WinSCANOPY: Canopy Structure and Solar Radiation Analysis
Image Analysis for Plant Science
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Regent Instruments Inc. since 1991

 

 

WinSCANOPY


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canopy analysis, solar radiation  

Program features

WinSCANOPY features per version  

The WinSCANOPY software program is available in three versions: Basic, Regular & Pro DSLR

Note: Versions can be upgraded at any time to a higher version simply by paying the difference of cost between the two versions.

General features
Basic
Reg
Pro
Description
Interactive or predefined region to analyse
Indicate the hemispherical part of the image to analyse by one of three methods; 1) clicking the image, 2) entering numerical values or 3) automatically extracting parameters from a calibration file (provided with lenses and cameras that we sell).
Analysis done in one or two steps (Pixels classification and canopy analysis)
1) view and modify the pixels classification, 2-) create or edit masks, 3-) create interactive gap regions and 4) choose analysis settings before it is complete. The analysis can be stopped at any time and can go backward by steps.
Interactive or batch mode analysis
Interactive analysis is done with operator interaction. A batch analysis can be done with or without operator supervision. Unlike some programs there is no limit on the number of images that can be processed in batch. Random or whole images lot analysis verification.
User selectable number of sky regions (sky grid)
Choose how many zenith rings or azimuthal slices the hemisphere is divided into.
Zenith rings of equal view angle
WinSCANOPY divides the hemisphere equally among a specified number of annuli. Each zenith ring has the same field of view.
Automatic data saving
Data are saved automatically, no need to activate specific commands.
Zenith rings of unequal view angle
You can specify the beginning and ending of each zenith ring.
Multiple passes analysis    
Analyse images in multiple passes by successively changing the analysis parameters (growing season, radiation model, sky grid divisions...). Details

 

Files features Basic Reg Pro Description
Analyse tiff, bmp or jpeg image files
These are the most common image file formats. Jpeg is produced by all digital cameras.
Analyse grey levels images with 10 to 16 bits per pixel    
Versions below the Pro can only analyse 8 bits per pixels grey levels images. These images can reproduce only 256 grey levels or light intensities. A 10 bits image can reproduce 1024 grey levels, while at 12 bits it is 4096 and finally at 16 bits it is 65536. An image with more bits per pixel has a wider dynamic range (difference between darkest and brightest light levels) and more tonal variations (light differences distinguished between the minimal and maximal light level). Light variations are merged when the number of grey levels is not sufficient. When subtle light variations are preserved, the image contains more details and this gives more choice for pixels classification into canopy and sky.
Print any image used in the analysis
Images can be printed with their analysis marks over them (suntracks, sky grid...) and with or without the accompanying graphic (which can also be printed/saved individually).
Export any image used in the analysis
You can save in tiff format readable by many programs; the pixels classification image, the color classes and color groups image (Pro and DSLR versions) and the original image with all its analysis settings.
Save the analysis with the image
An image that has been analysed can be saved in tiff format with its complete analysis. When such images are loaded in WinSCANOPY, the analysis is automatically recreated and displayed and can be modified. WinSCANOPY can take on the same settings as when the image was saved or discard the analysis and keep its setting.
Automatic extraction of photo number from images file name    
From the image file name (ex: ABC00123.jpeg), WinSCANOPY will automatically extract the number (123) and write it in the sample identification window at the beginning of the analysis.
Automatic extraction of camera settings from image files    
When a photo is acquired, most digital cameras store their settings in the image files. WinSCANOPY automatically extracts this information, displays it on screen before analysing the sample and save it with the analysis data. Some of the information that is automatically read and recopied to the analysis data comprises: 1-) lens focal length (to make sure that the lens was set to the focal length it was calibrated), 2-) lens aperture, 3-) camera exposure time, 3-) camera manufacturer, model and firmware, 4-) acquisition date and time, 5-) ISO and exposure program and 6-) metering mode. (This list is not complete).
Extract GPS information from image files     You can connect a GPS (global positioning system) to some high end cameras (we do sell one such model) to precisely know the location (latitude, longitude and altitude) where the images are acquired. WinSCANOPY will automatically extract this information when it is present in image files so you wont have to enter them during the analysis. Note: Regent Instruments do not sell GPS systems and not all GPS are compatible.
Choose which information and data are saved in analysis data files
This comprises full control over which title lines, analysis settings and measurements data that are written to files. It can go from the simplest format that comprises only the image identification number and a single measurement to a detailed file that includes all WinSCANOPY's settings.
Save and load from a file the analysis settings
Allows multiple persons to work with the program each with their own settings, without having to reenter them each time the program is started.

