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Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data

Overview of attention for article published in Anais da Academia Brasileira de Ciências, September 2017
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Title
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data
Published in
Anais da Academia Brasileira de Ciências, September 2017
DOI 10.1590/0001-3765201720160324
Pubmed ID
Authors

CARLOS A. SILVA, CARINE KLAUBERG, ANDREW T. HUDAK, LEE A. VIERLING, SCOTT J. FENNEMA, ANA PAULA D. CORTE

Abstract

Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 24%
Researcher 5 17%
Student > Ph. D. Student 5 17%
Student > Postgraduate 3 10%
Librarian 2 7%
Other 2 7%
Unknown 5 17%
Readers by discipline Count As %
Environmental Science 7 24%
Agricultural and Biological Sciences 5 17%
Engineering 4 14%
Arts and Humanities 1 3%
Earth and Planetary Sciences 1 3%
Other 3 10%
Unknown 8 28%