A comparison of different methods for assessing leaf area index in four canopy types

Cristina Ariza-Carricondo 1 , Francesca Di Mauro 2 , Maarten Op de Beeck 1 , Marilyn Roland 1 , Bert Gielen 1 , Domenico Vitale 2 , Reinhart Ceulemans 1 , 3 , and Dario Papale 2
  • 1 University of Antwerp, Department of Biology, Research Center of Excellence on Plants and Ecosystems, B–2610, Wilrijk, Belgium
  • 2 University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), 01100, Viterbo, Italy
  • 3 , CZ–603 00, Brno


The agreement of Leaf Area Index (LAI) assessments from three indirect methods, i.e. the LAI–2200 Plant Canopy Analyzer, the SS1 SunScan Canopy Analysis System and Digital Hemispherical Photography (DHP) was evaluated for four canopy types, i.e. a short rotation coppice plantation (SRC) with poplar, a Scots pine stand, a Pedunculate oak stand and a maize field. In the SRC and in the maize field, the indirect measurements were compared with direct measurements (litter fall and harvesting). In the low LAI range (0 to 2) the discrepancies of the SS1 were partly explained by the inability to properly account for clumping and the uncertainty of the ellipsoidal leaf angle distribution parameter. The higher values for SS1 in the medium (2 to 6) to high (6 to 8) ranges might be explained by gap fraction saturation for LAI–2200 and DHP above certain values. Wood area index –understood as the woody light-blocking elements from the canopy with respect to diameter growth– accounted for overestimation by all indirect methods when compared to direct methods in the SRC. The inter-comparison of the three indirect methods in the four canopy types showed a general agreement for all methods in the medium LAI range (2 to 6). LAI–2200 and DHP revealed the best agreement among the indirect methods along the entire range of LAI (0 to 8) in all canopy types. SS1 showed some discrepancies with the LAI–2200 and DHP at low (0 to 2) and high ranges of LAI (6 to 8).

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 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.

  • Bland, J. M., Altman, D. G., 1986: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 327:307–310.

  • Breda, N. J. J., 2003: Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of Experimental Botany, 54:2403–2417.

  • Broeckx, L. S., Verlinden, M. S., Ceulemans, R., 2012: Establishment and two-year growth of a bio-energy plantation with fast-growing Populus trees in Flanders (Belgium): effects of genotype and former land use. Biomass & Bioenergy, 42:151–163.

  • Broeckx, L. S., Vanbeveren, P. P. S., Verlinden, M. S., Ceulemans, R., 2015: First vs. second rotation of a poplar short rotation coppice: leaf area development, light interception and radiation use efficiency. iForest – Biogeosciences and Forestry, 8:565–573.

  • Carstensen, B., 2010: Comparing methods of measurement: Extending the LoA by regression. Statistics in Medicine, 29:401–410.

  • Chen, J. M. R., 1997: Leaf area index of boreal forests: Theory, techniques and measurements. Journal of Geophysical Research, 102:429–443.

  • Chen, J. M., Black, T. A., 1992: Defining leaf area index for non-flat leaves. Plant, Cell & Environment, 15:421–429.

  • Chen, J. M., Rich, P. M., Gower, S. T., Norman, J. M., Plummer, S., 1997: Leaf area index of boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research: Atmospheres, 102:29429–29443.

  • 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.

  • Chianucci, F., Disperati, L., Guzzi, D., Bianchini, D., Nardino, V., Lastri, C. et al., 2016: Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. International Journal of Applied Earth Observation and Geoinformation, 47:60–68.

  • Chiroro, D., Milford, J., Makuvaro, V., 2006: An investigation on the utility of the SunScan ceptometer in estimating the leaf area index of a sugarcane canopy. Proceedings of the South African Sugar Technologists Association, 80:143–147.

  • Cleveland, W. S., 1979: Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74:829–836.

  • Curiel, Y. J., Konôpka, B., Janssens, I. A., Coenen, K., Xiao, C. W., Ceulemans, R., 2005: Contrasting net primary productivity and carbon distribution between neighbouring stands of Quercus robur and Pinus sylvestris. Tree Physiology, 25:701–712.

