Biomass estimation equations for miombo woodland, Zambia

1985 ◽  
Vol 3 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Peter Stromgaard
Keyword(s):  
Biomass Estimation ◽  
Miombo Woodland ◽  
Estimation Equations

Generality of black spruce biomass estimation equations

1984 ◽  
Vol 14 (3) ◽  
pp. 468-470 ◽  
Author(s):  
D. F. Grigal ◽  
L. K. Kernik
Keyword(s):  
Black Spruce ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Tree Diameter ◽  
Estimation Equations ◽  
Stand Conditions

We developed a set of equations to estimate biomass of black spruce (Piceamariana (Mill.) B.S.P.), based on data from trees from bogs in Minnesota. The equation for total biomass, based on diameter alone, was similar to most other equations for black spruce in the literature. This relationship appears to have wide generality. Estimates of foliage biomass, arrived at by using the same tree diameter and height but various equations from the literature, differed greatly. These comparisons indicate that stand conditions, such as stocking, are apparently very important in determining mass of black spruce foliage.

Biomass estimation equations for Norway spruce in New York

1986 ◽  
Vol 16 (2) ◽  
pp. 413-415 ◽  
Author(s):  
E. J. Jokela ◽  
K. P. Van Gurp ◽  
R. D. Briggs ◽  
E. H. White
Keyword(s):  
Norway Spruce ◽  
Strong Predictor ◽  
Allometric Equation ◽  
Site Quality ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Dry Matter Accumulation ◽  
Individual Tree ◽  
Stem Wood ◽  
Estimation Equations

Biomass estimation equations for plantation-grown Norway spruce (Piceaabies (L.) Karst.) were developed from data of 30 sample trees and expressed using the linear form of the following allometric equation: In Y = b0 + b1 ln X + ln ε, where Y is dry weight and X is dbh or D2H. The accuracy of the equations for biomass estimates were ranked as follows: total tree > stem wood > stem bark > foliage > live branches > dead branches. Diameter alone was a strong predictor of biomass and the addition of height to the model only slightly reduced the standard error of the estimate for the stem component equations. Comparison of results to equations developed in Sweden showed similarity in predictions for total biomass, but also showed disparity in predictions for individual tree components. Factors that influence tree morphology and distribution patterns of dry matter accumulation, such as stocking and site quality, may be responsible for these differences.

scholarly journals A critical review of forest biomass estimation equations in India

2021 ◽  
pp. 100098
Author(s):  
Biplab Brahma ◽  
Arun Jyoti Nath ◽  
Chandraprabha Deb ◽  
Gudeta W Sileshi ◽  
Uttam Kumar Sahoo ◽  
...  
Keyword(s):  
Critical Review ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Estimation Equations

Individual tree biomass estimation equations for plantation-grown white spruce in northern Minnesota

1985 ◽  
Vol 15 (4) ◽  
pp. 738-739 ◽  
Author(s):  
R. B. Harding ◽  
D. F. Grigal
Keyword(s):  
Stand Structure ◽  
White Spruce ◽  
Biomass Estimation ◽  
Prediction Equations ◽  
Tree Biomass ◽  
Eastern Canada ◽  
Individual Tree ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Estimation Equations

Prediction equations for biomass of white spruce (Piceaglauca (Moench) Voss) were developed for 115 sample trees using the allometric models Y = ADB and Y = ADBHC, where Y is mass, D is diameter at breast height, and H is total height. The addition of height to the model reduced the Sy•x for all estimates except that for biomass of branches and needles. Comparison of results to other estimation equations developed in eastern Canada showed that biomass estimates were variable. Variations in stand structure and age between natural and plantation-grown trees are possible reasons for these differences.

Generalized biomass estimation equations for Betulapapyrifera Marsh.

1981 ◽  
Vol 11 (4) ◽  
pp. 837-840 ◽  
Author(s):  
Mark D. C. Schmitt ◽  
D. F. Grigal
Keyword(s):  
New Hampshire ◽  
Aboveground Biomass ◽  
Biomass Estimation ◽  
Mean Square ◽  
Data Set ◽  
Site Specific ◽  
Specific Data ◽  
Independent Variables ◽  
Breast Height ◽  
Estimation Equations

Aboveground biomass estimation equations were developed and compared for several components of Betulapapyrifera Marsh, trees using diameter at breast height (dbh) alone or dbh and height as independent variables. The data upon which the equations are based were collected by a number of different investigators working in Minnesota, Wisconsin, New Hampshire, and several sites in Maine and New Brunswick. Coefficients of determination ranged from 0.82 to 0.99, with higher values for bole than for crown components. The root mean-square deviation of the observations from the model was in the range 1 – 10 kg for any component. The largest trees in the data set (ca. 30 cm dbh) had total aboveground biomass of about 540 kg. In the absence of site-specific data, these equations provide acceptable estimates of biomass for B. papyrifera.

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