% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{George:901829,
      author       = {George, Jan-Peter and Yang, Wei and Kobayashi, Hideki and
                      Biermann, Tobias and Carrara, Arnaud and Cremonese, Edoardo
                      and Cuntz, Matthias and Fares, Silvano and Gerosa, Giacomo
                      and Grünwald, Thomas and Hase, Niklas and Heliasz, Michael
                      and Ibrom, Andreas and Knohl, Alexander and Kruijt, Bart and
                      Lange, Holger and Limousin, Jean-Marc and Loustau, Denis and
                      Lukeš, Petr and Marzuoli, Riccardo and Mölder, Meelis and
                      Montagnani, Leonardo and Neirynck, Johan and Peichl,
                      Matthias and Rebmann, Corinna and Schmidt, Marius and
                      Serrano, Francisco Ramon Lopez and Soudani, Kamel and
                      Vincke, Caroline and Pisek, Jan},
      title        = {{M}ethod comparison of indirect assessments of understory
                      leaf area index ({LAI}u): {A} case study across the extended
                      network of {ICOS} forest ecosystem sites in {E}urope},
      journal      = {Ecological indicators},
      volume       = {128},
      issn         = {1470-160X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-03852},
      pages        = {107841},
      year         = {2021},
      abstract     = {Leaf area index (LAI) is a key ecological indicator for
                      describing the structure of canopies and for modelling
                      energy exchange between atmosphere and biosphere. While LAI
                      of the forest overstory can be accurately assessed over
                      large spatial scales via remote sensing, LAI of the forest
                      understory (LAIu) is still largely ignored in ecological
                      studies and ecosystem modelling due to the fact that it is
                      often too complex to be destructively sampled or
                      approximated by other site parameters. Additionally, so far
                      only few attempts have been made to retrieve understory LAI
                      via remote sensing, because dense canopies with high LAI are
                      often hindering retrieval algorithms to produce meaningful
                      estimates for understory LAI. Consequently, the forest
                      understory still constitutes a poorly investigated research
                      realm impeding ecological studies to properly account for
                      its contribution to the energy absorption capacity of forest
                      stands. This study aims to compare three conceptually
                      different indirect retrieval methodologies for LAIu over a
                      diverse panel of forest understory types distributed across
                      Europe. For this we carried out near-to-surface measurements
                      of understory reflectance spectra as well as digital surface
                      photography over the extended network of Integrated Carbon
                      Observation System (ICOS) forest ecosystem sites. LAIu was
                      assessed by exploiting the empirical relationship between
                      vegetation cover and light absorption (Beer-Lambert- Bouguer
                      law) as well as by utilizing proposed relationships with two
                      prominent vegetation indices: normalized difference
                      vegetation index (NDVI) and simple ratio (SR). Retrievals
                      from the three methods were significantly correlated with
                      each other (r = 0.63–0.99, RMSE = 0.53–0.72), but
                      exhibited also significant bias depending on the LAI scale.
                      The NDVI based retrieval approach most likely overestimates
                      LAI at productive sites when LAIu > 2, while the simple
                      ratio algorithm overestimates LAIu at sites with sparse
                      understory vegetation and presence of litter or bare soil.
                      The purely empirical method based on the Beer-Lambert law of
                      light absorption seems to offer a good compromise, since it
                      provides reasonable LAIu values at both low and higher LAI
                      ranges. Surprisingly, LAIu variation among sites seems to be
                      largely decoupled from differences in climate and light
                      permeability of the overstory, but significantly increased
                      with vegetation diversity (expressed as species richness)
                      and hence proposes new applications of LAIu in ecological
                      modelling.},
      cin          = {IBG-3},
      ddc          = {630},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000663325100009},
      doi          = {10.1016/j.ecolind.2021.107841},
      url          = {https://juser.fz-juelich.de/record/901829},
}