Imputation of Individual Longleaf Pine (Pinus palustris Mill.) Tree Attributes from Field and LiDAR Data

Carlos A. Silva, Andrew T. Hudak, Lee A. Vierling, E. Louise Loudermilk, Joseph J. O’Brien, J. Kevin Hiers, Steve B. Jack, Carlos Gonzalez-Benecke, Heezin Lee, Michael J. Falkowski, Anahita Khosravipour

Research output: Contribution to journalArticlepeer-review

170 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)554-573
Number of pages20
JournalCanadian Journal of Remote Sensing
Issue number5
StatePublished - Sep 2 2016

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