- Finnish Geospatial Research Institute
National Forest Inventories (NFI) are key data and tools to better understand the role of forests in the global carbon budget. Traditionally inventories have been carried out as field work, which makes them laborious and expensive. In recent years, the development of various remote sensing techniques to improve the cost-efficiency of the NFIs has accelerated. The goal of this study is to determine the usability of open and free multitemporal multispectral satellite images from the European Space Agency's Sentinel-2 satellite constellation and to compare their usability in forest inventories against airborne laserscanning (ALS) and three-dimensional data obtained with high-resolution optical satellite images from WorldView-2 and Synthetic Aperture Radar (SAR) stereo data from TerraSAR-X. Ground reference consisted of field data collected over 74 boreal forest plots in Southern Finland in 2014 and 2016. Features utilizing both single- and multiple-date information were designed and tested for Sentinel-2 data. Due to high cloud cover, only four Sentinel-2 images were available for the multitemporal feature analysis of all reference plots within the monitoring window. Random Forest technique was used to find the best descriptive feature sets to model five forest inventory parameters (mean height, mean diameter at breast height, basal area, volume, above-ground biomass) from all input remote sensing data. The results confirmed that the higher spatial resolution input data correlated with more accurate forest inventory parameter predictions, which is in line with other results presented in literature. The addition of temporal information to the Sentinel-2 results showed limited variation in prediction accuracy between the single and multidate cases ranging from 0.45 to 1.5 percentage points, whereof mean height, basal area and aboveground biomass are lower for single date with relative RMSEs of 14.07%, 20.66% and 24.71% respectively. Diameter at breast height and volume are lower for multi date feature combination with relative RMSEs of 18.38% and 27.21%. The results emphasize the importance of obtaining more evenly distributed data acquisitions over the growing season to fully exploit the potential of temporal features.