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.com
Volume 9
Journal of Bioremediation & Biodegradation
ISSN: 2155-6199
Biofuel Congress 2018 &
Biomass 2018
September 04-06, 2018
JOINT EVENT
September 04-06, 2018 | Zurich, Switzerland
13
th
Global Summit and Expo on
Biomass and Bioenergy
&
12
th
World Congress on
Biofuels and Bioenergy
Above-ground bole carbon stock estimation using forest inventory and remote sensing data for
secondary forest ecosystem in Ibadan, Nigeria
Aghimien Ehimwenma Victor
Federal College of Forestry, Ibadan, Nigeria
S
econdary forest ecosystem contributes to global climate change mitigation through carbon sequestration. Above-ground
bole biomass (AGBB) is the major component for monitoring and estimating carbon stocks (CS) and fluxes in tropical
forests. Integrating remote sensing (RS) with forest inventory (FI) techniques had also been reported to provide accurate
estimation of above ground bole carbon stock (AGBCS). However, information on AGBCS for the International Institute of
Tropical Agriculture (IITA), which hosts relics of the undisturbed secondary forest ecosystem in south-western Nigeria, has
not been documented. Therefore, AGBCS of the secondary forest ecosystem was estimated using remote sensing and forest
inventory techniques. Forest inventory and remote sensing data were used for this study. One hundred and forty plots of 50 m
x 50 m were laid in IITA secondary forest using systematic sampling technique at 10% sampling intensity. Trees in each plot
were enumerated and identified to species level. The total height (TH) and diameter at breast height (DBH) of trees ≥10 cm
were measured to determine tree volume (TV). Sixty wood core samples were randomly collected from dominant trees species
at breast height for wood density (WD) estimation. The TV and WD were used to determine AGBB, which were converted
to CS using standard forest inventory method. Pleiades satellite imagery was acquired using RS technique and spectral data
for each sample plot extracted. The spectral indices used for AGBB estimation were: normalized difference vegetation index
(NDVI), difference vegetation index (DVI), infrared percentage vegetation index (IPVI), optimized soil adjusted vegetation
index (OSAVI) and renormalized difference vegetation index (RDVI). The RS data were integrated with FI data to develop
regression equations for the prediction of AGBB from where the total CS estimate was obtained. Data were analysed using
descriptive statistics and regression analysis at ᬬ0.05. A total of 9,985 individual trees comprising 121 tree species and
30 families were recorded. The highest and least frequency of species recorded were
Funtumia elastica
(61/ha) and
Cordia
alliodora
(1/ha) respectively. The TH and DBH ranged from 4.70 to 39.30 m and 10.76 to 74.50 cm, respectively, while TV
ranged from 129.57 to 167,186 m
3
/ha. The WD of tree species ranged from 0.23 to 0.89 kg/cm
3
. The AGBB and CS ranged from
101.06 to 881,834.92 kg/ha and 50.53 to 440,917.46 kg/ha, respectively. The DVI had the highest AGBB value which ranged
from 187 to 15,577 kg/ha, followed by IPVI, RDVI and OSAVI which ranged from 7,561 to 12,324 kg/ha, 64.0591 to 133.178
kg/ha, 0.0134 to 0.5621 kg/ha, respectively, while NDVI had the least values which ranged from -0.01 to 0.48 kg/ha. The best
AGBB estimation model was AGBB =
exp
(3,496.61 +0.99x(RDVI)
1/2
); (Coefficient of determination = 0.93, root mean square
error = 31.39, Bayesian information = 2129.34). The total carbon stock ranged from 11,035 to 18,774 kg/ha. Model with
renormalized difference vegetation index was most suitable among other indices for estimating above-ground bole carbon
stock when integrated with forest inventory data.
aghimien4@yahoo.comJ Bioremediat Biodegrad 2018, Volume 9
DOI: 10.4172/2155-6199-C1-015