, Volume 32, Issue 3, pp 713–723 | Cite as

An objective image analysis method for estimation of canopy attributes from digital cover photography

  • Alessandro Alivernini
  • Silvano Fares
  • Carlotta Ferrara
  • Francesco ChianucciEmail author
Original Article


Key message

A method was proposed to remove the subjectivity of gap size analyses approaches implemented by default in cover photography. The method yielded robust and replicable measurements of forest canopy attributes.


Digital cover photography (DCP) is an increasingly popular method to estimate canopy attributes of forest canopies. Compared with other canopy photographic methods, DCP is fast, simple, and less sensitive to image acquisition and processing. However, the image processing steps used by default in DCP have a large substantial subjective component, particularly regarding the separation of canopy gaps into large gaps and small gaps. In this study, we proposed an objective procedure to analyse DCP based on the statistical distribution of gaps occurring in any image. The new method was tested in 11 deciduous forest stands in central Italy, with different tree composition, stand density, and structure, which is representative of the natural variation of these forest types. Results indicated that the new method removed the subjectivity of manual and semi-automated gap size classifications performed so far in cover photography. A comparison with direct LAI measurements demonstrated that the new method outperformed the previous approaches and increased the precision of LAI estimates. Results have important implications in forestry, because the simplicity of the method allowed objective, reliable, and highly reproducible estimates of canopy attributes, which are largely suitable in forest monitoring, where measures are routinely repeated. In addition, the use of a restricted field of view enables implementation of this photographic method in many devices, including smartphones, downward-looking cameras, and unmanned aerial vehicles.


Canopy photography Leaf area index Gap size distribution Canopy cover Foliage clumping 



