Assessing Growing Stock and Stock changes
through Multi-Purpose National Forest Monitoring and Assessment
Dan Altrell (FAO), Anne Branthomme (FAO), Rebecca Tavani (FAO)
Introduction
For forest management and planning purposes, at both the national and stand level, it is vital to
know the capital of forests in terms of wood resources, as well as how much that capital is growing.
This is assessed through the volumetric measurement of the trees, commonly known as the volume
of the growing stock (GS), as well as through biomass estimates, expressed in terms of the dry weight
of living organisms.
Box 1. Definition of Growing Stock:
Volume over bark of all living trees more than X cm in diameter at breast height (DBH). Includes the
stem from ground level or stump height up to a top diameter of Y cm, and may also include branches
up to a minimum diameter of W cm (FAO, 2005).
NB: The definition above is typically applied as the “stem volume in forests of all living trees more
than 10 cm diameter at breast height (or above buttresses if these are higher), over bark measured
from stump to top of bole. Excludes: smaller branches, twigs, foliage, flowers, seeds and
roots.”(FAOTERM)
Box 2. Definition of Biomass Stock
Organic material both above-ground and below-ground, and both living and dead, e.g., trees, crops,
grasses, tree litter, roots etc. Biomass includes the pool definition for above - and below - ground
biomass (FAO,2006).
Growing Stock (GS) estimates are used to evaluate and monitor the commercial potential of a forest
(or other land uses) for timber and fuel wood production and for harvest potential. Information on
growing stock is also essential for understanding the ecological dynamics and productive capacity of
forest stands and allows managers the ability to manage stands within the limits of sustainability, as
defined by their dynamics of growth. The commercial volume is derived from the total GS and
constitutes the proportion of GS that is represented by commercial tree species, fulfilling minimum
criteria for quality, dimension and sometimes age/development stage.
Forest biomass is another important measure for analysing ecosystem productivity and also for
assessing the energy potential and the role of forests within the global carbon cycle. Although
closely correlated to – and often estimated directly from – growing stock, forest biomass constitutes
an important characteristic of the forest ecosystem. (FAO, 2008)
Consequently, estimating losses and increments in GS can provide good quantitative indicators to
assess how forests are degraded or enhanced over-time in terms of productivity.
The assessment of the GS primarily relies upon field measurements. Only biometric ground
inventories can capture data on the essential parameters for calculating growing stock and biomass
estimates. Continuous and consistent measurements are needed to assess changes in the GS. A
negative development of GS can be one of many indicators of forest degradation, but in order to
understand if a negative trend in GS is related to forest degradation, or to be able to plansustainable
forest management practices, a long-term monitoring of the forestry resources is needed. Growing
stock estimates by themselves are many times not enough to determine if a decrease in GS is a sign
of forest degradation, as often information on additional indicators are needed, such as existing
management plans, trends in land tenure, soil productivity, wild fires, land use conversion, local
populations, livelihoods, etc. Systems for monitoring forest degradation need to embrace not only
the multiple functions of forests, but also the multiple uses and users of forest resources. Multipurpose forest monitoring and assessment can fulfil a very significant role in such systems.
Methods
Forest inventories and monitoring systems are carried out to estimate the GS and to monitor its
change over time. Applying statistical sampling methods, forest inventories and monitoring systems
can be designed for stand-, regional-, or national level estimates. Forest inventories cover both
measurements and observations of a number of qualitative and quantitative parameters, which are
determined by information and data requirements. A system for long term forest monitoring is
typically composed of either only field surveys (FS) or a combination of both field surveys and remote
sensing surveys (RSS). However, it is mainly in the field that tree measurements (tree diameter,
height, etc), necessary for calculating the GS density, are carried out.
The availability of financial resources and human capacity determines the method for assessing the
GS. In the best case scenario, multi-purpose forest inventories are undertaken and primary data is
collected on tree species, diameter at breast height (dbh), height, land use, etc. On the other hand, in
some cases forest inventories are not possible because of very limited financial and human capacities.
Therefore partial inventory data and/or extrapolations from past inventories must be used for
volume or biomass calculations in lieu of more accurate and timely inventory-generated data.
Field survey
In a field survey, teams are sent to collect data on the ground. Full-cover inventories can be carried
out for very small areas, such as for harvesting (operational) inventories, but for large-scale
inventories, it is impractical and extremely costly to measure all trees so a sampling strategy and
design is adopted in order to restrict the measurements to representative sampling units (the
sample), and to subsequently expand results to the whole area (the population of interest).
