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Where can I get numerical data for the PAR (photosynthetically active radiation) curve?

Where can I get numerical data for the PAR (photosynthetically active radiation) curve?


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I'm trying to do some rough figuring to calculating efficiencies of various light sources for growing plants. I'm hampered, however, by the lack of a curve to multiply by the spectrum of light sources to determine the amount of photosynthetically active radiation that a typical plant will absorb and use.

I can find curves like below all over, but I can't get numerical data in the form of tables. Is there some source from where I can get such data?


Which part of the light spectrum is used for photosynthesis?

Plants use visible light for photosynthesis. Visible light ranges from low blue to far-red light and is described as the wavelengths between 380 nm and 750 nm. The region between 400 nm and 700 nm is what plants primarily use to drive photosynthesis and is typically referred to as Photosynthetically Active Radiation (PAR). Plant biologists quantify PAR using the number of photons in the 400-700 nm range received by a surface for a specified amount of time, or the Photosynthetic Photon Flux Density (PPFD) in the units μmol/s.


Batiuk, R. A., R. J. Orth, K. A. Moore, W. C. Dennison, J. C. Stevenson, L. Staver, V. Carter, N. Rybicki, R. E. Hickman, S. Kollar, S. Bieber, P. Heasly , and P. Bergstrom . 1992. Chesapeake Bay Submerged Aquatic Vegetation Habitat Requirements and Restoration Goals: A Technical Synthesis. U.S. Environmental Protection Agency, Chesapeake Bay Program, Annapolis, Maryland.

Carter, V., N. B. Rybicki, J. M. Landwehr , and M. Naylor . 2000. Light requirements for SAV survival and growth, p. 4–15.In R. A. Batiuk, P. Bergstrom, W. M. Kemp, E. Koch, L. Marray, J. C. Stevenson, R. Bartleson, V. Carter, N. B. Rybicki, J. M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K. A. Moore, S. Ailstock, and M. Teichberg (eds.), Chesapeake Bay Submerged Aquatic Vegetation Water Quality and Habitat-based Requirements and Restoration Targets: A Second Technical Synthesis. U.S. Environmental Protection Agency, Chesapeake Bay Program, Annapolis, Maryland.

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Dennison, W. C., R. J. Orth, K. A. Moore, J. C. Stevenson, V. Carter, S. Kollar, P. W. Bergstrom , and R. A. Batiuk . 1993. Assessing water quality with submersed aquatic vegetation.BioScience 43:86–94.

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Gallegos, C. and K. A. Moore . 2000. Factors contributing to water-column light attenuation, p. 16–27.In R. A. Batiuk, P. Bergstrom, W. M. Kemp, E. Koch, L. Murray, J. C. Stevenson. R. Bartleson, V. Carter, N. B. Rybicki, J. M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K. A. Moore, S. Ailstock, and M. Teichberg (eds.), Chesapeake Bay Submerged Aquatic Vegetation Water Quality and Habitat-based Requirements and Restoration Targets: A Second Technical Synthesis. U.S. Environmental Protection Agency, Chesapeake Bay Program, Annapolis, Maryland.

Geider, R. J. 1987. Light and temperature dependence of the carbon to chlorophylla ratio in microalgae and cyanobacteria: Implications for physiology and growth of phytoplankton.New Phytologist 106:1–34.

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Kemp, W. M., R. Bartleson , and L. Murray 2000. Epiphyte contributions to light attenuation at the leaf surface, p. 28–37.In R. A. Batiuk, P. Bergstrom, W. M. Kemp, E. Koch, L. Murray, J. C. Stevenson, R. Bartleson, V. Carter, N. B. Rybicki, J. M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K. A. Moore, S. Ailstock, and M. Teichberg (eds.), Chesapeake Bay Submerged Aquatic Vegetation Water Quality and Habitat-based Requirements and Restoration Targets: A Second Technical Synthesis. U.S. Environmental Protection Agency, Chesapeake Bay Program, Annapolis, Maryland.

Kirk, J. T. O. 1980. Relationship between nephelometric turbidity and scattering coefficients in certain Australian waters.Australian Journal of Marine and Freshwater Research 31:1–12.

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Kirk, J. T. O. 1994. Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press, Cambridge.

Koch, E. W. 2001. Beyond light: Physical, geological and geochemical parameters as possible submerged aquatic vegetation habitat requirements.Estuaries 24:1–17.

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Mobley, C. D. 1994. Light and Water. Radiative Transfer in Natural Waters. Academic Press, New York.

