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More about some of WinSCANOPY's features
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| A significant number of new features and measurements have been introduced in WinSCANOPY version 2006a. These are presented on a different page. Click for details. |
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The WinSCANOPY DSLR Version
DSLR stands for Digital Single Lens Reflex cameras. These cameras are high-end digital (filmless) 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 6 to 14 megapixels (subject to change). The images they produce have an outstanding clarity and definition.
The WinSCANOPY DSLR 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).
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Multiple Passes Analysis (Pro version)
To analyse images more than one time with different parameters in a single mouse click
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| 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 overlaping zenith ranges [Ex: 0-10º, 0-20º, 10-60º...]. |
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WinSCANOPY has five (Reg) or six (Pro) 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, gap fraction is insensitive to leaf angle and can be related to LAI by the Beer-Lambert extinction law.
LAI-2000 original: This 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 formulae 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.
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Pixels classification
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| 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 and DSLR versions offer 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.
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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.
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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.
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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. |
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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 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)
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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
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Masks
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| 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;
- 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.
- Parametrical masks are created by specifying geometrical parameters tied to the hemisphere position and size in the image. These parameters comprise the azimuth and elevation beginning and ending angles and how they vary in between.
- Coordinate masks are defined by entering a series of hemisphere coordinate points (azimuth and elevation or zenith).
- Image masks are created by loading an image and transforming it to a mask before an analysis.
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| 1) Interactive masks. They are made by drawing in the image. They usually have an irregular shape. |
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Inside masks. The masked area is the interior of the region outlined by the operator.
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Outside mask. The masked area is the exterior of the region outlined by the operator.
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| 2) Parametrical masks. They are made by specifying hemispherical parameters. They usually have a regular shape. |
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| 3) Coordinates masks. They are made by specifying image or hemispherical point coordinates. They can have any shape. |
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| 4) An image mask is an image stored in a tiff or jpeg file which indicates areas of the image to be masked (not analysed). They can be produced by many types of programs (image editors, GIS programs which compute view sheds based on digital elevation models,...) or within WinSCANOPY itself by drawing the mask with the edition tools. |
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Individual Gaps Measurement
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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.
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Sun Projection
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| The sun projection size in the image is taken into account when calculating under canopy radiation. There are three models of sun size projection. |
1- One Pixel
The sun projection area in the image is assumed to be equal to one pixel. For all pixels along the sunpath in the image, if the pixel is classified as canopy, no radiation is considered to be received under the canopy. If the pixel is classified as opening, 100% of above canopy radiation is considered to be received under. |
2- Real Projected Size
In a high resolution image, the area covered by the sun overlaps a few pixels. WinSCANOPY determines which pixels are covered by the sun and their percentage of cover. It then uses all this pixel information to compute the radiation level below the canopy at different moments. WinSCANOPY does not assume that the sun's projection shape is round. It takes into account its real ellipsoidal projection, which changes as a function of its height in the sky. |
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3-Real Projected Size Plus Penumbral Effect
The Real Projected Size + Penumbral Effect Method is identical to the Real Projected Size Method, except that the effect of sun penumbra (Anderson and Miller 1974 * and Miller and Norman 1971 **) is added. |
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| WinSCANOPY can transform a hemispherical image into a panoramic image which is easier for the human eye to interpret. |
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The panoramic image covers 360 degrees of azimuthal direction. It represents what you would see in all directions and elevations at the place where the hemispherical photo was taken. The three dimensional 360 degrees of azimuthal view are projected on a two dimensional plane.
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Download WinSCANOPY brochure (3.2 MB).
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