Assessment of national biomass in complex forests and technical capacity scenariosThis article is part of a larger document. View the larger document here.
Understanding forest ecosystems is paramount for their sustainable management and for the livelihoods and ecosystem services which depend on them. However, the complexity and diversity of these systems poses a challenge to interpreting data patterns. The availability and accessibility of data and tools often determine the method selected for forest assessment. Capacity building is fundamental to ensure that sampling methods, data analysis and use of tools are efficiently and sustainably appropriated. FAO has trained people from over 30 countries to develop tools and databases to improve forest resource assessment. In highly diverse inventory plots in the tropics, the use of one single pantropical allometric equation (models for the estimation of forest elements such as biomass and carbon stock) for all trees inventoried is the norm in many countries. However these equations present large biases and/or uncertainties for several tropical regions of the world, due to their compositional and structural complexity, and their limited representation in the original datasets used to build the pantropical model. In order to contribute to the elimination of bias and increasing accuracy, we propose a combined approach to stand biomass estimation following statistical methods that depends on both the availability of equations and/or destructive data, and the existing capacities in the country. We illustrate the methods through different scenarios of existing technical capabilities and data availability, taking GlobAllomeTree as a source for allometric equations. GlobAllomeTree provides access to existing available allometric equations and relevant documentation, supports the development of new models and builds a network of national experts for data-sharing and collaboration. The different alternative approaches proposed present a realistic roadmap towards the reduction of uncertainties and biases in reporting national stocks.