ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION USING DOUBLE SAMPLING APPROACH AND REMOTELY-SENSED DATA

Abstract

Tropical forest embraces a large stock of carbon and contributes to the enormous amount of aboveground biomass (AGB) in the global carbon cycle. In order to quantify the carbon inventory, field data is vital for accurately determining the forest parameter such as diameter at the breast height (DBH), height of the tree (h) ,crown diameter (CD) and tree species. The merging of the multi-sensory remote sensing which is LiDAR (Light Detection and Ranging) and very high resolution satellite imagery can reduce the labor intensive of field sampling for a large area of carbon inventory data. Double sampling approach which is combination of the field sampling plot measurement with ancillary remote sensing data used to improve the precision of AGB estimation compared by using field data alone. Hence, this study aims: (1) to describe the use of field data plots in a statistical way, and (2) to determine the potential of LiDAR data in a double sampling forest aboveground biomass and carbon stock inventories and (3) to compare the used of field data plot itself or combination with LiDAR data to quantify the aboveground biomass and carbon stock for upcoming inventories.