an algorithm for processing stem analysis data and sampling intensities for immature jack pine growth
abstract
this study examined two topics. in the first, a computer
algorithm was developed to process stem analysis data produced by
tree ring increment measure (trim) system. the algorithm
developed not only processed trim data for cumulative increment of
volume, height, and dbh by one-year intervals for individual trees,
but also calculated annual volume increment per unit area (vol./ha)
by one-year intervals for stands. a hashing technique with a linked
list data structure was used in the algorithm. the advantages of the
algorithm are to process stem analysis and manage outputs
efficiently and to provide a user with quick access to any processed
stem analysis tree records. in the second, sampling intensities on
both plot and tree levels were investigated. two forms of two-stage
sampling strategies were employed. the study indicated that
subsampling using probabilities proportional to size (pps) could
produce reliable estimates for an annual growth. the study
suggested that over 91 percent of precision of mean growth estimate
can be obtained with the sample plot intensities of 66 percent at the
first stage and with the sample tree intensities of 2.1 percent at the
second stage at the 95 percent confidence level. the study also
showed that subsampling with pps was superior to that with simple
random subsampling.
collections
- retrospective theses [1604]