GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN LOBLOLLY PINE PLANTATIONS

Cerne

Endereço:
Departamento de Ciências Florestais, Universidade Federal de Lavras, Caixa Postal 3037
Lavras / MG
0
Site: http://www.dcf.ufla.br/cerne
Telefone: (35) 3829-1706
ISSN: 1047760
Editor Chefe: Gilvano Ebling Brondani
Início Publicação: 31/05/1994
Periodicidade: Trimestral

GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN LOBLOLLY PINE PLANTATIONS

Ano: 2019 | Volume: 25 | Número: 4
Autores: Luan Demarco Fiorentin, Wagner Hugo Bonat, Allan Libanio Pelissari, Sebastião do Amaral Machado, Saulo Jorge Téo, Gabriel Orso
Autor Correspondente: Luan Demarco Fiorentin | [email protected]

Palavras-chave: Elastic net, Link function, Logistic regression, Ridge regression, Stepwise method

Resumos Cadastrados

Resumo Inglês:

To quantify the surviving trees in a forest stand and estimate the probability of an individual tree to survival are a fundamental task in forest management planning. Therefore, the main goal of this paper was to estimate the tree survival probability in loblolly pine (Pinus taeda L.) plantations based on generalized linear models (GLM). The data set was obtained from forest inventories carried out in the Midwest of Santa Catarina State, Brazil. The data analysis combined strategies for selecting covariates and different specifications of link functions in a Bernoulli GLM. We performed strategies for covariate selection at plotlevel along with the standard stepwise procedure, where we considered the elastic net approach, as well as its special cases the lasso and ridge penalization. Our analyses showed that the stepwise procedure combined with the complementary log-log link function provide the best fit. The variables that most contributed to assess tree survival were basal area, number of individuals, maximum diameter, diameter of the average cross-sectional area and the diameter coefficient of variation per plots. This model presents 81.5% of accuracy given by ROC curve. Finally, we evaluated the fitted model by means of the half-Normal plots and randomized quantile residuals, whose results showed evidence of a suitable fit. We suggest the stepwise procedure for selecting covariates for a tree survival probability model, besides a complementary log-log link function.