This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal
Prashant Paudel
Faculty of Forestry
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0982-4818
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Volume, biomass and carbon of forest ecosystem are generally estimated using lookup tables or allometric equations known as models. These general equation-based models are usually exclusively based on dimensional measurement such as diameter at breast height (DBH) and/or height, which sometimes makes it difficult to judge applicability of equation to given forest condition or types. It is therefore important to estimate carbon stock and develop models to predict biomass or carbon stock with stratification by categorical variables like crown cover, slope, forest types, etc. Stratification of forest by remote sensing approach while designing forest inventory not only improves the reliability of the estimation but also reduces the cost of measurement. Taking crown coverage (<25%, 25-50%, 50-75% and >75%) as a categorical variable, this study assessed the status of carbon stock and develop a regression model to predict carbon stock for each canopy class of Sal (Shorea robusta) forest in Nepal. DBH and height were measured for trees with more than 7 cm DBH in 82 sample plots (18, 22, 22 and 20 for <25%, 25-50%, 50-75% and >75% respectively). On average 297 stands per hectare were recorded with 94.80 m 3 /ha growing stock. Carbon stock was highest for >75% crown cover class (89.83 ton C/ha) and lowest for <25% crown cover class (27.47 ton C/ha) with average 60.41 ton C/ha, where per tree carbon stock was lowest in crown cover class 25-50% (0.16 ton C/tree). TukeyHSD shows that four pairs of crown cover classes have significant difference in carbon stock at 95% confidence interval. Regression model with natural logarithm of DBH 2 and total tree height was best fitted for estimation of carbon stock per tree in different crown cover class with adjusted R 2 >0.99 and residuals were normally distributed. Adjustment of model (natural logarithm of DBH 2 and height) with high accuracy (R 2 >0.99) shows the importance of stratification especially by crown cover for accurate estimation of carbon stock for optimization of carbon benefits.
Keywords
Sal forest, Crown cover, carbon stock, estimation, model
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Crown cover classification
Division of forest into four different crown cover classes shows that majority of forest areas fall under crown cover class 50-75 % whereas only around 50 ha of forest was covered by crown cover class <25 % (Table 1). Cohen’s kappa coefficient calculated from the error matrix shows that the overall accuracy of the classification was more than 85 %.
Table 1: Area coverage by crown cover class
|
Crown cover class |
Area (Ha) |
|
< 25% |
49.60 |
|
25-50% |
120.65 |
|
50-75% |
380.19 |
|
> 75% |
122.28 |
|
Total |
672.72 |
[Figure 1]
Carbon stock
There was a fluctuation in the number of stems with increase in crown cover, where on an average 297 stems per hectare were recorded in study area with highest number of stems in crown cover class >75 %. Similarly, per tree growing stock and carbon stock was highest for crown cover class >75 % and lowest for 25-25 % crown cover class despite having the second largest number of stems per ha in the class. Growing stock (GS), above ground biomass (AGB), total biomass (TB) and carbon stock (CS) was observed to have increased with increase in crown coverage. Table 2 shows that estimated variables by crown cover class and weighted average for the whole area.
Table 2: Number of stems, growing stock and biomass by crown cover
|
Variable |
Crown cover class (average) |
Average (weighted) |
|||
|
< 25 % |
25-50% |
50-75% |
> 75% |
||
|
No. of stands /ha |
161 |
345 |
288 |
375 |
297 |
|
Growing stock (m3/ha) |
44.42 |
91.07 |
100.47 |
137.99 |
94.80 |
|
Per tree GS (m3/ha) |
0.28 |
0.26 |
0.35 |
0.37 |
0.31 |
|
AGB (ton/ha) |
48.72 |
100.52 |
114.06 |
159.28 |
107.11 |
|
Total biomass (ton/ha) |
58.46 |
120.62 |
136.87 |
191.13 |
128.53 |
|
Total CS (ton/ha) |
27.47 |
56.69 |
64.33 |
89.83 |
60.41 |
|
Per tree CS (ton/ha) |
0.17 |
0.16 |
0.22 |
0.24 |
0.20 |
Four estimated variables (GS, AGB, TB, CS) were plotted against crown cover in Box-whisker plot for displaying the variation in data by crown cover class. It shows that the variability in data was seen more in crown cover class >75 % for all four estimated variables and less in crown cover class <25 %.
[Figure 2]
One-way ANOVA showed a significant difference in total carbon stock with crown cover class at 95 % confidence interval. TukeyHSD, test to explore the significance between different pairs of crown cover, showed that three pairs of crown cover category (<25 to 25-50, <25 to 50-75 and 25-50 to >100) significantly differ at 95 % confidence interval (Table 3) whereas <25 to >100 pairs were significantly different at 99.9 % confidence interval.
