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

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posted on 23.08.2019, 14:51 by C Silva, C Klauberg, A Hudak, L Vierling, W Jaafar, M Mohan, M Garcia, A Ferraz, A Cardil, S Saatchi
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.

Funding

National Council of Technological and Scientific Development (CNPq) via the Science without Borders Program (Process 249802/2013-9) and USDA Forest Service.

History

Citation

Forests, 2017, 8 (7), pp. 254-254

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment

Version

VoR (Version of Record)

Published in

Forests

Publisher

MDPI

eissn

1999-4907

Acceptance date

13/07/2017

Copyright date

2017

Available date

23/08/2019

Language

en

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