The field data collection is carried out on a sample of permanent and/or temporary field sampling
units. The sample area is the total area of all the sampling units on which the measurements are
carried out. The sampling procedure can be random or systematic. For forest monitoring systems it is
usually more efficient, in the absence of regular/systematic patterns present in the landscape, to
apply a systematic sampling method, as it tends to better represent the distribution of land uses and
forest types. The sampling can also be pre-stratified in order to intensify the sample in strata which
are more heterogeneous, or of higher priority, in order to increase the precision of the estimates.
The sampling units can be stands, plots, strips or points. Plots can have different shapes (circular,
rectangular, square, etc.) and can be of fixed or variable size. The size of the plots is defined
according to the parameter of interest and in which context it is assessed. For example, plots for
measuring small trees are often smaller than plots for measuring larger trees, as the former are often
more numerous than the latter. The number of plots is determined by considering the requirements
of statistical precision of the estimates for key parameters, and most importantly, by considering
actual costs and time constraints. Instead of a distribution of single plots, which require considerable
time for moving from one to another, cluster sampling is often used in forest inventories over large
areas, for economical and logistical reasons. In a cluster sampling design, the sampling units are
clusters (groups) of subplots.
Remote sensing survey
A Remote Sensing Survey (RSS) can use either a full-cover (wall-to-wall) approach or a sampling
approach. In a sample-based RSS, observations are made over sampling units (sample area), while in
a full-cover RSS the whole area of interest (region, country, etc) is studied. Remote sensing
observations (e.g. via aerial photos, LandSat, Aster images), can be used in particular to determine
the extent or area of land cover(/use) classes, which can greatly assist in extrapolating volume and
biomass densities over large areas, or to stratify the design of the field sampling.
Radar and laser-derived spaceborne or airborne remote sensing measurements can also be used to
remotely capture data for estimating both volume and biomass stocks with less intensive field survey,
however accuracy depends on the ability to calibrate and validate measurements with field data
from ground checks. These methods are still rather expensive and experimental but provide
promising results in particular for areas that are difficult to access.
Periodic survey
Data for estimating GS can be collected either from single or from periodic inventories in order to
monitor forest characteristics. Depending on the required precision of estimates, how often changes
occur, the needs of updating the information on GS status and trends, and, above all, on available
resources, the measurements are carried out at certain intervals in a monitoring system. Typical
measurement intervals are between five and ten years, but it can vary greatly according to above
mentioned criteria.
Data needs
To generate GS estimates the following basic data are typically needed:
 data on tree parameters used in GS models (from tree measurements and observations in the
field plots);
 the area of the sample where measurements are made (or inventoried area);
 the total extent of the population of interest (entire study area, e.g. country, Land Use/Cover
class, forest).
The data on tree parameters are collected through direct measurements and observations in the
field: diameter at breast height (dbh), height/length, branch length, species, health status, increment
rings, basal area, etc. The data required depend on which models are applied to calculate the GS
estimates (see examples in Annex 1).
It is critical to know the extent of the sample area in order to calculate the GS densities (GS/area).
The sample area is either fixed (case of fixed size plots), or measured in the field.
To produce GS density estimates by land use/cover class(es) (LUCC), the field plots need to be
classified according to a predefined LUCC system. Either the major LUCC is registered for the whole
plot or for predefined plot sections, or the inventory team sub-divides the plot into land use/cover
sections (LUCS) with a variable dynamic area according to the extent of the LUCCs present on the
plot. In the latter case, the size of the LUCS needs to be measured, which is done through measuring
length and width or through estimating their proportion of the whole plot (applied typically for
circular plots). To extrapolate the GS densities by LUCC to estimate the total GS by LUCC (population
of interest), the extent of these areas needs to be known.
The area of the total population of interest can be derived from existing records (e.g. size of the
country, province, etc), from plot data (e.g. proportion of sample for post stratification of data) or
from RS data (e.g. area of a Land Use/Cover Class in the country). RSS will usually give better
precision for assessing LUCC area, in particular for LUCCs which are less frequent in the landscape.