 

Images features Basic Reg Pro Description
Select a color channel for viewing and analysis.    
A color image can be viewed and analysed:1-) in RGB color space, 2-) in true grey levels (converted from the three color channels not just from the green channel as some cameras or programs do), 3-) in its red, green or blue channel. You can view an histogram of light intensity distribution for each of the preceding choices.
Image edition  
To remove artifacts or defects. Images can be edited with pre-defined colors or with any color present in the image. Can also be used to darken white stems to make sure they are classified as canopy elements.
Image sharpening
To enhance grey levels or color transitions so that objects boundaries are sharper and to prevent small gaps from being classified as canopy.
Panoramic view   Transforms the image from an hemispherical view to a panoramic view.

 

Fisheye lenses features Basic Reg Pro Description
Lens calibration  
9th order polynomial to compensate for non linearity in zenithal projection.
Support lenses with field of view (FOV) different from 180 degrees  
To analyse images acquired with a lens with a FOV smaller or larger than 180 degrees or to reduce the effective FOV of a standard 180 degree lens. Specify the sky grid's minimal and maximal viewing angles.

 

Pixels classification (into canopy and sky) features Basic Reg Pro Description
View the pixels classification before, during or after analysis. Valid for all methods available (see lines below) including color pixels classification (Pro). As you change the classification parameters, the resulting classification is shown in the displayed image.
Global threshold pixels classification method (automatic or manual) Light intensity (grey level) is used to determine if pixels belong to sky or canopy.
Adaptive threshold pixels classification     Light intensity (grey level) is used to determine if pixels belong to sky or canopy but criteria changes in function of the location in the image to adapt for lighting variations.
Hemispherical threshold pixels classification     Light intensity (grey level) is used to determine if pixels belong to sky or canopy taking into account the light variations of hemispherical lenses.
Solar threshold pixels classification     Light intensity (grey level) is used to determine if pixels belong to sky or canopy taking into account the sky's light variations (due to sun or other).
Color based pixels classification     True color information (RGB and hue, intensity and saturation) is used to determine if pixels belong to sky or canopy. Details
Interactive pixels classification modifications To verify and make modifications to the pixels classification for the whole image or regions of it. The regions can have any shape or can be the sky grid's zenith rings. Compare the effective classification to one that can be obtained with different classification criteria and retain the best. Details
Histogram Displays the light levels distribution for the grey levels and/or each color channel. Helps visualize thresholds selection.

 

Mask features Basic Reg Pro Description
Masks   To prevent some parts of the image which might contain non-canopy elements from being analysed. You can:
1) Create as many masks as you want and of any type.
2) Export and import masks (even from other programs) to files (unlimited number of masks per file).
3) Save them with the image (in the same tiff file).
4) View the mask over the image as you create them.
5) Create masks by 3 methods: a) drawing over the image, b) specifying parameters or c) entering a list of points in polar or image coordinates.
6) Revert a mask selected region (the masked area can be the inside the mask or outside of it).
7) Create them before or after the analysis.
8) Manipulate masks (save, load, apply) individually or as a group.

 

Display features Basic Reg Pro Description
User-customizable sample identification information. Choose which information is asked to the operator before each analysis (hide the information you do not need). This information is saved with the analysis data.
Graphic of measurements above the image.   To view measurement data (radiation above and below canopy per hour, leaf angle distribution, sunfleck distribution...) graphically while the image is being analysed. It can be viewed (or hidden) on screen, sent to a file or printed.
Choose which information/data is displayed over the image. This information (suntracks, dates, sky grid, masks...) is available at any time and over the original image (instead of a separate image as with some programs) without modifying it. You can also change the color used to display that information.

 

Radiation features Basic Reg Pro Description
Suntracks   Can be automatically generated for a user specified growing season (with variable day interval) or manually one at a time.
Substitute or modify theoretical direct and diffuse radiation.   Substitution or modification of default theoretical values can be done on an hourly basis by month.
Choice of two diffuse (indirect) radiation models. Uniform Overcast (UOC) and Standard Overcast (SOC) are two models commonly used to quantify diffuse radiation distribution in function of zenith.

