DOI: 10.5061/dryad.s9652
title: Data from: Standardizing the protocol for hemispherical photographs: accuracy assessment of binarization algorithms

description: Hemispherical photography is a well-established method to optically assess
 ecological parameters related to plant canopies; e.g. information about
 ground-level light regimes and the distribution of foliage within the
 crown space. Interpreting hemispherical photographs involves classifying
 pixels as either sky or vegetation. A wide range of automatic thresholding
 or binarization algorithms exists to classify these photographs. The
 variety in methodology hampers ability to compare results across studies.
 To identify an optimal threshold selection method, this study assessed the
 accuracy of seven binarization methods implemented in software currently
 available for the processing of hemispherical photographs. Therefore,
 binarizations obtained by the algorithms were compared to reference data
 generated through a manual binarization of a stratified random selection
 of pixels. This approach was adopted from the accuracy assessment of map
 classifications known from remote sensing studies. Percentage correct (PC)
 and kappa-statistics (K) were calculated. The accuracy of the algorithms
 was assessed for photographs taken with automatic exposure settings
 (auto-exposure) and photographs taken with settings which avoid
 overexposure (histogram-exposure). In addition, gap fraction values
 derived from hemispherical photographs were compared with estimates
 derived from the manually classified reference pixels. All tested
 algorithms were shown to be sensitive to overexposure. Three of the
 algorithms showed an accuracy which was high enough to be recommended for
 the processing of histogram-exposed hemispherical photographs:
 "Minimum" (PC 98.8%; K 0.952), "Edge Detection" (PC
 98.1%; K 0.950), and "Minimum Histogram" (PC 98.1%; K 0.947).
 The Minimum algorithm overestimated gap fraction least of all (11%). The
 overestimation by the algorithms Edge Detection (63%) and Minimum
 Histogram (67%) were considerably larger. For the remaining four evaluated
 algorithms (IsoData, Maximum Entropy, MinError, and Otsu) an
 incompatibility with photographs containing overexposed pixels was
 detected. When applied to histogram-exposed photographs, these algorithms
 overestimated the gap fraction by at least 180%.
description: reference_pixels_rawThe 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 operatorthresholdsThe 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_photographsThe 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°.accuracy_estimatesThe 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 kappa