It is also important to collect data that can help in understanding the development and dynamics of
the GS. These data can be gathered through direct measurements, observations, interviews, often
through multi-purpose forest inventories, or from auxiliary sources: LUCC conversion trends,
designation/protection status, regeneration (species, frequency), tree stumps (species, diameter at
stump height, years since cut), tree harvest/extraction (frequency, change trend, trend reason, users’
expected/desired future tree cover, timber exploitation system), silviculture system, tree
products/services (species, supply/demand trends), tree canopy cover, management plan/agreement,
environmental problem, stand history, soil (productivity, drainage, nutrients, pH), fire (type, area,
frequency), population on site (size, dynamics, main/secondary activity, settlement history), proximity
to infrastructure/accessibility (all-weather/seasonal roads, markets, settlements), etc.
To create a system for monitoring tree resources over time, often on permanent sample plots, and to
make the measurements transparent and verifiable it is also important to locate precisely the
measurements by indicating the coordinates of the plots and trees (or their position within the plot).
Data collection and equipment used
Equipment covered in this section refers primarily to the data which are collected for inventory
purposes. A variety of instruments and tools can be used to collect forest data, depending on budget
and accessibility of equipment. For those volume and biomass data need parameters collected
through direct measurements and observations, the following basic tools are of use:
Main tree parameters of interest
Diameter at breast height (Dbh) –
measured at 1.3m
Tree Height/Branch length
Species identification
Geographic Location
Equipment
Diameter tape
Biltmore Stick
Calipers
Clinometer
Altimeter
Stick/ruler + measuring tape
Range finder
Hypsometer
Relascope
Botanical field guide and/or local
knowledge
Plant press for sample collection
Global Positioning System (GPS) receiver
Measuring tape
Compass
Topographic maps
Diameter at breast height (Dbh) is typically measured
over bark at 1.3 meters above the ground. The most
common and practical tools used to measure Dbh are the
diameter tape (D-tape), the caliper and the Biltmore stick.
The d-tape is the most accurate tool to measure diameter,
taking into consideration the circumference of the tree
into the distance on the tape. The Biltmore stick and
calipers are less accurate, however often used in
commercial inventories because of ease and speed.
Height of the total tree refers to the vertical height from
the ground to the top of the tree. Commercial bole height
Figure 1: using a ruler to measure refers to the length of the trunk from the ground to the
vertical height of a tree
merchantable height of the tree, defined as the minimum
diameter able to be utilized as timber. Both total height
and commercial bole height can be estimated using a variety of tools, some ranging from very simple;
such as standing at a distance with a ruler or a stick until the entire length of the tree is in eye range
and then measuring the distance back to the base of the tree (Figure 1). As trees grow, however,
measuring their heights becomes increasingly complex. More accurate and expensive tools to
estimate tree height include the clinometer, which measures angles and enables the user to
determine the height of a tree when standing at a given distance. Laser tools may also be used such
as a range finder. While more costly, it can greatly speed up data collection. Measurements of tree
heights are particularly difficult in some tropical areas, where tree tops are not visible through a
dense canopy cover.
Species or species type should be registered to improve the accuracy of the GS estimates by
employing species specific volume and biomass models and/or wood densities, when these exist.
These models are derived through forestry research and are often species or region specific, as the
estimates can differ greatly from one species to another, and from one region to the next. For these
purposes, there is no substitution for local identification skills, particularly in countries with very high
ecological diversity. In addition to identification skills, field guides should be used in order to verify
species types. If species type is not found within the field guide, a sample is often taken back to the
office for subsequent analysis and identification.
The locations of the plots and the plot boundaries are essential, as they define which trees will be
included in the inventory. For this reason, GPS receivers, compass, maps and measuring tapes are
employed.
Data registration, validation and storage
During a field survey, the data are registered in field forms, which can be printed paper copies or
digital, if a digital data collector is used. The field forms are organised according to inventory topic
and level, and to save time and space, codes are often used to register predefined options, but to
accept indiscrete, or continuous values, some data must be entered as free text, or as “real” value.
The advantages of using digital data collectors in the field, compared to paper copies, are that: a)
validation criteria can be programmed to provide the inventory teams with immediate
indication/warning if data are inconsistent b) digital measurement instruments can register the
values directly in the data collector and c) the data can also be directly transferred to the main
database (DB) without additional manual data entry. However, the disadvantage with digital data
collectors is that they depend on electricity to function and that data can get lost if the data collector
breaks down. Another critical disadvantage with the use of digital data collectors and measurements
instruments is that the “human” validation of the data often is omitted. Moreover, digital
instruments are relatively expensive in countries with low labour cost with programming and
maintenance of digital instruments requiring highly-qualified expertise, which can be hard to find in
some countries. When paper forms are employed to register collected field data they are validated
by field team supervisors before the data are entered into the database. Once the data have been
collected, validated and entered into the database further validation and “cleaning” of the data are
made in the database to identify and eventually correct remaining data inconsistencies.
