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

Michael J. Falkowski, Alistair M.S. Smith, Paul E. Gessler, Andrew T. Hudak, Lee A. Vierling, Jeffrey S. Evans

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

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% canopy cover), detecting over 80% of all trees with measurements, and producing tree height and crown diameter estimates that are well correlated with field measurements. However, increasing tree canopy cover significantly decreased the accuracy of both SWA and VWF tree measurements. Neither SWA or VWF produced tree measurements within 25% of field-based measurements in high canopy cover (i.e., canopy cover >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.

Original languageEnglish (US)
Pages (from-to)S338-S350
JournalCanadian Journal of Remote Sensing
Volume34
DOIs
StatePublished - Nov 21 2008

Bibliographical note

Funding Information:
This research was funded through the Sustainable Forestry Initiative of Agenda 2020, which is a joint effort of the USDA Forest Service Research & Development and the American Forest and Paper Association. Partial funding for this work was also provided by the National Aeronautics and Space Administration (NASA) Synergy Program, the USDA Forest Service Rocky Mountain Research Station (04-JV-11222063-299), and the Forest Public Access Resource Center (ForestPARC), an Upper Midwest Aerospace Consortium (UMAC) group, which is in turn supported with funds from NASA.

Fingerprint

Dive into the research topics of 'The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data'. Together they form a unique fingerprint.

Cite this