Discrimination of deciduous tree species from time series of unmanned aerial system imagery

Technology advances can revolutionize Precision Forestry by providing accurate and fine forest information at tree level.
This paper addresses the question of how and particularly when Unmanned Aerial System (UAS) should be used in order to efficiently discriminate deciduous tree species.
A time series of high resolution UAS imagery was collected to cover the growing season from leaf flush to leaf fall.
Full benefit was taken of the temporal resolution of UAS acquisition, one of the most promising features of small drones.
The disparity in forest tree phenology is at the maximum during early spring and late autumn.
But the phenology state that optimized the classification result is the one that minimizes the spectral variation within tree species groups and, at the same time, maximizes the phenologic differences between species.
Sunlit tree crowns (5 deciduous species groups) were classified using a Random Forest approach for monotemporal, two-date and three-date combinations.
The end of leaf flushing was the most efficient single-date time window.
Multitemporal datasets definitely improve the overall classification accuracy.
But single-date high resolution orthophotomosaics, acquired on optimal time-windows, result in a very good classification accuracy (overall out of bag error of 16%).