The ability to accurately map and monitor forest carbon (C) has gained global attention as countries seek to comply with international agreements to mitigate climate change. However, attaining precise estimates of forest C storage is challenging due to the inherent heterogeneity occurring across different scales. To develop cost-effective sampling protocols, there is a need for more unbiased estimates of the current C stock, its distribution among forest compartments and its variability across different scales. As a contribution to this work, this dissertation used high-resolution field measurements of C collected from different forest compartments across a boreal forest stand in South East Norway. In the first paper, we combined the use of airborne scanning light detection and ranging (lidar) systems with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools across the forest. We found that predictor variables from lidar derived metrics delivered precise models of above and belowground tree C, which comprised the largest of the measured C pool in our study. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. In the search for an effective tool to measure and monitor forest C pools, we found the capabilities of lidar to map forest C encouraging. In the second paper, we used a geostatistical approach to analyze the fine-scale heterogeneity of the soil organic layer (forest floor) C storage. Our results showed that the C stocks were highly variable within each plot, with spatial autocorrelation distances < 3 m. Further, we established that a minimum of 20 to 25 inventory samples is needed to determine the organic layer C stock with a precision of �0.5 kg C m-2 in inventory plots of ~2000 m2. In the third paper, we investigated how the short-range spatial variability of organic layer C affects sampling strategies aiming to monitor and detect changes in the C stock. We found that sample repeatability rapidly declines with sample separation distance, and the a priori sample sizes needed to detect a change a fixed change in the organic layer C stock vary by a factor of ~4 over 15 to 125 cm separation distance. Unless care is taken by the surveyor to ensure spatial sampling precision, substantially larger samples sizes, or longer time intervals between baseline sampling and revisit are required to detect a change. In the final paper, we utilized the nested sampling protocol to investigate the spatial variability of organic layer C across different scales and incorporated inventory expenses in the development of a cost-optimal sampling approach. Because precise estimates are costly to obtain, it is of great interest for surveyors to develop cost-efficient sampling protocols aimed at maximizing the spatial coverage, while minimizing the estimate variance. We found that the majority of the estimate variance is confined within small subplots (100 m2) of the forest (25 km2), emphasizing the importance of considering the short-range variability when conducting a large-scale inventory. Further, this chapter demonstrated how optimal allocation of sampling units (plot, subplot and sample) is not only a function of the variance component within that dimension, but also changes with the sampling unit costs and the acceptable margin of error. We found that the costs of conducting an organic layer C inventory could be reduced by more than 60% by increasing the inventory uncertainty from �0.25 Mg C ha-1 to �0.5 Mg C ha-1. Finally, we established that sampling costs can be reduced with as much 80% by conducting a double sampling procedure that utilizes the correlation between organic layer C stock (r = 0.79 to 0.85) and measurements of layer thickness.