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One of the most common ways to calculate volume of a standing tree or of a log is by using the Doyle log rule. This formula uses the small end diameter of a log (D) (in inches) along with the log length (L) (in feet) to estimate the volume of a log. The Doyle log rule on average under estimates the volume of a log. See formula below:
The board foot or board-foot is a unit of measurement for the volume of lumber in the United States and Canada. [1] It equals the volume of a board that is one foot (30.5 cm) in length, one foot in width, and one inch (2.54 cm) in thickness, or exactly 2.359 737 216 liters. Board foot can be abbreviated as FBM (for "foot, board measure"), BDFT ...
The log scaler is subject to random "check scales" in which another scaler rescales exactly the same logs and the results are compared. The log scaler must be within + or - 1% of the gross scale and + or - 2% of the net scale to keep their certification to scale. The scale is used for payment, quality control and inventory purposes.
Girard form class is a form quotient calculated as the ratio of diameter inside bark at the top of the first 16 foot log to the diameter outside bark at breast height ().Its purpose is to estimate board-foot volume of whole trees from measurement of DBH, estimation of the number of logs, and estimation of the taper of the first log, based on the general relationships identified between the ...
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A board foot is a United States and Canadian unit of approximate volume, used for lumber. It is equivalent to 1 inch × 1 foot × 1 foot (144 cu in or 2,360 cm 3). It is also found in the unit of density pounds per board foot. In Australia and New Zealand the terms super foot or superficial foot were formerly used for this unit. The exact ...
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A base-10 log scale is used for the Y-axis of the bottom left graph, and the Y-axis ranges from 0.1 to 1000. The top right graph uses a log-10 scale for just the X-axis, and the bottom right graph uses a log-10 scale for both the X axis and the Y-axis. Presentation of data on a logarithmic scale can be helpful when the data: