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Forest Condition Monitoring in Finland – National report

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Photo: Liisa Ukonmaanaho

Estimation of canopy cover using planar photography method

By Liisa Ukonmaanaho & Jaakko Heikkinen

Summary

Estimation of forest canopy cover is an important part of forest inventories. We determined canopy cover in 18 Level II plots in August 2010 using digital camera and image analyses technique. Traditional canopy cover varied on Scots pine plots between 32 to 79%, on Norway spruce 30 and 91% and on birch plots 70 to 91%. The effective canopy cover% was less than traditional canopy cover %. In northermost plots the canopy cover was generally less than in southern plots.

Background

Forest canopy cover is an important ecological indicator, that can be used for example to characterize forest microclimate and light environment or to recognize habitants suited for several plant and animal species (e.g. Jennigs et al. 1999, Korhonen & Heikkinen 2009). Canopy cover is also an important ancillary variable in the estimation of leaf area index (LAI) using empirical or physically based vegetation reflectance models (Jasinski 1990, Kuusk and Nilson 2000). In addition, the international definition of a forest is based on canopy cover: at 0.5 ha area potential canopy cover should be at least 10% and potential tree height at least five meters (FAO 2000).

Canopy cover is defined as the proportion of the forest floor covered by the vertical projection of the tree crowns. However, it has been discussed, whether the gaps inside tree crowns should be counted as canopy or not. The traditional definition of canopy cover includes  canopy gaps in the cover measurement (=traditional canopy cover). In contrast the term effective canopy cover comprises only the leaves, branches and stems and not the empty spaces between them.

An estimate of the canopy cover can be obtained using e.g. field measurements, statistical models, remotely sensed information or laser scanner data. However, field measurement are the only way to define the true vertical projection of a canopy. Best known field method is the Cajanus tube (Sarvas 1953). However, nowadays canopy cover can be determined reliable and conveniently using digital camera and image analyses techniques.

Results and discussion

The traditional canopy cover was an average 59% on Scots pine stands, 71% on Norway spruce stands and 81% on birch stands (Table 1, pdf). The effective canopy cover was on average 40%, 59% and 63% correspondingly. The traditional canopy cover was on average 30% higher than effective canopy cover in Scots pine, and correspondingly 17% higher in Norway spruce and 22% higher in birch stands. The difference is due to structure of the tree species, obviously in pine stands there are more open gaps between branches and needles compared to spruce and birch stands. The lowest canopy cover % was on the northenmost plots, which are old growth forest  with lowest stem volume (Intensive and continuous monitoring...Table 4, pdf).

Material and methods

Photographing

Figure 1. Location of study sites.

The study was carried out in nine Norway spruce plots, seven Scots pine and two birch plots in August 2010 (Fig 1). Planar photographs were taken using standard digital camera. Digital cameras have considerably higher spatial resolution than traditional AOV (angle of view) instruments (densitometer, moosehorn) and therefore they are suitable for canopy cover measurements. The photos were taken in total 32 points from one of the subplots in each stand. Sixteen of the points were above litterfall collectors which have arranged in a systematic grid (10 x 10 m), other 16 points located in a systematical grid (10 x 10) starting from the south-east corner of the plot, both network covered the subplot area. Average of both network values were used to calculate traditional and effective canopy cover %. The images were taken pointing the camera in a near-vertical, skyward direction at breast height (1.5 m), clear sky in the middlepoint of the photo. It was possible to take photos in varying weather conditions, with the exception of rain, as raindrops in the images disturb analysis. Sunny weather was not an obstacle as long as the sun does not appear directly in the images or result in severe reflections from the canopy.

Image processing

Main steps of the canopy image analysis is shown in the flow-chart below. Image processing was done using Matlab numerical computing environment (MathWorks Inc. 2008).  

  1. Original RGB image.

  2. Blue component of RGB images is thresholded according to the method proposed by Nobis and Hunziker (2005). The method is based on edge detection. Basically, the idea is to find the value of the blue channel that gives the greatest contrast between the canopy and the sky.

  3. Thresholded image. The percentage of black and white pixels in the binary image is calculated -> effective canopy cover.

  4. Gaps inside the tree crowns are painted over using morphological dilation and erosion operations -> traditional canopy cover.

The steps of image processing is described in detail in study by Korhonen & Heikkinen (2009). Matlab-script used in canopy image analysis and can be obtained from Matlab file exchange (Heikkinen and Korhonen 2009).

The average cover of images represents the canopy cover of the plot.

References

FAO 2000. On definations of forest and forest change. Forest Resources Assessment Programme, working Paper 33. FAO, Rome, Italy. 15 p.

Heikkinen, J. & Korhonen. L. 2009. MATLAB codes for canopy image analyses.

Jasinski, M. F. 1990. Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale. Remote Sensing of Environment 32: 169–187.

Jennigs, S.B., Brown, N.D. & Sheil, D. 1999. Asseessing forest canopies and understorey illumination: canopy closure. canopy cover and other measures. Forestry 72(1): 59–74.

Korhonen, L. & Heikkinen, J. 2009. Automated analysis of in situ canopy images for the estimation of forest canopy cover. Forest Science 55 (4): 323–334.

Kuusk, A. & Nilson, T. 2000. A directional multispectral forest reflectance model. Remote Sensing of Environment 72: 244–252.

MathWorks Inc. 2008. Matlab- the language of technical computing.

Nobis, M. & Hunziker, U. 2005. Automatic thresholding for hemispherical canopy-photographs based on edge detection. Agricultural and Forest Meteorology 128: 243–250.

Sarvas, R. 1953. Measurement of the crown closure of the stand. Communicationes Instituti Foreststalis Fenniae 41 (6). 13 p.

Citation: Ukonmaanaho, L. and Heikkinen, J. (2013). Estimation of canopy cover using planar photography method. In: Merilä, P. & Jortikka, S. (eds.). Forest Condition Monitoring in Finland – National report. The Finnish Forest Research Institute. [Online report]. Available at http://urn.fi/URN:NBN:fi:metla-201305087581. [Cited 2013-05-07].

: Updated: 10.05.2013 /SJor |  Photo: Erkki Oksanen, Metla, unless otherwise stated  |  Copyright Metla  |  Feedback