Leaf area index in a forested mountain catchment
Abstract
Leaf area index (LAI) belongs among the catchment characteristics widely used in hydrological models but still associated with great uncertainties. In a mountain forest catchment, the leaf area affects retention and evapotranspiration loss, and it could be significantly modified by forestry practices. In this study, LAI in mature stands of Norway spruce (Picea abies) and European beech (Fagus sylvatica) was analysed in headwater catchments of the Jizera Mountains (Czech Republic) between 2012 and 2016. A comparison evaluation of LAI in harvested site with dominant herbaceous vegetation was taken into account by applying direct ground investigation what was compared with hemispherical canopy photography (Gap light analyser GLA-V2) and satellite remote sensing (Sentinel-2 mission). While the direct ground measurement includes only the foliage (leaves or needles), the Gap light analysis is affected by trunks and branches, and the remote sensing techniques by herbaceous understory. The results of the Gap light analyser underestimated the ground based LAI values by 52–76 per cent, and satellite interpretations by 29–73 per cent. The remote sensing is capable to provide effective information on the distribution of LAI within the time and space. However, in a catchment scale, the satellite detection underestimated average LAI values approx. by 42–62 per cent. Changes in the observed rainfall interception reflected well the LAI variation.
References
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. 2005. Crop evapotranspiration. FAO Irrigation and Drainage Paper 56. Rome, Food and Agriculture Organization.
Anderson, M.C. 1971. Radiation and crop structure. In Plant photosynthetic production, manual of methods. Eds.: Šesták, Z., Čatský, J. and Jarvis, P.G., Hague, Dr. W. Junk, 77–90.
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. https://doi.org/10.1093/jxb/erg263
Brenner, A.J., Cueto Romero, M., Garcia Haro, J., Gilabert, M.A., Incoll, L.D., Martinez Fernandez, J., Porter, E., Pugnaire, F.I. and Younis, M.T. 1995. A comparison of direct and indirect methods for measuring leaf and surface areas of individual bushes. Plant, Cell and Environment 18. 1332–1340. https://doi.org/10.1111/j.1365-3040.1995.tb00193.x
Campbell, G.S. 1986. Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agricultural and Forest Meteorology 36. 317–321. https://doi.org/10.1016/0168-1923(86)90010-9
Cowling, S.A. and Field, C.B. 2003. Environmental control of leaf area production: Implications for vegetation and land-surface modeling. Global Biogeochemical Cycles 17. 1–11. https://doi.org/10.1029/2002GB001915
ESA, 2017. Sentinel-2. https://sentinel.esa.int/web/sentinel/missions/sentinel-2 Accessed on 24.03.2017.
Federer, A. 1993. BROOK 90: A simulation model for evapotranspiration, soil water and streamflow. Durham (US), USDA Forest Service.
Fehrmann, L. and Kleinn, C. 2006. General considerations about the use of allometric equations for biomass estimation on the example of Norway spruce in central Europe. Forest Ecology and Management 236. 412–421. https://doi.org/10.1016/j.foreco.2006.09.026
Frazer, G.W., Canham, C.D. and Lertzman, K.P. 1999. Gap Light Analyzer (GLA), Version 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, user's manual and program documentation. Burnaby (British Columbia) – Millbrook (N.Y.), Simon Fraser University – Institute of Ecosystem Studies.
Garrigues, S., Allard, D., Baret, F. and Weiss, M. 2006. Influence landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment 105. 286–298. https://doi.org/10.1016/j.rse.2006.07.013
Gash, J.H.C., Wright, I.R. and Loyd, C.R. 1980. Comparative estimates of interception loss from three coniferous forest in Great Britain. Journal of Hydrology 48. 89–105. https://doi.org/10.1016/0022-1694(80)90068-2
Gosa, A.G., Schaepman-Strub, G., Kooistra, L., Schaepman, M. and Pellikka, P. 2007. Estimation of leaf area index using optical field instruments and imaging spectroscopy. In Proceeding 5th Earsel Workshop on Imaging Spectroscopy. Bruges, Belgium, 1–12.
