plotted observation points, in order to give the best prediction model. The result of regression correlation analysis will show the strength and the type of model
between the parameter of trees biomass, DBH, total height, vegetation index and LAI. The equation derived from the regression model type will be used to
calculate the dependent variable y.
4.3.1 Biomass and Diameter Breast Height DBH
The correlation between the trees biomass and DBH for each cover type was tested independently among all plots observation for each cover type by using
regression analysis and ANOVA test. Analysis of variant ANOVA result and homogeneity test shows that there no significant different between the parameter
of each observation plot for all forest cover type statistical result in appendix.
yA1 = 0.762x
2
+ 19.31x - 306.3 R
2
= 0.9322
yA2 = 1.3693x
2
- 19.005x + 58.998 R
2
= 0.9917 yA3 = 1.2573x
2
- 17.131x + 76.402 R
2
= 0.997 yA4 = 1.0387x
2
+ 4.4035x - 160.96 R
2
= 0.8467
-5000 5000
10000 15000
20000
20 40
60 80
100 120
140
DBH cm Tr
e e
b iom
a s
s k
g pl
a nt
A1 A2
A3 A4
Poly. A1 Poly. A2
Poly. A3 Poly. A4
Figure 4.6 Tree biomass and DBH of forest cover type A using polynomial regression.
The independent test analysis used for each plot observation A separately using polynomial regression model Figure 4.6, the result of the correlation
52
regression pattern, visually shows that there is no significant different between each plot observation data within forest cover type A. It is mean that the
regression model derived from each correlation regression analysis is also allowed to be implemented because DBH highly correlated with the dependent variable of
trees biomass. The plot observation data of B1, B2 and B3 are also compared using
independent test. The pattern trend derived from polynomial and power correlation regression of B1, B2 and B3 Figure 4.7 and Figure 4.8 is visually
show that DBH influence on trees biomass is not significantly difference between each observation plot. The correlation between the parameter is equally high
about 98.
yB1 = 1.1329x
2
- 2.9149x - 73.494 R
2
= 0.9939
yB3 = 1.3842x
2
- 26.444x + 160.96 R
2
= 0.9722 yB2= 1.3002x
2
- 26.401x + 197.27 R
2
= 0.9972
-5000 5000
10000 15000
20000 25000
30000
20 40
60 80
100 120
140 160
DBH cm Tr
e e b
io m
ass kg
p lan
t
B1 B2
B3 Poly. B1
Poly. B3 Poly. B2
Figure 4.7 Tree biomass and DBH of forest cover type B using polynomial regression.
53
yB1 = 0.0993x
2.5703
R
2
= 0.9861 yB2 = 0.0717x
2.6451
R
2
= 0.9599 yB3 = 0.0919x
2.5796
R
2
= 0.9844
5000 10000
15000 20000
25000 30000
35000 40000
20 40
60 80
100 120
140 160
DBH cm Tr
ee bi om
ass k
g pl
ant
B1 B2
B3 Power B1
Power B2 Power B3
Figure 4.8 Tree biomass and DBH of forest cover type B using power regression.
The independent power regression trend pattern of forest cover type C Figure 4.9 for each plot, show that there is no different gap of DBH influence on
biomass between the observation plots. The correlation coefficient of the power model is equally high about 90.
yC2 = 0.140x
2.4316
R
2
= 0.9398 yC1 = 0.123x
2.499
R
2
= 0.9889
yC3 = 0.065x
2.6596
R
2
= 0.9625
1000 2000
3000 4000
5000 6000
20 40
60 80
DBH cm Tr
ee bi
om a
ss k
g pl
ant
C1 C2
C3 Power C2
Power C1 Power C3
Figure 4.9 Tree biomass and DBH of forest cover type C using power regression.
