The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data

Authors

Michael J. Falkowski, University of IdahoFollow
Alistair M.S. Smith, University of IdahoFollow
Paul Gessler, University of IdahoFollow
Andrew T. Hudak, Rocky Mountain Research StationFollow
Lee A. Vierling, University of IdahoFollow
Jeffery S. Evans, Rocky Mountain Research Station

Document Type

Article

Date of this Version

2008

Citation

Can. J. Remote Sensing, Vol. 34, Suppl. 2, pp. S1-S13, 2008

Abstract

Individual tree detection algorithms can provide accurate measurements of individual tree locations, crown diameters (from aerial photography and light detection and ranging (lidar) data), and tree heights (from lidar data). However, to be useful for forest management goals relating to timber harvest, carbon accounting, and ecological processes, there is a need to assess the performance of these image-based tree detection algorithms across a full range of canopy structure conditions. We evaluated the performance of two fundamentally different automated tree detection and measurement algorithms (spatial wavelet analysis (SWA) and variable window filters (VWF)) across a full range of canopy conditions in a mixed-species, structurally diverse conifer forest in northern Idaho, USA. Each algorithm performed well in low canopy cover conditions (50%) conditions. The results presented herein suggest that future algorithm development is required to improve individual tree detection in structurally complex forests. Furthermore, tree detection algorithms such as SWA and VWF may produce more accurate results when used in conjunction with higher density lidar data.