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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles by Eero Muinonen

Category: Research article

article id 983, category Research article
Sakari Tuominen, Juho Pitkänen, Andras Balazs, Kari T. Korhonen, Pekka Hyvönen, Eero Muinonen. (2014). NFI plots as complementary reference data in forest inventory based on airborne laser scanning and aerial photography in Finland. Silva Fennica vol. 48 no. 2 article id 983. https://doi.org/10.14214/sf.983
Highlights: Using NFI plots in forest management inventories could provide a way for rationalising forest inventory data acquisition; NFI plots were used as additional reference data in laser scanning and aerial image based forest inventory; NFI plots improved the estimates of some forest variables; There are differences between the two inventory types that cause difficulties in combining the data.
In Finland, there are currently two, parallel sample-plot-based forest inventory systems, which differ in their methodologies, sampling designs, and objectives. One is the National Forest Inventory (NFI), aimed at unbiased inventory results at national and regional level. The other is the Forest Centre’s management-oriented forest inventory based on interpretation of airborne laser scanning and aerial images, with the aim of locally accurate stand-level forest estimates. The National Forest Inventory utilises relascope sample plots with systematic cluster sampling. This inventory method is optimised for accuracy of regional volume estimates. In contrast, the management-oriented forest inventory utilises circular sample plots with an allocation system covering certain pre-defined forest classes in the inventory area. This method is optimised to produce reference data for interpretation of the remote-sensing materials in use. In this study, we tested the feasibility of the National Forest Inventory sample plots in provision of additional reference data for the management-oriented inventory. Various combinations of NFI plots and management inventory plots were tested in the interpretation of the laser and aerial-image data. Adding NFI plots in the reference data generally improved the accuracy of the volume estimates by tree species but not the estimates of total volume or stand mean height and diameter. The difference between the plot types in the NFI and management inventories causes difficulties in combination of the two datasets.
  • Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: sakari.tuominen@metla.fi (email)
  • Pitkänen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: juho.pitkanen@metla.fi
  • Balazs, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: andras.balazs@metla.fi
  • Korhonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: kari.t.korhonen@metla.fi
  • Hyvönen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: pekka.hyvonen@metla.fi
  • Muinonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: eero.muinonen@metla.fi
article id 1022, category Research article
Eero Muinonen, Perttu Anttila, Jaakko Heinonen, Jukka Mustonen. (2013). Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints. Silva Fennica vol. 47 no. 4 article id 1022. https://doi.org/10.14214/sf.1022
The technical potential of forest chips from final fellings in Central Finland was estimated using a method based on biomass maps derived from a multi-source forest inventory technique. Image segmentation techniques were applied to a satellite image mosaic to detect stand boundaries. The technical potential of forest chips was computed based on primary forestry residues, i.e. logging residues and stumps from final fellings. Harvesting level definitions for final fellings were established using realized statistics for roundwood at the municipality level as well as larger area statistics. The sensitivity of the potential to ecological and technical constraints in the model was also examined. The technical recovery rate of stump harvesting according to biomass harvesting guidelines was evaluated separately. The critical prerequisites for using the advanced, spatially explicit approach to analysing forest energy potentials may lie in the existence of spatially explicit forest inventory data and the biometric models for tree biomass assortments. The method applied was capable of taking into account the constraints that rely upon map data, such the actual forwarding distance or steepness of the slope in the terrain. The calculation results can be used for strategic decision making in the field of forest bioenergy production.
  • Muinonen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: eero.muinonen@metla.fi (email)
  • Anttila, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: perttu.anttila@metla.fi
  • Heinonen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: jaakko.heinonen@metla.fi
  • Mustonen, Stora Enso, Talvikkitie 40 C, FI-01300 Vantaa, Finland ORCID ID:E-mail: jukka.mustonen@storaenso.com
article id 368, category Research article
Eero Muinonen. (2005). Generating a raster map presentation of a forest resource by solving a transportation problem. Silva Fennica vol. 39 no. 4 article id 368. https://doi.org/10.14214/sf.368
Necessary tools for raster map generation, for the approach based on the calibration estimator, were developed and implemented. The allocation of the area weight of each pixel to sample plots was formulated as a transportation problem, using a spectral distance measure as a transportation cost, and solved using the transportation simplex algorithm. Pixel level accuracy was calculated for the methods based on the calibration estimator so that the results could be compared with the results of the nearest neighbour estimation, the reference sample plot method (RSP) at pixel level. Local averaging in a 3 x 3 window was performed for each generated raster map as a postprocessing phase to smooth the map. Test plot results were calculated both for the unfiltered raster map and the filtered raster map. RSP produced the smallest RMSE in the pooled test data. Local averaging with a 3 x 3 filter decreased the pixel level error – and the bias – and the differences between the methods are smaller. Without local averaging, the pixel level errors of the methods based on solving the transportation problem were high. Raster map generation using the methods of this study forms an optional part – followed possibly by the classification of the pixel level results – of the whole computation task, when the area weight computation is based on the calibration estimation. For larger areas than in the present study, such as municipalities, the efficiency of the method based on the transportation model must be improved before it is a usable tool, in practice, for raster map generation. For nearest neighbour methods, the area size is not such a problem, because the inventory area is processed pixel by pixel.
  • Muinonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: eero.muinonen@metla.fi (email)

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