Mobley, C. D., B. Gentili, H. R. Gordon, Z. Jin, G. W. Kattawar, A. Morel, P. Reinersman, K. Stamnes , and R. H. Stavn . 1993. Comparison of numerical models for computing underwater light fields.Applied Optics 32:1–21.

Moore, K. A., W. M. Kemp, V. Carter , and C. Gallegos . 2000. Future needs for continued management application, p. 77–78.In R. A. Batiuk, P. Bergstrom, W. M. Kemp, E. Koch, L. Murray, J. C. Stevenson, R. Bartleson, V. Carter, N. B. Rybicki, J. M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K. A. Moore, S. Ailstock, and M. Teichberg (eds.), Chesapeake Bay Submerged Aquatic Vegetation Water Quality and Habitat-based Requirements and Restoration Targets: A Second Technical Synthesis. U.S. Environmental Protection Agency, Chesapeake Bay Program, Annapolis, Maryland.

Moore, K. A., R. L. Wetzel , and R. J. Orth . 1997. Seasonal pulses of turbidity and their relations to eelgrass (Zostera marina L.) survival in an estuary.Journal of Experimental Marine Biology and Ecology 215:115–134.

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2 thoughts on &ldquoPhotosynthesis Response Curve with Floating Disk Assay&rdquo

Great way to measure photosynthesis and respiration. Shouldn’t you do both and subtract to get net photosynthesis?

Is ten disks really sufficient? It would appear to allow for large random error. How about 20?

Using bicarbonate, how much variation can you have in pH before affecting the results. If light and not CO2 limits, then you’re set. Discovering the variation of photosynthesis with pH is a good experiment too.

Using colored filters always seemed a bit crude to me. These days, you can readily obtain bright LEDs in many colors. These are close to monochromatic. The problems you face are measuring the photon intensity reliably across different wavelengths and having enough LEDs to achieve saturation without prior experimentation. It’s easy enough to vary light intensity by changing the number of LEDs that are lit and by adjusting the distance to the petrie dish.

I’m interested in the potential for redoing photosynthesis this way instead of the classic DPIP approach, which I found to be fraught with errors from a variety of sources. I have very many filmed experiments with varying light color, light intensity, and pH. I believe that students will better relate to the floating disk approach. Once you have the films, it’s easy enough to provide the counting just as we now do with seed germination.

Great way to measure photosynthesis and respiration. Shouldn’t you do both and subtract to get net photosynthesis?

The disk rise due to excess oxygen production—you are actually measuring net photosynthesis. You can infer gross photosynthesis by measuring the rate of respiration (also possible with this work) and then adding this to the net.

Is ten disks really sufficient? It would appear to allow for large random error. How about 20?

Depends on the variability of your sample—in this case I plotted two standard errors above and below each mean. Plus or minus two standard errors describes the boundaries for approx. 96% confidence interval. In other words I’m better than 96% confident that the true mean of my population I’m sampling at each light intensity lies between those error bars. One way to determine sample size is as a function of standard error….

Using bicarbonate, how much variation can you have in pH before affecting the results. If light and not CO2 limits, then you’re set. Discovering the variation of photosynthesis with pH is a good experiment too.

True. That is why I mentioned that I did not use a buffer to maintain a pH around 7….I might get even better results if I had. Likewise I did not indicate the concentration of the bicarb—its is low. We’ve done work to show that very small concentrations of bicarb permit photosynthesis in this technique—too much inhibits its. I’m convinced it was not problem in this investigation–at least not until the temp. got too high. More work to do….

Using colored filters always seemed a bit crude to me. These days, you can readily obtain bright LEDs in many colors. These are close to monochromatic. The problems you face are measuring the photon intensity reliably across different wavelengths and having enough LEDs to achieve saturation without prior experimentation. It’s easy enough to vary light intensity by changing the number of LEDs that are lit and by adjusting the distance to the petrie dish.

LED’s are a great possibility—one I have encouraged my students to use. Generally, I let my students design how they wish to measure color transmission and absorbance. That is one of the reasons I got the PAR meters—so that we could change colors but maintain equivalent photon flux….

I’m interested in the potential for redoing photosynthesis this way instead of the classic DPIP approach, which I found to be fraught with errors from a variety of sources. I have very many filmed experiments with varying light color, light intensity, and pH. I believe that students will better relate to the floating disk approach. Once you have the films, it’s easy enough to provide the counting just as we now do with seed germination.

The evidence seems to indicate that students (and teachers) find this technique more accessible and it promotes true student inquiry.


