J. For. Sci., X:X | DOI: 10.17221/42/2026-JFS

Comparison of indirect optical methods for estimating effective leaf area index in young Norway spruce standsOriginal Paper

Jakub Černý ORCID...1,2, Jaroslav Čepl ORCID...1, Zdeněk Vacek ORCID...1, Jan Cukor ORCID...1,2, Stanislav Vacek ORCID...1
1 Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
2 Forestry and Game Management Research Institute, Jíloviště, Czech Republic

Forest canopy structure is crucial for regulating light interception, carbon exchange, and water balance, making accurate estimation of leaf area index (LAI) essential for forest ecology and management. This study compared three indirect optical methods (LAI-2200 PCA, LaiPen LP 110, and digital hemispherical photography – DHP) for estimating effective leaf area index (LAIe) in young Norway spruce [Picea abies (L.) Karst.] stands. It evaluated the influence of the field of view (FOV) and seasonal timing on inter-method agreement. The research was conducted in young, even-aged Norway spruce stands at the Křivina experimental site in the Czech Republic, using three research plots with varying stand densities. Results showed that LAIe estimates were influenced by the selected FOV, with mean values generally decreasing as FOV increased, particularly for the LAI-2200 PCA. Narrow FOV configurations produced greater local variability among sampling positions, whereas broader FOVs resulted in more homogeneous spatial canopy patterns. The strongest agreement among methods was observed between LaiPen LP 110 and LAI-2200 PCA, with correlation coefficients ranging from 0.71 to 0.84 across temporally matched measurements. In contrast, DHP exhibited weaker and more variable relationships with LaiPen LP 110. Differences among research plots were most pronounced between the dense control stand and the heavily thinned stand, indicating that canopy density and structural heterogeneity substantially affected LAIe estimation. The study demonstrates that indirect optical methods are sensitive to both canopy structure and measurement configuration; therefore, careful instrument selection, FOV standardisation, and synchronised measurements are essential for obtaining reliable and comparable LAIe estimates.

Keywords: canopy transmittance; digital hemispherical photography; field of view; gap fraction; LaiPen LP 110; LAI 2200 PCA; Picea abies

Received: May 18, 2026; Revised: May 22, 2026; Accepted: May 25, 2026; Prepublished online: June 5, 2026 

Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Abegg M., Boesch R., Schaepman M.E., Morsdorf F. (2021): Impact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests. IEEE Transactions on Geoscience and Remote Sensing, 59: 8153-8167. Go to original source...
    2. Bednář P., Souček J., Krejza J., Černý J. (2022): Growth and morphological patterns of Norway spruce [Picea abies (L.) Karst.] juveniles in response to light intensities. Forests, 13: 1804. Go to original source...
    3. Bréda 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. Go to original source... Go to PubMed...
    4. Čater M., Schmid I., Kazda M. (2013): Instantaneous and potential radiation effect on underplanted European beech below Norway spruce canopy. European Journal of Forest Research, 132: 23-32. Go to original source...
    5. Černý J. (2023): Pre-commercial thinning in young Norway spruce stands as a tool mitigating climate change? Zprávy lesnického výzkumu, 68: 149-158. (in Czech with an English summary)
    6. Černý J., Pokorný R. (2021): Field measurement of effective leaf area index using optical device in vegetation canopy. Journal of Visualized Experiments, 173: e62802. Go to original source...
    7. Černý J., Krejza J., Pokorný R., Bednář P. (2018): LaiPen LP 100 - A new device for estimating forest ecosystem leaf area index compared to the etalon: A methodologic case study. Journal of Forest Science, 64: 455-468. Go to original source...
    8. Černý J., Vacek Z., Cukor J., Báňa D., Vacek S. (2025): Leaf area index and soil water content responses to pre-commercial thinning in Norway spruce plantations under climate change. Journal of Forest Science, 71: 599-613. Go to original source...
    9. Cescatti A. (1997): Modelling the radiative transfer in discontinuous canopies of asymmetric crowns: II. Model testing and application in a Norway spruce stand. Ecological Modelling, 101: 275-284. Go to original source...
    10. Cescatti A. (1998): Effects of needle clumping in shoots and crowns on the radiative regime of a Norway spruce canopy. Annals of Forest Science, 55: 89-102. Go to original source...
    11. Chang L.H., Fan H.C., Zhu N.N., Dong Z. (2022): A two-stage approach for individual tree segmentation from TLS point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 8682-8693. Go to original source...
    12. Chen J.M. (1996): Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology, 80: 135-163. Go to original source...
    13. Chen J.M., Black T.A., Adams R.S. (1991): Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand. Agricultural and Forest Meteorology, 56: 129-143. Go to original source...
    14. Chen J.M., Govind A., Sonnentag O., Zhang Y., Barr A., Amiro B. (2006): Leaf area index measurements at Fluxnet-Canada forest site. Agricultural and Forest Meteorology, 140: 257-268. Go to original source...
    15. Chianucci F. (2020): An overview of in situ digital canopy photography in forestry. Canadian Journal of Forest Research, 50: 227-242. Go to original source...
    16. Chianucci F., Cutini A. (2012): Digital hemispherical photography for estimating forest canopy properties: Current controversies and opportunities. iForest - Biogeosciences and Forestry, 5: 290-295. Go to original source...
    17. Danner M., Locherer M., Hank T., Richter K. (2015): Measuring Leaf Area Index (LAI) with the Li-Cor LAI 2200C or LAI-2200 (+2200 Clear Kit) - Theory, Measurement, Problems, Interpretation. EnMAP Field Guide Technical Report. Potsdam, GFZ Data Services: 24.
    18. Deblonde G., Penner M., Royer A. (1994): Measuring leaf area index with the LI-COR LAI-2000 in pine stands. Ecology, 75: 1507-1511. Go to original source...
    19. Disney M. (2019): Terrestrial LiDAR: A three-dimensional revolution in how we look at trees. New Phytologist, 222: 1736-1741. Go to original source... Go to PubMed...
    20. Fang H., Baret F., Plummer S., Schaepman-Strub G. (2019): An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics, 57: 739-799. Go to original source...
    21. Fassnacht K.S., Gower S.T., Norman J.M., McMurtrie R.E. (1994): A comparison of optical and direct methods for estimating foliage surface area index in forests. Agricultural and Forest Meteorology, 71: 183-207. Go to original source...
    22. Fleck S., Raspe S., Čater M., Schlappi P., Ukonmaanaho L., Greve M., Hertel C., Weiss M., Rumpf S., Thimonier A., Chianucci F., Beckschäfer P. (2020): Part XVII: Leaf area measurements. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual of Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests. Eberswalde, Thünen Institute of Forest Ecosystems: 17-35.
    23. Giagli K., Vavrčík H., Tsalagkas D., Černý J., Leugner J., Hacurová J., Gryc V. (2023): Effect of different stand densities on xylem and phloem formation in Norway spruce plantations. IAWA Journal, 45: 227-242. Go to original source...
    24. Giagli K., Vavrčík H., Tsalagkas D., Leugner J., Hacurová J., Gryc V., Černý J. (2026): Changing stand densities influence xylem and phloem formation in Norway spruce plantations. Dendrochronologia, 95: 126467. Go to original source...
    25. Goude M., Nilsson U., Holmström E. (2019): Comparing direct and indirect leaf area measurements for Scots pine and Norway spruce plantations in Sweden. European Journal of Forest Research, 138: 1033-1047. Go to original source...
