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Provedor de dados:  Anais da ABC (AABC)
País:  Brazil
Título:  Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data
Autores:  SILVA,CARLOS A.
KLAUBERG,CARINE
HUDAK,ANDREW T.
VIERLING,LEE A.
FENNEMA,SCOTT J.
CORTE,ANA PAULA D.
Data:  2017-09-01
Ano:  2017
Palavras-chave:  Forest Inventory
LiDAR metrics
Multiple Linear Regression
Remote Sensing
Resumo:  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.
Tipo:  Info:eu-repo/semantics/article
Idioma:  Inglês
Identificador:  http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401895
Editor:  Academia Brasileira de Ciências
Relação:  10.1590/0001-3765201720160324
Formato:  text/html
Fonte:  Anais da Academia Brasileira de Ciências v.89 n.3 2017
Direitos:  info:eu-repo/semantics/openAccess
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