Forescasting forest growth of unevenaged forest stands based on fitting diameter distribution functions in southeastern Sinaloa, Mexico
Abstract
This study was conducted to develop a forest growth model with information collected from conventional forest inventories in southeastern Sinaloa, Mexico. The growth model was developed with data from 80 sample plots, which were sampled in a systematic pattern and classified in 10 age classes. The model was based on fitting a diameter distribution function to diametric classes of unevenaged forest stands. The diameter classes were characterized by the three-parameter Weibull probability density function. The location, shape, and scale parameters of the Weibull distribution, as well as the number of trees per ha and total height were statistically related to average stand diameter at breast height. Predicting average diameter at breast height results in estimates of total wood volumen and the diameter class distribution. The error effect by forescasting these parameters was evaluated as a function of the total wood volume by sensitivity analysis. Total wood volume was estimated with the predicted parameters plus one standard error. These preliminary results showed that the growth model can be used to forescast forest growth of irregular forest stands with data collected from conventional forest inventories of southeastern Sinaloa, Mexico. The model estimated total wood volume with a deviation of less than 4 p. 100 in comparison to the estimate of total wood volume by another more conventional procedure. Sensitivity analysis had errors in total wood volume of 10, 2, 9, 10 and 4 p. 100 considering the standard error of the shape, scale, location, average number of trees per ha and average trunk height parameters, respectively. The proposed growth model could be further refined by classifying the sampling plots by site index and floristic composition.Downloads
© CSIC. Manuscripts published in both the printed and online versions of this Journal are the property of Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a “Creative Commons Attribution 4.0 International” (CC BY 4.0) License. You may read here the basic information and the legal text of the license. The indication of the CC BY 4.0 License must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the published by the Editor, is not allowed.