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3330171 
Journal Article 
Mapping Individual Tree Species in an Urban Forest Using Airborne Lidar Data and Hyperspectral Imagery 
Zhang, C; Qiu, F 
2012 
Photogrammetric Engineering and Remote Sensing
ISSN: 0099-1112 
78 
10 
1079-1087 
We developed a neural network based approach to identify urban tree species at the individual tree level from lidar and hyperspectral imagery. This approach is capable of modeling the characteristics of multiple spectral signatures within each species using an internally unsupervised engine, and is able to catch spectral differences between species using an externally supervised system. To generate a species-level map for an urban forest with high spatial heterogeneity and species diversity, we conducted a treetop-based species identification. This can avoid the problems of double-sided illumination, shadow, and mixed pixels, encountered in the crown-based species classification. The study indicates lidar data in conjunction with hyperspectral imagery are not only capable of detecting individual trees and estimating their tree metrics, but also identifying their species types using the developed algorithm. The integration of these two data sources has great potential to take the place of traditional field surveys.