Review Combining national forest inventory field plots and remote sensing data for forest databases Erkki Tomppo a, ⁎ , Håkan Olsson b , Göran Ståhl b , Mats Nilsson b , Olle Hagner b , Matti Katila a a Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland b Swedish University of Agricultural Sciences, Umeå, Sweden Received 12 November 2006; received in revised form 9 February 2007; accepted 24 March 2007 Abstract Information about forest cover is needed by all of the nine societal benefit areas identified by the Group of Earth Observation (GEO). In particular, the biodiversity and ecosystem areas need information on landscape composition, structure of forests, species richness, as well as their changes. Field sample plots from National Forest Inventories (NFI) are, in combination with satellite data, a tremendous resource for fulfilling these information needs. NFIs have a history of almost 100 years and have developed in parallel in several countries. For example, the NFIs in Finland and Sweden measure annually more than 10,000 field plots with approximately 200 variables per plot. The inventories are designed for five-year rotations. In Finland nationwide forest cover maps have been produced operationally since 1990 by using the k-NN algorithm to combine satellite data, field sample plot information, and other georeferenced digital data. A similar k-NN database has also been created for Sweden. The potentials of NFIs to fulfil diverse information needs are currently analyzed also in the COST Action E43 project of the European Union. In this article, we provide a review of how NFI field plot information has been used for parameterization of image data in Sweden and Finland, including pre-processing steps like haze correction, slope correction, and the optimization of the estimation variables. Furthermore, we review how the produced small-area statistics and forest cover data have been used in forestry, including forest biodiversity monitoring and habitat modelling. We also show how remote sensing data can be used for post-stratification to derive the sample plot based estimates, which cannot be directly estimated from the spectral data. © 2008 Elsevier Inc. All rights reserved. Keywords: National forest inventory; k-NN estimation; Post-stratification; Biodiversity monitoring; Habitat modelling; Satellite images Contents 1. Introduction ............................................................. 1983 2. National forest inventories ...................................................... 1984 2.1. History of inventories .................................................... 1984 2.2. Current field inventories ................................................... 1984 2.3. Need for multi-source methods ............................................... 1985 3. Current multi-source methods .................................................... 1986 3.1. Input data .......................................................... 1986 3.1.1. Field data ...................................................... 1986 3.1.2. Satellite images ................................................... 1986 3.1.3. Digital map data .................................................. 1986 Available online at www.sciencedirect.com Remote Sensing of Environment 112 (2008) 1982 – 1999 www.elsevier.com/locate/rse ⁎ Corresponding author. Tel.: + 358 10 211 2170. E-mail address: erkki.tomppo@metla.fi (E. Tomppo). 0034-4257/$ - see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2007.03.032