Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401895 |
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. |
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Anais da Academia Brasileira de Ciências (Online) |
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Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR dataForest InventoryLiDAR metricsMultiple Linear RegressionRemote SensingABSTRACT 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.Academia Brasileira de Ciências2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401895Anais da Academia Brasileira de Ciências v.89 n.3 2017reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201720160324info:eu-repo/semantics/openAccessSILVA,CARLOS A.KLAUBERG,CARINEHUDAK,ANDREW T.VIERLING,LEE A.FENNEMA,SCOTT J.CORTE,ANA PAULA D.eng2019-11-29T00:00:00Zoai:scielo:S0001-37652017000401895Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-11-29T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
title |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
spellingShingle |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data SILVA,CARLOS A. Forest Inventory LiDAR metrics Multiple Linear Regression Remote Sensing |
title_short |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
title_full |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
title_fullStr |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
title_full_unstemmed |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
title_sort |
Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data |
author |
SILVA,CARLOS A. |
author_facet |
SILVA,CARLOS A. KLAUBERG,CARINE HUDAK,ANDREW T. VIERLING,LEE A. FENNEMA,SCOTT J. CORTE,ANA PAULA D. |
author_role |
author |
author2 |
KLAUBERG,CARINE HUDAK,ANDREW T. VIERLING,LEE A. FENNEMA,SCOTT J. CORTE,ANA PAULA D. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
SILVA,CARLOS A. KLAUBERG,CARINE HUDAK,ANDREW T. VIERLING,LEE A. FENNEMA,SCOTT J. CORTE,ANA PAULA D. |
dc.subject.por.fl_str_mv |
Forest Inventory LiDAR metrics Multiple Linear Regression Remote Sensing |
topic |
Forest Inventory LiDAR metrics Multiple Linear Regression Remote Sensing |
description |
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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401895 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401895 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765201720160324 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.89 n.3 2017 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302864811884544 |