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    Author(s): Carlos A. Silva; Andrew T. Hudak; Lee A. Vierling; E. Louise LoudermilkJoseph J. O'Brien; J. Kevin Hiers; Steve B. Jack; Carlos Gonzalez-Benecke; Heezin Lee; Michael J. Falkowski; Anahita Khosravipour
    Date: 2016
    Source: Canadian Journal of Remote Sensing. 42(5): 554-573.
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: Download Publication  (1.0 MB)


    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.

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    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.


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    Light Detection and Ranging, LiDAR, longleaf pine, Pinus palustris

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