A review on aboveground biomass estimation methods utilizing forest structural characteristics

Forest structure and its composition (species) are influenced by three major components: ecology, disturbance type and climatic conditions. These play a crucial role in the photosynthesis of vegetation, driving the dynamic growth and accumulation of aboveground biomass (AGB). Moreover, forest structural characteristics, such as tree density, vertical stratification and species composition, influence AGB concentration. In addition, it remains unclear about the relationship between forest structure and remote sensing metrics in different environments on AGB model accuracy. This review explores 2D and 3D remote sensing methods, emphasizing diverse forest types and structures for AGB estimation. We covered studies from various geographical locations and forest structures, including boreal forests, tropical forests, temperate forests, etc. Satellite images collect canopy reflectance and backscatter information from forests, and surface metrics like vegetation indices and texture measures are derived to model age-wise and stand-wise aboveground biomass distribution. Among image-based methods, shadow fraction method is effective for forests with stand-alone trees, while stand-based methods offer better results in temperate and tropical forests. Textural-based methods allow better biomass modelling for degraded forests. Microwave backscatter data facilitate biomass modelling of varied forest structures to some extent branches. From point cloud data, detailed geometric information (horizontal and vertical) about forest structure through derived LiDAR metrics to model AGB at stand and tree level. Moreover, in fusion-based methods, decision-level fusion is more popular than fusion-based methods at the stand level and vice versa at the tree level. As later demands high computation and the removal of redundant information. Combining both methods can help estimating biomass in forests of complex structures. Biomass model accuracy is dependent on the spatial and spectral resolution of remote-sensing data used, area and type of forest, forest condition, area and number of field plots and regression techniques.