Journal of Biosciences

, Volume 37, Issue 4, pp 723–730 | Cite as

Simple luminosity normalization of greenness, yellowness and redness/greenness for comparison of leaf spectral profiles in multi-temporally acquired remote sensing images

  • Ryoichi DoiEmail author


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 spectral-profiled 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.


Public availability and feasibility rooftop invariants remote sensing image spatio-temporal variability of leaf spectral profile temporal variation of brightness 


  1. Adams ML, Philpot WD and Norvell WA 1999 Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. Int. J. Remote Sens. 20 3663–3675Google Scholar
  2. Balasubramanian V, Morales AC, Cruz RT, Thiyagarajan TM, Nagarajan R, Babu M, Abdulrachman S and Hai LH 2000 Adaptation of the chlorophyll meter (SPAD) technology for real-time N management in rice: A review. Intl. Rice Res. Notes 25 4–8Google Scholar
  3. Bunting P and Lucas R 2006 The delineation of tree crowns in Australian mixed species forests using hyperspectral compact airborne spectrographic imager (CASI) data. Remote Sens. Environ. 101 230–248Google Scholar
  4. Coste S, Baraloto C, Leroy C, Marcon E, Renaud A, Richardson AD, Roggy JC, Schimann H, Uddling J and Herault B 2010 Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana. Ann. For. Sci. 67 607Google Scholar
  5. Doi R 2012 Quantification of leaf greenness and leaf spectral profile in plant diagnosis using an optical scanner. Cienc. Agrotec. 36 309–317Google Scholar
  6. Doi R and Mahaut S 2006 Effect of extract of Curcuma alismatifolia inoculated with Ralstonia solanacerum and cultured in vitro on detection of the bacterium using a medium: a case study. Rev. Cienc. Agr. 29 241–252Google Scholar
  7. Doi R and Ranamukhaarachchi SL 2009 Correlations between soil microbial and physicochemical variations in a rice paddy: implications for assessing soil health. J. Biosci. 34 969–976PubMedCrossRefGoogle Scholar
  8. Doi R and Ranamukhaarachchi SL 2010 Discriminating between canopies of natural forest and acacia plantation plots in a Google Earth Image to evaluate forest land rehabilitation by acacia species. Intl. J. Agric. Biol. 12 921–925Google Scholar
  9. Doi R, Wachrinrat C, Teejuntuk S, Sakurai K and Sahunalu P 2010 Semiquantitative color profiling of soils over a land degradation gradient in Sakaerat, Thailand. Environ. Monit. Assess. 170 301–309CrossRefGoogle Scholar
  10. Fujimoto S 2007 Analysis of prediction methods for budburst days based on the phenological observation in 29 broad-leaved tree species for 10 years. J. Jpn. For. Soc. 89 253–261CrossRefGoogle Scholar
  11. Hadjimitsis DG, Clayton CR I and Retalis A 2009 The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery. Intl. J. Appl. Earth Obs. 11 192–200Google Scholar
  12. Handschuh S, Schwaha T and Metscher BD 2010 Showing their true colors: a practical approach to volume rendering from serial sections. BMCDev. Biol. 10 41Google Scholar
  13. Hayes DJ and Sader, SA 2001 Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photogramm. Eng. Rem. S. 67 1067–1075Google Scholar
  14. Khanduri VP and Sharma CM 2010 Male and female reproductive phenology and annual production of male cones in two natural populations of Cedrus deodara. Nordic J. Bot. 28 119–127Google Scholar
  15. Kirk K, Andersen HJ, Thomsen AG, Jorgensen JR and Jorgensen RN 2009 Estimation of leaf area index in cereal crops using red-green images. Biosystems Eng. 104 308–317Google Scholar
  16. Kondo N, Ahmad U, Monta M and Murase H 2000 Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Comput. Electron. Agric. 29 135–147CrossRefGoogle Scholar
  17. Lev-Yadun S and Gould KS 2007 What do red and yellow autumn leaves signal? Bot. Rev. 73 279–289Google Scholar
