Airbone laser scanning (ALS) data are aften used for downscaling point based forest inventory (FI) measurements in order to obtain spatially distributed estimates of forest parameters. Such downscaling algorithms usually consist in a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, adequate co-registration between FI and ALS data is an essential pre-processing step in order to get accurate predictive relationships. The current paper presents a new, automated co-registration approach which iteratively searches for the best match between an ALS based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to vatious types of FI sampling configurations, the co-registration approach has been specifically developed to take into account the tree selection criteria posed by angel count sampling. Several criteria are employed to detect possible ambiguous solutions and to reduce post-processing efforts by an image operator. Model valiation was based on National Forest Inventory (NFI) and ALS data of the Austrian Vorarlberg province. Results show that 67% of the sample plots could be accurately automatically co-registered (i.e, distance to reference data set <4m). All solutions with deviations from the reference data set > 4m were correctly marked by the algorithm as being ambiguous. Applying the automatically co-registered sample plots in a growing stock model provided estimates that were clearly superior to those obtaines with the original plot positions and even slightly outperformed those based on manual co-registration. As the developed algorithm will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance.
