Gains by growth—need improved guidance: Need regrowth rate of stand after logging per unit area per year for multiple years Regrowth occurs only in and around the gaps and may not enhance carbon accumulation (smaller trees and gap closure) Occurs over limited time frame How to estimate—need time course of carbon accumulation by ingrowth of new trees, growth of surviving trees, and delayed mortality of previously damaged trees: Long-term plots established around logging gaps and measured over time Establish and measure a chronosequence of logging gaps with a standard design The gain side of the gain-loss equation is difficult to quantify, and several methodological options exist. Long-term plots can be established around newly created logging gaps, and these are then measured through time for ingrowth of new trees, growth of surviving trees, and any delayed mortality of previously damaged trees. Or a chronosequence can be developed: a set of plots established in previously logged areas with similar forest type and similar method of timber extraction and where the year the earlier trees were logged is known. However, there is a need for improved guidance to measure the gain side of the gain-loss equation. It is often assumed that the rate of C sequestration could be enhanced, as thinning forests is a typical silvicultural practice used to enhance growth of desired species. While this process can enhance the radial growth of the smaller trees, the biomass carbon increment of multiple smaller trees will often be lower than the biomass increment of a single missing large diameter tree. Thus, loss of a large timber tree with large canopy area could actually lead to a net reduction in sequestration, i.e., so no enhancement in the absolute carbon sequestration rate. This lack of stimulation of C sequestration has been observed in many studies.

In summary The IPCC recommends that it is good practice to use higher Tiers for the measurement of significant sources/sinks. The stock-difference method is most commonly applied for estimating emissions from deforestation. The gain-loss method is the most suitable method to estimate emissions form forest degradation. Allometric equations that link tree variables (DBH, height, wood density) to tree biomass are used to estimate C emissions from deforestation. Emissions from forest degradation can be estimated by summing emissions from extracted logs, logging damage, and infrastructure damage. The use of SOPs and quality assessment/quality control are important to ensure the quality of estimates and to minimize errors.

Country examples and exercises
Estimating emission factors (biomass and soil) for forest cover change in Guyana Exercises Tutorial on how to estimate EFs: Estimating Emissions from Deforestation Estimating Emissions from Degradation by Logging

Recommended modules as follow-up
Module 2.4 to learn about involving communities / local experts in monitoring changes in forest area and carbon stocks. Module 2.5 to estimate carbon emissions from deforestation and forest degradation Modules 3.1 to 3.3 to proceed REDD+ assessment and reporting.

References Brown, S., F. M. Casarim, S. K. Grimland, and T. Pearson Carbon Impacts from Selective Logging of Forests in Berau District, East Kalimantan, Indonesia: Final Report to the Nature Conservancy. Arlington, VA: Nature Conservancy. logging-forests-berau-district-east-kalimantan-indonesia. Chave, J., et al “Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests.” Oecologia 145: 87–99. improved-estimation-carbon-stocks-and-balance-tropical-forests. GFOI (Global Forest Observations Initiative) Integrating Remote-sensing and Ground-based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observations Initiative. (Often GFOI MGD.) Geneva, Switzerland: Group on Earth Observations, version GOFC-GOLD (Global Observation of Forest Cover and Land Dynamics) A Sourcebook of Methods and Procedures for Monitoring and Reporting Anthropogenic Greenhouse Gas Emissions and Removals Associated with Deforestation, Gains and Losses of Carbon Stocks in Forests Remaining Forests, and Forestation. (Often GOFC-GOLD Sourcebook.) Netherland: GOFC-GOLD Land Cover Project Office, Wageningen University.

IPCC IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use. Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan. (Often referred to as IPCC AFOLU GL) Pearson, T. R. H., S. Brown, and F. M. Casarim “Carbon Emissions from Tropical Forest Degradation Caused by Logging.” Environmental Research Letters 9: doi: / /9/3/ UNFCCC COP (United Nations Framework Convention on Climate Change Conference of the Parties) Decisions. This module refers to and draws from various UNFCCC COP decisions. Specific decisions for this module are listed in the “Background Material” slides. All COP decisions can be found from the UNFCCC webpage “Search Decisions of the COP and CMP.”

Module developers: Sandra Brown, Winrock International

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