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.