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Automated Analysis of in Situ Canopy Images for the Estimation of Forest Canopy Cover

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Abstract:

Forest canopy cover is a commonly used ecological indicator variable, but its reliable estimation in the field with, e.g., the line intersect sampling method is laborious. One alternative for obtaining this information quickly is to sample a larger area of canopy from each prespecified location within a sample plot by using digital cameras and then use image analysis techniques to extract canopy cover from the images. The first aim of this study was to implement an automated method for the estimation of canopy cover from skyward-looking canopy images obtained with standard digital cameras. The second aim was to compare the estimates obtained with the cameras and the automated analysis technique to control values measured with a vertical tube by using two different plot types and a varying angular field of view (1‐50°). The results indicate that the automated analysis technique, involving an automated image thresholding algorithm and morphological image processing operations to eliminate the effect of within-crown gaps, can reliably replace manual postprocessing. With the tested sampling schemes, a 40° angle of view yielded the best approximation of the control.

Keywords: angle of view; canopy cover; digital photographs; forest canopy; image analysis

Document Type: Research Article

Publication date: August 1, 2009

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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