    26. Iio A., Hikosaka K., Anten N.P.R., Nakagawa Y., Ito A. (2013): Global dependence of field-observed leaf area index in woody species on climate: A systematic review. Global Ecology and Biogeography, 23: 274-285. Go to original source...
    27. 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. Agricultural and Forest Meteorology, 121: 19-35. Go to original source...
    28. King D.A. (1990): The adaptive significance of tree height. The American Naturalist, 135: 809-838. Go to original source...
    29. Konôpka B., Pajtík J., Marušák R., Bošeľa M., Lukac M. (2016): Specific leaf area and leaf area index in developing stands of Fagus sylvatica L. and Picea abies Karst. Forest Ecology and Management, 364: 52-59. Go to original source...
    30. Kucharik C.J., Norman J.M., Gower S.T. (1998): Measurement of branch area and adjusting leaf area index indirect measurements. Agricultural and Forest Meteorology, 91: 69-88. Go to original source...
    31. Küßner R., Mosandl R. (2000): Comparison of direct and indirect estimation of leaf area index in mature Norway spruce stands of eastern Germany. Canadian Journal of Forest Research, 30: 440-447. Go to original source...
    32. LI-COR (2011): Instruction manual. LAI-2200 Plant Canopy Analyzer. Lincoln, LI-COR: 259.
    33. Lotz S., Kattenborn T., Frey J., Soltani S., Göritz A., Jaksztat T., Katal N. (2026): Litter vs. lens: Evaluating LAI from litter traps and hemispherical photos across view zenith angles and leaf fall phases. Biogeosciences, 23: 1949-1963. Go to original source...
    34. Niinemets Ü. (2010): A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecological Research, 25: 693-714. Go to original source...
    35. Olivas P.C., Oberbauer S.F., Clark D.B., Clark D.A., Ryan M.G., O'Brien J.J., Ordonez H. (2013): Comparison of direct and indirect methods for assessing leaf area index across a tropical rain forest landscape. Agricultural and Forest Meteorology, 177: 110-116. Go to original source...
    36. Parker G.G. (2020): Tamm review: Leaf Area Index (LAI) is both a determinant and a consequence of important processes in vegetation canopies. Forest Ecology and Management, 477: 118496. Go to original source...
    37. Peel M.C., Finlayson B.L., McMahon T.A. (2007): Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11: 1633-1644. Go to original source...
    38. Pokorný R., Stojnić S. (2012): Leaf area index of Norway spruce stands in relation to age and defoliation. Beskydy, 5: 173-180. Go to original source...
    39. Pokorný R., Tomášková I., Havránková K. (2008): Temporal variation and efficiency of leaf area index in young mountain Norway spruce stand. European Journal of Forest Research, 127: 359-367. Go to original source...
    40. R Core Team (2025): R: A language and environment for statistical computing. Vienna, R Foundation for Statistical Computing. Available at: https://www.r-project.org/
    41. Rautiainen M., Stenberg P. (2015): On the angular dependency of canopy gap fractions in pine, spruce and birch stands. Agricultural and Forest Meteorology, 206: 1-3. Go to original source...
    42. Ryan M.G., Binkley D., Fownes J.H. (1997): Age-related decline in forest productivity: Pattern and process. Advances in Ecological Research, 27: 213-262. Go to original source...
    43. Schaefer M.T., 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 Products. Version 1.1. St. Lucia, TERN AusCover: 88-118.
    44. Seidel D., Fleck S., Leuschner C., Hammet T. (2011): Review of ground-based methods to measure the distribution of biomass in forest canopies. Annals of Forest Science, 68: 225-244. Go to original source...
    45. 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. Go to original source...
    46. Viewegh J., Kusbach A., Mikeska M. (2003): Czech forest ecosystem classification. Journal of Forest Science, 49: 74-82. Go to original source...
    47. Wang Y., Fang H. (2020): Estimation of LAI with the LiDAR technology: A review. Remote Sensing, 12: 3457. Go to original source...
    48. Welles J.M., Cohen S. (1996): Canopy structure measurement by gap fraction analysis using commercial instrumentation. Journal of Experimental Botany, 47: 1335-1342. Go to original source...
    49. Woodgate W., Jones S.D., Suarez L., Hill M.J., Armston J.D., Wilkes P., Soto-Berelov M., Haywood A., Mellor A. (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. Go to original source...
    50. Xu J., Quackenbush L.J., Volk T.A., Im J. (2020): Forest and crop leaf area index estimation using remote sensing: Research trends and future directions. Remote Sensing, 12: 2934. Go to original source...
    51. Yan G., Hu R., Luo J., Weiss M., Jiang H., Mu X., Xie D., Zhang W. (2019): Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265: 390-411. Go to original source...
    52. Yan G., Jiang H., Luo J., Mu X., Li F., Qi J., Hu R., Xie D., Zhou F. (2021): Quantitative evaluation on leaf inclination angle distribution on leaf area index retrieval of coniferous canopies. Journal of Remote Sensing, 2021: 2708904. Go to original source...
    53. Ye Q., Zhao Z. (2026): Remote sensing based modelling of forest structural parameters: Advances and challenges. Forests, 17: 209. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return