Open AccessASSORTMENT DOWNGRADE CORRECTION FOR CLEAR CUT OF Pinus taeda L. WITH USE OF TRANSITION MATRIX
Author(s) -
Thiago Floriani Stepka,
Klerysson Julio Farias
Publication year - 2021
Publication title -
floresta
Language(s) - English
Resource type - Journals
eISSN - 1982-4688
pISSN - 0015-3826
DOI - 10.5380/rf.v51i4.73576
Subject(s) - downgrade , stochastic matrix , markov chain , pinus <genus> , tapering , computer science , volume (thermodynamics) , function (biology) , mathematical optimization , mathematics , machine learning , physics , botany , computer graphics (images) , computer security , quantum mechanics , evolutionary biology , biology
It is very important that the quantification of a forest's stock is determined efficiently and accurately, in such a way that more detailed information is desirable for the knowledge of the different multiproducts originated. However, the use of taper functions, a precise adjustment method for determining the assortments, does not foresee the appearance of defects in the stem that could disqualify the logs during the forest harvest. This study aimed to use the transition matrix method, a model traditionally used for predict the diametric structure of uneven-aged forests, to correct assortment estimates made by tapering functions, such as failures in tree processing, that provide a disqualification of the logs in the market values. Adapting the concept of the Markov Chain, for the correction of the assortment downgrade, the transition of the assortment classes can be obtained by dividing the actual volume obtained after the operation of the harvesting machine by the volume estimated by the tapering function. In this case, applying this alternative to the clear cut of a 16-year-old Pinus taeda plantation, it was possible to verify the existence of changes, mainly in assortment classes with a thin end diameter of 24 and 18 cm and presenting efficient correction in the estimates. In order to make realistic corrections to the assortment transition, probability matrices must be built for each compartment or forest site to be estimated. Divergences between processing machines or forestry operator’s qualifications can be decisive for the calibration of the model.