  18. Lu D and Weng Q 2007 A survey of image classification methods and techniques for improving classification performance. Intl. J. Remote Sens. 28 823–870Google Scholar
  19. Mori M, Suzuki K and Kohzaki R 2000 Variations in chlorophyll and carotenoid content in the growth process of the Ginkgo leaf. J. Jpn. Soc. Food Sci. Technol. 47 448–451Google Scholar
  20. Nandris D, Vancanh T, Geiger JP, Omont H and Nicole M 1985 Remote-sensing in plant-diseases using infrared color aerial-photography - applications trials in the Ivory Coast to root diseases of Heveabrasiliensis. Eur. J. For. Pathol. 15 11–21CrossRefGoogle Scholar
  21. Newton A, Hill R, Echeverria C, Golicher D, Benayas J, Cayuela L and Hinsley S 2009 Remote sensing and the future of landscape ecology. Prog. Phys. Geog. 33 528–546CrossRefGoogle Scholar
  22. Okamoto M 1989 A comparative study of the ontogenetic development of the cupules in Castanea and Lithocarpus (Fagaceae). Pl. Syst. Evol. 168 7–18CrossRefGoogle Scholar
  23. Olszewska M, Grzegorczyk S, Alberski J, Baluch-Malecka A and Kozikowski A 2008 Effect of copper deficiency on gas exchange parameters, leaf greenness (SPAD) and yield of perennial ryegrass (Lolium perenne l.) and orchard grass (Dactylis glomerata l.). J. Elementol. 13 597–604Google Scholar
  24. Pagani A, Echeverria HE, Andrade FH and Rozas HR S 2009 Characterization of corn nitrogen status with a greenness index under different availability of sulfur. Agron. J. 101 315–322CrossRefGoogle Scholar
  25. Pallardy SG 2008 Physiology of woody plants 3rd ed. (Burlington, Ma.: Academic Press)Google Scholar
  26. Pollnac FW, Rew LJ, Maxwell BD and Menalled FD 2008 Spatial patterns, species richness and cover in weed communities of organic and conventional no-tillage spring wheat systems. Weed Res. 48 398–407Google Scholar
  27. Prior L, Bowman D and Eamus D 2004 Seasonal differences in leaf attributes in Australian tropical tree species: family and habitat comparisons Funct. Ecol. 18 707–718Google Scholar
  28. Schott JR, Salvaggio C and Volchok WJ 1988 Radiometric scene normalization using pseudoinvariant features. Remote Sens. Environ. 28 1–16Google Scholar
  29. Shuvalov VA 2007 Electron and nuclear dynamics in many-electron atoms, molecules and chlorophyll-protein complexes: A review. BBA-Bioenergetics 1767 422–433PubMedCrossRefGoogle Scholar
  30. Sicher RC 1999 Photosystem-II activity is decreased by yellowing of barley primary leaves during growth in elevated carbon dioxide. Intl. J. Plant Sci. 160 849–854Google Scholar
  31. Smith, B. 1999 An RGB to spectrum conversion for reflectances. J. Graph. Tools 4 11–22Google Scholar
  32. Spurr SH 1948 Aerial photographs in forestry (New York: Ronald Press)Google Scholar
  33. Steele MR, Gitelson AA and Rundquist DC 2008 A comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves. Agron. J. 100 779–782CrossRefGoogle Scholar
  34. Summy KR and Little CR 2008 Using color infrared imagery to detect sooty mold and fungal pathogens of glasshouse-propagated plants. HortScience 43 1485–1491Google Scholar
  35. Van Niel TG and McVicar TR 2004 Current and potential uses of optical remote sensing in rice-based irrigation systems: a review. Aust. J. Agric. Res. 55 155–185CrossRefGoogle Scholar
  36. Verhoeve GJJ 2009 Providing an archaeological bird's-eye view – an overall picture of ground-based means to execute low-altitude aerial photography (LAAP) in archaeology. Archeol. Prospect. 16 233–249CrossRefGoogle Scholar
  37. Yang Z, Willis P and Mueller R 2008 Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Proc. Pecora 17

Copyright information

© Indian Academy of Sciences 2012

Authors and Affiliations

  1. 1.Graduate School of Agricultural and Life SciencesThe University of TokyoBunkyo-kuJapan

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