%0 Research article
%T Tree species classification using airborne LiDAR – effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type
%A Korpela, Ilkka
%A Ørka, Hans Ole
%A Maltamo, Matti
%A Tokola, Timo
%A Hyyppä, Juha
%D 2010
%J Silva Fennica
%V 44
%N 2
%R doi:10.14214/sf.156
%U https://silvafennica.fi/article/156
%X Tree species identification constitutes a bottleneck in remote  sensing-based forest inventory. In passive images the differentiating  features overlap and bidirectional reflectance hampers analysis.  Airborne LiDAR provides radiometric and geometric information. We  examined the single-trees-level response of two LiDAR sensors in over 13  000 forest trees in southern Finland. We focused on the commercially  important species. Our aims were to 1) explore the relevant LiDAR  features and study their dependencies on stand and tree variables, 2)  examine two sensors and their fusion, 3) quantify the gain from  intensity normalizations, 4) examine the importance of the size of the  training set, and 5) determine the effects of stand age and site  fertility. A set of 570 semiurban broad-leaved trees and exotic conifers  was analyzed to 6) examine the LiDAR signal in the economically less  important species. An accuracy of 88 90% was achieved in the  classification of Scots pine, Norway spruce, and birch, using intensity  variables. Spruce and birch showed the highest levels of confusion.  Downsizing the training set from 30% to 2.5% of all trees had only a  marginal effect on the performance of classifiers. The intensity  features were dependent on the absolute and relative sizes of trees,  especially for birch. The results suggest that leaf size, orientation,  and foliage density affect the intensity, which is thus not affected by  reflectance only. Some of the ecologically important species in Finland  may be separable, since they gave rise to high intensity values.  Comparison of the sensors implies that performance of the intensity data  for species classification varies between sensors for reasons that  remained uncertain. Both range and gain receiver normalization improved  species classification. Weighting of the intensity values improved the  fusion of two LiDAR datasets.