Horntvedt, R. 1993. Crown density of spruce trees related to needle biomass. Forest Ecology and Management 59. 225–235. https://doi.org/10.1016/0378-1127(93)90004-7
Chen, J.M., Black, T.A. and 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. https://doi.org/10.1016/0168-1923(91)90108-3
Chen, J.M., Menges, C.H. and Leblanc, S.G. 2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment 97. 447–457. https://doi.org/10.1016/j.rse.2005.05.003
Jones, H.G. 1992. Plant and microclimate. 2nd edition. Cambridge (UK), Cambridge University Press.
Křeček, J. and Hořická, Z. 2010. Recovery of headwater catchments and lakes affected by the acid atmospheric deposition. In Integrated watershed management. Eds.: Beheim, E. et al. Dordrecht, Springer, 200–207. https://doi.org/10.1007/978-90-481-3769-5_17
Křeček, J. and Krčmář, V. 2015. Landsat imagery applications to identify vegetation recovery from acidification in mountain catchments. Hungarian Geographical Bulletin 64. (2): 121–126. https://doi.org/10.15201/hungeobull.64.2.3
Křeček, J., Nováková, J. and Hořická, Z. 2010. Ellenberg's indicator in water resources control: the Jizera Mountains, Czech Republic. Ecological Engineering 36. 1112–1117. https://doi.org/10.1016/j.ecoleng.2010.01.011
Křeček, J., Palán, L. and Stuchlík, E. 2017. Acid atmospheric deposition in a forested mountain catchment. iForest 10. 680–686.
Liu, Ch. and Westman, C.J. 2009. Biomass in a Norway spruce – Scots pine forest: a comparison of estimation methods. Boreal Environment Research 14. 875–888.
Lovett, G.M. and Reiners, W.A. 1986. Canopy structure and cloud water deposition in subalpine coniferous forests. Tellus B 38. 319–327. https://doi.org/10.3402/tellusb.v38i5.15140
Mueller-Wilm, U., Devignot, O. and Pessiot, L. 2016. Sen2Cor Configuration and User Manual. France, ESA.
Punčochář, P., Křeček, J. and Van de Griend, A. 2012. Interception storage in a small Alpine catchment, In Integrated watershed management. Eds.: Křeček, J. et al. Dordrecht, Springer, 200–207.
Ramiréz-García, J., Quemada, M. and Almendros, P. 2012. Ground cover and leaf area index relationship in a grass, legume and crucifer crop. Plant, Soil and Environment 58. 385–390. https://doi.org/10.17221/195/2012-PSE
Shiver, B.D. and Borders, B.E. 1996. Sampling techniques for forest resource inventory. London, John Wiley & Sons.
Tolasz, R. 2007. Climate atlas of the Czech Republic. Prague, Czech Hydro-meteorological Institute.
Watson, D.J. 1947. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany 11. 41–76. https://doi.org/10.1093/oxfordjournals.aob.a083148
Watts, S.B. and Tolland, L. 2005. Forestry Handbook for British Columbia. Vancouver. Faculty of Forestry, University of British Columbia.
Weiss, M. and Baret, F. 2016. S2ToolBox Level 2 products: LAI, FAPAR, FCOVER, Version 1.1. Paris, French National Institute for Agricultural Research (INRA).
Weiss, M., Baret, F., Myneni, R.B., Pragnère, A. and Knyazikhin, Y. 2000. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 20. 3–2. https://doi.org/10.1051/agro:2000105
Zhang, Y., Chen, J.M. and Miller, J.R. 2005. Determining digital hemispherical photograph exposure for leaf area index estimation. Agricultural and Forest Meteorology 133. 166–181. https://doi.org/10.1016/j.agrformet.2005.09.009
Copyright (c) 2018 Ladislav Palán, Josef Křeček, Yoshinobu Sato
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
