Tree species discrimination in temperate woodland using high spatial resolution Formosat-2 time series

Mapping tree species is an important issue for forest ecosystem services and habitat assessment. In this study, the ability of Formosat-2 multispectral image time series to discriminate thirteen tree species of temperate woodland is investigated. The discrimination is performed using several learning classifiers and testing three levels of classification. The classification accuracies in terms of kappa vary from 0.80 to 0.96 highlighting the benefits of using seasonal variations in spectral reflectance for tree species identification. The results suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrate the potential contribution of the forthcoming Sentinel-2 images for studying forest ecosystems.