Anett Schibalski, Aleksi Lehtonen, Thomas Hickler, Boris Schröder. (2017). Identifying important topics for model refinement in a widely used process-based model informed by correlative model analyses in a boreal forest. Silva Fennica vol. 51 no. 4 article id 6977. https://doi.org/10.14214/sf.6977

Keywords: Finland; treeline; competition; LPJ-GUESS; statistical modelling; forest ecology

Highlights: Continental-scale model parameterization of widely used LPJ-GUESS experiences problems when applied on the regional level; Competition, disturbances and soil conditions are crucial for explaining treeline position in Finland, besides climatic limitation; Picea abies is overly dominant in LPJ-GUESS model, as key competitive mechanisms are not implemented in sufficient detail.

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Models attempting to predict treeline shifts in changing climates must include the relevant ecological processes in sufficient detail. A previous correlative model study has pointed to nutrients, competition, and temperature as the most important factors shaping the treelines of Pinus sylvestris L., Picea abies (L.) H. Karst. and Betula pubescens Ehrh. in Finnish Lapland. Here, we applied a widely used process-based dynamic vegetation model (LPJ-GUESS) to (i) test its capability to simulate observed spatial and temporal patterns of the main tree species in Finnish Lapland, and (ii) to explore the model representation of important processes in order to guide further model development. A European parameterization of LPJ-GUESS overestimated especially P. abies biomass and the species’ northern range limit. We identified implemented processes to adjust (competition, disturbance) and crucial processes in boreal forests to include (nutrient limitation, forest management) which account for the model’s failure to (edaphically) restrict P. abies in Finnish Lapland and the resulting species imbalance. Key competitive mechanisms are shade and drought tolerance, nutrient limitation, fire resistance, and susceptibility to disturbances (storm, herbivory) which we discussed with respect to boreal ecology and promising model developments to provide a starting point for future model development.

Schibalski, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, D-38106 Braunschweig, Germany E-mail: a.schibalski@tu-braunschweig.de
Lehtonen, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: aleksi.lehtonen@luke.fi
Hickler, Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, D-60325 Frankfurt am Main, Germany; Department of Physical Geography, Goethe University, Altenhöferallee 1, D-60438 Frankfurt am Main, Germany E-mail: thomas.hickler@senckenberg.de
Schröder, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, D-38106 Braunschweig, Germany; Berlin-Brandenburg Institute of Advanced Biodiversity Research BBIB, Altensteinstr. 6, D-14195 Berlin, Germany E-mail: boris.schroeder@tu-bs.de

article id 458, category Research article

Sakari Tuominen, Kalle Eerikäinen, Anett Schibalski, Markus Haakana, Aleksi Lehtonen. (2010). Mapping biomass variables with a multi-source forest inventory technique. Silva Fennica vol. 44 no. 1 article id 458. https://doi.org/10.14214/sf.458

Keywords: National Forest Inventory; remote sensing; biomass models; biomass maps

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Map form information on forest biomass is required for estimating bioenergy potentials and monitoring carbon stocks. In Finland, the growing stock of forests is monitored using multi-source forest inventory, where variables are estimated in the form of thematic maps and area statistics by combining information of field measurements, satellite images and other digital map data. In this study, we used the multi-source forest inventory methodology for estimating forest biomass characteristics. The biomass variables were estimated for national forest inventory field plots on the basis of measured tree variables. The plot-level biomass estimates were used as reference data for satellite image interpretation. The estimates produced by satellite image interpretation were tested by cross-validation. The results indicate that the method for producing biomass maps on the basis of biomass models and satellite image interpretation is operationally feasible. Furthermore, the accuracy of the estimates of biomass variables is similar or even higher than that of traditional growing stock volume estimates. The technique presented here can be applied, for example, in estimating biomass resources or in the inventory of greenhouse gases.

Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
Eerikäinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ke@nn.fi
Schibalski, University of Potsdam, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam, Germany E-mail: as@nn.de
Haakana, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mh@nn.fi
Lehtonen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: al@nn.fi

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