We focus on boreal landscapes and take under consideration the use of a valuable existing data base - National Forest Inventory (NFI) - that is common to mahy European countries, using Finland as a case study. We aim at showing how good quality fields data when combined with remote sensing data and spatial analysis can be turned into a powerful tool for monitoring biodiversity. Futhermore, the tools developed can be applied in assessing biodiversity value of both managed and protected forest areas to help decisionmaking concerning the protection of valuable habitats. Biodiversity issues have gained importance in forestry as a result of increased awareness of forest landscape changes, but still there is much to do before forest management meets reasonable goals in relation to forest protection and renewal of biodiversity. In order to achieve efficient monitoring systems that focus on the understanding of changes and their linkage to ecological processes, a thorough detailed spatial knowledge of the landscape is needed. We used wall-to-wall output thematic maps from the Finnish muti-source National Forest Inventory (MS-NFI) to evaluate landscape level structure and composition of the Finnish forest. MS-NFI maps are based on K-nearest-neighbour (k-nn) estimation from field data and satellite image data. In addition we used other related databases from permanent inventories. dead wood and other structural aspects of frest stands are potentially important biodivesity indicators. The advantage is that the indicators used constitute a good surrogate for biodiversity value and therefore provide important insights for monitoring and protection for large areas. The Habitat index model developed seems to have combined the input data in a logical way. Comaparisons to other descriptive habitat layers produced from NFI data seem to affirm the rationality of the model Trends from biodiversity indicators show regional differences as well as different patterns within southern Finland revealing different management history and different driving environmental factors.