Effective management of forest resources requires reliable and timely information on their status. In this regard, remote sensing techniques have played an important role, as they allow collection of data on extensive, remote and inaccessible areas. Historically, aerial photographs were the primary remote sensing data source in forest inventory and mapping, and they are still extensively for visual photo-interpretation. In this thesis, we show that their use can be improved thanks to automatic processing and an application using digitised orthophotos is provided. Satellite-based remote sensing has been regarded as an alternative, low-cost and rapid, data source to aerial photography and ground survey. Indeed, it has proved to be effective at the continental and global scales, but applications for local forest management purposes are still rare. The main reason for this is that the spatial resolution of satellite remote sensing data that was available until recently (mainly from Landsat TM/ETM and SPOT HRV) was too coarse for stand level information. Satellite images with enhanced spatial resolution (such as IKONOS) should overcome this limitation. This thesis investigates their actual capabilities for forest stand mapping and characterisation. We show that they are well suited for forest stand type classification and for retrieval of several dendrometric variables in coniferous stands with an accuracy similar to that of field sampling. For the sake of solutions to provide more precise and detailed information on forest stand, we assessed also the contribution of hyperspectral and multiple-view angle data acquired by CHRIS/PROBA. Although the winter season scene did not fully permit utilisation of the hyperspectral dimension of this dataset, the study provides insights into directional effects. This work makes, hopefully, a step towards automated processing and effective integration of satellite-based remote sensing data into the forest management information system and, by upscaling, into the national forest inventories.