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Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation

Authors :
CARLOS ALBERTO SILVA
CARINE KLAUBERG
ANDREW T. HUDAK
LEE A. VIERLING
VERALDO LIESENBERG
LUIZ G. BERNETT
CLEWERSON F. SCHERAIBER
EMERSON R. SCHOENINGER
Source :
Anais da Academia Brasileira de Ciências, Vol 90, Iss 1, Pp 295-309
Publisher :
Academia Brasileira de Ciências.

Abstract

ABSTRACT Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

Details

Language :
English
ISSN :
16782690 and 00013765
Volume :
90
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Anais da Academia Brasileira de Ciências
Publication Type :
Academic Journal
Accession number :
edsdoj.15646c47b82c4f44be88bcdcb76779e5
Document Type :
article
Full Text :
https://doi.org/10.1590/0001-3765201820160071