*
 

iForest - Biogeosciences and Forestry

*

The estimation of canopy attributes from digital cover photography by two different image analysis methods

Francesco Chianucci   , Ugo Chiavetta, Andrea Cutini

iForest - Biogeosciences and Forestry, Volume 7, Issue 4, Pages 255-259 (2014)
doi: https://doi.org/10.3832/ifor0939-007
Published: Mar 26, 2014 - Copyright © 2014 SISEF

Research Articles


Proximal sensing methods using digital photography have gained wide acceptance for describing and quantifying canopy properties. Digital hemispherical photography (DHP) is the most widely used photographic technique for canopy description. However, the main drawbacks of DHP have been the tedious and time-consuming image processing required and the sensitivity of the results to the image analysis methods. Recently, an alternative approach using vertical photography has been proposed, namely, digital cover photography (DCP). The method captures detailed vertical canopy gaps and performs canopy analysis by dividing gap fractions into large between-crown gaps and small within- crown gaps. Although DCP is a rapid, simple and readily available method, the processing steps involved in gap fraction analysis have a large subjective component by default. In this contribution, we propose an alternative simple, more objective and easily implemented procedure to perform gap fraction analysis of DCP images. We compared the performance of the two image analysis methods in dense deciduous forests. Leaf area index (LAI) estimates from the two image analysis methods were compared with reference LAI measurements obtained through the use of litter traps to measure leaf fall. Both methods provided accurate estimates of the total gap fraction and, thus, accurate estimates of the LAI. The new proposed procedure is recommended for dense canopies because the subjective classification of large gaps is most error-prone in stands with dense canopy cover.

  Keywords


Digital Cover Photography, Canopy Cover, Gap Fraction, Leaf Area Index, Dense Canopy

Authors’ address

(1)
Francesco Chianucci
Ugo Chiavetta
Andrea Cutini
Consiglio per la Ricerca e la sperimentazione in Agricoltura - Forestry Research Centre, v.le Santa Margherita 80, I-52100 Arezzo (Italy)

Corresponding author

 
Francesco Chianucci
francesco.chianucci@entecra.it

Citation

Chianucci F, Chiavetta U, Cutini A (2014). The estimation of canopy attributes from digital cover photography by two different image analysis methods. iForest 7: 255-259. - doi: 10.3832/ifor0939-007

Academic Editor

Francesco Ripullone

Paper history

Received: Dec 20, 2012
Accepted: Mar 03, 2014

First online: Mar 26, 2014
Publication Date: Aug 01, 2014
Publication Time: 0.77 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 47499
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 40024
Abstract Page Views: 1891
PDF Downloads: 4302
Citation/Reference Downloads: 53
XML Downloads: 1229

Web Metrics
Days since publication: 3655
Overall contacts: 47499
Avg. contacts per week: 90.97

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2014): 15
Average cites per year: 1.50

 

Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Beckschäfer P, Seidel D, Kleinn C, Xu J (2013)
On the exposure of hemispherical photographs in forests. iForest - Biogeosciences and Forestry 6 (4): 228-237.
CrossRef | Gscholar
(2)
Bréda NJ (2003)
Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of Experimental Botany 54 (392): 2403-2417.
CrossRef | Gscholar
(3)
Cescatti A (2007)
Indirect estimates of canopy gap fraction based on the linear conversion of hemispherical photographs. Agricultural and Forest Meteorology 143 (1-2): 1-12.
CrossRef | Gscholar
(4)
Chen J, Cihlar J (1995)
Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods. IEEE Transactions on Geoscience and Remote Sensing 33 (3): 777-787.
CrossRef | Gscholar
(5)
Chianucci F, Cutini A (2012)
Digital hemispherical photography for estimating forest canopy properties: current controversies and opportunities. iForest - Biogeosciences and Forestry 5 (6): 290-295.
CrossRef | Gscholar
(6)
Chianucci F, Cutini A (2013)
Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography. Agricultural and Forest Meteorology 168: 130-139.
CrossRef | Gscholar
(7)
Evans GD, Coombe DE (1959)
Hemispherical and woodland canopy photography and the light climate. Journal of Ecology 47: 103-113.
CrossRef | Gscholar
(8)
Jennings SB, Brown, ND Sheil D (1999)
Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry 72: 59-74.
CrossRef | Gscholar
(9)
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F (2004)
Review of methods for in situ leaf area index determination. Agricultural and Forest Meteorology 121 (1-2): 19-35.
CrossRef | Gscholar
(10)
Kucharik CJ, Norman JM, Gower ST (1998)
Measurements of branch area and adjusting leaf area index indirect measurements. Agricultural and Forest Meteorology 91 (1-2): 69-88.
CrossRef | Gscholar
(11)
Leblanc SG (2008)
DHP-TRACWin Manual (version 1.03). Natural Resources Canada, Saint-Hubert, Quebec, pp. 29.
Gscholar
(12)
Liu J, Pattey E (2010)
Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agricultural and Forest Meteorology 150: 1485-1490.
CrossRef | Gscholar
(13)
Macfarlane C (2011)
Classification method of mixed pixels does not affect canopy metrics from digital images of forest overstorey. Agricultural and Forest Meteorology 151 (7): 833-840.
CrossRef | Gscholar
(14)
Macfarlane C, Hoffman M, Eamus D, Kerp N, Higginson S, Mcmurtrie R, Adams M (2007)
Estimation of leaf area index in eucalypt forest using digital photography. Agricultural and Forest Meteorology 143 (3-4): 176-188.
CrossRef | Gscholar
(15)
Nadkarni NM, Parker GG, Lowman MD (2011)
Forest canopy studies as an emerging field of science. Annals of Forest Science 68: 217-224.
CrossRef | Gscholar
(16)
Pau G, Oles A, Smith M, Sklyar O, Huber W (2014)
EBImage: Image processing toolbox for R. R package version 4.4.0.
Online | Gscholar
(17)
R Development Core Team (2013)
R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Gscholar
(18)
Warton DI, Wright IJ, Falster DS, Westoby M (2006)
Bivariate line-fitting methods for allometry. Biological Reviews 81 (2): 259-291.
CrossRef | Gscholar
(19)
Woebbecke DM, Meyer GE, Von Bargen K, Mortensend D (1995)
Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the American Society of Agricultural and Biological Engineers 38: 259-269.
CrossRef | Gscholar
 

This website uses cookies to ensure you get the best experience on our website. More info