Overview of the Forest Biomass Models

Abstract

The diversity of species, tree allometry, and stand structure makes modelling forest biomass a challenge. At tree level diameter at breast height, and height are the most frequently used explanatory variables. Yet, other variables that encompass the variability in tree allometry due to species, stand structure, competition between trees, and site allow better performances of the biomass models. Similarly, at area level, the biomass functions have large variability in the data and explanatory variables used for modelling. This is due to the differences in species, stand structure, and their correlation with the remote sensing data. The combination of different remote sensing data sets from passive and/or active sensors linked with ancillary data enabled to improve models’ performance. Furthermore, a wide set of mathematical methods have been used to capture the stands and forests diversity and variability and accommodate it in the models to improve predictions. Overall, the wide range of biomass models corresponds to a continuous need to develop biomass functions that enable assessing, monitoring and predicting total or per component biomass.