Tree species classification in boreal forests with hyperspectral data

Tree species mapping in forest areas is an important topic in forest inventory. In recent years, several studies have been carried out using different types of hyperspectral sensors under various forest conditions. The aim of this work was to evaluate the potential of two high spectral and spatial resolution hyperspectral sensors (HySpex-VNIR 1600 and HySpex-SWIR 320i), operating at different wavelengths, for tree species classification of boreal forests. To address this objective many experiments were carried out, taking into consideration: i) three classifiers (Support Vector Machines, Random Forest, and Gaussian Maximum Likelihood); ii) two spatial resolutions (1.5 m and 0.4 m pixel sizes); iii) two subsets of spectral bands (all and a selection); and iv) two spatial levels (pixel and tree level). The study area is characterized by the presence of four classes 1) Norway spruce, 2) Scots pine, together with 3) scattered Birch and 4) other broadleaves. Our results showed that: i) the HySpex VNIR 1600 sensor is effective in boreal tree species classification with kappa accuracies over 0.8 (with Pine and Spruce reaching producer’s accuracies higher than 95%); ii) the role of the HySpex-SWIR 320i is limited, and its bands alone are able to properly separate only Pine and Spruce species; iii) the spatial resolution has a strong effect on the classification accuracy (an overall decrease of more than 20% between 0.4 m and 1.5 m spatial resolution); and iv) there is no significant difference between Support Vector Machines or Random Forest classifiers.