Forest inventories describe the quantity and quality of trees and other organisms
of the forest and the characteristics of the land on which the forest grows.
A forest assessment assigns values to the resource. The main objectives of
a forest inventory are to obtain estimates of the timber volume to prepare
an area for timber sale and to prepare operational plans for logging based
on the quantity and location of the timber. Data recorded during inventories
(e.g. timber volumes, growth rates, size distribution pattern, species composition,
stand conditions and location of stands) are also important for sound forest
management (Taylor et al. 1989a).
Cost and reliability are important considerations when designing an inventory.
Forest inventories can involve large areas and a 100-percent inventory is
often not possible and for most purposes unnecessary (it might be a specific
research requirement). Complete inventories are expensive, tedious and as
a result non-sampling errors (incorrect recording of tree diameters, heights
and quality) tend to increase (Bell 1997). For this reason, sampling has been
introduced, which if properly done, provides reasonable estimates of the true
population. It is crucial to select a sampling method or combination of sampling
methods that allows for the most efficient collection of data.
There are numerous different sampling designs and no particular design meets
the needs of all inventories. Each forest area varies and in meeting the objectives
of an inventory a design must be selected that provides best estimates at
a reasonable cost. Seven basic factors influence the choice of the design
(Taylor et al. 1989b):
The basic inventory designs include the following categories:
In probability sampling, the probability of selecting any sampling unit is known
prior to actual chance of selection. In non-random sampling, the units that
constitute the sample are not chosen by probability laws, but by personal
judgment or systematically.
Random sampling requires an equal chance of selecting all possible combinations
of the n sampling units from the total population. In systematic sampling,
the sampling units are spaced at fixed intervals throughout the population.
This approach has many advantages, which explains its regular use. It is able
to provide good estimates of the population means by spreading the sample
over the entire population. Inventories based on systematic sampling are usually
faster and less expensive to execute since the choice of sample locations
is mechanical and uniform. Travel between sample units is easier and shorter
as fixed directional bearings are followed.
We often have some knowledge of a population, which can be used to increase the
precision or usefulness of our sample. Stratified random sampling is a method
that takes advantage of available information about the population.�
In this method, the units of the population are grouped together on
the basis of similarity of some characteristics. Each group or stratum is
then sampled and the group estimates are combined to provide population estimates.
Stratification is achieved by sub-dividing the forest area into strata on
the basis of some features such as topography, forest types, density classes,
volume, age and site. Stratified random sampling has two main advantages over
unrestricted random sampling:
As each stratum is more uniform it should give less variation within forest types
than between types.� This in turn will
give more precise estimates of parameters (e.g. basal area and number of stems).
Forest stratification should be considered to increase the cost-effectiveness
of an inventory. In addition, in some cases it can be used to reduce the total
number of plots.
Normally, forest stratification is based on remotely sensed data such as aerial
photographs and satellite images. However, the ability to stratify tropical
forests with reliable precision has been questioned. Hence, forests are classified
usually according to available historical information. Logged areas may be
classified according to years since logging (e.g. 1-10 years, 11-20 years,
20-30 years after logging). Such an approach has been adopted for national
forest inventories in Malaysia.
Recent technological advances in remote sensing (i.e. high-resolution images)
have enhanced the capability of foresters to stratify and classify forests
more accurately. Nezry et al. (2000) reported obtaining fairly reliable
estimates of standing tree volume in Sarawak, Malaysia, based on Synthetic
Aperture Radar and optical synergy and forestry knowledge (forest structure
models). New approaches in data analysis have also assisted greatly in the
stratification of tropical forests. One such development is the Canopy Density
Mapping and Monitoring Model (in short the FCD model), which was developed
by Rikimaru with assistance from the International Tropical Timber Organization
(ITTO). Rikimaru (2000) developed a modelling software that utilizes Landsat
imagery and assesses the forest status based on canopy density through several
pertinent indices such as advanced vegetation index, bare soil index, shadow
index and thermal index (Rikimaru 2002). Studies in Indonesia (Urquizo 1998)
and Malaysia (Alias et al. 2001) have also reported positive results
in forest stratification.