Table 3: Pair-wise comparison of carbon stock by crown cover
|
Crown cover Percentage |
Diff |
Lower |
Upper |
P-value |
Significance level |
|
0-25 to 25-50 |
28.96 |
1.65 |
56.27 |
0.033 |
* |
|
0-25 to 50-75 |
36.53 |
9.22 |
63.84 |
0.004 |
* |
|
0 -25 to 75-100 |
61.81 |
33.89 |
89.72 |
0.000 |
*** |
|
25-50 to 50-75 |
7.57 |
-18.34 |
33.48 |
0.860 |
|
|
25-50 to 75-100 |
32.85 |
6.29 |
59.39 |
0.009 |
* |
|
50-75 to 75-100 |
25.28 |
-1.27 |
51.83 |
0.070 |
|
Confidence level ***’ 99.9% ‘**’ 99% ‘*’ 95%
4.3 Model for Carbon Stock Estimation
Regression equation to model each crown cover class was developed from DBH and height as independent variables where crown cover was used as a categorical variable. Models were selected after the intercept of DBH and height were positive, adjusted R2 value was above 0.99 and residual areas normally distributed and homogeneous.
Fitted model for estimating carbon stock where DBH and Height are independent variables with base of natural logarithm is given as:
[Due to technical limitations, the formula could not be displayed here. Please see the supplementary files section to access the formula.]
Where, CS is in kg/tree, DBH in cm and Height in m.
Table 4: Estimated intercepts of DBH, Height for estimating CS by different CC
|
CC % |
Intercept (SE) (c) |
DBH (SE) (a) |
Height (SE) (b) |
R2 |
p-value |
|
0-25 |
-3.22 (0.062) |
1.05 (0.01) |
0.72(0.035) |
0.9984 |
<2.2e-16 |
|
25-50 |
-3.31 (0.023) |
1.04(0.005) |
0.76 (0.014) |
0.9986 |
< 2.2e-16 |
|
50-75 |
-3.32 (0.034) |
1.04 (0.006) |
0.77 (0.019) |
0.9986 |
< 2.2e-16 |
|
75-100 |
-3.42 (0.03) |
1.01 (0.005) |
0.86 (0.016) |
0.9987 |
< 2.2e-16 |
Similar biomass model was fitted for four different crown cover classes where all models were statistically different with decreasing order of intercept. The fitted models have adjusted R2 value of more than 0.99 (Table 3) and residuals are normally distributed (Figure 5). Selected models were plotted with natural logarithm of DBH and height along with estimated carbon stock in 3d scatter diagram as shown in Figure 5.
[Figure 3]
This is a list of supplementary files associated with this preprint. Click to download.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 23 Dec, 2019
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Forestry
Learn more about our company.
This is a preprint. It has not completed peer review.
Research
Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal
Prashant Paudel
Faculty of Forestry
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0982-4818
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 23 Dec, 2019
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Forestry
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Volume, biomass and carbon of forest ecosystem are generally estimated using lookup tables or allometric equations known as models. These general equation-based models are usually exclusively based on dimensional measurement such as diameter at breast height (DBH) and/or height, which sometimes makes it difficult to judge applicability of equation to given forest condition or types. It is therefore important to estimate carbon stock and develop models to predict biomass or carbon stock with stratification by categorical variables like crown cover, slope, forest types, etc. Stratification of forest by remote sensing approach while designing forest inventory not only improves the reliability of the estimation but also reduces the cost of measurement. Taking crown coverage (<25%, 25-50%, 50-75% and >75%) as a categorical variable, this study assessed the status of carbon stock and develop a regression model to predict carbon stock for each canopy class of Sal (Shorea robusta) forest in Nepal. DBH and height were measured for trees with more than 7 cm DBH in 82 sample plots (18, 22, 22 and 20 for <25%, 25-50%, 50-75% and >75% respectively). On average 297 stands per hectare were recorded with 94.80 m 3 /ha growing stock. Carbon stock was highest for >75% crown cover class (89.83 ton C/ha) and lowest for <25% crown cover class (27.47 ton C/ha) with average 60.41 ton C/ha, where per tree carbon stock was lowest in crown cover class 25-50% (0.16 ton C/tree). TukeyHSD shows that four pairs of crown cover classes have significant difference in carbon stock at 95% confidence interval. Regression model with natural logarithm of DBH 2 and total tree height was best fitted for estimation of carbon stock per tree in different crown cover class with adjusted R 2 >0.99 and residuals were normally distributed. Adjustment of model (natural logarithm of DBH 2 and height) with high accuracy (R 2 >0.99) shows the importance of stratification especially by crown cover for accurate estimation of carbon stock for optimization of carbon benefits.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Crown cover classification
Division of forest into four different crown cover classes shows that majority of forest areas fall under crown cover class 50-75 % whereas only around 50 ha of forest was covered by crown cover class <25 % (Table 1). Cohen’s kappa coefficient calculated from the error matrix shows that the overall accuracy of the classification was more than 85 %.
