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    Author(s): Wade T. Tinkham; Alistair M. S. Smith; Chad Hoffman; Andrew T. Hudak; Michael J. Falkowski; Mark E. Swanson; Paul E. Gessler
    Date: 2012
    Source: Canadian Journal of Forest Research. 42: 413-422.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (363.01 KB)


    Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR's ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with LiDAR data. Prior to applying LiDAR in operational management, it is necessary to understand the errors in Li- DAR-derived estimates of forest inventory metrics (i.e., tree height). Most LiDAR-based forest inventory metrics require creation of digital elevation models (DEM), and because metrics are calculated relative to the DEM surface, errors within the DEMs propagate into delivered metrics. This study combines LiDAR DEMs and 54 ground survey plots to investigate how surface morphology and vegetation structure influence DEM errors. The study further compared two LiDAR classification algorithms and found no significant difference in their performance.

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    Tinkham, Wade T.; Smith, Alistair M. S.; Hoffman, Chad; Hudak, Andrew T.; Falkowski, Michael J.; Swanson, Mark E.; Gessler, Paul E. 2012. Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories. Canadian Journal of Forest Research. 42: 413-422.


    LiDAR, ground surface errors, forest inventories, digital elevation models (DEM)

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