The correlation coefficient of non linear regression model derived from the analysis is higher than linear regression model. However, the equation model
from the linear regression model is still usable, because the correlation coefficient is strong more than 80. The curve pattern from non linear regression model
polynomial and power is highly representing the pattern of biomass that influenced by diameter of each tree in forest cover A, B, C and D.
The high of correlation value of forest cover A, B, C and D between the tree biomass and DBH shows that the diameter breast, the parameter reflecting the
trees stem influence much on biomass compared with another growth parameter, as the previous research proved that the stem biomass gives the biggest percentage
to the total tree biomass. The trees biomass on Acacia mangium tropical forest tress 80 of biomass is on stem, 11 on branch, 5 on leaves and 4 on small
branch Wicaksono, 2004. Another result from the biomass in tropical forest trees study of Adinugroho 2002 also resulted the resemble distribution percentage of
biomass.
4.3.2 Biomass and Total Height
The independent correlation test and ANOVA test was also conducted for trees biomass and tree total height parameter in each forest cover type.
Analysis of variant ANOVA result shows that there is no significant different between the parameter of each observation plot for all forest cover type statistical
result in appendix. The power regression pattern of forest cover type A and B visually show
that there is significant different between total height influence of the observation plot Figure 4.17 and Figure 4.18. The correlation between the parameter of each
59
pattern plot is strong, depicted in correlation coefficient is more than 80 resulted from power regression model.
yA1 = 0.0391x
3.0505
R
2
= 0.8446 yA2 = 0.0309x
3.2342
R
2
= 0.8792 yA3 = 0.0317x
3.2418
R
2
= 0.9253 yA4 = 0.045x
3.0335
R
2
= 0.9046
2000 4000
6000 8000
10000 12000
14000 16000
18000 20000
10 20
30 40
50 60
Total height m Tr
ee bi
o m
a s
s k
g p
la n
t
A1 A2
A3 A4
Power A1 Power A2
Power A3 Power A4
Figure 4.17 Tree biomass and total height of forest cover type A using power regression.
yB1 = 0.0175x
3.4075
R
2
= 0.8915 y B2= 0.1525x
2.6609
R
2
= 0.8066 yB3 = 0.0244x
3.3282
R
2
= 0.8843 5000
10000 15000
20000 25000
30000
10 20
30 40
50
Total he ight m Tree bi
om ass
k g
pl ant
B1 B2
B3 Power B1
Power B2 Power B3
Figure 4.18 Tree biomass and total height of forest cover type B using power regression.
60
The pattern of power regression model of forest cover C Figure 4.19 show that the difference value of total height on tree biomass is different when the
total height reach more than 25 m. It is due to the condition of plot observation of forest cover C that has different vegetation density, but the difference is tolerable
because the pattern of changing is equally the same.
yC1 = 0.0063x
3.7457
R
2
= 0.8894 yC2= 0.1615x
2.4668
R
2
= 0.6312 yC3 = 0.1509x
2.6875
R
2
= 0.8201
1000 2000
3000 4000
5000 6000
7000 8000
10 20
30 40
50 60
Total height m Tr
e e
b iom
a s
s k
g pl
a n
t
C1 C2
C3 Power C1
Power C2 Power C3
Figure 4.19 Tree biomass and total height of forest cover type C using power regression.
yD1 = -0.0276x
2
+ 76.26x - 302.15 R
2
= 0.7563 yD5 = 0.6605x
2
+ 73.085x - 316.09 R
2
= 0.7357 yD4 = 2.5675x
2
- 12.876x + 15.241 R
2
= 0.7135 yD3 = 0.3641x
2
+ 59.397x - 261.65 R
2
= 0.8471 yD6 = 1.0147x
2
+ 2.8755x - 3.1677 R
2
= 0.9015
-500 500
1000 1500
2000 2500
3000 3500
4000
5 10
15 20
25 30
35 40
45
Total height m Tr
ee bi om
ass kg
pl ant
D1 D5
D4 D3
D6 Poly. D1
Poly. D5 Poly. D4
Poly. D3 Poly. D6
Figure 4.20 Tree biomass and total height of forest cover type D using polynomial regression.
61
The polynomial regression pattern of forest cover D derived from independent analysis, shows that there is no significant different on influencing
total height to tree biomass between the observation plot. The plot observation data for all points of forest cover type A, B, C and
D is used to derived the regression model that the trend lines of dispersion plot values are fitted using polynomial, power and linear model. The correlation
coefficient of the trees biomass and total height of each cover type is lower than its correlation with the trees diameter, but the correlation is still high range about
70-90 from non linear model Figure 4.21 - Figure 4.25 . The equation model between tree biomass and total height derived from each forest cover type is
summarized in Table 4.3.
y = 0.0354x
3.1512
R
2
= 0.8875 y = 182.43x - 2055.1
R
2
= 0.4505 y = 7.3733x
2
- 135.75x + 496.5 R
2
= 0.5792
-4000 -2000
2000 4000
6000 8000
10000 12000
14000 16000
18000 20000
5 10
15 20
25 30
35 40
45 50
55
Total height m Tree B
iom ass kgplant
Plot A Power Plot A
Linear Plot A Poly. Plot A
Figure 4.21 Tree biomass and total height correlation regression model of forest cover type A.
62
y = 0.0483x
3.0561
R
2
= 0.8593 y = 5.3769e
0.2x
R
2
= 0.7997
2000 4000
6000 8000
10000 12000
14000 16000
18000 20000
22000 24000
26000 28000
10 20
30 40
50
Total heightm Tr
ee Bi om
ass k
g pl
a n
t
Plot B Power Plot B
Expon. Plot B
Figure 4.22 Tree biomass and total height correlation regression model of forest cover type B.
y = 0.0626x
2.9134
R
2
= 0.767
1000 2000
3000 4000
5000 6000
10 20
30 40
50 60
Total Height m T
ree B io
m ass
kg p
lan t
Plot C Power Plot C
Figure 4.23 Tree biomass and total height correlation regression model of forest cover type C.
63
y = 79.011x - 363.25 R
2
= 0.6869 y = 0.9688x
2
+ 46.49x - 222.83 R
2
= 0.7014 y = 0.1475x
2.8426
R
2
= 0.7552
-1000 1000
2000 3000
4000 5000
6000 7000
5 10
15 20
25 30
35 40
45
Total height m Tr
ee Bi
om as
s k
g pl
ant
Plot D Linear Plot D
Poly. Plot D Power Plot D
Figure 4.24 Tree biomass and total height correlation regression model of forest cover type D.
y = 0.0049x
2
- 0.0652x + 0.196 R
2
= 0.4615 y = 0.0002x
2.6932
R
2
= 0.8245
-5 5
10 15
20 25
30
25 50
75
Total Height m T
ree B io
m a
ss kg
p lan
t
Series1 Poly. Series1
Power Series1
Figure 4.25 Tree biomass and DBH correlation regression model of tropical rain forest covering type A, B, C and D.
64
The more strength relation is derived from non linear regression model of forest cover A, B, C and D. The pattern curve of the model is curvilinear. It means
that the total trees height will influence significantly on biomass when the trees reach certain total height.
4.3.3 Biomass and LAI
