Examining spectral properties of Landsat 8 OLI for predicting above-ground carbon of Labanan Forest, Berau
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Date
2018-04-01Author
Suhardiman, Ali
Tampubolon, Benny Aryef
Sumaryono, M.
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Show full item recordAbstract
Many studies revealed significant correlation between satellite image properties and
forest data attributes such as stand volume, biomass or carbon stock. However, further study is
still relevant due to advancement of remote sensing technology as well as improvement on
methods of data analysis. In this study, the properties of three vegetation indices derived from
Landsat 8 OLI were tested upon above-ground carbon stock data from 50 circular sample plots
(30-meter radius) from ground survey in PT. Inhutani I forest concession in Labanan, Berau,
East Kalimantan. Correlation analysis using Pearson method exhibited a promising results
when the coefficient of correlation (r-value) was higher than 0.5. Further regression analysis
was carried out to develop mathematical model describing the correlation between sample plots
data and vegetation index image using various mathematical models. Power and exponential
model were demonstrated a good result for all vegetation indices. In order to choose the most
adequate mathematical model for predicting Above-ground Carbon (AGC), the Bayesian
Information Criterion (BIC) was applied. The lowest BIC value (i.e. -376.41) shown by
Transformed Vegetation Index (TVI) indicates this formula, AGC = 9.608*TVI21.54, is the best
predictor of AGC of study area.
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- J - Forestry [128]