Abstract

Photosynthetically active radiation (PAR) is absorbed by plants to carry out photosynthesis. Its estimation is important for many applications such as ecological modeling. In this study, a broadband transmittance scheme for solar radiation at the PAR band is developed to estimate clear-sky PAR values. The influence of clouds is subsequently taken into account through sunshine-duration data. This scheme is examined without local calibration against the observed PAR values under both clear- and cloudy-sky conditions at seven widely distributed Surface Radiation Budget Network (SURFRAD) stations. The results indicate that the scheme can estimate the daily mean PAR at these seven stations under all-sky conditions with root-mean-square error and mean bias error values ranging from 6.03 to 6.83 W m −2 and from −2.86 to 1.03 W m −2 , respectively. Further analyses indicate that the scheme can estimate PAR values well with globally available aerosol and ozone datasets. This suggests that the scheme can be applied to regions for which observed aerosol and ozone data are not available.


Modelling Canopy Production. II. From Single-Leaf Photosynthesis Parameters to Daily Canopy Photosynthesis

This paper presents a simple algorithm for calculating daily canopy photosynthesis given parameters of the single-leaf light response, the canopy extinction coefficient, canopy leaf area index, daylength, daily solar irradiance and daily maximum and minimum temperatures. Analytical expressions are derived for total daily production by a canopy of leaves whose light response is either a rectangular hyperbola or a Blackman response. An expression which gives an excellent approximation to canopy photosynthesis for an arbitrary hyperbolic light response is then derived. These expressions assume photosynthetically active radiation (PAR) within the canopy follows Beer's law, light-saturated photosynthetic rate at any point in the canopy is proportional to the ratio of local PAR to full-sun PAR, diurnal variation of PAR is sinusoidal, and parameters of the single-leaf photosynthetic light response do not vary diurnally. It is shown how these expressions can be used to accommodate diurnal temperature variation of photosynthesis in a simple manner. The accuracy of the approximation to the basic integral of leaf photosynthesis over the canopy and over time is illustrated by applying the algorithm to compute the seasonal variation of daily canopy photosynthesis and comparing these data with corresponding values obtained by numerical integration.


Acknowledgements

The authors gratefully acknowledge funding of the work by the German Research Foundation (DFG) in the scope of the DFG-Research Unit RU816 ‘Biodiversity and Sustainable Management of a Megadiverse Mountain Ecosystem in South Ecuador’, subprojects C3.1, B3.1 and Z1.1. We further would like to thank Prof. Dr. Michael Richter (University of Erlangen-Nürnberg) for providing the data of the ECSF meteorological station that he collected in his subproject B1.4. B.Silva gratefully appreciates granting by the Brazilian Council of Technological and Scientific Development (CNPq). The authors thank three anonymous reviewers for their valuable comments, which helped to improve the paper.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.


Photosynthesis

That's light intensity, we'll now move to photosynthesis and photosynthesis efficiency. The light that we're interested in for photosynthesis is about 400nm (UV-A) to 700nm (deep red) and is known as photosynthetically active radiation (PAR). It actually extends a little lower than 400nm but it's very inefficient.

The whole concept of photosynthesis that is relevant to the grower is a plant takes in water, carbon dioxide and light to make sugar and oxygen and is expressed in the simplified equation of 6 CO2 (six carbon dioxide molecules from the air) + 6 H2O (six water molecules from the roots) powered by light equals C6H12O6 (one sugar molecule that the plant uses for energy) + 6 O2 (6 oxygen molecules given off as a gas). It's all about making sugar which is transported through the plant via the phloem network. (It's important to note that the uptake of water and nutrients is via the xylem network from the roots and doesn't mean adding sugar to your soil is absorbed by the plant).

No fresh air means a low photosynthesis rate in a small volume since the carbon dioxide in the air is rapidly consumed unless CO2 enhancement is used such as a tank/regulator. Being in the same room with the plants will raise CO2 levels. A typical exhaled breath is 4500-5000 ppm CO2

There's four charts that people often get confused: chlorophyll and other pigments dissolved in a solvent, leaf absorption, action spectra and quantum yield. If you're going off a chart that has sharp peaks and talk about very specific wavelengths needed for photosynthesis optimization, then you're probably using the wrong chart. This is the pigments dissolved in a solvent chart. Also, if you're using a chart with a really deep dip in the green/yellow/orange area then it's likely for algae or aquatic plants. This is the correct chart (PDF file chart C) for land plants and are the average of dozens of plants. The relative quantum yield chart is what we want to use since this is ultimately a measure of how much sugar is produced. Keep in mind that this is for monochromatic light only which below you'll see why is problematic and that these are relative charts and not absolute charts.

