Confusion matrix.

<p>The matrix is calculated for the  = 24 reference pixels of each of  = 16 gray value-strata of a hemispherical photograph. Column and row sums show how many pixels were classified by a binarization algorithm (image classified data) and by an operator (reference data) into the categories vegetation (V) and sky (S). The four central cells show how many pixels were classified by an algorithm and the operator in agreement (, ) and in disagreement (, ). The image marginal proportion displays the overall fraction of pixels classified by the algorithm as vegetation () or sky () within the respective stratum. Adapted from Congalton and Green 2009.</p

reference_pixels_raw

The file contains the raw data of the reference pixel classification by an operator. Column description: id: unique id refering to a single reference pixel / pic: the picture where the pixel derived from originally / x, y: x- and y- coordinates of the pixles within the pixture / gvcl: One of 16 gray value classes used for the stratified sampling approach to select the reference pixels / r, g, b: the red, green and blue gray value of the pixel (8-bit) / class: classification of the pixel by a human operato

accuracy_estimates

The file contains accuracy estimates of binarization algorithms of hemispherical photographs. Column description: pic: the name of the picture. From row 142 statistics over all pictures are given. / 'sub' refers to a subset of pictures without sites VIII and IX. 'all' refers to all pictures without exclusion. / bin: name of the algorithms. See the methodology section of the publication for details / pc: percentage correct estimate / var_pc: variance of pc / lower_pc: lower bound of the confidence interval of pc / upper_pc: upper bound of the confidence interval of pc / kap: kappa estimate / var_kap: variance of kappa / lower_kap: lower bound of the confidence interval of kappa / upper_kap: upper bound of the confidence interval of kapp

Examples of reference pixels.

<p>(A) Pixel with a low gray value in a dark region of the sky; (B) blooming effect (overexposed vegetation pixel); (C) reflection at vegetation.</p

Overexposure of sky regions in hemispherical photographs.

<p>Gray value histograms of the blue color plane of the photographs taken on site VI with auto-exposure (A) and histogram-exposure (B).</p

hemispherical_photographs

The processed hemispherical photographs. The original photographs were cropped and LZW-compressed to save memory but are not modified in any other way. The filenames indicate the site number and the used exposure protocol. 'auto_exp' stands for the automatic exposure settings of the camera. 'hist_exp' stands for the histogram exposure protocol introduced by Beckschäfer et al. (2013) (DOI:10.3832/ifor0957-006). The camera was a Nikon D70s DSLR camera equipped with a Sigma Circular Fisheye 4.5 mm 1:2.8 lens with a field of view of 180°

thresholds

The file contains thresholds of the hemispherical photographs which have been used in the publications. For details about the thresholding algorithms see the methodology section of the paper

Hemispherical photographs with high and low amounts of mixed pixels.

<p>Histogram-exposed photographs of site V (A) with foliated vegetation and a low amount of mixed pixels and site VIII (B) with defoliated vegetation and a high amount of mixed pixels.</p

Accuracy assessment of seven binarization algorithms for hemispherical photographs

<p>. Percentage correct and kappa values of binarizations obtained from different algorithms applied to hemispherical photographs. Photographs were taken at ten locations (indicated by latin numbers) and either histogram- or auto-exposed. Whiskers represent confidence intervals with a confidence level of 95%.</p

Binarization methods implemented in currently available software for the processing of digital hemispherical photographs.

<p>Binarization methods implemented in currently available software for the processing of digital hemispherical photographs.</p