Skip to Main Content
Predicting stem total and assortment volumes in an industrial Pinus taeda L. forest plantation using airborne laser scanning data and random forestAuthor(s): Carlos Alberto Silva; Carine Klauberg; Andrew Thomas Hudak; Lee Alexander Vierling; Wan Shafrina Wan Mohd Jaafar; Midhun Mohan; Mariano Garcia; Antonio Ferraz; Adrian Cardil; Sassan Saatchi
Source: Forests. 8: 254.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
Download Publication (4.0 MB)
DescriptionImprovements 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.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationSilva, Carlos Alberto; Klauberg, Carine; Hudak, Andrew Thomas; Vierling, Lee Alexander; Jaafar, Wan Shafrina Wan Mohd; Mohan, Midhun; Garcia, Mariano; Ferraz, Antonio; Cardil, Adrian; Saatchi, Sassan. 2017. Predicting stem total and assortment volumes in an industrial Pinus taeda L. forest plantation using airborne laser scanning data and random forest. Forests. 8: 254.
Keywordsforest inventory, lidar, remote sensing, supply chain
- Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data
- Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in North-Central Idaho
- A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data
XML: View XML