LED grow light manufactures tend to use the solvent absorption charts which are wildly off in the green/yellow/orange area to boost their claims of very high yields per watt. It's all BS. This forum gets spammed a few times per month by LED grow light manufacturers or related people. Look at the spectrum of HPS vs quantum yield charts and you'll see that it has a very high efficiency and not the 10% ballpark efficiency that is often claimed. A 600 and 1000 watt SunMaster HPS put out 215 and 358 PAR watts perspectively. This is 35.8% PAR efficient so its 31.5 % efficient with at a .87 magnetic ballast loss and 33.2% with at a .93 digital ballast loss.

Some commercially available LEDs have surpassed this number and lab samples exist that are much higher. But, if you go in to Home Depot and check out their white LED lights they're less efficient than CFL at the time of this writing (but the LED spot lights have the advantage of luminaire efficiency which for our purposes is how much light out of the light source is coupled to the plant. A CFL without a reflector or close by reflective surface above a plant would have a very low luminaire efficiency since there's a lot of wasted light).

Although red light is generally most efficient in photosynthesis, one thing that a lot of people don't understand is that green light is also actively used in photosynthesis. In fact, with a bright white light source it can be the case that adding more green rather than red or blue is how to increase photosynthesis efficiency since green can reach in to deeper chloroplasts in the leaves. The green light absorption in healthy, high nitrogen level pot leaves is in the 85-87% ballpark as can be seen in this shot of a couple of pot leaves on a 18% gray card (gray card reflects 18%, absorbs 82%) with the camera's sensor balanced to the top bounce, diffused light source. You can analyze the levels in different parts of the pic in Photoshop.

I've always found it odd that people would say that plants don't use green light or that leaves somehow reflect all green light. They generally reflect a little more green light than red or blue. That's it. An extreme case would be iceberg lettuce which absorbs around 50%. A healthy Douglas-fir tree is closer to 90% (the source is the “green rather than red or blue” research paper link just above).

Don't forget side lighting or intracanopy lighting as a strategy if one wants to boost yield per area or volume.


Spatial Characteristics:

The FIFE study area, with areal extent of 15 km by 15 km, is located south of the Tuttle Reservoir and Kansas River, and about 10 km from Manhattan, Kansas, USA. The northwest corner of the area has UTM coordinates of 4,334,000 Northing and 705,000 Easting in UTM Zone 14.

Spatial Coverage:

The PARABOLA data was collected at the following locations:

Spatial Coverage Map:

Spatial Resolution:

Ranges from 2 square meter at nadir to 5.7 square meter at a 45 degrees off-nadir angle, to 17 square meters at 60 degrees of nadir.

Projection:

Grid Description:

Temporal Characteristics:

Temporal Coverage:

The overall time period of PARABOLA data acquisition was from June 6, 1987 through October 11, 1987 and on August 4, 1989.

Temporal Coverage Map:

Temporal Resolution:

Data were collected at intervals during the daylight hours. The PARABOLA measures a 4 pi hemisphere area with 15 degree IFOV sectors in 11 seconds.

Data Characteristics:

The SQL definition for this table is found in the PARABOLA.TDF file located on FIFE CD-ROM Volume 1.

Decode the FIFE_DATA_CRTFCN_CODE field as follows:

The primary certification codes are: EXM Example or Test data (not for release). PRE Preliminary (unchecked, use at your own risk). CPI Checked by Principal Investigator (reviewed for quality). CGR Checked by a group and reconciled (data comparisons and cross-checks).

The certification code modifiers are: PRE-NFP Preliminary - Not for publication, at the request of investigator. CPI-MRG PAMS data that are "merged" from two separate receiving stations to eliminate transmission errors. CPI-. Investigator thinks data item may be questionable.

Sample Data Record:


Where can I get numerical data for the PAR (photosynthetically active radiation) curve? - Biology

The WinSCANOPY Pro Version

Multiple Passes Analysis (Pro version)

To analyse images more than one time with different parameters in a single mouse click.

