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Simple luminosity normalization of greenness, yellowness and redness/greenness for comparison of leaf spectral profiles in multi-temporally acquired remote sensing images.
J Biosci ; 2012 Sep; 37 (4): 723-730
Article in English | IMSEAR | ID: sea-161735
ABSTRACT
Observation of leaf colour (spectral profiles) through remote sensing is an effective method of identifying the spatial distribution patterns of abnormalities in leaf colour, which enables appropriate plant management measures to be taken. However, because the brightness of remote sensing images varies with acquisition time, in the observation of leaf spectral profiles in multi-temporally acquired remote sensing images, changes in brightness must be taken into account. This study identified a simple luminosity normalization technique that enables leaf colours to be compared in remote sensing images over time. The intensity values of green and yellow (green+red) exhibited strong linear relationships with luminosity (R2>0.926) when various invariant rooftops in Bangkok or Tokyo were spectralprofiled using remote sensing images acquired at different time points. The values of the coefficient and constant or the coefficient of the formulae describing the intensity of green or yellow were comparable among the single Bangkok site and the two Tokyo sites, indicating the technique’s general applicability. For single rooftops, the values of the coefficient of variation for green, yellow, and red/green were 16% or less (n=6−11), indicating an accuracy not less than those of well-established remote sensing measures such as the normalized difference vegetation index. After obtaining the above linear relationships, raw intensity values were normalized and a temporal comparison of the spectral profiles of the canopies of evergreen and deciduous tree species in Tokyo was made to highlight the changes in the canopies’ spectral profiles. Future aspects of this technique are discussed herein.

Full text: Available Index: IMSEAR (South-East Asia) Language: English Journal: J Biosci Year: 2012 Type: Article

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Full text: Available Index: IMSEAR (South-East Asia) Language: English Journal: J Biosci Year: 2012 Type: Article