This research was supported by the Italian national project URBANFOR3, funded by Lazio Innova (CUP: C82I16000000005). Francesco Chianucci was also supported by the research project “ALForLab” (PON03PE_00024_1) co-funded by the (Italian) National Operational Programme for Research and Competitiveness (PON R&C) 2007–2013, through the European Regional Development Fund (ERDF) and national resource (Revolving Fund - Cohesion Action Plan (CAP) MIUR). CaCo software is available online on Github ( We acknowledged Editor and Reviewers for the constructive comments, which helped to improve the quality of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Beckschäfer P, Seidel D, Kleinn C, Xu J (2013) On the exposure of hemispherical photographs in forests. iForest-Biogeosci For 6(4):228–237CrossRefGoogle Scholar
  2. 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 T Geosci Remote Sens 33:777–787CrossRefGoogle Scholar
  3. Chianucci F (2016) A note on estimating canopy cover from digital cover and hemispherical photography. Silva Fennica 50.
  4. Chianucci F, Cutini A (2013) Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography. Agric For Meteorol 168:130–139CrossRefGoogle Scholar
  5. Chianucci F, Chiavetta U, Cutini A (2014a) The estimation of canopy attributes from digital cover photography by two different image analysis methods. iForest-Biogeosci For 7:255–259CrossRefGoogle Scholar
  6. Chianucci F, Cutini A, Corona P, Puletti N (2014b) Estimation of leaf area index in understory deciduous trees using digital photography. Agric For Meteorol 198:259–264CrossRefGoogle Scholar
  7. Chianucci F, Puletti N, Venturi E, Cutini A, Chiavetta U (2014c) Photographic assessment of overstory and understory leaf area index in beech forests under different management regimes in Central Italy. Forest Stud 61:27–34CrossRefGoogle Scholar
  8. Chianucci F, Macfarlane C, Pisek J, Cutini A, Casa R (2015a) Estimation of foliage clumping from the LAI-2000 plant canopy analyzer: effect of view caps. Trees 29(2):355–366CrossRefGoogle Scholar
  9. Chianucci F, Puletti N, Giacomello E, Cutini A, Corona P (2015b) Estimation of leaf area index in isolated trees with digital photography and its application to urban forestry. Urban For Urban Green 14(2):377–382CrossRefGoogle Scholar
  10. Chianucci F, Disperati L, Guzzi D, Bianchini D, Nardino V, Lastri C, Rindinella A, Corona P (2016) Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. Int J Appl Earth Obs Geoinf 47:60–68CrossRefGoogle Scholar
  11. Chianucci F, Pisek J, Raabe K, Marchino L, Ferrara C, Corona P (2017) A dataset of leaf inclination angles for temperate and boreal broadleaf woody species. Mendeley Data, V2, [dataset].
  12. De Bei R, Fuentes S, Gilliham M, Tyerman S, Edwards E, Bianchini N, Smith J, Collins C (2016) VitiCanopy: A free computer App to estimate canopy vigor and porosity for grapevine. Sensors 16(4):585CrossRefPubMedCentralGoogle Scholar
  13. Fuentes S, Palmer AR, Taylor D, Zeppel M, Whitley R, Eamus D (2008) An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI. Funct Plant Biol 35(10):1070–1079CrossRefGoogle Scholar
  14. Fuentes S, De Bei R, Pozo C, Tyerman S (2012) Development of a smartphone application to characterise temporal and spatial canopy architecture and leaf area index for grapevines. Wine Vitic J 6:56–60Google Scholar
  15. Glatthorn J, Beckschäfer P (2014). Standardizing the protocol for hemispherical photographs: accuracy assessment of binarization algorithms. PLoS One 9(11):e111924CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hwang Y, Ryu Y, Kimm H, Jiang C, Lang M, Macfarlane C, Sonnentag O (2016) Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography. Agric For Meteorol 222:32–44CrossRefGoogle Scholar
  17. 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 Part I. Theories, sensors and hemispherical photography. Agric For Meteorol 121:19–35CrossRefGoogle Scholar
  18. Jones E, Oliphant E, Peterson P et al. (2001). SciPy: Open source scientific tools for Python. (Online). Accessed 18 July 2017
  19. Kucharik CJ, Norman JM, Gower ST (1998) Measurements of branch area and adjusting leaf area index indirect measurements. Agric For Meteorol 91:69–88CrossRefGoogle Scholar
  20. Lang ARG, Xiang Y (1986) Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agric For Meteorol 35:229–243CrossRefGoogle Scholar
  21. Leblanc SG (2002) Correction to the plant canopy gap-size analysis theory used by the Tracing Radiation and Architecture of Canopies instrument. Appl Opt 41(36):7667CrossRefPubMedGoogle Scholar
  22. Leblanc SG (2008) DHP-TRACWin manual. Canada Centre for Remote Sensing, Québec, p 29Google Scholar
  23. Leblanc SG, Chen JM, Fernandes R, Deering DW, Conley A (2005) Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests. Agric For Meteorol 129:187–207CrossRefGoogle Scholar
  24. Macfarlane C, Ogden GN (2012) Automated estimation of foliage cover in forest understorey from digital nadir images. Methods Ecol Evol 3(2):405–415CrossRefGoogle Scholar
  25. Macfarlane C, Coote M, White DA, Adams MA (2000) Photographic exposure affects indirect estimation of leaf area in plantations of Eucalyptus globulus Labill. Agric For Meteorol 100(2):155–168CrossRefGoogle Scholar
  26. Macfarlane C, Grigg A, Evangelista C (2007a) Estimating forest leaf area using cover and fullframe fisheye photography: thinking inside the circle. Agric For Meteorol 146:1–12CrossRefGoogle Scholar
  27. Macfarlane C, Hoffman M, Eamus D, Kerp N, Higginson S, McMurtrie R, Adams MA (2007b) Estimation of leaf area index in eucalypt forest using digital photography. Agric For Meteorol 143:176–188CrossRefGoogle Scholar
  28. Macfarlane C, Ryu Y, Ogden GN, Sonnentag O (2014) Digital canopy photography: exposed and in the raw. Agric For Meteorol 197:244–253CrossRefGoogle Scholar
  29. Miller JB (1967) A formula for average foliage density. Aust J Bot 15:141–144CrossRefGoogle Scholar
  30. Mora M, Avila F, Carrasco-Benavides M, Maldonado G, Olguín-Cáceres J, Fuentes S (2016) Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Comput Electron Agric 123:195–202CrossRefGoogle Scholar
  31. Pekin B, Macfarlane C (2009) Measurement of crown cover and leaf area index using digital cover photography and its application to remote sensing. Remote Sens 1(4):1298–1320CrossRefGoogle Scholar
  32. Piayda A, Dubbert M, Werner C, Vaz Correia A, Pereira JS, Cuntz M (2015) Influence of woody tissue and leaf clumping on vertically resolved leaf area index and angular gap probability estimates. For Ecol Manage 340:103–113CrossRefGoogle Scholar
  33. Pisek J, Ryu Y, Alikas K (2011) Estimating leaf inclination and G-function from leveled digital camera photography in broadleaf canopies. Trees 25:919–924CrossRefGoogle Scholar
  34. Pisek J, Sonnentag O, Richardson AD, Mõttus M (2013) Is the spherical leaf inclination angle distribution a valid assumption for temperate and boreal broadleaf tree species? Agric For Meteorol 169:186–194CrossRefGoogle Scholar
  35. Poblete-Echeverría C, Fuentes S, Ortega-Farias S, Gonzalez-Talice J, Yuri JA (2015) Digital cover photography for estimating leaf area index (LAI) in apple trees using a variable light extinction coefficient. Sensors 15(2):2860–2872CrossRefPubMedPubMedCentralGoogle Scholar
  36. Prewitt J, Mendelsohn ML (1966) The analysis of cell images. Ann N Y Acad Sci 128:1035–1053CrossRefPubMedGoogle Scholar
  37. Raabe K, Pisek J, Lang M, Korhonen L (2017) Estimating the beyond-shoot foliage clumping at two contrasting points in the growing season using a variety of field-based methods. Trees. Google Scholar
  38. Ryu Y, Nilson T, Kobayashi H et al (2010) On the correct estimation of effective leaf area index: does it reveal information on clumping effects? Agric For Meteorol 150:463–472CrossRefGoogle Scholar
  39. Salas-Aguilar V, Sánchez-Sánchez C, Rojas-García F, Paz-Pellat F, Valdez-Lazalde J, Pinedo-Alvarez C (2017) Estimation of vegetation cover using digital photography in a regional survey of central Mexico. Forests 8(10):392CrossRefGoogle Scholar
  40. Van Rossum G (2007) Python programming language. USENIX Annu Tech Conf 41:36Google Scholar
  41. Wagner S, Hagemeier M (2006) Method of segmentation affects leaf inclination angle estimation in hemispherical photography. Agric For Meteorol 139(1–2):12–24CrossRefGoogle Scholar
  42. Zhang Y, Chen JM, Miller JR (2005) Determining digital hemispherical photograph exposure for leaf area index estimation. Agric For Meteorol 133:166–181CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Consiglio per la ricerca in agricoltura e l′analisi dell’economia agraria (CREA)Research Centre for Agriculture and EnvironmentRomeItaly
  2. 2.Consiglio per la ricerca in agricoltura e l′analisi dell’economia agraria (CREA)Research Centre for Forestry and WoodArezzoItaly

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