Introduction
Forest aboveground biomass (AGB) 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 is critical to implementing cost-effective carbon emission mitigation strategies.
Forest AGB stocks are traditionally assessed using field-based plot inventory and destructive biomass sampling approaches. 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. In general, optical, radar and lidar sensors mounted on ground-based, airborne and spaceborne platforms collect remotely-sensed data. Researchers and scientists use plot inventory, remotely-sensed data, and parametric or non-parametric machine learning (ML) approaches to model forest AGB.
Parametric approaches comprise statistical regression models and semi-empirical models. In general, parametric methods assume that the relationship between the dependent and independent predictor variables has explicit model structures, interpretable a priori through parameters. For example, multiple linear regression models have been commonly used to predict forest AGB. However, the relationship between forest AGB and remote sensing variables is often too complex and non-linear. Therefore, researchers and scientists have adopted data-driven non-parametric ML approaches to model complex non-linear relationships between forest AGB and predictor variables.
This course introduces methods and tools to model forest canopy height and AGB. We will use EO data such as NASA’s Global Ecosystem Dynamics Investigation (GEDI), Sentinel (S-1 and Sentinel-2), Landsat data, and machine learning methods (e.g., a random forest regression model).