The Multi-source National Forest Inventory of Finland - methods and results 2011 - Tomppo, Erkki; Katila, Matti; Mäkisara, Kai; Peräsaari, Jouni (Finnish Forest Research Institute, 2014)
This article presents the results of the Finnish multi-source forest inventory corresponding to year 2011. For these products, the method uses satellite images from years 2009-2012, digital map data, and field data. The main purpose of the article is to make multi-source forest inventory results available for the foresters, and all the readers interested in forests and forest environment. The field data originates from the 10th Finnish National Forest Inventory from years 2007 and 2008, and from the 11th Finnish National Forest Inventory from years 2009 to 2011. The field data has been computationally updated to the date 31 July, 2011. The basic features of the employed improved k-NN, ik-NN, method are described. The results are presented by the regions of the Public Service unit (regions) of the Finnish Forest Centre and within the regions by municipalities, the boundaries as on 1.1.2011. The estimates are given, for example,for land areas, areas of tree species dominance, age, and development classes and often separately for forests available for wood supply. The mean volume and total volume estimates are given in manydifferent ways: mean volumes by tree species and by timber assortments for forest land, and combined forest land and poorly productive forest land and also for forests available for wood supply, as well as by age and development classes. The biomass estimates are given, in addition to the total biomass estimates,by tree species groups in young thinning stands in which first commercial thinning was proposed for the first 5-year period, separately for stem and bark and branches and foliage. The biomass estimates of mature forests are presented separately for branches, foliage and stem residuals, and stumps and large roots by tree species groups. These biomass estimates are given separately for land available for wood supply. All presented results can be employed in calculating forest resource estimates also for regions larger thanmunicipalities. The error sources for the estimates as well as the reliability of the estimates are briefly discussed in Section 3.4.4 and Chapter 5.
Keywords: genetic algorithm; k-nearest neighbours; kansaallinen metsien inventointi; monilähde-inventointi; satellittikuvat; small-area estimation; statistical calibration; stratification; genetic algorithms; k-nearest neighbours; multi-source forest inventory; national forest inventory; satellite images; small-area estimation; statistical calibration; stratification