Introduction
An accurate estimation of forest aboveground biomass (AGB) density is required to provide the baseline of forest carbon stocks and quantify the anthropogenic emissions caused by deforestation and forest degradation. In addition, accurate estimation of forest AGB density is critical to implementing cost-effective carbon emission mitigation strategies. Traditionally, forest researchers use field-based plot inventory and destructive biomass sampling approaches to estimate forest AGB density. Forest researchers select some plots and collect tree structural parameters such as diameter at breast height (DBH) and tree height. Then scientists develop allometric equations to estimate forest AGB based on the tree structural parameters. This conventional approach is valuable to a certain extent. However, it is expensive and time-consuming over a vast forest area, limiting scalability. Furthermore, field-based inventory and destructive biomass sampling approaches can introduce sampling bias at a local scale.
Earth observation (EO) technology allows for large-scale assessments of the forest ecosystem, structure, and functionality. Forest researchers and scientists can cost-effectively employ field-based plot inventory and remotely-sensed data. Optical, radar and lidar sensors mounted on ground-based, airborne and spaceborne platforms collect data. Researchers and scientists use plot inventory, remotely-sensed data, and parametric or non-parametric machine learning (ML) approaches to model forest AGB.