National Forest Inventories are important primary data sources for large-scale forest resource surveys, in which volume estimates of sampled trees are essential for quantitative analysis. Volume prediction models in natural forests are scarce in Brazil due to legal restrictions for cutting trees, especially in the Atlantic Forest. This study aimed to fit volume models for the main forest types and timber species of the Atlantic Forest in Rio de Janeiro state, considering two hypotheses: (I) generic volume models provide greater generalizability of estimates; however, (II) they may reduce the accuracy of forest type- and species-specific predictions. Four linear models with logarithmic transformation of variables were evaluated to fit volume models for generic and specific datasets, which correspond to the main forest types and timber species. Goodness-of-fit statistics were calculated to compare the accuracy and efficiency of the models, and selected models were validated through leave-one-out cross-validation procedures. The estimates obtained by generic and specific models were compared by non-parametric hypothesis tests. Generic models showed similar predictions to the specific models for forest types and timber species, with similar potential for stem and total volume predictions. Therefore, generic models can be used for Atlantic Forests in Rio de Janeiro state, while specific models are recommended to obtain more detailed local estimates.
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Citation
Cysneiros VC, Gaui TD, Silveira Filho TB, Pelissari AL, Machado SA, De Carvalho DC, Moura TA, Amorim HB (2020). Tree volume modeling for forest types in the Atlantic Forest: generic and specific models. iForest 13: 417-425. - doi: 10.3832/ifor3495-013
Academic Editor
Nicola Puletti
Paper history
Received: May 05, 2020
Accepted: Jul 08, 2020
First online: Sep 16, 2020
Publication Date: Oct 31, 2020
Publication Time: 2.33 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2020
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This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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