The database is the core of the information system and it safeguards the forest inventory data and all
the information related to the inventory, therefore the sustainability of the monitoring system highly
depends on the maintenance of the database.
From field data to GS & biomass estimates
Data collected in the field are later processed, employing models for volume and biomass estimates.
For yielding GS and biomass stock estimations, data from field inventories are combined with
available allometric equations, models and functions which embrace the quantitative relationship
between different tree parameters and allow for the estimation of complex tree parameters, such as
volume and biomass, from more easily-measured parameters, such as diameter at breast height
(Dbh).
However, many countries still lack specific functions and allometric equations for estimating GS and
biomass and have to refer to more general models. For that reason, considerable research is still
required in many countries to improve the accuracy of their GS and biomass estimates.
Box 3. Example of allometric equation to calculate GS
An example of commonly used equation to estimate Growing Stock using field data is the following:
GS = ∑(Dbh2/4 * Htot * π * fform)
Where:
GS = Inventoried Growing Stock
Dbh = Diameter at breast-height (of inventoried living trees)
Htot = Total height/length (of inventoried living trees)
π = 3.1415…
fform = Stem form factor (of inventoried living trees)Aside from the need for Dbh and tree height data, a tree
stem form factor (fform) is also needed to complete the equation. Form factors refer to the form of a
given species of tree and were conceived as a method of relating form and volume. It is defined as its
comparative volume (as determined by Dbh) of the tree stem at specific heights. It typically ranges
from 0.3-0.8 depending on the shape of a tree species. Apart from diameter, the form factor is the
most important variable that can be used to predict volume of a tree stem (Husch, 2002). When
species-specific tree stem form factors are not available, a less accurate country-specific default
value may be used which generalizes the forms of all trees found within the country.
GS estimation and reporting
To derive statistics on GS or biomass density, first the GS / biomass in the sample area needs to be
estimated, which is done by employing allometric functions for each individual tree and then
aggregating the tree-wise estimates to derive total inventoried volume. The GS density (m3/ha) or
biomass density (tonnes/ha) are calculated by dividing total inventoried tree volume or biomass by
the sample area. Some volume functions with measurements at stand level (e.g. basal area) generate
directly the volume density.
To calculate the total GS and biomass stock these densities are extrapolated to the total population
area.
In order to generate statistical estimates of GS volume and biomass the collected data are processed
and analysed. Data are sorted and filtered through queries in the database and in order to compute
the statistics, either additional queries are made in the database, or the data are exported and
analysed on a spreadsheet.
Box 4. Biomass calculation
For any biomass calculation, irrespective of whether for above-ground biomass, below-ground
biomass or deadwood, the choice of method is determined by available data and country-specific
biomass estimation methods. The following list indicates some options, starting with the method
that provides the most precise estimates (FAO, 2008):
1. If a country has developed biomass functions for directly estimating biomass from forest
inventory data, or has not established country-specific factors for converting growing stock to
biomass, using these should be the first choice
2. Second choice is to use other biomass functions and/or conversion factors that are considered to
give better estimates than the default regional/biome-specific conversion factors published by
IPCC (e.g. functions and/or factors from neighbouring countries)
3. Third choice is to use the IPCC default factors and values (IPCC, 2006). These have been improved
since the 2003 Good Practice Guidance and are now available for different geographical regions
and ecological zones
Reported estimates of GS and biomass are expressed as volume density (m3/ha) and biomass density
(tonnes/ha), as well as total volume (m3) and total biomass (tonnes) over the area of the population
of interest. The estimates always include a margin of error represented by the sampling errors,
measurement errors and other uncertainties in the design and implementation of the methodology.
The only error that can be estimated is the sampling error. However, the other uncertainties related
to the estimates are often bigger, therefore much attention should be dedicated to quality control
and quality assurance in order to minimise the overall error (Lanly, 1973).
For reporting purposes, it is advisable to undertake careful documentation of the threshold values
used for the definition of growing stock. These values will also be needed in order to harmonize data
between inventories or countries and for global reporting.