WinSCANOPY has five (Reg version) or six (Pro version) methods of calculating LAI. The Mini version has two methods (LAI-2000 & LAI-2000 Generalised). Most of them are available in two variations the linear and the log average (the latter is for foliage clumping compensation with the Lang and Xiang 86 method):

  • Bonhomme and Chartier : This method is based on the assumption that at 57.5 degrees of elevation (user changeable), gap fraction is insensitive to leaf angle and can be related to LAI by the Beer-Lambert extinction law.
  • LAI-2000 original : The method is based on the work of Miller (1967) and Welles and Norman (1991). It uses linear regression to relate LAI to gap fractions at different zenith angles. It can also be used to measure isolated tree leaf density by substituting the default path lengths (valid for a continuous canopy) to those of the tree.
  • LAI-2000 generalized : This method is similar to the LAI-2000 original method. The formula used for calculations originate from the same theory but have been generalized for any number of elevation rings and field of view.
  • Spherical : This method assumes that leaf area distribution in canopies is identical to that of a sphere.
  • Ellipsoid : This method (Campbell, 1985) assumes that leaf area distribution in canopies is similar to that of an ellipsoid and uses non-linear curve fitting to relate LAI to gap fractions.
  • 2D projected area : method to measure individual tree leaf area is the area meter method first described by Lindsey and Bassuk 1992 and later modified and tested by Peper and McPherson 1998. We have enhanced the method so that calibration is much easier than described by the authors.

    • Bonhomme R. & Chartier P. 1972. The Interpretation and Automatic Measurement of Hemispherical Photographs to Obtain Sunlit Foliage Area and Gap Frequency. Israel Journal of Agricultural Research 22. pp. 53-61.
    • Miller J.B. 1967, A formula For Average Foliage Density. Aust. J. Bot. 15, pp. 141-144.
    • Welles J. M. and Norman J. M. 1991, Instrument for Indirect Measurement of Canopy Architecture, Agronomy Journal 83, pp. 818-825.
    • Campbell G.S., 1985. Extinction Coefficients for Radiation in Plant Canopies Calculated Using an Ellipsoidal Inclination Angle Distribution. Agric. For. Meteorol., 36, pp. 317-321.
    • Lang A.R.G., Xiang Y.Q., 1986, Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agric. For. Meteor. 37: pp. 229-243.
    • Lindsey P.A. and Bassuk N. L., 1992. A nondestructive image analysis technique for estimating whole-tree leaf area. HortTechnology, 2 (1) pp. 66-72.
    • Peper P. J. and McPherson E. G., 1998. Comparison of five methods for estimating leaf area index of open grown deciduous trees. Journal of Arboriculture, 24 (2), pp. 98-111.

    An accurate classification of pixels into sky (gaps) and canopy categories is a pre-requisite to get precise canopy analyses from hemispherical images. WinSCANOPY offers different methods to do this classification and to modify it after if required.

    All WinSCANOPY versions have two automatic threshold methods. These use grey levels information (light intensity from a color or grey levels image) to decide in which class (sky or canopy) pixels belongs to. With a global threshold, the classification criterium is the same for all pixels of the hemisphere.

    The Pro version offers four additional methods to classify pixels, two of which are specific to hemispherical images.

    • An hemispherical threshold that takes into account the light variation of hemispherical lenses which are brighter at the zenith and darker at the horizon.
    • A threshold that takes into account the light variations due to the sun position in the image (indicated by the operator).
    • Classification based on true color (Pro version). This algorithm is more tolerant to sky conditions variations. For example, it allows to analyse images with white clouds and dark blue sky, a task difficult to do with grey levels thresholds (the dark blue sky tend to be classified as canopy).

    The result of the pixels classification can be viewed before the analysis or after. As you change the parameters, the resulting classification is shown in the displayed image allowing you to choose the best method.

    The pixels classification can be verified and modified for specific image regions. Pixels that fall into the canopy group are drawn a different color over the original image as the threshold (pixel classification criteria) is modified by moving a slider bar. This allows for a simultaneous view of the original and pixels classification images.

    Select a region to be reclassified. It can be the whole hemisphere, a sub-region of any shape or a sky grid's zenith ring.

    As you move a slider, pixels classified into the canopy groups are drawn green over the original image.

    Adjust the slider so that all canopy elements are covered by green pixels (but not the sky). The analysis is updated automatically.


    Color analysis is more tolerant to sky condition variations. Images with clouds and blue sky or blue sky alone can often be analyzed.

    The image to the left has a non-uniform sky light distribution. It is well analysed with our solar threshold which adjusts its strength in function of position in the hemisphere. In this case, a global threshold is not efficient.

    The left side image is more easily analysed in color than in grey levels due to the presence of dark blue sky which tends to be classified as canopy in a grey levels analysis.