 

Miscellaneous features Basic Reg Pro Description
WinSCANOPY software updated regularly. Typically one version released per year (some of which are free). For a list of improvements made over the last years, see WinSCANOPY most recent version.
Free technical support. Technical support is done exclusively by email by qualified personnel (often by the programmers of WinSCANOPY) within typically one hour (when message is received on working hours eastern America).

 

WinSCANOPY measurements per version  
Canopy structure measurements
Basic
Reg
Pro
Description
Gap fraction The fraction of pixels classified as open sky (unobstructed by vegetation) in a sky grid region in the image (in a two dimensional space). It is available as a global value for the hemisphere, per azimuthal direction, zenith ring, sky region and individual gap regions (outlined interactively).
Openness(or percent open sky) The fraction of open sky (unobstructed by vegetation) in a certain region of the canopy above the lens (in a three dimensional space). It takes into account the relative sphere area occupied by each zenith ring. Openness is available as a global value for the hemisphere and per zenith ring.
Highest obstruction analysis 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. Details
Leaf area index (LAI)   A measure of leaf material quantity. It is defined as the leaf area (m2) per unit of ground area (m2). It is calculated by five methods, each with linear and logarithm averaging (the latter is for clumped canopies).
Leaf angle distribution (LAD) and mean leaf angle (MLA)   LAD = Leaf surface inclinations distribution in function of zenith.
Leaf projection coefficient in function of zenith angle.   Details
Isolated tree leaf density   By substitution of the default normalized canopy path lengths of an infinite canopy by those of the tree canopy.
Isolated tree LAI     From 2D projected tree leaf area optionally calibrated from LAI values obtained with another method such as destructive sampling and measurement with a leaf area meter (such as WinFOLIA). Details
Analysis of non-hemispherical images for Individual leaf area and soil cover     Turns WinSCANOPY into a basic individual leaf area meter, disease quantifier (see WinFOLIA for more sophisticated measurements) and soil foliage cover quantifier.
Individual canopy gaps analysis     Gap area, position, gap fraction and radiation data are measured for each region that you outline in the image.
Gap size distribution     For foliage clumping compensation and more accurate LAI estimates.
Clumping index from the Lang and Xiang 86 method (log average)   For more accurate LAI estimates. Available for the hemisphere or the sky FOV you choose. Details
Clumping index from the gap size distribution analysis (Chen & Cihlar 96)     For more accurate LAI estimate. Available for the hemisphere, the sky FOV you choose, each zenith ring and sky region. Details
Cover image analysis     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 image analysis     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. Details

 

Radiations (PAR) Measurements
Basic
Reg
Pro
Description
Diffuse (indirect) radiation above and below canopy  
Theoretical models (user modifiable) are used to estimate the diffuse radiation levels (PPFD, photosynthetically active photon flux density) above and below the canopy. They are available as instantaneous and total daily values. When data are processed with XLScanopy, these data are also available as daily average per week, month and growing season periods of time.
Direct radiation above and below canopy  
Theoretical models (user modifiable) are used to estimate the direct radiation levels (PPFD, photosynthetically active photon flux density) above and below the canopy. They are available as instantaneous and total daily values. When data are processed with XLScanopy, these data are also available as daily average per week, month and growing season periods of time.
Indirect (diffuse) site factor
Relative amount of incident diffuse radiation that penetrates below canopy for a specified period of time (the growing season).
Direct site factor  
Relative amount of incident direct radiation that penetrates below canopy for a specified period of time (the growing season).
Total site factor  
Relative amount of incident total (direct+diffuse) radiation that penetrates below canopy for a specified period of time.
Percentage of time direct radiation is received below canopy  
The percentage of time that direct radiation reaches the site location per hour and month.
Ground slope  
To get more accurate LAI and radiations calculations when the ground is not horizontal.
Sunfleck distribution and daily duration  
Sunfleck distribution is the number of sunflecks in function of the duration in minutes for the growing season. Sunfleck daily duration is the total duration in minutes of all sunflecks for each day of the growing season.

