Several countries are researching new methods to improve accuracies and cost effec- tiveness in forest inventory. Finland was one of the first countries to research and develop a new inventory method based not only on field sample plot information, but also on satellite images and digital map data [88, 14, 89]. Nationwide forest cover maps have been produced operationally since 1990 in Finland using the k- nearest neighbor (kNN) algorithm on multi-source input data. To a large extent, Finland and Sweden use similar approaches as described in [14, 90]. The goal of these inventories is to monitor the composition and fragmentation of ecosystems, to monitor biodiversity at the species level and in the context of forest planning to maintain wood production and fulfill forestry rules and legislation. International treaties and associations like the United Nations Convention on Biological Diver- sity also demand diverse and detailed information. Finland and Sweden developed methods to combine national forest inventory field plot data with optical satellite data to obtain nationwide wall-to-wall raster databases for small-area statistics and forest resources [14]. Forest inventory field plots measured in subsequent years were used and therefore needed to be updated with respect to incremental changes due to natural growth and the felling of trees. They were adjusted to match the year and growth period of the image acquisition date of the used optical data. Several preprocessing steps were used including geometrical rectification of the data using a digital elevation model (DEM) to achieve geometric fits between image data and field plot data. Another step was illumination correction to reduce topographic ef- fects and a correction for within-scene differences in atmospheric optical depth. In [14], it was reported that at the pixel level, the obtained prediction errors measured with relative RMSE were high with e.g. 50 − 80 % when predicting volumes for field plot pixels. The precision of the multi-source inventory estimates for small areas showed relative RMSE of 5 − 15 % for mean volumes of pine, spruce, birch and all together mean volumes. The kNN product was not considered accurate enough for planning silviculture, cutting regimes and other forestry operations. It was stated that the error was relatively high when the area in question was small and that the kNN method had poor capabilities to extrapolate beyond the variation of the range of the reference data.
of a plot-based system of permanent observational units located on a national grid as described in [91]. The measurement of ground plots were synchronized with the interpretation of photo plots and remote sensing data was used to assess the location of plots, the extent of change and to define the need to revisit plots. It was stated that the integration of remote sensing could result in a calibrated forest resource cover type map for all of Canada. In [92], the need to assess data more accurately and at a high resolution for the entire boreal region in order to assist wildlife habitat monitoring was observed. It was stated that space-based sensors like the TM and enhanced thematic mapper (ETM) on board the Landsat satellites and MODIS were inadequate for classifying forest cover or vegetation in terms of spatial resolution and thematic precision for wildlife habitat monitoring. The recorded attributes included crown closure, species composition, height, mean canopy or stand origin age, stand structure, moisture regime, site class or site index, non-forested cover types, non- vegetated cover types, disturbance history, ecosite and wetlands. A PostGIS open source spatial database engine was used. The scope of applications was reported to be limited at the time, due to various data sharing agreements and restrictions. But many applications in the areas of wildlife habitat modeling, environmental impact assessments, developing objectives and priorities for conservation planning, identi- fying and assessing priority habitats and evaluating management scenarios at large scale and temporal scales were expected to emerge in [91].
The Southern Annual Forest Inventory System employed in the USA, which was based only on field sample plot data was described in [93]. The need for developing automated procedures for forest mapping and area determination was stated. It was also suggested, that wall-to-wall forest mapping and area determination from remote sensing satellite data provided a viable solution and that the estimation of forest area using maps was discussed. In [94], the forest inventory and analysis (FIA) database, which replaced the two regional databases for the eastern and western states of the USA, was described. Remote sensing classifications of land use and more detailed classes for forested land were used. Ground plots were measured to adjust the remote sensing sample for changes since acquisition date and to correct misclassifications. The FIA database was designed to meet the specified sampling errors at the state level at 67 % confidence limit.
In [95], a study on user requirements for remote sensing applications in forestry was presented. National forest inventory was not sufficient for the purpose of the
federal state forest administration in Bavaria/Southern Germany. Data was re- quired on forest stand level and should be collected at least every 5 years. The most important parameters were stated to be tree species composition, forest areas, forest boundaries and forest stand heights. Standardized remote sensing products were preferred by two-third of the forest professionals included in the survey. The demand for additional forest information was reported to be high and although some differences regarding spatial resolution and ecological condition existed, there were strong similarities in the requirement profiles for remote sensing applications in forestry and forest administrations.