    White stems (right image) are often misclassified as sky with a threshold based method. With color classification this is not a problem (when no white clouds are present)

    It is possible to mask some areas of the image to prevent them from being analysed. These regions might contain non-canopy elements (operator, building…). They can have any shape and can be created by different methods (see below).

    There are four types of masks and two variations of them (the Mini version has only Interactive masks):

    1. Interactive masks are created simply by drawing in the image with a lasso tool. The masked area can be inside (as the first two images below) or outside the outlined area (as the right image below).

    2. Parametric pie masks are defined numerically (center position, view angle, extinction. )

    3. Coordinate masks are defined by entering a series of hemisphere coordinate points (azimuth and elevation or zenith).

    4. Image masks are created by loading an image in which non zero pixels values are the regions to mask.

    Individual Gaps Measurement

    The position and size (area) of canopy gaps can be measured by outlining them in the image.

    Gap Size Distribution Analysis (Pro)

    Gap size distribution (GSD), i.e. the number of gaps in function of their size, can be used in combination with gap fractions to quantify the degree of clumpiness at the tree level and to use this information to increase the accuracy of LAI measurements. For a canopy of a given gap fraction with randomly distributed foliage elements, it is possible to make a theoretical probability of gaps occurring in function of their size. By comparing the measured GSD to this theoretical distribution, foliage clumpiness can be measured.

    At t the base of GSD analysis, is the classification of gaps in two categories those which are normally expected for a given randomly distributed leaf area and those which are not. The latter are larger gaps that are present because of foliage clumping at the crown level and can be seen between tree crowns. These are called between-crown gaps while random gaps are called within-crown gaps. WinSCANOPY has two methods of classifying gaps into these two groups Chen and Cihlar 95's method based on transect length (a one dimensional data), which is also used in a sunfleck based commercial instrument, and a new simpler, but efficient, method of our own based on gap area (a two dimensional data).

    GSD analyses can be done on hemispherical or cover images. Between-crown gaps are drawn blue, within-crown gaps yellow.

    • On-screen visualisation of between-crown and within-crown gaps. Can also be saved to standard tiff image files.
    • The automatic gap classification can be modified with simple mouse clicks. It can also be done completely manually.
    • Clumping index is measured in function of view zenith angle and globally for the hemisphere or for any view angles range that you choose. Clumping index in function of zenith can be displayed in the graphic above the image during the analysis.

    Canopy Cover Images Analysis (Pro)

    Canopy cover images have a narrow view angle (5 to 25 degrees) directed toward the zenith or close to it (see figure below). This kind of analysis is an alternative method to hemispherical images analysis to compute LAI and other canopy structural parameters (crown porosity, crown cover, foliage cover, clumping index).

    Full-Frame Fish-Eye Images Analysis (Pro)

    Full-frame fish-eye images are acquired with a fish-eye lens but do not have a circular projection. The 180 degrees (or less) typical field-of-view spans over the diagonal of the image sensor rather than the vertical image dimension. One of their advantage is to increase the effective image resolution as all pixels are used for canopy and sky information (no black pixels).

    Two methods to measure LAI or leaf density of isolated tree

    One method (Pro) was first described by Lindsey and Bassuk 1992 and later modified and tested by Peper and McPherson 1998. The other (Reg) is a modification to the LAI2000 LAI method which consists in substituting the default normalized path lengths for those of the tree canopy (length traveled by light in the canopy at the five rings view angle).

    Individual leaf area measurement from non fish-eye images (Pro)

    Turns WinSCANOPY into a basic individual leaf area meter, disease quantifier (see WinFOLIA for more sophisticated measurements) and soil foliage cover quantifier.

    Leaf projection coefficient in function of view zenith angle (Reg)

    Highest obstruction per azimuth analysis (Reg)

    It gives the zenith angle of the highest obstacle (canopy, building or any object other than sky) in function of azimuth. Useful for shading analysis (solar panels, architecture) and communication equipment site comparisons.


    Watch the video: Maths Tutorial: Categorical and Numerical Data (September 2022).


Comments:

  1. Kirkwood

    I have no doubt about it.

  2. Noah

    What science.

  3. Kendrik

    This is a common convention

  4. Chait

    Not caught, not high! Why is it called prayer when you talk to God, and schizophrenia when God is with you? When you decide to shake off the old days, make sure that it does not fall off !!! Anything good in life is either illegal, immoral, or obese

  5. Sahak

    I consider, that you commit an error. I can defend the position.

  6. Safiy

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  7. Fitzadam

    Why did you raise the panic here?



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