Cluster and discriminant analyses for stem volume modelling of tree species groups in an amazon rainforest

Figueiredo-Filho A., . and Pelissari A. L., . and David H. C., . and Cysneiros V. C., . and Machado S. A., . Cluster and discriminant analyses for stem volume modelling of tree species groups in an amazon rainforest. pp. 325-333. ISSN 0128-1283

The diversity of species in native tropical forests causes difficulty in the interpretation of data that support their management and conservation. Species grouping based on characteristics of interest reduces significantly the number of volume equations and helps solve the problem of undersampling rare species. This study aims to group 32 Amazonian trees species of commercial interest based on regression coefficients of the Schumacher and Halls model and their fit statistics. To accomplish this we employ a two-stage approach in which we first applied cluster analysis to classify species with higher sampling intensity (n 30). This phase allowed us to allocate poorly sampled species (n 30) to groups created by discriminant analysis resulting in the second stage. This proposed approach has proven adequate for grouping timber species in the Amazon forest and so the stem volume can be modelled on consistent groups of species. The grouping of Amazon rainforest commercial species based on the regression coefficients and fit statistics performs better in aggregation for the stem volume modelling providing stabilisation of estimation error and supplying few equations for the evaluation of standing stock

Full text available from: Official URL: https://www.frim.gov.my/v1/jtfsonline/jtfs/v29n3/3...

Abstract

The diversity of species in native tropical forests causes difficulty in the interpretation of data that support their management and conservation. Species grouping based on characteristics of interest reduces significantly the number of volume equations and helps solve the problem of undersampling rare species. This study aims to group 32 Amazonian trees species of commercial interest based on regression coefficients of the Schumacher and Halls model and their fit statistics. To accomplish this we employ a two-stage approach in which we first applied cluster analysis to classify species with higher sampling intensity (n 30). This phase allowed us to allocate poorly sampled species (n 30) to groups created by discriminant analysis resulting in the second stage. This proposed approach has proven adequate for grouping timber species in the Amazon forest and so the stem volume can be modelled on consistent groups of species. The grouping of Amazon rainforest commercial species based on the regression coefficients and fit statistics performs better in aggregation for the stem volume modelling providing stabilisation of estimation error and supplying few equations for the evaluation of standing stock

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Item Type:	Article
AGROVOC Term:	Rain forests
AGROVOC Term:	Cluster sampling
AGROVOC Term:	Discriminant analysis
AGROVOC Term:	Stems
AGROVOC Term:	Species
AGROVOC Term:	Species diversity
AGROVOC Term:	Tropical forests
AGROVOC Term:	Forest management
AGROVOC Term:	Timber
AGROVOC Term:	Volume
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	24 Apr 2025 06:28
URI:	http://webagris.upm.edu.my/id/eprint/23625

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