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Software Program Features

WinSCANOPY is available in three versions: Mini LS (LAI Solar), Reg & Pro

WinSCANOPY measurements per version

Note: Versions can be upgraded at any time to a higher end version simply by paying the price difference between them.

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WinSCANOPY Features per version

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More about some of WinSCANOPY features 

The WinSCANOPY Pro Version

Multiple Passes Analysis (Pro version)

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

LAI Methods

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.

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  • 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 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.

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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.

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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.

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The image above 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 image below (left) 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).

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Masks

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).This is the first WinDENDRO version made and tested under Windows 10.

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2) Parametric pie masks are defined numerically (center position, view angle, extinction...)WinDENDRO 2016a

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

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

Individual Gap Measurement




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

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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.

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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. One of their advantage is to increase the effective image resolution as all pixels are used for canopy and sky information (no black pixels).

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).

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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.

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

Highest obstruction per azimuth analysis (Regular)

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.

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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

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