National mapping and estimation of forest area by dominant tree species using Sentinel-2 data

Abstract

Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) data for modelling and subsequent mapping of spruce, pine and deciduous forest in Norway in 16 m $\times$ 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42.000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by approximately 10%. Nonetheless, efficiencies of national forest area estimates increased by 20% to 50% for model-assisted (MA) estimates and up to 90% for poststratification (PS). Greater minimum number of observations, however, constrained the use of PS. For estimates of sub-populations (municipalities), efficiencies improved by up to a factor of 8. However, even for municipalities with a decent number of NFI plots, direct NFI estimates were sometimes more precise than MA estimates. PS estimates were always more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is freely available as part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.