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

article id 244, category Research article

Georg E. Kindermann, Ian McCallum, Steffen Fritz, Michael Obersteiner. (2008). A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica vol. 42 no. 3 article id 244. https://doi.org/10.14214/sf.244

Keywords: biomass map; downscaling; regression analysis

Abstract | View details | Full text in PDF | Author Info

Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/below-ground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www.iiasa.ac.at/Research/FOR/.

Kindermann, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: kinder@iiasa.ac.at
McCallum, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: im@nn.at
Fritz, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: sf@nn.at
Obersteiner, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: mo@nn.at

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