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    Author(s): Michael J. Falkowski; Alistair M.S. Smith; Paul E. Gessler; Andrew T. Hudak; Lee A. Vierling; Jeffrey S. Evans
    Date: 2008
    Source: Canadian Journal of Remote Sensing. 34(2): S338-S350.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (2.27 MB)

    Description

    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.

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    Citation

    Falkowski, Michael J.; Smith, Alistair M.S.; Gessler, Paul E.; Hudak, Andrew T.; Vierling, Lee A.; Evans, Jeffrey S. 2008. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Canadian Journal of Remote Sensing. 34(2): S338-S350.

    Keywords

    conifer forest canopy cover, tree detection algorithms, lidar data, spatial wavelet analysis (SWA), variable window filters (VWF)

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https://www.fs.usda.gov/treesearch/pubs/32022