Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
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. palus...
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| Format: | Article |
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Universidade Federal Rural do Rio de Janeiro
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| Series: | Floresta e Ambiente |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106&lng=en&tlng=en |
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| author | Aline Araújo Farias Salvador A. Gezan Melissa Pisaroglo de Carvalho Antonio Carlos Ferraz Filho Carlos Pedro Boechat Soares |
| author_facet | Aline Araújo Farias Salvador A. Gezan Melissa Pisaroglo de Carvalho Antonio Carlos Ferraz Filho Carlos Pedro Boechat Soares |
| author_sort | Aline Araújo Farias |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T10:28:21Z |
| format | Article |
| id | doaj.art-d53ad1ee1d1342f58da6296242b96cf3 |
| institution | Directory Open Access Journal |
| issn | 2179-8087 |
| language | English |
| last_indexed | 2024-12-10T10:28:21Z |
| publisher | Universidade Federal Rural do Rio de Janeiro |
| record_format | Article |
| series | Floresta e Ambiente |
| spelling | doaj.art-d53ad1ee1d1342f58da6296242b96cf32022-12-22T01:52:39ZengUniversidade Federal Rural do Rio de JaneiroFloresta e Ambiente2179-808726spe110.1590/2179-8087.040318S2179-80872019005000106Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United StatesAline Araújo FariasSalvador A. GezanMelissa Pisaroglo de CarvalhoAntonio Carlos Ferraz FilhoCarlos Pedro Boechat SoaresABSTRACT 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106&lng=en&tlng=enforest managementmodelingregression |
| spellingShingle | Aline Araújo Farias Salvador A. Gezan Melissa Pisaroglo de Carvalho Antonio Carlos Ferraz Filho Carlos Pedro Boechat Soares Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States Floresta e Ambiente forest management modeling regression |
| title | Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
| title_full | Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
| title_fullStr | Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
| title_full_unstemmed | Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
| title_short | Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
| title_sort | allometric equations to predict pinus palustris biomass in the southeastern united states |
| topic | forest management modeling regression |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106&lng=en&tlng=en |
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