Modern remote sensing-based forest inventory methods utilize airborne light detection and ranging (LiDAR) and optical image data for the prediction of forest attributes by tree species. These methods assume that the three-dimensional information provided by LiDAR can be used to predict the total growing stock attributes, while the spectral reflectance of tree crowns, contained in optical image data, are beneficial for the discrimination of tree species. In Finland, airborne image data has been found suitable for the discrimination of the most common tree species: pine (Pinus sylvestris), spruce (Picea abies) and broadleaves (mainly Betula pendula and Betula pubescens). There are, however, numerous issues in the collection and use of two different types of datasets in the inventory process, such as incorrect co-registration of datasets and increased data acquisition and processing costs.
In the wake of advances in algorithms and hardware, two new data sources have been merged as single sensor solutions for tree species-specific forest inventories: stereo matching of aerial images and multispectral airborne LiDAR. Both data sources offer structural and optical information beneficial in tree species classification. However, due to differences in observational geometry, the interpretation, and, thus, the usefulness of the optical information may differ between these two data sources. It is, therefore, essential to examine whether the differences in data characteristics between stereo matching of aerial images and multispectral airborne LiDAR affect the performance of the inventory.
In this thesis, stereo matching data and multispectral airborne LiDAR data are evaluated as single sensor solutions for tree species-specific forest inventories. The results provide a unique insight as to how these data sources compare to the traditional use of single wavelength airborne LiDAR and aerial images. The findings can be used to support future species-specific forest inventories on the selection of remotely sensed data.