To determine changes in GS, repeated measurements on permanent sample plots can be carried out,
and differences between two successive inventories can be assessed. The difference in volume
between these two points in time is what is referred to as GS change and allows managers and
planners to better predict forest stocks from year to year.
∆GSt2-t1 = GSt2 – GSt1
Where,
∆GSt2-t1 = Change in Growing Stock from year 1 to year 2
GSt1 = Growing Stock year 1
GSt2 = Growing Stock year 2
Conclusion
Systematic field measurements of tree parameters are fundamental for assessing the growing stock,
even though other assessments methods, like remote sensing surveys, can contribute considerably in
improving the precision in the estimation of the total GS. To assess the changes in the GS a
monitoring system with repeated and consistent measurements is required. In order to better
understand if a negative development of the GS is an indication of forest degradation, or a part of
sustainable management of tree resources, long-term monitoring is needed, as well as an
assessment of additional indicators of forest degradation and information on uses and users of the
forestry resources (FAO, 2009). Multi-purpose forest monitoring and assessment are efficient
systems for monitoring GS and possible forest degradation as they take account of the multiple
functions of forests and the multiple uses and users of forest and tree resources.
Annex 1: Examples of models and Calculations for Volume, Biomass and
Carbon estimations
Volume
Stem Volume (Growing Stock or Dead Wood):
Example of volume function of a tree stem including bark (=growing stock, GS):
Volstem = (Dbh2)/4 * Htot * π * fform
(cylinder volume adjusted with a stem form factor)
Volstem
Dbh
Htot
π
fform
= Tree stem volume incl. bark (=Growing stock if including only living trees and dead wood if
including only dead/dying trees)
= Tree stem diameter (at breast height)
= Tree total height/length
= 3,141596
= Tree stem form factor (0,3 – 0,8)
~0,5 for broadleaved trees species and ~0,65 for coniferous tree species
Commercial Stem Volume:
Example of commercial volume function of a tree stem including bark (=growing stock, GS):
Volcomm = (Dbh2)/4 * Hcomm * π * fcomm (cylinder volume adjusted with a stem form factor)
Volcomm
Dbh
Hcomm
fcomm
= Commercial tree stem volume incl. bark (for commercial tree species)
= Tree stem diameter (at breast height)
= Commercial tree stem height/length (national definitions)
= Tree commercial stem form factor (0,5 – 0,9)
~0,7 for broadleaved trees species and ~0,8 for coniferous tree species
Branch Volume (for timber estimates):
Example of volume function of a branch excluding bark to estimate timber volume:
Volbranch = (Davg2)/4 * Ltimber * π
Volbranch
Davg
Ltimber
= Branch volume excl. bark (=Branch timber volume)
= Branch average (or at the middle of the timber length) diameter under bark
= Branch timber length (to where the branch reaches minimum timber diameter)
Harvested Stem Volume (to estimate harvested and extracted tree volume):
Example of volume function of a harvested and extracted tree stem incl. bark and an optional
reduction for stump volume if stumps are above default stump height:
Volextract = ((Dsh * fDsh )2)/4 * Htable * π * fform [ - (Dsh2)/4 * (Hstump – Hdef) * π * fsred] (cylinder
volume based on an adjusted stump diameter, to approximate Dbh, and a tree stem form
factor)
Volextraxt
Dsh
fDsh
Htable
fform
Hstump
Hdef
fsred
= Extracted tree stem volume incl. bark
= Stump diameter (at stump height or at 1,3m if higher than Dbh)
= Stump diameter adjustment factor; 1,0 (0,6 – 1,0) to approximate Dbh of extracted tree
stem
= Tree total height/length (from Height–Diameter table, which can be generated from the
forest inventory data)
= Tree stem form factor (0,3 – 0,8)
~0,5 for broadleaved trees species and ~0,65 for coniferous tree species
= Stump height
= Stump default height; 0,3m (0,2m – 0,5m), height at which tree is usually felled
= Stump reduction form factor; 1,0 (0,8 – 1,2) optional if stumps are above default stump
height to approximate part of stem that was not extracted
Stump Volume (to estimate extractable stump volume):
Example of volume function of a stump incl. bark and top coarse roots:
Volstump = (Dsh2)/4 * Hstump * π * fstump (cylinder volume adjusted with a stump form factor)
Dsh
Hstump
fstump
= Stump diameter (at stump height or at 1,3m if higher than Dbh)
= Stump height
= Stump form factor 1,5 (1,3 – 2,0)
Biomass
Stem Biomass (Growing Stock Biomass (GSB) / Dead Wood Biomass):
Bstem = Volstem * Dwood
BGS = Volstem * Dwood
BDW = Volstem * Dwood
Bstem
BGS
BDW
Volstem
Dwood
= Stem Biomass (= dry biomass of stem incl. bark)
= Growing Stock Biomass (= dry biomass of stem, incl. bark, for living trees)
= Dead Wood Biomass (= dry biomass of stem incl. bark, for dead/dying trees)
= Tree stem volume incl. bark (=Growing stock if including only living trees and dead wood if
including only dead/dying trees)
= Wood Density (tonnes dry matter/m3 of stem volume) (see IPPC default factors Table
3A.1.9-1 and Table 3A.1.9-1)
Above Ground Biomass (AGB):
AGB = BGS * BEF
AGB
BGS
BEF
= Total Above Ground living Biomass (incl. living tree-; stem, bark, branches, leaves, fruits,
flowers, nuts, etc.)
