Modelling and mapping of above ground biomass (AGB) of oil palm plantations in Malaysia using remotely-sensed data

摘要

Estimating accurate above ground biomass (AGB) of oil palm plantation in Malaysia is crucial as it serves as an important indicator to assess the role of oil palm plantations in the global carbon cycle, particularly whether it serves as carbon source or sink. Research on oil palm AGB in Malaysia using remote sensing is almost insignificant and it has known that remote sensing provides easy, inexpensive and less time consuming over larger areas. Therefore, this study focuses on evaluating the potential of Landsat Thematic Mapper (TM) data with combination of field data survey to predict AGB estimates and mapping the oil palm plantations. The relationships of AGB with individual TM bands and various selected vegetation indices were examined. In addition, various possibilities of data transform were explored in statistical analysis. The potential models selected were obtained using backward elimination method where R-2, adjusted R-2 (R-2 (adj)), standard error of estimate (SEE), root mean squared error (RMSE) and Mallows's C-p criterion were examined in model development and validation. It was found that the most promising model provides moderately good prediction of about 62% of the variability of the AGB with RMSE value of 3.68 tonnes (t) ha(-1). In conclusion, Landsat TM offers the low cost AGB estimates and mapping of oil palm plantations with moderate accuracy in Malaysia.