Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images

Abstract The objectives of this study were to identify useful predictive factors for tree species identification of individual trees and to compare classifications based on a combination of LiDAR data and multi‐spectral images with classification by the use of each individual data source. Crown segments derived from LiDAR data were mapped to multi‐spectral images for extraction of spectral data within individual tree crowns. Several features, related to height distribution of laser returns in the canopy, canopy shape, proportion of different types of laser returns, and intensity of laser returns, were derived from LiDAR data. Data from a test site in southern Sweden were used (lat. 58°30′ N, long. 13°40′ E). The forest consisted of Norway spruce (Picea abies), Scots pine (Pinus sylvestris), and deciduous trees. Classification into these three tree species groups was validated for 1711 trees that had been detected in LiDAR data within 14 field plots (sizes of 20×50 m2 or 80×80 m2). The LiDAR data were acquired by the TopEye MkII system (50 LiDAR measurements per m2) and the multi‐spectral images were taken by the Zeiss/Intergraph Digital Mapping Camera. The overall classification accuracy was 96% when both data sources were combined. Acknowledgements This work was financed by the Carl Tryggers Foundation (J. Holmgren) and by the Swedish Armed Forces Research and Technology Development Program (U. Söderman and Å. Persson). The latter were part of funding for a project at the Swedish Defence Research Establishment (FOI) aiming for detailed mapping using laser scanning. The field data and remote sensing data were financed by the Hildur and Sven Wingquist Foundation. We would also like to thank TopEye for delivering the LIDAR data and the Swedish National Land Survey for delivering the DMC images. The authors would like to thank Heather Reese for comments on the manuscript and for improving the language.