= Growing Stock Biomass (= dry biomass of stem, incl. bark, for living trees)
= Biomass Expansion Factor (expanding growing stock biomass to total AGB) (see IPPC
default factors Table 3A.1.10)
Below Ground Biomass (BGB):
BGB = AGB * RRoot-Shoot
BGB
AGB
RRoot-Shoot
= Average Below Ground living Biomass (incl. living tree roots.)
= Total Above Ground living Biomass (incl. living tree-; stem, bark, branches, leaves, fruits,
flowers, nuts, etc.)
= Root-Shoot Ratio of BGB to AGB (tonnes dry matter/tonne dry matter) (see IPPC default
factors Table 3A.1.8)
Carbon stocks
Above Ground Carbon (AGC) stock:
AGC = AGB * DC-AGB
AGC
AGB
DC-AGB
= Total Above Ground Carbon stock (tonnes of C/ha)
= Total Above Ground living Biomass (incl. living tree-; stem, bark, branches, leaves, fruits,
flowers, nuts, etc.)
= Carbon density in AGB (tonnes of C/tonne of dry biomass); 0,5 (0,48-0,52)
Below Ground Carbon (AGC) stock:
BGC = BGB * DC-BGB
BGC
BGB
DC-BGB
= Total Below Ground Carbon stock (tonnes of C/ha)
= Total Below Ground living Biomass (incl. living tree roots)
= Carbon density in BGB (tonnes of C/tonne of dry biomass); 0,5 (0,48-0,52)
Dead Wood Carbon (DWC) stock:
DWC = BDW * DC-DW
DWC
BDW
DC-DW
= Dead Wood Carbon stock (tonnes of C/ha)
= Dead Wood Biomass (=tonnes of dry dead coarse biomass/ha)
= Carbon density in BDW (tonnes of C/tonne of dry biomass); 0,5 (0,48-0,52)
Litter Carbon (LC) stock:
LC = DLitter * Area
LC
DLitter
Area
= Litter Carbon stock (tonnes of C/ha)
= Litter Carbon density per hectare (see IPPC default factors Table 3.2.1)
= Area of study
Soil Organic Carbon (SOC) stock:
SOC = DSOC * Area
SOC
DSOC
Area
= Soil Organic Carbon stock (tonnes of C/ha)
= Soil Organic Carbon density per hectare (see IPPC default factors Table 3.2.4)
= Area of study
Extracted Carbon (EC) stock:
Example of Extracted Carbon Stock model based on extraction of tree stems without branches:
EC = Volextraxt * Dwood * DC-EW
Example of Extracted Carbon Stock model based on extraction of tree stems with branches:
EC = Volextraxt * Dwood * BEF * DC-EW
EC
Volextraxt
Dwood
DC-EW
BEF
= Extracted Carbon stock
= Extracted tree stem volume incl. bark (see volume function on tree stem volume above)
= Wood Density (tonnes dry matter/m3 of stem volume) (see IPPC default factors Table
3A.1.9-1 and Table 3A.1.9-1)
= Carbon density in extracted wood biomass (tonnes of C/tonne of dry biomass); 0,5 (0,480,52)
= Biomass Expansion Factor (expanding growing stock biomass to total AGB) (see IPPC
default factors Table 3A.1.10)
References
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