ASSESSING ALTERNATIVES TO ESTIMATE THE STEM VOLUME OF A SEASONAL SEMI-DECIDUOUS FOREST | Zendy

Jadson Coelho de Abreu | Zendy; Carlos Pedro Boëchat Soares | Zendy; Hélio Garcia Leite | Zendy

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ASSESSING ALTERNATIVES TO ESTIMATE THE STEM VOLUME OF A SEASONAL SEMI-DECIDUOUS FOREST

Author(s) -

Jadson Coelho de Abreu,

Carlos Pedro Boëchat Soares,

Hélio Garcia Leite

Publication year - 2017

Publication title -

floresta

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.386

H-Index - 13

eISSN - 1982-4688

pISSN - 0015-3826

DOI - 10.5380/rf.v47i4.54259

Subject(s) - deciduous , forestry , random forest , statistics , mathematics , computer science , geography , artificial intelligence , biology , ecology

The objective of this study was to evaluate the use of linear and hybrid linear models, artificial neural networks (ANN) and support vector machine (SVM) in the estimation of the stem volume in a Seasonal Semi-deciduous Forest. Cubing data of 99 sample-trees of 15 species were used for this purpose. After analysis, we verified that the inclusion of the species as random effect did not contribute to increase the accuracy of the estimates in the structure of a hybrid model. Artificial neural networks and support vector machines, including species as input categorical variables, were the best alternatives to estimate the stem volume of trees of the Seasonal Semi-deciduous Forest.

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