Attached
Stem volume is the most important variable in commercial forestry because all the
management decisions are taken on the volume production of trees. However, it is also the
most difficult variable to measure and therefore it is necessary to accurate volume
prediction methods.
For the present study a growth model was constructed to predict the stem volume of
individual Tectona grandis L.f. (TEAK) trees of the 29 year old plantation (ID No: Block
01, Sub-block 29) in Mihintale Beat of the Anuradhapura Forest Division. The age of this
even-aged plantation was 29 year and the size was 34.0 ha.
In order to collect the data, ten 0.02 ha circular sample pots were randomly laid. Diameter
at breast height (dbh), total height and crown height of the trees in all the sample plots
were measured as the first step. Tree basal area, stand basal area and top height were
calculated using these data. For the second step of data collection, each tree stem was
divided into 3-5 m sections without felling them using the Blume-Leiss altimeter. Then the
bottom, middle and top diameters of each section were measured using Spiegal relascope.
The volume of each section was calculated separately using Newton's formula and the
stem volume was determined by summing the section volumes together. For this reason,
the final section of the tree was considered as a cone. The sample plot data were divided
into two as construction (75%) and validation (25%) and the latter was not used for
building the model.
A theoretical model was developed to predict the individual tree volume using the
relationship of form factor with volume, basal area and total height. It was fitted to the
collected data using multiple linear regression in MINIT AB. Three site factors and four
transformations which are biologically accepted were used to enhance the quality of the
models
After fitting 13 models were selected for further analysis due to their high R2 values which
were over 85% and good distribution of standard residuals. For these selected models,
average model bias and modelling efficiency were tested to select the best model. The
biases indicated by all the models were insignificant and the model with the highest
modelling efficiency (0.982) was selected for the field use. When the final model was
validated with independent data reserved at the beginning of the model construction, the
results proved the ability of using the selected model in the field without producing errors.
The finally selected model for the field use is .rv = 0.0567.Jba * lit + 0.00356topht.
