We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byJosh Hollers
Modified about 1 year ago
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, firstname.lastname@example.org (2) Laboratory of Soil and Water Management, Katholieke Universiteit Leuven (K.U.Leuven), Celestijnenlaan 200E,BE3001, Heverlee, Belgium, email@example.com (3) University of Antwerp, Research group Plant- and Vegetation Ecology. Department of Biology, Universiteitsplein 1, BE2600 Wilrijk, Belgium. firstname.lastname@example.org (4) University of Antwerp, Research group Land and Forest Management, Department of Applied Biological Sciences, Groenenborgerlaan 171, BE2020 Antwerp, Belgium. (*) Corresponding email@example.com@firstname.lastname@example.org 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
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
Do In and Post-Season Plant-Based Measurements Predict Corn Performance and/ or Residual Soil Nitrate? Patrick J. Forrestal, R. Kratochvil, J.J Meisinger.
DROUGHT MONITORING THROUGH THE USE OF MODIS SATELLITE Amy Anderson, Curt Johnson, Dave Prevedel, & Russ Reading.
Chlorophyll Estimation Using Multi-spectral Reflectance and Height Sensing C. L. JonesResearch Engineer N. O. Maness Professor M. L. Stone Regents’ Professor.
Mirza Muhammad Waqar HYPERSPECTRAL REMOTE SENSING 1 Contact:
Resolution Resolution. Landsat ETM+ image Learning Objectives What are the four types of resolution that we must consider with remotely sensed data?
PREFER 1 st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy PREFER WP 3.2 Information support to Recovery/Reconstruction Task 7 Damage Severity Map PREFER.
Resolution Resolution. Landsat ETM+ image Learning Objectives Be able to name and define the four types of data resolution. Be able to calculate the.
Statistics Josée L. Jarry, Ph.D., C.Psych. Introduction to Psychology Department of Psychology University of Toronto June 9, 2003.
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
PREFER 1 st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy PREFER WP 3.2, Task 7.
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Introduction to Remote Sensing Images By:
Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture JAVIER MERÁS FERNÁNDEZ MSc.
Introduction Stomatal conductance regulates the rates of several necessary processes in plants including transpiration, carbon dioxide assimilation, and.
Modeling Digital Remote Sensing Presented by Rob Snyder.
U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.
Initial testing of longwave parameterizations for broken water cloud fields - accounting for transmission Ezra E. Takara and Robert G. Ellingson Department.
Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
Radiometric Correction and Image Enhancement Modifying digital numbers.
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
Development of Vegetation Indices as Economic Thresholds for Control of Defoliating Insects of Soybean James BoardVijay MakaRandy PriceDina KnightMatthew.
Learning Objectives Nature of Light Color & Spectroscopy ALTA Spectrophotometer Spectral Signature of Substances Interpretation of Satellite Images.
Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev.
BOT / GEOG / GEOL 4111 / Field data collection Visiting and characterizing representative sites Used for classification (training data), information.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Detecting Emerald Ash Borer Infestation with Hyperspectral data using Spectral Mixture Analysis Silvia Petrova Objective The objective of this project.
Overview of Biomass Mapping The Woods Hole Research Center Alessandro Baccini, Wayne Walker and Ned Horning November 8 – 12, Samarinda, Indonesia.
Remote Sensing Hyperspectral Remote Sensing. 1. Hyperspectral Remote Sensing ► Collects image data in many narrow contiguous spectral bands through the.
Soil Moisture Estimation Using Hyperspectral SWIR Imagery Poster Number IN43B-1184 D. Lewis, Institute for Technology Development, Building 1103, Suite.
Statistical Atmospheric Correction of Lake Surface Temperature from Landsat Thermal Images Hyangsun Han and Hoonyol Lee Department of Geophysics, Kangwon.
SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.
Applications of Spatial Statistics in Ecology Introduction.
Observing Laramie Basin Grassland Phenology Using MODIS Josh Reynolds with PROPOSED RESEARCH PROJECT Acknowledgments Steven Prager, Dept. of Geography.
Questions How do different methods of calculating LAI compare? Does varying Leaf mass per area (LMA) with height affect LAI estimates? LAI can be calculated.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
1 Copyright © Cengage Learning. All rights reserved. 3 Functions and Graphs 3.6 Quadratic Functions.
Date of download: 6/22/2016 Copyright © 2016 SPIE. All rights reserved. Schematic representation of the near-infrared (NIR) structured illumination instrument,
Materials: LI-COR LAI-2200 Plant Canopy Analyzer FV2200 software Excel Rstudio Methods and Study Site: LAI measurements using an indirect, optical method.
Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.
A COMPARATIVE STUDY OF LAND USE AND LAND COVER ANALYSIS OF KARACHI USING MODIS AND LANDSAT DATASETS JIBRAN KHAN, DAWOOD CO-AUTHORS: MARYAM ALTAF & INTIKHAB.
LiDAR Remote Sensing of Forest Vegetation Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota.
Spatial Statistics in Ecology: Case Studies Lecture Five.
ABRF meeting 09 Light Microscopy Research Group. Why are there no standards? Imaging was largely an ultrastructure tool Digital imaging only common in.
Comprehensive evaluation of Leaf Area Index estimated by several method Comprehensive evaluation of Leaf Area Index estimated by several method ― LAI-2000,
Digital Images The nature and acquisition of a digital image.
ESTIMATION OF RIVER DISCHARGE WITH MODIS IMAGES The University of Tokyo, Institute of Industrial Science (IIS) Kohei Hashimoto and Kazuo Oki.
SGM as an Affordable Alternative to LiDAR February 2016 by Frank Wilson of ControlCam, LLC 1.
© 2017 SlidePlayer.com Inc. All rights reserved.