foliage and branch biomass prediction an allometric approach

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Foliage and Branch Biomass Prediction an allometric approach

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Page 1: Foliage and Branch Biomass Prediction an allometric approach

Foliage and Branch Biomass Prediction

an allometric approach

Page 2: Foliage and Branch Biomass Prediction an allometric approach

Problem

The prediction of crown biomass

(foliage and branches) is more

difficult to make because of

•sophisticated structures, and

• irregular distributions (not

continuous and less uniform)

Page 3: Foliage and Branch Biomass Prediction an allometric approach

Virtual Density

Assume that crown biomass

distributes uniformly on crown

cross-area and continuously along

crown depth and thus continuous

functions can be applied for

describing density variation .

Page 4: Foliage and Branch Biomass Prediction an allometric approach

Virtual Foliage Density Distribution

lH

hh

0

Where r=density

H=total tree height,

l=crown length,

h=the distance from tree top, and

h=distance increment.

r

Page 5: Foliage and Branch Biomass Prediction an allometric approach

•the value of the density should be zero at

the top of tree•the density increases along crown depth

until it reaches a maximum and then

decreases

Page 6: Foliage and Branch Biomass Prediction an allometric approach

Candidates

Weibull

Maxima

flexibility symbolicsolution

appliedbefore

Yes

Yes

N/A

Yes

Yes

N/A

Page 7: Foliage and Branch Biomass Prediction an allometric approach

Distribution Function SelectedMaxima Function

hβαher Where and are the coefficients

Page 8: Foliage and Branch Biomass Prediction an allometric approach

Distribution & Foliage Biomass

Assume that

dhπrF 2B

Where FB is foliage biomass.

Page 9: Foliage and Branch Biomass Prediction an allometric approach

Foliage Biomass Function(Integration)

l

0

hγ22l

0

2hβB dhehkαdh)hek(αF

Where 2βγ

k is the transition coefficient.

Page 10: Foliage and Branch Biomass Prediction an allometric approach

Foliage Biomass Equation

]2)ell(γγ)e[2(1γ

kαF lγlγ

3

2

B

= an adjustment term of crown length

)e2(1 lγ = the assimilation rate according to the Lambert-Beer’s law

lγ2)ell(γγ

Page 11: Foliage and Branch Biomass Prediction an allometric approach

Foliage Biomass & Sapwood area

According to the pipe model theory, foliage

biomass is proportional to the sapwood area at

breast height:

2A ηdbhS

Where SA = sapwood area is the proportionality coefficient

Page 12: Foliage and Branch Biomass Prediction an allometric approach

Foliage Biomass & Age

Foliage biomass was affected by age and a

proposed function relationship is:

τB AF

Where A is tree age is a coefficient

Page 13: Foliage and Branch Biomass Prediction an allometric approach

Constant Transition Method

Let k (in foliage biomass equation) be equal to:

τ2Aηdbh

Page 14: Foliage and Branch Biomass Prediction an allometric approach

Foliage Biomass Equation(revised)

τlγlγ3

22

B ]A2)ell(γγ)e[2(1γ

αηdbhF

or:

τlγlγ2B ]A2)ell(γγ)e[2(1ξdbhF

Where is a coefficient

Page 15: Foliage and Branch Biomass Prediction an allometric approach

Branch Biomass Equation

A linear relationship exists between foliage

biomass and branch biomass, that is:

BB ζFB

Where BB is branch biomass

is a coefficient

Page 16: Foliage and Branch Biomass Prediction an allometric approach

Fertilization Impact

Fertilization significantly

increased foliage biomass. The

distribution of leaf biomass

could be shifted higher for

fertilized trees. Therefore, the

distribution coefficient should

be adjusted for fertilized trees.

Page 17: Foliage and Branch Biomass Prediction an allometric approach

Region Impact

Physiographic region is also a factor that affects

foliage and branch biomass. Thus, parameters

and in both biomass prediction models should

differ by region.

Page 18: Foliage and Branch Biomass Prediction an allometric approach

Data

Data came from the Consortium for

Accelerated Pine Plantation Studies (CAPPS),

which was initiated in 1987 and maintained by

the School of Forest Resources, University of

Georgia.

Page 19: Foliage and Branch Biomass Prediction an allometric approach
Page 20: Foliage and Branch Biomass Prediction an allometric approach

•H - complete vegetation control

•F- annual fertilization

•HF- both H and F

•C- check plot

Treatments

Page 21: Foliage and Branch Biomass Prediction an allometric approach
Page 22: Foliage and Branch Biomass Prediction an allometric approach

• In the winter of 1999, 192 trees were

harvested in the lower coastal plain of

Georgia for research on foliage, branches,

and stem biomass.

• In the winter of 2000, the same amount

trees were harvested in the piedmont of

Georgia for the same purpose.

Foliage and Branch Samples

Page 23: Foliage and Branch Biomass Prediction an allometric approach
Page 24: Foliage and Branch Biomass Prediction an allometric approach

Data Analysis

•complete vegetation control did not

significantly affect foliage biomass

• fertilization significantly increased foliage

biomass.

•age is a significant predictor of foliage

biomass

• foliage and branch biomass differ significantly

by region

Page 25: Foliage and Branch Biomass Prediction an allometric approach

Model Fitting

•Nonlinear mixed-effects system modeling

method was employed in order to obtain

consistent and unbiased estimates.

•Calculated foliage biomass were applied for an

independent variable in the branch biomass

prediction model fitting to eliminate

simultaneous equation bias.

