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Combination Between Empirical Modeling and Remote Sensing Technology in Estimating Biomass and Carbon Stock of Oil Palm in Salim Indoplantation, Riau Province
Thesis Abstract:
The study combined remote sensing technology and empirical modeling in estimating biomass and carbon stock of oil palm (Elaeis guineensis) in Salim Indoplantation in Riau, Sumatra. Remote sensing technology using satellite images has a view of large areas of the surface to estimate the normalized difference vegetation index (NDVI) values. Empirical modeling was chosen to estimate leaf area index (LAI), biomass, and carbon content using field data.
Estimation of LAI was based on NDVI values and correlation coefficient of linear regression between NDVI values and LAI (R2), with the equation LAI = (15.58*NDVI − 8.08). The correlation coefficient between LAI and plant height was 0.86, with formula of H(field) A = (125.23 × LAI − 171.1). Measured data of LAI and plant height were obtained from 22 sample blocks. The developed regression equations were then used for the whole study area.
Biomass was calculated based on plant height as follows: biomass (ton/ha) = (0.3747 × (H + 3.6334) × P/1000) (FAO 1997). Estimation of carbon was based on carbon (ton/ha) = (37.87 × biomass/100). The total biomass was 11366.64 ton (320.16 ton/y) and total carbon content was 4304.55 ton (121.25 ton/y) for the whole study area. There were nine age groups in all the study area positively correlated with biomass and the correlation coefficient is 0.63.
* FAO (Food and Agriculture Organization). 1997. “Biomass estimations and carbon stock
calculations in the oil palm plantations of African derived savannas using IKONOs data.” Yale University, Connecticut, USA. http://www.isprs.org/commission1/paper/00012.pdf.