Table 1: Area coverage by crown cover class
|
Crown cover class |
Area (Ha) |
|
< 25% |
49.60 |
|
25-50% |
120.65 |
|
50-75% |
380.19 |
|
> 75% |
122.28 |
|
Total |
672.72 |
[Figure 1]
Carbon stock
There was a fluctuation in the number of stems with increase in crown cover, where on an average 297 stems per hectare were recorded in study area with highest number of stems in crown cover class >75 %. Similarly, per tree growing stock and carbon stock was highest for crown cover class >75 % and lowest for 25-25 % crown cover class despite having the second largest number of stems per ha in the class. Growing stock (GS), above ground biomass (AGB), total biomass (TB) and carbon stock (CS) was observed to have increased with increase in crown coverage. Table 2 shows that estimated variables by crown cover class and weighted average for the whole area.
Table 2: Number of stems, growing stock and biomass by crown cover
|
Variable |
Crown cover class (average) |
Average (weighted) |
|||
|
< 25 % |
25-50% |
50-75% |
> 75% |
||
|
No. of stands /ha |
161 |
345 |
288 |
375 |
297 |
|
Growing stock (m3/ha) |
44.42 |
91.07 |
100.47 |
137.99 |
94.80 |
|
Per tree GS (m3/ha) |
0.28 |
0.26 |
0.35 |
0.37 |
0.31 |
|
AGB (ton/ha) |
48.72 |
100.52 |
114.06 |
159.28 |
107.11 |
|
Total biomass (ton/ha) |
58.46 |
120.62 |
136.87 |
191.13 |
128.53 |
|
Total CS (ton/ha) |
27.47 |
56.69 |
64.33 |
89.83 |
60.41 |
|
Per tree CS (ton/ha) |
0.17 |
0.16 |
0.22 |
0.24 |
0.20 |
Four estimated variables (GS, AGB, TB, CS) were plotted against crown cover in Box-whisker plot for displaying the variation in data by crown cover class. It shows that the variability in data was seen more in crown cover class >75 % for all four estimated variables and less in crown cover class <25 %.
[Figure 2]
One-way ANOVA showed a significant difference in total carbon stock with crown cover class at 95 % confidence interval. TukeyHSD, test to explore the significance between different pairs of crown cover, showed that three pairs of crown cover category (<25 to 25-50, <25 to 50-75 and 25-50 to >100) significantly differ at 95 % confidence interval (Table 3) whereas <25 to >100 pairs were significantly different at 99.9 % confidence interval.
Table 3: Pair-wise comparison of carbon stock by crown cover
|
Crown cover Percentage |
Diff |
Lower |
Upper |
P-value |
Significance level |
|
0-25 to 25-50 |
28.96 |
1.65 |
56.27 |
0.033 |
* |
|
0-25 to 50-75 |
36.53 |
9.22 |
63.84 |
0.004 |
* |
|
0 -25 to 75-100 |
61.81 |
33.89 |
89.72 |
0.000 |
*** |
|
25-50 to 50-75 |
7.57 |
-18.34 |
33.48 |
0.860 |
|
|
25-50 to 75-100 |
32.85 |
6.29 |
59.39 |
0.009 |
* |
|
50-75 to 75-100 |
25.28 |
-1.27 |
51.83 |
0.070 |
|
Confidence level ***’ 99.9% ‘**’ 99% ‘*’ 95%
4.3 Model for Carbon Stock Estimation
Regression equation to model each crown cover class was developed from DBH and height as independent variables where crown cover was used as a categorical variable. Models were selected after the intercept of DBH and height were positive, adjusted R2 value was above 0.99 and residual areas normally distributed and homogeneous.
Fitted model for estimating carbon stock where DBH and Height are independent variables with base of natural logarithm is given as:
[Due to technical limitations, the formula could not be displayed here. Please see the supplementary files section to access the formula.]
Where, CS is in kg/tree, DBH in cm and Height in m.
Table 4: Estimated intercepts of DBH, Height for estimating CS by different CC
|
CC % |
Intercept (SE) (c) |
DBH (SE) (a) |
Height (SE) (b) |
R2 |
p-value |
|
0-25 |
-3.22 (0.062) |
1.05 (0.01) |
0.72(0.035) |
0.9984 |
<2.2e-16 |
|
25-50 |
-3.31 (0.023) |
1.04(0.005) |
0.76 (0.014) |
0.9986 |
< 2.2e-16 |
|
50-75 |
-3.32 (0.034) |
1.04 (0.006) |
0.77 (0.019) |
0.9986 |
< 2.2e-16 |
|
75-100 |
-3.42 (0.03) |
1.01 (0.005) |
0.86 (0.016) |
0.9987 |
< 2.2e-16 |
Similar biomass model was fitted for four different crown cover classes where all models were statistically different with decreasing order of intercept. The fitted models have adjusted R2 value of more than 0.99 (Table 3) and residuals are normally distributed (Figure 5). Selected models were plotted with natural logarithm of DBH and height along with estimated carbon stock in 3d scatter diagram as shown in Figure 5.
[Figure 3]