Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.

[1]  Marco Heurich,et al.  Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[2]  M. E. Schaepman,et al.  Assessing Vegetation Function with Imaging Spectroscopy , 2019, Surveys in Geophysics.

[3]  Zuzana Lhotáková,et al.  Heritable variation in needle spectral reflectance of Scots pine (Pinus sylvestris L.) peaks in red edge , 2018, Remote Sensing of Environment.

[4]  Andrew K. Skidmore,et al.  Impact of Vertical Canopy Position on Leaf Spectral Properties and Traits across Multiple Species , 2018, Remote. Sens..

[5]  Jarkko Salojärvi,et al.  Genotype- and provenance-related variation in the leaf surface secondary metabolites of silver birch , 2018 .

[6]  Matti Mottus,et al.  Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements , 2018, Remote. Sens..

[7]  Andrea Nardini,et al.  Sampling intraspecific variability in leaf functional traits: Practical suggestions to maximize collected information , 2017, Ecology and evolution.

[8]  Miina Rautiainen,et al.  A spectral analysis of 25 boreal tree species , 2017 .

[9]  L. Alonso,et al.  Spatial Variation of Leaf Optical Properties in a Boreal Forest Is Influenced by Species and Light Environment , 2017, Front. Plant Sci..

[10]  Giorgio Matteucci,et al.  Investigating the European beech (Fagus sylvatica L.) leaf characteristics along the vertical canopy profile: leaf structure, photosynthetic capacity, light energy dissipation and photoprotection mechanisms. , 2016, Tree physiology.

[11]  Aditya Singh,et al.  Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity , 2016, Remote. Sens..

[12]  J. Nauš,et al.  Analysis of the effect of chloroplast arrangement on optical properties of green tobacco leaves , 2016 .

[13]  André Große-Stoltenberg,et al.  The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae) , 2015, Remote. Sens..

[14]  Arvo Tullus,et al.  Elevated air humidity affects hydraulic traits and tree size but not biomass allocation in young silver birches (Betula pendula) , 2015, Front. Plant Sci..

[15]  Frank Veroustraete,et al.  Leaf reflectance variation along a vertical crown gradient of two deciduous tree species in a Belgian industrial habitat. , 2015, Environmental pollution.

[16]  Sarita Keski-Saari,et al.  Colonization of a host tree by herbivorous insects under a changing climate , 2015 .

[17]  Sébastien Debuisson,et al.  Nondestructive diagnostic test for nitrogen nutrition of grapevine (Vitis vinifera L.) based on dualex leaf-clip measurements in the field. , 2015, Journal of agricultural and food chemistry.

[18]  Roberta E. Martin,et al.  Quantifying forest canopy traits: Imaging spectroscopy versus field survey , 2015 .

[19]  Lea Hallik,et al.  A worldwide analysis of within-canopy variations in leaf structural, chemical and physiological traits across plant functional types. , 2015, The New phytologist.

[20]  G. Mozgeris,et al.  Visible and near infrared hyperspectral imaging reveals significant differences in needle reflectance among Scots pine provenances , 2014 .

[21]  Clayton C. Kingdon,et al.  Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. , 2014, Ecological applications : a publication of the Ecological Society of America.

[22]  K. Hikosaka,et al.  Optimal nitrogen distribution within a leaf canopy under direct and diffuse light. , 2014, Plant, cell & environment.

[23]  M. Rousi,et al.  Variation in 13 leaf morphological and physiological traits within a silver birch (Betula pendula) stand and their relation to growth , 2014 .

[24]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[25]  John A. Gamon,et al.  Effects of irradiance and photosynthetic downregulation on the photochemical reflectance index in Douglas-fir and ponderosa pine , 2013 .

[26]  Miina Rautiainen,et al.  Optical properties of leaves and needles for boreal tree species in Europe , 2013 .

[27]  John A. Gamon,et al.  Facultative and constitutive pigment effects on the Photochemical Reflectance Index (PRI) in sun and shade conifer needles , 2012 .

[28]  Cyrille Violle,et al.  The return of the variance: intraspecific variability in community ecology. , 2012, Trends in ecology & evolution.

[29]  Philip A. Townsend,et al.  Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature , 2011, Journal of experimental botany.

[30]  Barbara Hinterstoisser,et al.  Qualitative Assessment of Acetylated Wood with Infrared Spectroscopic Methods , 2011 .

