The thesis presented hereby proposes as a general objective the estimation of forest inventory parameters (e.g. trunk location, height, basal area, crown area, species, etc..) from the combination of Airborne Laser Scanning (ALS) and Hyperspectal Imaging (HI). The research is centered around three main topics: the development of new individual tree segmentation algorithms, the assessment of direct and indirect dendrometry methods, tree species classification based on ALS and HI features. A common dependency of these topics is the availability of reliable reference datasets for the calibration and validation (error assessment) of algorithms. This requirement is addressed with the development of an interactive software application and procedures to facilitate the manual extraction of trees and visual identification of species from ALS points clouds. The results of this research can be useful to the operational domain in several ways: providing tools and procedures to characterize areas that are not covered by field inventories (e.g. private forests, low accessibility areas), act as a decision support (e.g. preparing plot maps, identifying priority intervention zones, etc.) when planning field surveys or logging, improving the integration of field and remote sensing measurements for forest inventories.