Thomas Seifert and Stefan Seifert

2.1 Introduction

A primary objective of sustainable bioenergy production is to quantify the available resource supply because all further planning of the value chain hinges on the available biomass that can be converted. Since biomass is costly to transport, the spatial quantification of the resource is also important. Thus, modern approaches to biomass supply chain management must embrace the resource quantity and location as a key element of the supply chain. Data on resource availability are usually obtained from different sources such as remote sensing and terrestrial inventories, as discussed in Chap. 2, which provide information on the spatial distribution of forests and trees and their dimensions but are, as such, not capable of estimating biomass directly with the necessary accuracy. Thus the main purpose of the application of modelling and simulation techniques in this context is the estimation of the biomass resource from broadly available tree and stand variables. This auxiliary information could be sourced from inventories and remote sensing or could be provided by model projections from growth models to estimate the biomass availability.

Biomass modelling is a typical upscaling process based on statistical modelling. Depending on the modelling and sampling method of choice, different upscaling steps are involved. An upscaling process normally involves two steps: upscaling from the biomass samples to the individual tree and from the tree to the stand (Fig. 3.1).

Each upscaling step is normally characterised by sampling and a regression modelling components. The biomass models are first established on a subset of data of a bigger population with independent variables that are easier to measure than

T. Seifert (H) • S. Seifert

Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa e-mail: seifert@sun. ac. za

T. Seifert (ed.), Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26, DOI 10.1007/978-94-007-7448-3__3,

© Springer Science+Business Media Dordrecht 2014

sampling

the biomass itself. In a next step this auxiliary data from the bigger population of interest is entered into the model to estimate the biomass for the entire population. This applies equally to upscaling from samples to the individual tree and from trees to the stand.

Each upscaling step is subject to a specific error. To assess the uncertainty of the biomass estimation the error of the biomass has to be estimated as well. Since upscaling can be complex as a result of the combined involvement of various regression models and sampling procedures that all contribute to a specific error budget, the calculation of the error-propagation forms an essential part of biomass estimation.

This chapter follows the structure of the upscaling concept, describing the sampling and modelling for upscaling from samples to trees in Sect. 3.2 and details the modelling process for upscaling from a tree to a stand in Sect. 3.3. This is followed by Sect. 3.4 on error-propagation where the error budgets of both upscaling steps are integrated. Finally, some implications of using biomass models in growth simulators are discussed in Sect. 3.5.