Abstract
Key message
coveR is an R package for estimating canopy attributes from digital cover photography (DCP) images. The simplicity of the method and the open-accessibility of coveR can effectively extend the accessibility and applicability of DCP to a wider audience.
Abstract
Digital cover photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for hemispherical photography, whereas open-access tools for DCP are lacking. We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline while ensuring quality-checking of each processing step. A wrapper function ‘coveR()’ is also created to perform all the image processing workflow in a single function. A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. By providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the possibility to implement DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts.
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Data availability
The ‘coveR’ package can be installed from gitLab (https://gitlab.com/fchianucci/coveR). All the images used in the case study, along with the results of image processing using coveR, are available at Chianucci, Francesco (2022), “Dataset of digital cover photography (DCP) images of beech (Fagus sylvatica) canopies”, Mendeley Data, V1, https://doi.org/10.17632/w6ptkf48jr.1
References
Alivernini A, Fares S, Ferrara C, Chianucci F (2018) An objective image analysis method for estimation of canopy attributes from digital cover photography. Trees 32:713–723. https://doi.org/10.1007/s00468-018-1666-3
Anderson M (1964) Studies of the woodland light climate: I. The photographic computation of light conditions. J Ecol 52:27–41. https://doi.org/10.2307/2257780
Breda NJJ (2003) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J Exp Bot 54:2403–2417. https://doi.org/10.1093/jxb/erg263
Chen JM, Black T (1992) Defining leaf area index for non-flat leaves. Plant Cell Environ 15:421–429
Chianucci F (2016) A note on estimating canopy cover from digital cover and hemispherical photography. Silva Fenn. https://doi.org/10.14214/sf.1518
Chianucci F (2020) An overview of in situ digital canopy photography in forestry. Can J for Res 50:227–242. https://doi.org/10.1139/cjfr-2019-005
Chianucci F, Cutini A (2013) Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography. Agric Meteorol 168:130–139
Chianucci F, Macek M (2022) hemispheR: an R package for fisheye canopy image analysis. bioRxiv. https://doi.org/10.1101/2022.04.01.486683
Chianucci F, Puletti N, Giacomello E et al (2015) Estimation of leaf area index in isolated trees with digital photography and its application to urban forestry. Urban for Urban Green 14:377–382. https://doi.org/10.1016/j.ufug.2015.04.001
Chianucci F, Pisek J, Raabe K et al (2018) A dataset of leaf inclination angles for temperate and boreal broadleaf woody species. Ann Sci 75:50. https://doi.org/10.1007/s13595-018-0730-x
Chianucci F, Bajocco S, Ferrara C (2021) Continuous observations of forest canopy structure using low-cost digital camera traps. Agric Meteorol 307:108516. https://doi.org/10.1016/j.agrformet.2021.108516
De Bei R, Fuentes S, Gilliham M et al (2016) VitiCanopy: a free computer app to estimate canopy vigor and porosity for grapevine. Sensors 16:585. https://doi.org/10.3390/s16040585
Evans GC, Coombe DE (1959) Hemispherical and woodland canopy photography and the light climate. J Ecol 47:103–113. https://doi.org/10.2307/2257250
Glatthorn J, Beckschäfer P (2014) Standardizing the protocol for hemispherical photographs: accuracy assessment of binarization algorithms. PLoS ONE 9:e111924. https://doi.org/10.1371/journal.pone.0111924
Grotti M, Calders K, Origo N et al (2020) An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning. Agric Meteorol 280:107766. https://doi.org/10.1016/j.agrformet.2019.107766
Harvey P (2020) ExifTool. Read, write and edit meta information
Hijmans RJ (2021) raster: geographic data analysis and modeling. R package version 3.4-10
Hwang Y, Ryu Y, Kimm H et al (2016) Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography. Agric Meteorol 222:32–44. https://doi.org/10.1016/j.agrformet.2016.03.008
Jennings S (1999) Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry 72:59–74. https://doi.org/10.1093/forestry/72.1.59
Jonckheere I, Fleck S, Nackaerts K et al (2004) Review of methods for in situ leaf area index determination. Agric Meteorol 121:19–35. https://doi.org/10.1016/j.agrformet.2003.08.027
Kim J, Ryu Y, Jiang C, Hwang Y (2019) Continuous observation of vegetation canopy dynamics using an integrated low-cost, near-surface remote sensing system. Agric Meteorol 264:164–177. https://doi.org/10.1016/j.agrformet.2018.09.014
