Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation

Despite numerous studies existing for tree species classification the difficult situation in dense and mixed temperate forest is still a challenging task. This study attempts to extend the existing limitations by investigating comprehensive sets of different types of features derived from multiple data sources. These sets include features from full-waveform LiDAR, LiDAR height metrics, texture, hyperspectral data and colour infrared (CIR) images. Support vector machines (SVM) are used as an appropriate classifier to handle the high dimensional feature space and an internal ranking method allows the determination of the most important parameters. In addition, for species discrimination, focus is put on single tree applicable scale. While most experiences within these scales derive from boreal forests and are often restricted to two or three species, we concentrate on more complex temperate forests. The four main species pine (Pinus sylvestris), spruce (Picea abies), oak (Quercus petraea) and beech (Fagus sylvatica) are classified with an accuracy of 89.7%, 88.7%, 83.1% and 90.7%, respectively. Instead of directly classifying delineated single trees a raster cell based classification is conducted. This overcomes problems with erroneous polygons of merged tree crowns, which occur frequently within dense deciduous or mixed canopies. Lastly, we further test the possibility to correct these failures by combining species classification with single tree delineation.

► Investigating features from different data sources for tree species classification. ► Nearly 90% classification accuracy for main species of a European temperate forest. ► High resolution tree species classification on a single tree applicable scale. ► Improving single tree delineation results by combination with tree species.