Abstract
Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. 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 the 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 () and kappa-statistics () 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” ( 98.8%; 0.952), “Edge Detection” ( 98.1%; 0.950), and “Minimum Histogram” ( 98.1%; 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%.
Highlights
Hemispherical photography is an important and frequently applied technique to assess light conditions and canopy structure in forests [1]
Its accuracy was higher with auto-exposed photographs than with histogram-exposed ones; its accuracy estimates showed a high variation with some values being similar to those of the first group but very low values for some photographs as well
All three algorithms were appropriate for the binarization of hemispherical photographs; at this point no recommendation can be given which of them should be preferred
Summary
Hemispherical photography is an important and frequently applied technique to assess light conditions and canopy structure in forests [1]. Information about available radiation, as derived from hemispherical photographs, e.g. allows for investigating light response of natural regeneration or habitat choice by insects [2]. This information can be used to model tree growth in forest ecosystems, e.g. with the software BWINPro [3]. The techniques major drawback is that obtained values are often not comparable among studies due to non-standardized exposure determination procedures applied during the acquisition [4, 5] and nonstandardized binarization methods applied in the processing of hemispherical photographs [6, 7]. For dark canopy conditions [5] found that gap fraction values can be up to 900% higher if photographs were auto-exposed and not non-overexposed as recommended by e.g. Auto-exposed photographs were included in the study because auto-exposure is still an often applied exposure determination method in hemispherical photography [5]
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