 

More about some of WinSCANOPY features

The WinSCANOPY ProVersion

DSLR stands for Digital Single Lens Reflex cameras. These cameras are high-end digital (film less) models that are based on and operate like professional 35mm film cameras. DSLR camera lenses are fully interchangeable, they don’t have a non removable lens like point-and-shoot cameras. They accept standard high quality lenses made for 35mm film cameras or newer lenses made especially for them. Our DSLR cameras are completely automatic (the camera sets the proper exposure and aperture) but all of them allow complete manual control over all camera settings. The typical resolution of images produced by DSLR cameras is in the range of 10 to 20 Megapixels (subject to change). The images they produce have an outstanding clarity and definition.

The WinSCANOPY Pro version has its default settings configured for DSLR cameras images which have a different aspect ratio than Point and Shoot cameras. It can also analyse images with 10 to 16 bits of grey levels information produced by such high end cameras and it can extract GPS information from camera image files when a GPS unit is connected to the camera (only some models support this feature and GPS units are not sold by Regent Instruments).

Multiple Passes Analysis (Pro version)

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

WinSCANOPY can process images in multiple passes by successively analyzing them under a different set of parameters (sky grids, lens FOV...) for each pass. Some applications can be:
1) to obtain the same sky divisions as another research project for comparison purposes, while at the same time analyzing the image under optimal (or other standard) divisions and
2) to analyse gap fractions at different overlapping zenith ranges [Ex: 0-10º, 0-20º, 10-60º...].

LAI Methods

WinSCANOPY has five (Reg version) or six (Pro version) methods of calculating LAI. 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.
LAI-2000 original
    • 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.

Pixels Classification

An accurate classification of pixels into the 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 a global automatic threshold method which has been improved in 2006. This method uses 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 criteria 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 adaptive threshold that changes value in function of the location in the hemisphere. It adapts itself in function of the lighting variations.
  • 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). The attenuation compensation can be linear or like the SOC diffuse radiation distribution.
  • Classification based on true color. 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 simple 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 at any moment of the analysis. 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 classified. It can be the whole image, a sub-region of any shape defined by outlining it or a pre-defined circular regions corresponding to the sky grid's zenith rings or circular regions centered on the sky brightest position (below).

As you move the slider, pixels classified into the canopy groups are drawn in 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 or a threshold adjusted in function of the sky grid (centered on the zenith) 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)

 
   

10 to 16 bits of grey levels per pixels images (like those produced by DSLR cameras) offers new pixels classification opportunities.

An image with more bits per pixel has a wider dynamic range (difference between darkest and brightest light levels) and more tonal variations (smallest light variations that can be distinguished). When the number of grey levels is not sufficient, light variations are merged into the same grey level information is lost. When subtle light variations are preserved, this gives more choices for pixels classification into canopy and sky.

Masks

It is possible to mask some areas of the image to prevent them from being analysed. These regions might contain non-canopy elements like an operator, a building or a light that indicates the north direction. There can be as many as you wish, they can have any shape and can be created by different methods (see below). Unlike their hardware counter part, they can be added and modified after the image has been taken. You can export and import masks (even from other programs) to files (unlimited number of masks per file), save them with the image (in the same tiff file) and revert a masked region (the masked area can be the inside or outside of the mask).

There are four types of masks and two variations of them:

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. Interactive masks are created simply by drawing in the image with a lasso tool. The masked area can be inside or outside the outlined area.

   

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 and transforming it to a mask before an analysis.

Individual Gaps Measurement

The position, size (area), gap fraction and radiation data of canopy gaps can be measured by outlining them in the image. See also the automatic gaps size distribution analysis

 

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 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 in blue, within-crown gaps are drawn in yellow.

   
   

Other features of GSD:

  • 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. For a given image sensor size, a full frame fish-eye image is obtained by using a fish-eye lens with a longer focal length than a regular fish-eye circular projection lens or simply by zooming in (Point & Shoot camera). One of their advantage is to increase the effective image resolution as all pixels are used for canopy and sky information (no black pixels). The analysis that can be done on these images are identical to those of regular fish-eye images.

  Standard fish-eye image Full-frame fish-eye image Cover image  
   

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 (Basic)

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.

   

References

  • Chen J.M. and Cihlar J., 1995, Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index, Applied Optics Vol. 34. no. 27, pp. 6211-6222
  • 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