Automatic thresholding for hemispherical canopy-photographs based on edge detection

The analysis of hemispherical photographs is nowadays an established method for assessing light indirectly and describing canopy structures. In this article, we present an automatic threshold algorithm for separating canopy and sky by edge detection. The algorithm was evaluated under different canopy conditions by comparing its results for canopy openness, fractal dimension and diffuse transmittance with those from multiple manual thresholding and direct measurements of the percent photosynthetic photon flux density (PPFD). We show that the automatic threshold algorithm is appropriate to replace the widely used manual interactive processing. It also improves the accuracy of results, especially in comparison with single manual thresholding. Whereas manual threshold setting has often been criticised as subjective and a major source of error the less time-consuming edge detection approach is objective, reproducible and can be applied to a large number of images.