  • Daughtry, C. S. T., 1990: Direct measurements of canopy structure. Remote Sensing Reviews, 5:545–60.

  • Duchemin, B., Hadriab, R., Errakib, S., Bouleta, G., Maisongrandea, P., Chehbounia, A. et al., 2006: Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79:1–27.

  • Facchi, A., Baroni, G., Boschetti, M., Gandolfi, C., 2010: Comparing optical and direct methods for leaf area index determination in a maize crop. Journal of Agricultural Engineering, 1:33–40.

  • Fang, F., 2005: The retrieval of leaf inclination angle and leaf area index in maize. Master of Science thesis, Geo-Information Science and Earth Observation for Environmental Modelling and Management program. University of Lund, Sweden, 64 p.

  • Fang, H., Li, W., Wei, S., Jiang, C., 2014: Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Inter-comparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods. Agricultural and Forest Meteorology, 198–199:126–14.

  • Gebauer, R., Cermak, J., Plichta, R., Spinlerova, Z., Urban, J., Volarik, D. et al., 2015: Within-canopy variation in needle morphology and anatomy of vascular tissues in a sparse Scots pine forest. Trees, 29:1447–1457.

  • Gielen, B., De Vos, B., Campioli, M., Neirynck, J., Papale, D., Verstraeten, A. et al., 2013: Biometric and eddy covariance-based assessment of decadal carbon sequestration of a temperate Scots pine forest. Agricultural and Forest Meteorology, 174–175:135–143.

  • Gower, S. T., Kucharik, C. J., Norman, J. M., 1999: Direct and indirect estimation of Leaf Area Index, fAPAR, and Net Primary Production of terrestrial ecosystems. Remote Sensing Environment, 70:29–51.

  • Homolová, L., Malenovský, Z., Hanuš, J., Tomášková, I., Dvořáková, M., Pokorný, R., 2007: Comparison of different ground techniques to map leaf area index of Norway spruce forest canopy. International Society for Photogrammetry and Remote Sensing (ISPRS), XXXVI, 499–504. [online] URL: http://www.isprs.org/proceedings/XXXVI/7-C50/papers/P95.pdf (accessed 15.01.2019).

  • Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Cop-pin, P., Weiss, M. et al., 2004: Review of methods for in situ leaf area index determination. Part I: Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121:19–35.

  • Jonckheere, I., Muys, B., Coppin, P., 2005: Allometry and evaluation of in situ optical LAI determination in Scots pine: a case study in Belgium. Tree Physiology, 25:723–732.

  • Jones, H. G., 2014: Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology. Third edition, Cambridge University Press, NY, USA.

  • Konôpka, B., Pajtík, J., 2014: Similar foliage area but contrasting foliage biomass between young beech and spruce stands. Lesnícky časopis - Forestry Journal, 60:205–213.

  • Lang, A. R. G., Xiang, Y., 1986: Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agricultural and Forest Meteorology, 37:229–243.

  • Leblanc, S. G., Chen, J. M., Fernandes, R., Deering, D. W., Conley, A., 2005: Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests. Agricultural and Forest Meteorology, 129:187–207.

  • Lin, A., Zhu, H., Wang, L., Gong, W., Zou, L. 2016: Characteristics of long-term climate change and the ecological responses in central China. Earth Interactions, 20:1–24.

  • Lopez-Lozano, R., Baret, F., Chelle, M., Rochdi, N., España, M., 2007: Sensitivity of gap fraction to maize architectural characteristics based on 4D model simulations. Agricultural and Forest Meteorology, 143:217–229.

  • Mason, G. E., Diepstraten, M., Pinjuv, G. L., Lasserre, J-P., 2012: Comparison of direct and indirect leaf area index measurements of Pinus radiate D. Don. Agricultural and Forest Meteorology, 166–167:113–119.

  • Macfarlane, C., Hoffman, M., Eamus, D., Kerp, N., Higginson, S., McMurtrie, R. et al., 2007: Estimation of leaf area index in eucalypt forest using digital photography. Agricultural and Forest Meteorology, 143:176–188.

