Normal diameter, height and volume predictions of Abies religiosa from stump diameter

Control and monitoring of forest management, conducting forest audits in harvested areas and / or quantify short clandestine, requires the estimation of the normal diameter (d), total height (h) or volume (v) to characterize the original stand and from it, estimate the extracted volumes. When the tr...

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Bibliografische gegevens
Hoofdauteurs: García Cuevas, Xavier, Hernández Ramos, Jonathan, García Magaña, J. Jesús, Hernández Ramos, Adrián, Herrera Ávila, Victorino, González Peralta, Alfredo, Garfias Mota, Enrique Jonathan
Formaat: Online
Taal:spa
Gepubliceerd in: Instituto de Ecología, A.C. 2017
Online toegang:https://myb.ojs.inecol.mx/index.php/myb/article/view/1528
Omschrijving
Samenvatting:Control and monitoring of forest management, conducting forest audits in harvested areas and / or quantify short clandestine, requires the estimation of the normal diameter (d), total height (h) or volume (v) to characterize the original stand and from it, estimate the extracted volumes. When the tree no longer exists, not taken as the normal diameter (d), but the estimation of the same or other variables can be performed depending of the stump diameter (dt). This allometric relationship can be used to calculate the missing volumes. Therefore, the aim was to adjust equations that describe the normal diameter, height and volume as a function of the diameter of the tree stump of Abies religiosa (Kunth) Schltdl. et Cham. in Tancítaro, Michoacan, Mexico. Through sampling of 71 sites and 1745 data pairs dt-d, dt-h and dt-v; including all diametric and heights categories. By Proc Model and maximum likelihood method, linear and nonlinear models were fitted to predict d, h and v. Based on the level significance of parameters of the models, their estimates of goodness of fit test normality of the data and the predictive capacity thereof, we can inferred that these are valid to predict d, h and v in dt function. All models explained above 91% of the data variability.