Page 26: Foliage and Branch Biomass Prediction an allometric approach

Estimates (the Piedmont)

Parameter Estimate STD LCL UCLRegion (the piedmont) 0.0639 0.0064 0.0514 0.0764

0.6385 0.0386 0.5623 0.7142

f 0.6913 0.0416 0.6096 0.7730

0.8224 0.0358 0.7521 0.8928

f 0.8785 0.0360 0.8078 0.9492

2.2533 0.0640 2.1277 2.3790

Page 27: Foliage and Branch Biomass Prediction an allometric approach

Estimates (the Lower Coastal Plain)

Parameter Estimate STD LCL UCL

Region (the lower coastal plain)

0.0449 0.0063 0.0565 0.0813

0.6385 0.0386 0.5623 0.7142

f 0.6913 0.0416 0.6096 0.7730

0.8224 0.0358 0.7521 0.8928

f 0.8785 0.0360 0.8078 0.9492

2.5846 0.0640 2.1277 2.3790

Page 28: Foliage and Branch Biomass Prediction an allometric approach

Fit Statistics

Model Efficiency RMSE

FB 0.9636 0.6836 (kg)

BB 0.9648 1.6492 (kg)

Page 29: Foliage and Branch Biomass Prediction an allometric approach

Predictions & Observationsfoliage biomass in the Piedmont

0

1

2

3

4

5

5 10 12

Age

Dry

Fo

liag

e B

iom

ass (

kg

)

F=0

F=1

F=0 (ob)

F=1 (ob)

Page 30: Foliage and Branch Biomass Prediction an allometric approach

Predictions & Observationsfoliage biomass in the Lower Coastal Plain

0

1

2

3

4

5

6 10 12

Age

Dry

Fo

liag

e B

iom

ass

(kg

)

F=0

F=1

F=0 (ob)

F=1 (ob)

Page 31: Foliage and Branch Biomass Prediction an allometric approach

Predictions & Observationsbranch biomass in the Piedmont

0

1

2

3

4

5

6

7

8

9

10

11

5 10 12

Age

Dry

Bra

nch

Bio

mas

s (k

g)

F=0

F=1

F=0 (ob)

F=1 (ob)

Page 32: Foliage and Branch Biomass Prediction an allometric approach

0

1

2

3

4

5

6

7

8

9

10

11

6 10 12

Age

Dry

Bra

nch

Bio

mas

s (k

g)

F=0

F=1

F=0 (ob)

F=1 (ob)

Predictions & Observationsbranch biomass in the Lower Coastal Plain

Page 33: Foliage and Branch Biomass Prediction an allometric approach

Growth Trend

•Foliage and branch biomass growth of

fertilized trees keep from dropping until age

12 in both regions.

•Foliage and branch biomass growth of

unfertilized trees drop from age 10 in the

piedmont.

Page 34: Foliage and Branch Biomass Prediction an allometric approach

Dry Foliage Biomasssame dbh (18 cm), the Piedmont

0

1

2

3

4

5 6 7 8 9 10

Crown Length (m)

Dry

Fo

liag

e B

iom

ass (

kg

) F=1

F=0

Page 35: Foliage and Branch Biomass Prediction an allometric approach

Dry Foliage Biomasssame dbh (18 cm), the Lower Coastal Plain

0

1

2

3

4

5

5 6 7 8 9 10

Crown Length (m)

Dry

Fo

liag

e B

iom

ass (

kg

)

F=1

F=0

Page 36: Foliage and Branch Biomass Prediction an allometric approach

Fertilized vs Unfertilized

•Dry foliage biomass of a unfertilized tree is

more than the fertilized tree with the same

dbh.

•A plausible explanation- a tree in unfertilized

stands may be more dominant than the

fertilized tree with the same dbh.

Page 37: Foliage and Branch Biomass Prediction an allometric approach

Position of the Maximum Density

Let the first order derivation of the virtual

density r

)βheα(edh

dr hβhβ

be zero, i.e.,

0dh

dr

Page 38: Foliage and Branch Biomass Prediction an allometric approach

Position of the Maximum Density

That is,

Where r reaches the maximum value.

β

1h

Page 39: Foliage and Branch Biomass Prediction an allometric approach

Position of the Maximum Density

For unfertilized trees

For fertilized trees

treeoftopthefrommeters1.570.6385

1h

treeoftopthefrommeters1.450.6913

1h

Page 40: Foliage and Branch Biomass Prediction an allometric approach

Position of the Maximum Density

The average crown length is 6.98 meters for

unfertilized trees and 7.47 meters for fertilized

trees. The position is at about upper 78%

(100(1-1.57/6.98)) tree crown for unfertilized

trees and upper 81% (100(1-1.45/7.47)) tree

crown for fertilized trees.

Page 41: Foliage and Branch Biomass Prediction an allometric approach

Age & Foliage Biomass

If a tree reaches larger size at younger age, it

should gain more foliage biomass. The foliage

biomass of a fertilized tree with dbh 18 cm and

crown length 8 m at age 10 is about 5 kg, versus

a unfertilized tree with the same dbh and crown

length at age 12, 4.75 kg. That is, the younger

fertilized trees gained more than 5% foliage

biomass.

Page 42: Foliage and Branch Biomass Prediction an allometric approach

Number of Parameters

The allometric approach significantly reduced

the number of parameters to be estimated. The

developed foliage and branch biomass prediction

models used only four parameters, compared

with the empirical models, where eight

parameters were used for the same purpose.

Page 43: Foliage and Branch Biomass Prediction an allometric approach