Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2020
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Muhammad Azher Bhatti Lars Olav Eik Geir Steinheim Tormod Ådnøy David Laurence Hopkins Leif Jarle AsheimAbstract
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Cathrine Brekke Linn Fenne Groeneveld Theodorus Meuwissen Nina Svartedal Steffen Weigend Peer BergAbstract
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Min-Rui Wang Zhibo Hamborg Rune Slimestad Abdelhameed Elameen Dag-Ragnar Blystad Sissel Haugslien Gry Skjeseth Qiao‑Chun WangAbstract
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Rolf NestbyAbstract
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Guro Brodal Heidi Røsok Bye Eleonora Høst Martin Pettersson Inger Sundheim Fløistad Øyvind Meland Edvardsen Venche TalgøAbstract
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Olalla Díaz-Yáñez Blas Mola-Yudego Volkmar Timmermann Mari Mette Tollefsrud Ari Hietala Jonas OlivaAbstract
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Jan-Peter George Jan Pisek Tobias Biermann Arnaud Carrara Edoardo Cremonese Matthias Cuntz Silvano Fares Giacomo Gerosa Thomas Grünwald Niklas Hase Michal Heliasz Andreas Ibrom Alexander Knohl Bart Kruijt Hikdeki Kobayashi Holger Lange Jean-Marc Limousin Denis Loustau Petr Lukes Riccardo Marzuoli Meelis Mölder Leonardo Montagnani Johan Neirynck Matthias Peichl Corinna Rebmann Marius Schmidt Francisco Ramon Lopez Serrano Kamel Soudani Caroline Vincke Roman ZweifelAbstract
Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions. Liu Y. et al. (2017): Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.