Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data

Michael J. Falkowski, Alistair M.S. Smith, Andrew T. Hudak, Paul E. Gessler, Lee A. Vierling, Nicholas L. Crookston

Research output: Contribution to journalArticlepeer-review

178 Scopus citations

Abstract

We describe and evaluate a new analysis technique, spatial wavelet analysis (SWA), to automatically estimate the location, height, and crown diameter of individual trees within mixed conifer open canopy stands from light detection and ranging (lidar) data. Two-dimensional Mexican hat wavelets, over a range of likely tree crown diameters, were convolved with lidar canopy height models. Identification of local maxima within the resultant wavelet transformation image then allowed determination of the location, height, and crown diameters of individual trees. In this analysis, which focused solely on individual trees within open canopy forests, 30 trees incorporating seven dominant North American tree species were assessed. Two-dimensional (2D) wavelet-derived estimates were well correlated with field measures of tree height (r = 0.97) and crown diameter (r = 0.86). The 2D wavelet-derived estimates compared favorably with estimates derived using an established method that uses variable window filters (VWF) to estimate the same variables but relies on a priori knowledge of the tree height – crown diameter relationship. The 2D spatial wavelet analysis presented herein could potentially allow automated, large-scale, remote estimation of timber board feet, foliar biomass, canopy volume, and aboveground carbon, although further research testing the limitations of the method in a variety of forest types with increasing canopy closures is warranted.

Original languageEnglish (US)
Pages (from-to)153-161
Number of pages9
JournalCanadian Journal of Remote Sensing
Volume32
Issue number2
DOIs
StatePublished - Apr 2006

Bibliographical note

Funding Information:
This research was funded through the Sustainable Forestry component of Agenda 2020, a joint effort of the USDA Forest Service Research & Development and the American Forest and Paper Association. The authors also acknowledge partial funding for this work from the following additional sources: the National Aeronautics and Space Administration (NASA) Synergy program, and the USDA Forest Service Rocky Mountain Research Station (04-JV-11222063-299). Additional funds were also provided by the Forest Public Access Resource Center (ForestPARC), an Upper Midwest Aerospace Consortium (UMAC) group, which is in turn supported with funds from NASA. The authors thank Curtis Kvamme, K.C. Murdock, Jacob Young, Tessa Jones, Jennifer Clawson, Bryn Parker, Kasey Prestwich, Stephanie Jenkins, Kris Poncek, and Jeri Stewart for their assistance in the field. The authors also thank Jeff Evans for the lidar-derived CHM, David Hann for SWA code development, Eric Rowell for his assistance with IDL programming, and anonymous reviewers for their detailed comments on an earlier version of this manuscript.

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