Landini G, Randell DA, Fouad S, Galton A (2017) Automatic thresholding from the gradients of region boundaries: automatic thresholding. J Microsc 265:185–195. https://doi.org/10.1111/jmi.12474
Lang M, Nilson T, Kuusk A et al (2017) Digital photography for tracking the phenology of an evergreen conifer stand. Agric Meteorol 246:15–21. https://doi.org/10.1016/j.agrformet.2017.05.021
Macfarlane C (2011) Classification method of mixed pixels does not affect canopy metrics from digital images of forest overstorey. Agric Meteorol 151:833–840. https://doi.org/10.1016/j.agrformet.2011.01.019
Macfarlane C, Coote M, White DA, Adams MA (2000) Photographic exposure affects indirect estimation of leaf area in plantations of Eucalyptus globulus Labill. Agric Meteorol 100:155–168. https://doi.org/10.1016/S0168-1923(99)00139-2
Macfarlane C, Grigg A, Evangelista C (2007a) Estimating forest leaf area using cover and fullframe fisheye photography: thinking inside the circle. Agric Meteorol 146:1–12. https://doi.org/10.1016/j.agrformet.2007.05.001
Macfarlane C, Hoffman M, Eamus D et al (2007b) Estimation of leaf area index in eucalypt forest using digital photography. Agric Meteorol 143:176–188. https://doi.org/10.1016/j.agrformet.2006.10.013
Macfarlane C, Ryu Y, Ogden GN, Sonnentag O (2014) Digital canopy photography: exposed and in the raw. Agric Meteorol 197:244–253. https://doi.org/10.1016/j.agrformet.2014.05.014
Miller JB (1967) A formula for average foliage density. Aust J Bot 15:141–144. https://doi.org/10.1071/bt9670141
Mora M, Avila F, Carrasco-Benavides M et al (2016) Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Comput Electron Agric 123:195–202. https://doi.org/10.1016/j.compag.2016.02.011
O’Brien J (2020a) rasterDT: fast raster summary and manipulation. R package version 0.3.1
O’Brien J (2020b) exiftoolr: ExifTool functionality from R. R package version 0.1.7
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66
Pau G, Fuchs F, Sklyar O et al (2010) EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26:979–981. https://doi.org/10.1093/bioinformatics/btq046
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 1:1298–1320. https://doi.org/10.3390/rs1041298
Pisek J, Adamson K (2020) Dataset of leaf inclination angles for 71 different Eucalyptus species. Data Brief 33:106391. https://doi.org/10.1016/j.dib.2020.106391
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 Meteorol 169:186–194. https://doi.org/10.1016/j.agrformet.2012.10.011
Pueschel P, Buddenbaum H, Hill J (2012) An efficient approach to standardizing the processing of hemispherical images for the estimation of forest structural attributes. Agric Meteorol 160:1–13. https://doi.org/10.1016/j.agrformet.2012.02.007
Ryu Y, Baldocchi DD, Kobayashi H et al (2011) Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Glob Biogeochem Cycles. https://doi.org/10.1029/2011GB004053
Ryu Y, Verfaillie J, Macfarlane C et al (2012) Continuous observation of tree leaf area index at ecosystem scale using upward-pointing digital cameras. Remote Sens Environ 126:116–125. https://doi.org/10.1016/j.rse.2012.08.027
Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675. https://doi.org/10.1038/nmeth.2089
Toda M, Nakai T, Kodama Y, Hara T (2018) Using digital cover photography to track the canopy recovery process following a typhoon disturbance in a cool-temperate deciduous forest. Can J Res. https://doi.org/10.1139/cjfr-2018-0005
Urbanek S (2021) jpeg: read and write JPEG images. Version 0.1-9. https://CRAN.R-project.org/package=jpeg
Yan G, Hu R, Luo J et al (2019) Review of indirect optical measurements of leaf area index: recent advances, challenges, and perspectives. Agric Meteorol 265:390–411. https://doi.org/10.1016/j.agrformet.2018.11.033
Yin G, Qu Y, Verger A et al (2022) Smartphone digital photography for fractional vegetation cover estimation. Photogramm Eng Remote Sens. https://doi.org/10.14358/PERS.21-00038R2
Acknowledgements
We thank the anonymous reviewers for their helpful comments, which improved the original version of the manuscript.
Funding
The study was financially supported by the Research Project PRECISIONPOP (Sistema di monitoraggio multiscalare a supporto della pioppicoltura di precisione nella Regione Lombardia) funded by the Lombardy Region, Italy, grant number: E86C18002690002.
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Chianucci, F., Ferrara, C. & Puletti, N. coveR: an R package for processing digital cover photography images to retrieve forest canopy attributes. Trees 36, 1933–1942 (2022). https://doi.org/10.1007/s00468-022-02338-5
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DOI: https://doi.org/10.1007/s00468-022-02338-5