Forest-Based Biomass Basic Computations - Mathematics and Statistics for the Environment - page 330

13.3.3.1 Estimation of Biomass Weights from Forestry Volume Data
Equation 13.5 is used to estimate merchantable biomass from merchantable volume assuming that
the specific gravity and moisture content are known and the specific gravity basis corresponds to the
moisture content of the volume involved (Briggs, 1994). Specific gravity (SG) is a critical element
of the volume to biomass estimation equation. The SG content should correspond to the moisture
content of the volume involved. SG varies considerably from species to species, differs for wood
and bark, and is closely related to the moisture content as explained in graphs and tables in Briggs
(1994). The wood specific gravity of species can be found in several references, although the mois-
ture content basis is not generally given. Briggs (1994) suggested using a moisture content of 12%
as the standard upon which many wood properties measurements should be based.

Weight = Volume × Specific Gravity × Density of H 2 O × (1 + MC od /100) (13.5)

where the volume is expressed in cubic feet or cubic meters, the density of H 2 O is 62.4 lb/ft 3 or 1000
kg/m 3 , and MC od is the oven-dry moisture content.

■ EXAMPLE 13.1
Problem: What is the weight of fiber in a 44-ft 3 oven-dry log with a specific gravity of 0.40?
Solution:

Weight = 44 ft 3 × 0.40 × 62.4 lb ft 3 × (1 + 0/100) = 1098 lb, or 9.549 dry ton

13.3.3.2 Biomass Expansion Factors
Schroeder et al. (1997) described methods for estimating total aboveground dry biomass per unit
area from growing stock volume data in the U.S. Forest Service Forest Inventory and Analysis
(FIA) database. The growing stock volume data are limited to trees with diameters greater than or
equal to 12.7 cm. It is highly recommended that the paper be studied for details of how the biomass
expansion factors (BEFs) for oak-hickory and beech-birch were developed.

13.3.4 s tand -l evel b iomass e stimation
At the individual field or stand level, biomass estimation is relatively straightforward, especially if it
is being done for plantation-grown trees that are relatively uniform in size and other characteristics.
The procedure involves first developing a biomass equation that predicts individual tree biomass as
a function of diameter at breast height (DBH) or of DBH plus height. Second, the equation param-
eters (DBH and height) must be measured on a sufficiently large sample size to minimize variation
around the mean values. Finally, the mean individual tree weight results are scaled to the area of
interest based on percent survival or density information (trees per acre or hectare). Regression
estimates are developed by directly sampling and weighing enough trees to cover the range of sizes
being included in the estimation. They often take the form of

ln Y = - Factor 1 + Factor 2 × ln X (13.6)

where Y is weight in kilograms, and X is DBH or DBH 2 + height/100.
Regression equations can be found for many species in a wide range of literature. Examples for
trees common to the Pacific Northwest are provided in Briggs (1994). The equations will differ
depending on whether foliage or live branches are included, so care must be taken in interpreting the
biomass data. For plantation trees grown on cropland or marginal cropland, it is usually assumed
that tops and branches are included in the equations but foliage is not. For trees harvested from for-
ests on lower quality land, it is usually recommended that tops and branches should not be removed
(Pennsylvania DCNR, 2007) to maintain nutrient status and reduce erosion potential, thus biomass
equations should assume regressions based on the stem weight only.