Examining spectral properties of Landsat 8 OLI for predicting above-ground carbon of Labanan Forest, Berau

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 t...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2018-04, Vol.144 (1), p.12064
Main Authors: Suhardiman, A, Tampubolon, B A, Sumaryono, M
Format: Article
Language:English
Subjects:
Bayesian analysis
Carbon
Correlation analysis
Data analysis
Landsat
Landsat satellites
Mathematical analysis
Mathematical models
Regression analysis
Remote sensing
Satellite imagery
Satellites
Vegetation
Vegetation index
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Summary: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.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/144/1/012064