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Forests 2017, 8(7), 254; doi:10.3390/f8070254

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

1
Department of Natural Resources and Society, College of Natural Resources, University of Idaho (UI), 875 Perimeter Drive, Moscow, ID 83843, USA
2
US Forest Service (USDA), Rocky Mountain Research Station, RMRS, 1221 South Main Street, Moscow, ID 83843, USA
3
Jet Propulsion Laboratory, California Institute of Technology 4800 Oak Grove Drive, Pasadena, CA 91109, USA
4
School of Geosciences, University of Edinburgh, Edinburgh EH8 9XL, UK
5
Department of Forestry and Environmental Resources, North Carolina State University, 2800 Faucette Drive, Raleigh, NC 27695, USA
6
Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester LE1 7RH, UK
7
Tecnosylva Parque Tecnológico de León, 24009 León, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Timothy A. Martin
Received: 27 April 2017 / Revised: 22 June 2017 / Accepted: 13 July 2017 / Published: 17 July 2017
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Abstract

Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume. View Full-Text
Keywords: forest inventory; lidar; remote sensing; supply chain forest inventory; lidar; remote sensing; supply chain
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Silva, C.A.; Klauberg, C.; Hudak, A.T.; Vierling, L.A.; Jaafar, W.S.W.M.; Mohan, M.; Garcia, M.; Ferraz, A.; Cardil, A.; Saatchi, S. Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest. Forests 2017, 8, 254.

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