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Untreated wooden surfaces degrade when exposed to natural weathering. In this study thin wood samples were studied for weather degradation effects utilising a hyperspectral camera in the near infrared wavelength range in transmission mode. Several sets of samples were exposed outdoors for time intervals from 0 days to 21 days, and one set of samples was exposed to ultraviolet (UV) radiation in a laboratory chamber. Spectra of earlywood and latewood were extracted from the hyperspectral image cubes using a principal component analysis-based masking algorithm. The degradation was modelled as a function of UV solar radiation with four regression techniques, partial least squares, principal component regression, Ridge regression and Tikhonov regression. It was found that all the techniques yielded robust prediction models on this dataset. The result from the study is a first step towards a weather dose model determined by temperature and moisture content on the wooden surface in addition to the solar radiation.

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