The most efficient sampling design is one that, for a specific cost gives the
smallest error for the parameter to be estimated, or for an accepted error
is the least cost (Taylor et al. 1989b). Defining efficiency is straightforward, but determination
of the most efficient design is complicated for several reasons:
The kind of sampling units, their size and shape, the number of units, their
distribution, their measurement and their analysis are important considerations
in the overall inventory methodology. It is common to used fixed area plots
(e.g. strip plots, rectangular, square, L-shaped and circular plots of various
sizes). Variable plots, such as point sampling, are also used. Some approaches
rely on a combination of fixed and variable plots.
Fixed plots have a fixed size with a certain shape. Unbiased estimates of stand
parameters can be obtained from any plot size or shape, although the precision
and survey cost can vary significantly. It is usually more efficient to use
small sampling units as they exhibit higher variability. However, in very
variable forests, small sampling units may result in a high coefficient of
variation (CV).
Plots can assume any shape. Theoretically, a rectangular plot with long axes
at right angles to the contours is most efficient as the plots tend to cross
the range of stand conditions (Taylor et al. 1989b). Plot shapes do
not affect the size of the sampling error, but the characteristics of the
shape influence the ease of establishment and the length of the plot perimeter.
Circular plots have the advantage that a single dimension, the plot radius,
can be used to define the perimeter. Their biggest advantage is that they
have the smallest area to perimeter ratio of any shape, which reduces the
number of borderline trees. Fewer borderline trees reduce the number of measurement
errors.
In many inventory practices employing fixed plots, sub-plots of varying sizes
are also established to enumerate trees within specified size classes on each
sub-plot. The intent is to reduce the plot area for small trees and increase
it for the larger trees to tally approximately an equal number of trees in
several sized classes. When sub-plots are used, the sampling intensities of
the sub-plots are always less than those of the main primary plot.
Variable plot cruising is one of the latest methods to have been developed but
it is still not practised widely in the Asia-Pacific region. The method, also
called point sampling or plotless cruising, has been used extensively in temperate
countries and has also been thoroughly tested in a wide variety of tropical
situations. Many features of this method are similar to the fixed plots (e.g.
determination of plot locations and numbers, measurements of tree diameter,
defects and tree quality). However, its application has many differences,
and thus comparisons should be limited to the relative costs of obtaining
the same pre-determined sampling error, or relative sampling error at a fixed
cost. (Samsudin and Kasinathan 1998)
Variable radius , instead of fixed radius plots, are used in the variable plot method. It is
actually a multi-plot with each tree having its own plot size depending on
its diameter. It does not require measurement of plot diameters or tree diameters
to compare the basal area per hectare. A tree that has a diameter large enough
to be within the fixed critical angle of the angle gauge (e.g. relaskop or
wedge prism) is tallied. Trees too small or too distant are ignored. In fixed
plot sampling, the probability of tree selection is proportional to stem frequency;
in point sampling it is proportional to the stem basal area.
Inventories undertaken at various levels involve different intensities and cover
differing spatial scales. Stand inventories provide information on a stand-to-stand
basis for detailed management planning. This level of inventory is called
the management level inventory and is usually undertaken for planning purposes
and the development of mid-term management plans. More intense sampling is
the operational-level inventory and it is called the district inventory. Sampling
intensities are higher and normally cover 10 percent of an area for such purposes
as pre-felling inventories. The information is used for calculating allowable
cuts for sustained yield management. The land units include several stands
and they may range from several hundred (such as in Malaysia) to several thousand
hectares. Sampling errors tolerated at this level normally would be less than
20 percent. National inventories are also conducted to provide baseline information
for the whole country for policy and broad planning purposes. In Malaysia,
three national inventories have been undertaken at 10-year intervals. Currently,
the Fourth National Inventory is being implemented. Systematic sampling and
cluster sampling are the two most commonly used methods. In some countries,
existing information from management inventories is compiled to obtain national
estimates. The upscaling of management inventory data to national data is
problematic, as they may yield results that are 20 to 30 percent above the
results from adding up these lower level inventories (Pelz 1993).�
