Worldwide approximately 3 billion m3 of wood is harvested and removed from forests annually. Forest plantations play an important role in forest harvesting providing 46% of the total industrial roundwood produced in the world, while they account for only 7% of the world’s forested area. Modern harvesting systems are mechanised for productivity, costs, and safety reasons. Due to the advances and availability of both computing and sensor technologies, mechanised machinery is a platform for integration of these technologies with electronic control systems capable of monitoring machine functions, estimating measurements, and recording data. One of the most popular mechanized harvesting systems is Cut-To-Length (CTL). The CTL system typically consists of two types of machines, a harvester, which fells and processes the trees into logs in the stand, and a forwarder that extracts the logs. CTL machines were developed in Scandinavia and are now used worldwide. They are the preferred technology for harvesting fast growing forest plantations in some South American countries such as Uruguay. Harvesters are equipped with a system called StanForD that provides a mechanism to automatically record data from forest harvesters in a series of file formats. When harvesters are equipped with a Global Navigation Satellite System (GNSS) receiver, these data include a locational reference and a time stamp. GNSS-enabled data provide site-specific information that is a valuable input for both stand level forest management and harvesting operation assessment. The objective of this thesis is to demonstrate the usefulness of GNSS-enabled StanForD files as a tool for evaluating variables affecting harvesting operations and the forest management process. To achieve the objective two independent studies were carried out. Chapter 2 explores opportunities to manage harvesting operations. The goal of this study was to demonstrate the effectiveness of using the geospatial and time information contained in StanForD files to model harvester productivity. A harvester dataset obtained from Uruguay comprised over 63,000 cycles of felled and processed stems (stm files) and 1440 shift hours (drf files). With two thirds of this cycle time data, a mixed effects model was fitted to evaluate harvester productivity as a function of stem diameter at breast height (DBH), species, shift (day/night), slope, and operator. A slope surface derived from a digital terrain model was overlaid with GNSS stem records. The reserved third of the data was used to validate the model. DBH was the most influential variable in harvester productivity, showing a positive correlation and a R2 value of 0.73 in the validation model. Operator and species also had significant effects. There was no significant slope effect, whereby the study area only had flat and mildly sloping terrain. Shift did not have a significant effect, indicating there was no drop in night shift productivity. The model developed constitutes the first published harvester productivity model in South America based on data automatically collected by harvesters. Chapter 3 and 4 explore opportunities to provide feedback to improve the forest management process using the site-specific harvester data. Stand productivity of fast-growing forest plantation varies across short distances depending on site and forest characteristics. As plantation forest silviculture is typically resource intensive in establishment, forest management would benefit from a site-specific approach. A tool to characterize such stand productivity variations are yield maps and a cost effective source of data is automatically collected by harvesters. To create such maps we need to understand the effect of geospatial accuracy of tree location recorded by the harvester. The objective of Chapter 3 is to improve our understanding of spatial resolution for studying variations in volume and stocking across forested stands, and establish guidance for actual spatial resolution that would allow the development of fit-for-purpose forest yield maps from harvester data. This study investigated data sets from seven stands: two had very accurate tree location, and five were harvester data files that have inaccuracy associated with both the GNSS recording under forest canopy and the physical dislocation of the GNSS relative to the harvested tree location. The GNSS unit is on the cabin of the machine, but the tree is felled using a boom and could be up to 12 meters from the cabin. A spatial resolution for studying variations in stand productivity and stocking across stands was established to allow the development of forest yield maps from harvester data. By assessing the variability across a range of cell sizes, it was concluded that a cell size between 40 and 60 m is suitable to use as a reference for calculating volume per hectare and stocking. Based on the outputs of Chapter 3, the objective of Chapter 4 was to develop models to map stand productivity from GNSS enabled harvester data. This chapter first explores several models using the same two stands with accurate tree location used in Chapter 3. It assesses their accuracy, then applies the models to the harvester data stands, and finally compares the results of the models to determine the most suitable models. The assessment of the models includes the comparison of productivity maps created from inventory plots. Chapter 5 is a synthesis of the findings, contributions, limitations of the studies, and views on future research needs resulting from this work. Key words: StanForD files, GNSS, Eucalyptus spp., Uruguay, harvester, productivity model, forest productivity maps, Geostatistics.