Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
Tipo de material:
ArtigoAssunto(s): Recursos online:
Em: Floresta e Ambiente (Brazil) v. 26(special number n.1) p. 1-7; (2019)Sumário:
ABSTRACT
Pinus palustris Mill. ecosystem is considered one of the most threatened of North America.
In this context, studies on biomass quantification are fundamental for forest management
plans. Thus, the objective of this study was to develop a set of allometric equations to predict
total P. palustris stump-biomass. Biomass data were collected at different locations in the
southeastern United States. A total of 119 allometric equations were fitted from the combination
of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G),
number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models
that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE)
was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the
insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass
prediction for trees from different sites.
Keywords: forest management, modeling, regression.
| Tipo de material | Biblioteca atual | Coleção | Número de chamada | Informaçaõ do volume | Situação | Devolução em | Código de barras |
|---|---|---|---|---|---|---|---|
Periódicos
|
Biblioteca Nacional de Agricultura - Binagri Agrobase - Periódicos | Periódicos agrícolas | 2019 26( n. especial 1) | Online | 2025-0453 |
Publicação on-line; 22 ref.; 4 tables; 1 illus.; Summary (En)
ABSTRACT
Pinus palustris Mill. ecosystem is considered one of the most threatened of North America.
In this context, studies on biomass quantification are fundamental for forest management
plans. Thus, the objective of this study was to develop a set of allometric equations to predict
total P. palustris stump-biomass. Biomass data were collected at different locations in the
southeastern United States. A total of 119 allometric equations were fitted from the combination
of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G),
number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models
that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE)
was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the
insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass
prediction for trees from different sites.
Keywords: forest management, modeling, regression.
BINAGRI