Density is one of the most important variables in forest and wood science as it is crucial for understanding tree structures and functions, and is relevant for timber properties and energy content of the material. Many of the density variations within a tree can be ascribed to the anatomical structure of wood, such as characteristics of vessels and fibres (Roque and Filho, 2007
), while wood density also serves as an indicator of wood quality due to its strong positive correlation with, for example, mechanical strength properties (Nepveu, 1976
). Furthermore, reliable models of biomass and carbon content include density measurements (Chave et al., 2005
The most common, direct method for estimating wood density consists of a gravimetric procedure in which wood samples with clearly defined dimensions are weighed and measured with a vernier calliper (Kollmann, 1951
). In this study, sampling with a Pressler borer resulted in wood cores that were easy to weigh. Their volume, however, was difficult to determine due to irregular sample shapes. As such, the gravimetric assessment of wood density is a time-consuming and destructive procedure because wood cores are split into small, irregularly shaped pieces. Each subsample has to be weighed and its volume needs to be measured accurately by water displacement methods or via specialized techniques such as gas pycnometry. Moreover, gravimetry is only suitable for low-resolution assessments of wood density variations (millimetre scale).
There are also numerous indirect methods to estimate wood density. Most of these densitometric methods are based on high-resolution imaging of differences in attenuation of non-visible (short-wavelength) radiation by the objects that are studied. In the following overview, methods were ranked by the dimension of their outputs: one-, two- or three-dimensional information.
Typical one-dimensional (1-D) estimates for density are obtained from tree-ring series in which tree-ring widths are related to density variations (Alvarado et al., 2010
), from resistance drillings (Rinn, 1996
), the measurement of cell-wall thickness with a transmission light microscope (Decoux et al., 2004
) and high-frequency densitometry, which measures relative density variations along wood surfaces using the dielectric properties of wood (Schinker et al., 2003
Two-dimensional estimates of wood density are typically extracted from radiographies: X-ray (Bergsten et al., 2001
; Moya and Filho, 2009
) and gamma-ray (Macedo et al., 2002
) as ionizing radiation techniques, neutron imaging (Lehmann et al., 2001
; Mannes et al., 2007
), colour video camera imaging (Clauson and Wilson, 1991
), magnetic resonance imaging (Müller et al., 2002
) and microwave polarimetry (Kästner and Niemz, 2004
). However, the two last methods focus on changes in moisture content for rot detection and the detection of cavities instead of density. Other 2-D density estimates can be generated by thermograms (e.g. infrared thermography) (Wyckhuyse and Maldague, 2001
) and acoustics methods (Martinis et al., 2004
; Bucur, 2005
). But again, these two techniques are related rather with decay diagnosis.
Among the above-mentioned methods, the use of ionizing radiation probably has the longest tradition in the field of research on wood density (Polge and Nicholls, 1972
; Lenz et al., 1976
; Lindgren, 1991
; Lehmann et al., 2001
; Bucur, 2005
) and the highest resolution, up to sub-micrometre level (Trtik et al., 2007
; Van den Bulcke et al., 2009
). Both X-ray and neutron imaging are applied in this study, but the equipment is expensive and bound to the laboratory. In particular, the number of neutron imaging beam lines is very low. Therefore, the results of these laboratory methods are compared with resistance drillings, which are obtained with a fast, low-cost, semi-destructive technique that is employable in the field. In addition, all three techniques are compared with standard gravimetrical analysis.
The studied tree species is Terminalia superba
Engl. & Diels (commercial name: limba), a pioneer species, characterized by large buttresses and typically found in secondary forests and fallows (Groulez and Wood, 1985
). Limba has a very large distribution area (from Sierra Leone to Angola) and is one of the major veneer timber species exported by African timber producers (Lamprecht, 1989
). The species also has characteristics that reduce its popularity: the formation of a darker heart in some cases (the so-called limba noir
) and heart rot in older trees. From a commercial and ecological point of view, the assessment of a tree and its characteristics before harvesting are important, especially in the tropics, in order to limit the impact on the forest. We consider limba as a model species in which precise information on density fluctuations in the trunk might be relevant to judge the presence of either unwanted heart rot or sought-after heartwood figures. During this study, density in limba trees and wood cores was estimated in order to answer to the following questions: Do microdensitometric profiles assessed by means of high-resolution computed tomography (CT) correspond with gravimetrically obtained data? Do neutron and X-ray densitometric scans produce similar microdensity patterns? Are drilling resistance measurements reliable as a quick estimate of these density variations? Can density variations be related to the presence of brown heart in limba trees?