Model-based local thresholding for canopy hemispherical photography

Canopy hemispherical photography (HP) is widely used to estimate forest structural variables. To achieve good results with HP, a classification algorithm is needed to produce binary images to accurately estimate the gap fraction. We aimed to develop a local thresholding method for binarizing carefully acquired hemispherical photographs. The method was implemented in the R package â caimanâ . Working with photographs of artificial structures and using a linear model, our method turns the cumbersome problem of finding the optimal threshold value into a simpler one, which is estimating the digital number (DN) of the sky. Using hemispherical photographs of a deciduous forest, we compared our method with several standard and state-of-the-art binarization techniques. Our method was as accurate as the best-tested binarization techniques, regardless of the exposure, as long as it was between 0 and 2 stops over the open sky auto-exposure. Moreover, our method did not require knowing the exact relative exposure. Intending to balance accuracy and practicality, we mapped the sky DN using the values extracted from gaps. However, we discussed whether a more accurate but less practical way to map sky DN could provide, along with our method, a new benchmark.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author