Wall-to-wall tree type classification using airborne lidar data and CIR images

International Journal of Remote Sensing, Taylor & Francis, ISSN 0143-1161

Volume 35, 9, 2014

DOI:10.1080/01431161.2014.894670, Dimensions: pub.1014909110,



  1. (1) University of Copenhagen, grid.5254.6, KU






Extensive ground surveys of forest resources are expensive, and remote sensing is commonly used to extend surveys to large areas for which no ground data are available to provide more accurate estimates for forest management decisions. Remote-sensing data for tree type classification are usually analysed at the individual tree level (object-based). However, due to computational challenges, most object-based studies cover only smaller areas and experience of larger areas is lacking. We present an approach for an object-based, unsupervised classification of trees into broadleaf or conifer using airborne light detection and ranging (lidar) data and colour infrared (CIR) images on a countrywide scale. We adjusted the classification procedure using field data from countrywide tree species trial (TST) plots, and verified it on data from the National Forest Inventory (NFI). Results of the object-based classification of the TST plots showed an overall accuracy of 84% and a kappa coefficient (

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Times Cited: 7

Field Citation Ratio (FCR): 1.61