Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data

  • Authors: Silva, Carlos A.; Hudak, Andrew T.; Vierling, Lee A.; Loudermilk, E. Louise; O''Brien, Joseph J.; Hiers, J. Kevin; Jack, Steve B.; Gonzalez-Benecke, Carlos; Lee, Heezin; Falkowski, Michael J.; Khosravipour, Anahita
  • Publication Year: 2016
  • Publication Series: Scientific Journal (JRNL)
  • Source: Canadian Journal of Remote Sensing. 42(5): 554-573.
  • DOI: 10.1080/07038992.2016.1196582

Abstract

Light Detection and Ranging (LiDAR) has demonstrated potential for forest inventory at the individual-tree level. The aim in this study was to predict individual-tree height (Ht; m), basal area (BA; m2), and stem volume (V; m3) attributes, imputing Random Forest k-nearest neighbor (RF k-NN) and individual-tree-level-based metrics extracted from a LiDAR-derived canopy height model (CHM) in a longleaf pine (Pinus palustris Mill.) forest in southwestern Georgia, United States. We developed a new framework for modeling tree-level forest attributes that comprise 3 steps: (i) individual tree detection, crown delineation, and tree-level-based metrics computation from LiDAR-derived CHM; (ii) automatic matching of LiDAR-derived trees and field-based trees for a regression modeling step using a novel algorithm; and (iii) RF k-NN imputation modeling for estimating tree-level Ht, BA, and V and subsequent summarization of these metrics at the plot and stand levels. RMSDs for tree-level Ht, BA, and V were 2.96%, 58.62%, and 8.19%, respectively. Although BA estimation accuracy was poor because of the longleaf pine growth habitat, individual-tree locations, Ht, and V were estimated with high accuracy, especially in low-canopy-cover conditions. Future efforts based on the findings could help improve the estimation accuracy of individual-tree-level attributes such as BA.

  • Citation: Silva, Carlos A.; Hudak, Andrew T.; Vierling, Lee A.; Loudermilk, E. Louise; O'Brien, Joseph J.; Hiers, J. Kevin; Jack, Steve B.; Gonzalez-Benecke, Carlos; Lee, Heezin; Falkowski, Michael J.; Khosravipour, Anahita. 2016. Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data. Canadian Journal of Remote Sensing. 42(5): 554-573.
  • Keywords: Light Detection and Ranging, LiDAR, longleaf pine, Pinus palustris
  • Posted Date: September 15, 2016
  • Modified Date: January 6, 2017
  • Print Publications Are No Longer Available

    In an ongoing effort to be fiscally responsible, the Southern Research Station (SRS) will no longer produce and distribute hard copies of our publications. Many SRS publications are available at cost via the Government Printing Office (GPO). Electronic versions of publications may be downloaded, printed, and distributed.

    Publication Notes

    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
    • To view this article, download the latest version of Adobe Acrobat Reader.