%0 Research article
%T Large-area inventory of species composition using airborne laser scanning and hyperspectral data
%A Ørka, Hans Ole
%A Hansen, Endre H.
%A Dalponte, Michele
%A Gobakken, Terje
%A Næsset, Erik
%D 2021
%J Silva Fennica
%V 55
%N 4
%R doi:10.14214/sf.10244
%U https://silvafennica.fi/article/10244
%X <p>Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m<sup>2</sup> collected over 350 km<sup>2</sup> of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (<em>Picea abies</em> (L.) Karst.), Scots pine (<em>Pinus sylvestris</em> L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.</p>