  • Op de Beeck, M., Gielen, B., Jonckheere, I., Samson, R., Janssens, I. A., Ceulemans, R., 2010: Needle age-related and seasonal photosynthetic capacity variation is negligible for modelling yearly gas exchange of a sparse temperate Scots pine forest. Biogeosciences, 7:199–215.

  • Passing, H., Bablok, W., 1983: A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. Journal of Clinical Chemistry and Clinical Biochemistry, 21:709–720.

  • Ridler, T. W., Calvard, S., 1978: Picture thresholding using an iterative selection method. IEEE Transactions on System, Man and Cybernetics, 8:630–632.

  • Ryu, Y., Nilson, T., Kobayashi, H., Sonnentag, O., Law, BE., Baldocchi, D. D., 2010a: On the correct estimation of effective leaf area index: Does it reveal information on clumping effects? Agricultural and Forest Meteorology, 150:463–472.

  • Ryu, Y., Sonnentag, O., Nilson, T., Vargas, R., Kobayashi, H., Wenk, R. et al., 2010b: How to quantify tree leaf area index in an open savanna ecosystem: a multiinstrument and multi-model approach. Agricultural and Forest Meteorology, 150:63–76.

  • Schaefer, MT., Farmer, E., Soto-Berelov, M., Woodgate, W., Jones, S., 2015: Overview of ground based techniques for estimating LAI. In: Held, A., Phinn, S., Soto-Berelov, M. & Jones, S. (eds.): AusCover Good Practice Guidelines: A technical handbook supporting calibration and validation activities of remotely sensed data product, 88–118. Version 1.1. TERN AusCover, ISBN 978-0-646-94137-0.

  • Scrucca, L., 2011: Model-based SIR for dimension reduction. Computational Statistics & Data Analysis, 55:3010–3026.

  • Sone, C., Saito, K., Futakuchi, K., 2009: Comparison of three methods for estimating leaf area index of upland rice cultivars. Crop Science, 49:1438–1443.

  • Thimonier, A., Sedivy, I., Schleppi, P., 2010: Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129:543–562.

  • Verlinden, M. S., Broeckx, L. S., Ceulemans, R., 2015: First vs. second rotation of a poplar short rotation coppice: Above-ground biomass productivity and shoot dynamics. Biomass & Bioenergy, 73:174–185.

  • Webb, N., Nichol, C., Wood, J., Potter, E., 2013: User Manual for the SunScan Canopy Analysis System type SS1 Version: 3.0, Delta-T Devices Ltd. 37–39: 49–56. [online] URL: http://www.delta-t.co.uk/wp-content/uploads/2016/10/SS1-SunScan-User-Manual-v2-0.pdf (accessed 15.01.19).

  • Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., Cop-pin, P., 2004: Review of methods for in situ leaf area index (LAI) determination. Part II: Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology, 121:37–53.

  • Wilhelm, W., Ruwe, K., Schlemmer, M. R., 2000: Comparison of three leaf area index meters in a corn canopy. Crop Science, 40:1179–1183.

  • Woodgate, W., Jones, S. D., Suarez, L., Hill, M. J., Armston, J. D., Wilkes, P. et al., 2015: Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems. Agricultural and Forest Meteorology, 205:83–95.

  • Zheng, G. and Moskal, L. M., 2009: Retrieving Leaf Area Index (LAI) using remote sensing: theories, methods and sensors. Sensors, 9:2719–2745.

  • GCOS, 2011: Systematic Observation Requirements for Satellite-based Data Products for Climate. WMO, Switzerland. [online] URL:http://www.wmo.int/pages/prog/gcos/Publications/gcos-154.pdf (accessed 15.01.2019).

  • ICOS Ecosystem Thematic Center. [online] URL: http://www.icos-etc.eu/icos/ (accessed 15.01.2019).

  • LI-COR, 2009. LAI–2200 Plant Canopy Analyzer Instruction Manual. Lincoln, NE, USA. [online] URL: https://www.licor.com/documents/6n3conpja6uj9aq1ruyn (accessed 15.01.2019).

  • POPFULL Project. [online] URL: http://uahost.uantwerpen.be/popfull/ (accessed 15.01.2019).


Journal + Issues