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Sensitivity of colour indices for discriminating leaf colours from digital photographs
Author(s) -
Mizunuma Toshie,
Mencuccini Maurizio,
Wingate Lisa,
Ogée Jérôme,
Nichol Caroline,
Grace John
Publication year - 2014
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12260
Subject(s) - hue , digital camera , digital photography , sky , digital image , digital imaging , artificial intelligence , remote sensing , mathematics , computer vision , photography , computer science , geography , image processing , art , meteorology , image (mathematics) , visual arts
Summary Digital images of tree canopies have been analysed to understand how forest phenology responds to climate change. Researchers have used different colour indices to carry out quantitative analyses, but uncertainties over the performance of the various indices are hampering progress in their use. To compare the various indices under controlled conditions, we carried out experiments using a low‐cost off‐the‐shelf digital camera with a set of standard colour charts as model leaves for different stages: emerging leaves, yellowish green; newly expanded leaves, green; fully mature leaves, dark green; senescent leaves, yellow. Two models of cameras, a compact digital camera and a surveillance ‘live image’ camera were used, and photographs were taken by two cameras for each model under clear or overcast sky conditions with two colour balance settings. The indices were also compared with those derived from spectral reflectance. Colour indices based on hue distinguished leaf colour samples with only a small influence of camera models, balance setting and sky conditions, while indices based on green were strongly influenced by camera models and were relatively insensitive to leaf colours. The strength of the green channel relative to the total of digital numbers took similar values for the mature and senescent replica leaves, highlighting its poor ability to identify the change of colour in autumn. Spectral‐based hue was also sensitive to the gradation of leaf colours and showed a good correlation with the digital representation of hue regardless of camera models and balance setting. Remarkably, the primitive digital number of red, N red , also discriminated leaf colours well, with a small influence of the factors investigated here, showing a good correlation with the reflectance of the red band, except from images taken by the surveillance cameras with auto balance. Hue was a robust index across the image set, while the green‐based indices often used to quantify canopy phenology in previous studies performed poorly. Hue was well correlated with spectral reflectance indices and worked better than all other indices to discriminate leaf colours. We recommend using hue as a colour index for tracking different stages of leaf development.