Standardizing the protocol for hemispherical photographs: accuracy assessment of binarization algorithms

PLoS One. 2014 Nov 24;9(11):e111924. doi: 10.1371/journal.pone.0111924. eCollection 2014.

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 (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%.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Ecology / instrumentation
  • Ecology / methods*
  • Image Processing, Computer-Assisted / instrumentation
  • Image Processing, Computer-Assisted / methods*
  • Image Processing, Computer-Assisted / standards
  • Photography / instrumentation
  • Photography / methods*
  • Photography / standards
  • Reference Standards
  • Reproducibility of Results
  • Software

Associated data

  • Dryad/10.5061/dryad.S9652

Grants and funding

The authors' thanks are due to the Advisory Group on International Agricultural Research (BEAF) at the German Agency for International Cooperation (GIZ) and the German Ministry for Economic Cooperation (BMZ) for funding the research project MMC (“Making the Mekong Connected”, Project No. 08.7860.3-001.00) within which this study had been carried out. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.