1. Physical modelling of Nikon Coolpix camera RGB responses - Application in non-destructive leaf chlorophyll imaging Frank Veroustraete (1,4,*), Willem W. Verstraeten (2), Koen Hufkens (3), Bert Gielen (3) and Filip Colson (3) (1) Flemish institute for Technological Research (VITO), Centre for Remote Sensing and Earth Observation Processes, (TAP). Boeretang 200, BE2400 Mol, Belgium, frank.veroustraete@vito.be (2) Laboratory of Soil and Water Management, Katholieke Universiteit Leuven (K.U.Leuven), Celestijnenlaan 200E,BE3001, Heverlee, Belgium, willem.verstraeten@biw.kuleuven.be (3) University of Antwerp, Research group Plant- and Vegetation Ecology. Department of Biology, Universiteitsplein 1, BE2600 Wilrijk, Belgium. koen.hufkens@ua.ac.be (4) University of Antwerp, Research group Land and Forest Management, Department of Applied Biological Sciences, Groenenborgerlaan 171, BE2020 Antwerp, Belgium. (*) Corresponding author.frank.veroustraete@vito.bewillem.verstraeten@biw.kuleuven.bekoen.hufkens@ua.ac.be Conclusions » Commercial portable chlorophyll meters have limited resolution, are not reliable for thick leaves and measure only very small sample sizes (Yadava 1986) which precludes their application to thick leaves with heterogeneous chlorophyll distribution. In the application described in this paper, the spatial resolution of chlorophyll imagery is high enough to make quantitative assessments of chlorophyll heterogeneity. » Image analysis, permits non-destructive, non-invasive and quantitative measurement of chlorophyll content of entire leaves, within a fraction of the time required compared to invasive methods. The method demonstrates sufficient sensitivity to detect very small differences in leaf chlorophyll concentration, yet each measurement is quickly accomplished through simple, routine sampling once a calibration is completed. » A wide range of leaf sizes can be analyzed, the maximum size being determined by the camera fore-optic focal length and the distance between the camera and leaf specimen. The image acquisition and analysis system has the added value of application in many laboratory and field measurement conditions. For example, the described procedure can be directly applied for the quantification of leaf necrosis or chlorosis during the development of a disease or leaf physiological disorder. Introduction This poster describes the emerging technology of computer aided leaf digital image analysis. It is based on a fast, non-destructive measurement technique of leaf chlorophyll content imaging based on measurements of leaf reflectance. The validity of the method is demonstrated by direct comparison of conventional extraction of both leaf chlorophyll pigments from the same species with chlorophyll estimates based on leaf reflectance imagery. The leaves of the selected species are characterized by heterogeneous chlorophyll distributions. The application of software developed for image analysis at the spatial level (2D) of physiological processes or state variables does allow to reveal the morphological structures at the origin of the spatial variation of leaf chlorophyll. Keywords: Physical modelling, leaf chlorophyll imaging, spatial analysis, RGB camera. Materials and Methods Digital imagery of leaves of different plant species has been acquired according to a method which makes use of a consumer electronics digital camera and a Spectralon ® panel used as a calibration reference target for reflectance. A single leaf dataset consists of a digital image acquisition of the adaxial side of a leaf as well as a digital image acquisition of the Spectralon ® reference panel. The camera is a 10 MPix digital camera. Three plant species, Tilia sp, Cornus sp. and Zea mays L. are used for leaf chlorophyll determinations. A large enough number of leaves per species (6 or more) are selected for statistical purposes. Destructive measurements include the measurement of chlorophyll a, b, total chlorophyll content, fresh and dry weight and hence water content of the different leaves sampled from the species mentioned. Raw data consist of leaf digital number (DN) imagery in lossless tiff image format. Once acquired, it is transferred from the camera to a laptop equipped with IDL/ENVI © 3.6 (Research Systems Inc., Boulder, Colorado) image processing software. The conversion of image digital numbers to leaf reflectance, is performed by converting a raw leaf image into a Red (R), 1 byte DN image. Calibrated reflectance is calculated, by dividing the R leaf reflectance 1 byte digital numbers (DN R,l ) by the Spectralon ® reference panel 1 byte digital number (DN ref,l ). Calibrated spectral reflectance for the R spectral band of a leaf image pixel is subsequently calculated using the equation below: r l,l and r r,l are the leaf and reference target reflectance’s respectively for band l, with l indicating the R, spectral band of the camera. DN l,l and DN r,l are the digital numbers [range: 0–255] of leaf pixel reflectance and the reflectance of the Spectralon ® reference panel, respectively. Results Relationship between leaf spectral reflectance in the R (red dots), G (green dots) and B (blue dots) channels (r r,z, r g,z, r b,z ) and destructively measured total chlorophyll concentrations for Zea mays L. Leaf reflectance (expressed as a fraction) in the horizontal direction for an average of six Cornus sp. leaves. The reflectance in the G band is the highest while that for the B band is the lowest. The vertical measurements (not shown), which are taken parallel with the leaf veins, have a higher reflectance than the horizontal ones. Since the veins have a lower chlorophyll content than the inter-vein tissues, the cross-vein-cut sampling leads to higher reflectance values relative to the parallel vein sampling configuration. Relationship between pooled species leaf adaxial reflectance for the R band and destructively measured chlorophyll a, b and a + b concentrations Relationship between r r, r g and r b and destructively measured chlorophyll a + b concentrations for all species pooled, compared with two transfer functions simulated with the LIBERTY and PROSPECT leaf radiative transfer models. P in the legends represents the PROSPECT model and L represents the LIBERTY model. The coloured lines are plots based on RTF simulations. The black line is the regression line based on destructive measurements. A general multispecies transfer function between calibrated leaf adaxial red reflectance and total leaf chlorophyll [µg.cm - ²] can be defined as in the equation below: [C a+b ] = e (– ( r R – 0.5532)/0.103) A Tilia sp. leaf, processed - per pixel - with IDL/ENVI ® from a raw 3 byte RGB reflectance image (left) into a total chlorophyll image (right) using the calibration function as well as the transfer function above. When sampling the reflectance of a leaf adaxial surface, the measuring protocol with Spectralon ® as described earlier is applied. The figure below, illustrates leaf reflectance for the R, G and B bands in the horizontal (HZ) directions for Cornus sp leaves. The graphs are the mean of six leaf measurements. Results cont’d