54
Independent test of forest cover type D using polynomial and power regression model Figure 4.10 and Figure 4.11, visually show that there is no
significant different of observation plot data DBH on tree biomass.
y1_3_5_7_9 = 1.1654x
2
- 21.351x + 89.158 R
2
= 0.9213 y2_4_6_8 = 1.3466x
2
- 26.466x + 128.01 R
2
= 0.8795
-1000 1000
2000 3000
4000 5000
6000 7000
20 40
60 80
100
DBH cm T
ree b io
m ass
kg p
lan t
D1_D3_D5_D7_D9 D2_D4_D6_D8
Poly. D1_D3_D5_D7_D9 Poly. D2_D4_D6_D8
Figure 4.10 Tree biomass and DBH of forest cover type D using polynomial regression.
yD1 = 0.0752x
2.4878
R
2
= 0.9665 yD3 = 0.051x
2.6571
R
2
= 0.9632 yD5 = 0.0375x
2.7194
R
2
= 0.9821 yD4 = 0.0414x
2.7072
R
2
= 0.9702 yD6 = 0.0108x
3.0931
R
2
= 0.8638
500 1000
1500 2000
2500 3000
3500 4000
4500
10 20
30 40
50 60
70 80
DBH cm Tr
ee bi om
ass k
g pl
ant
D1 D3
D5 D4
D6 Power D1
Power D3 Power D5
Power D4 Power D6
Figure 4.11 Tree biomass and DBH of forest cover type D using power regression.
The independent test resulted from statistical analysis of forest cover A,
55
B, C and D infer that the plot observation data within each forest cover type is possible to be used to further regression analysis to derive the equation regression
model for trees biomass prediction, because there is no significant different among each plot for all forest cover type.
The plot observation data for all point of forest cover type A, B, C and D is used to derived the regression model that the trend line of dispersion plot value
are fitted using polynomial, power and linear model. The correlation is about 90 for all forest cover type show the strong correlation between tree biomass and
DBH. The equation model derived from each forest cover type is summarized in Table 4.3.
y = 109x - 1597 R
2
= 0.834 y = 1.2888x
2
- 16.508x + 60.506 R
2
= 0.9656 y = 0.0829x
2.6291
R
2
= 0.9823
-4000 -2000
2000 4000
6000 8000
10000 12000
14000 16000
18000 20000
22000 24000
26000 28000
10 20
30 40
50 60
70 80
90 100
110 120
130
DBH cm T
ree B io
m ass
kg p
lan t
Plot A Linear Plot A
Poly. Plot A Power Plot A
Figure 4.12 Tree biomass and DBH correlation regression model of forest cover type A
The strong correlation of forest cover type B, C and D Figure 4.13 and Figure 4.14 derived from power regression model.
56
y = 66.366x - 822.57 R
2
= 0.8314 y = 1.2921x
2
- 17.999x + 70.513 R
2
= 0.9533 y = 0.0835x
2.6142
R
2
= 0.9774
-2000 -1000
1000 2000
3000 4000
5000 6000
7000 8000
20 40
60 80
DBH cm Tr
ee Bi om
ass k
g pl
an t
Plot B Linear Plot B
Poly. Plot B Power Plot B
Figure 4.13 Tree biomass and DBH correlation regression model of forest cover type B.
y = 1.1435x
2
- 15.462x + 65.672 R
2
= 0.9069 y = 0.1079x
2.5216
R
2
= 0.9653
1000 2000
3000 4000
5000 6000
5 10
15 20
25 30
35 40
45 50
55 60
65 70
75 80
DBH cm T
ree B iom
ass k
g p
lan t
Plot C Poly. Plot C
Power Plot C
Figure 4.14 Tree biomass and DBH correlation regression model of forest cover type C.
57
The regression coefficient of each cover type and general tropical forest is about 90, it shows a high correlation between biomass and DBH vegetation of
tropical rainforest.
y = 41.756x - 362.3 R
2
= 0.7105 y = 1.1716x
2
- 21.001x + 88.968 R
2
= 0.9129 y = 0.0631x
2.5214
R
2
= 0.9622
-1000 1000
2000 3000
4000 5000
6000 7000
10 20
30 40
50 60
70 80
90
DBH cm Tr
ee Bi om
ass k
g pl
ant
Plot D Linear Plot D
Poly. Plot D Power Plot D
Figure 4.15 Tree biomass and DBH correlation regression model of forest cover type D.
y = 0.0873x - 1.0319 R
2
= 0.654 y = 6E-05x
2.6705
R
2
= 0.9627 y = 0.0013x
2
- 0.0159x + 0.0347 R
2
= 0.8649
-5 5
10 15
20 25
30 35
40
20 40
60 80
100 120
140 160
DBH cm Tr
e e Bi
om as
s ton
pl an
t
Trop. Forest Linear Trop. Forest
Power Trop. Forest Poly. Trop. Forest
Figure 4.16 Tree biomass and DBH correlation regression model of tropical rain forest covering type A, B, C and D.
58
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