[31]  John R. Miller,et al.  Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection , 2009 .

[32]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[33]  K. Soudani,et al.  Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .

[34]  U. Niinemets,et al.  Photosynthesis and resource distribution through plant canopies. , 2007, Plant, cell & environment.

[35]  Alexander Ac,et al.  Differences in pigment composition, photosynthetic rates and chlorophyll fluorescence images of sun and shade leaves of four tree species. , 2007, Plant physiology and biochemistry : PPB.

[36]  Gabriele Langsdorf,et al.  Chlorophyll fluorescence imaging of photosynthetic activity in sun and shade leaves of trees , 2007, Photosynthesis Research.

[37]  A. Skidmore,et al.  Red edge shift and biochemical content in grass canopies , 2007 .

[38]  Tomáš Polák,et al.  Does the azimuth orientation of Norway spruce (Picea abies/L./Karst.) branches within sunlit crown part influence the heterogeneity of biochemical, structural and spectral characteristics of needles? , 2007 .

[39]  R. Olsen Introduction to Remote Sensing , 2007, Scientia Militaria.

[40]  Alessandro Cescatti,et al.  Complex adjustments of photosynthetic potentials and internal diffusion conductance to current and previous light availabilities and leaf age in Mediterranean evergreen species Quercus ilex. , 2006, Plant, cell & environment.

[41]  A. Viña,et al.  Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity , 2006 .

[42]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[43]  P. Gemperline,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[44]  Pablo J. Zarco-Tejada,et al.  Hyperspectral Remote Sensing of Forest Condition: Estimating Chlorophyll Content in Tolerant Hardwoods , 2003, Forest Science.

[45]  G. Agati,et al.  New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. , 2001, Journal of photochemistry and photobiology. B, Biology.

[46]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[47]  P. G. Jarvis,et al.  Photosynthetic capacity in a central Amazonian rain forest. , 2000, Tree physiology.

[48]  Frans Bongers,et al.  The effect of tree height and light availability on photosynthetic leaf traits of four neotropical species differing in shade tolerance , 2000 .

[49]  Hartmut K. Lichtenthaler,et al.  The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants , 1999 .

[50]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[51]  B. Datt Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves , 1998 .

[52]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[53]  Mark E. Kubiske,et al.  Ecophysiological responses to simulated canopy gaps of two tree species of contrasting shade tolerance in elevated CO2 , 1997 .

[54]  Steven F. Oberbauer,et al.  Leaf optical properties along a vertical gradient in a tropical rain forest canopy in Costa Rica. , 1995 .

[55]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[56]  Edgar Wagner,et al.  Photoinactivation of catalase in needles of Norway spruce , 1994 .

[57]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[58]  G. Carter Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[59]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[60]  Mark D. Atkinson Betula pendula Roth (B. verrucosa Ehrh.) and B. pubescens Ehrh. , 1992 .

[61]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[62]  J. Dungan,et al.  Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.

[63]  F. Boochs,et al.  Shape of the red edge as vitality indicator for plants , 1990 .

[64]  John R. Miller,et al.  Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model , 1990 .

[65]  D. Meier,et al.  Photosynthetic activity, chloroplast ultrastructure, and leaf characteristics of high-light and low-light plants and of sun and shade leaves , 1981, Photosynthesis Research.

[66]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[67]  E. B. Knipling Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[68]  G. Birth,et al.  Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1 , 1968 .

[69]  Holly Croft,et al.  Leaf Pigment Content , 2018 .

[70]  Quan Wang,et al.  Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests , 2017, Ecol. Informatics.

[71]  SUPAPORN BUAJAN,et al.  THE EFFECT OF LIGHT ON MICROENVIRONMENT AND SPECIFIC LEAF AREA WITHIN THE GAP , SUBTROPICAL FOREST , CHINA , 2017 .

[72]  Diego González-Aguilera,et al.  Remote Sens , 2015 .

[73]  赵文吉 Zhao Wenji,et al.  Estimation model for plant leaf chlorophyll content based on the spectral index content , 2014 .

[74]  S. Ollinger Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[75]  Duncan S. Wilson,et al.  Sources of within- and between-stand variability in specific leaf area of three ecologically distinct conifer species , 2011, Annals of Forest Science.

[76]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[77]  Bisun Datt,et al.  A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves , 1999 .

[78]  D. M. Gates,et al.  Spectral Properties of Plants , 1965 .