Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches

Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume i...

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Autores: Vieira Leite, Rodrigo, Hummel do Amaral, Cibele, De Paula Pires, Raul, Silva, Carlos Alberto, Boechat Soares, Carlos Pedro, Paulo Macedo, Renata, Araújo Lopes Da Silva, Antonilmar, North Broadbent, Eben, Mohan, Midhun, Garcia Leite, Hélio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/69807
Acceso en línea:https://doi.org/10.3390/rs12091513
http://hdl.handle.net/10459.1/69807
Access Level:acceso abierto
Palabra clave:LiDAR
Eucalyptus
Tree detection
Machine learning
Remote sensing
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spelling Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based ApproachesVieira Leite, RodrigoHummel do Amaral, CibeleDe Paula Pires, RaulSilva, Carlos AlbertoBoechat Soares, Carlos PedroPaulo Macedo, RenataAraújo Lopes Da Silva, AntonilmarNorth Broadbent, EbenMohan, MidhunGarcia Leite, HélioLiDAREucalyptusTree detectionMachine learningRemote sensingForest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( ryyˆ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( ryyˆ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance.The first author was granted with a scholarship from the CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/rs12091513http://hdl.handle.net/10459.1/69807reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a: https://doi.org/10.3390/rs12091513Remote Sensing, 2020, vol. 12, núm. 9, p. 1513cc-by (c) Vieira Leite, Rodrigo et al., 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/698072026-06-24T12:42:17Z
dc.title.none.fl_str_mv Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
title Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
spellingShingle Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
Vieira Leite, Rodrigo
LiDAR
Eucalyptus
Tree detection
Machine learning
Remote sensing
title_short Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
title_full Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
title_fullStr Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
title_full_unstemmed Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
title_sort Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches
dc.creator.none.fl_str_mv Vieira Leite, Rodrigo
Hummel do Amaral, Cibele
De Paula Pires, Raul
Silva, Carlos Alberto
Boechat Soares, Carlos Pedro
Paulo Macedo, Renata
Araújo Lopes Da Silva, Antonilmar
North Broadbent, Eben
Mohan, Midhun
Garcia Leite, Hélio
author Vieira Leite, Rodrigo
author_facet Vieira Leite, Rodrigo
Hummel do Amaral, Cibele
De Paula Pires, Raul
Silva, Carlos Alberto
Boechat Soares, Carlos Pedro
Paulo Macedo, Renata
Araújo Lopes Da Silva, Antonilmar
North Broadbent, Eben
Mohan, Midhun
Garcia Leite, Hélio
author_role author
author2 Hummel do Amaral, Cibele
De Paula Pires, Raul
Silva, Carlos Alberto
Boechat Soares, Carlos Pedro
Paulo Macedo, Renata
Araújo Lopes Da Silva, Antonilmar
North Broadbent, Eben
Mohan, Midhun
Garcia Leite, Hélio
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv LiDAR
Eucalyptus
Tree detection
Machine learning
Remote sensing
topic LiDAR
Eucalyptus
Tree detection
Machine learning
Remote sensing
description Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( ryyˆ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( ryyˆ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/rs12091513
http://hdl.handle.net/10459.1/69807
url https://doi.org/10.3390/rs12091513
http://hdl.handle.net/10459.1/69807
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/rs12091513
Remote Sensing, 2020, vol. 12, núm. 9, p. 1513
dc.rights.none.fl_str_mv cc-by (c) Vieira Leite, Rodrigo et al., 2020
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Vieira Leite, Rodrigo et al., 2020
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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