Skip to main content
U.S. flag

An official website of the United States government

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

Author(s):

Carlos A. Silva
Lee A. Vierling
J. Kevin Hiers
Steve B. Jack
Carlos Gonzalez-Benecke
Heezin Lee
Michael J. Falkowski
Anahita Khosravipour

Year:

2016

Publication type:

Scientific Journal (JRNL)

Primary Station(s):

Rocky Mountain Research Station

Historical Station(s):

Southern Research Station

Source:

Canadian Journal of Remote Sensing. 42(5): 554-573.

Description

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.

Cited

Publication Notes

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
https://www.fs.usda.gov/treesearch/pubs/52704