Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression

Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric va...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fernando J. Aguilar, Abderrahim Nemmaoui, Manuel A. Aguilar, Alberto Peñalver
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	terrestrial laser scanning allometric models machine learning regression teak plantations forest inventory Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/63d156813db946388d991daa73f8f2b3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:63d156813db946388d991daa73f8f2b3
record_format	dspace
spelling	oai:doaj.org-article:63d156813db946388d991daa73f8f2b32021-11-11T15:12:15ZBuilding Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression10.3390/app1121101392076-3417https://doaj.org/article/63d156813db946388d991daa73f8f2b32021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10139https://doaj.org/toc/2076-3417Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (<i>Tectona grandis</i> Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that allometric models involving both H and CD to estimate DBH performed better than those based solely on H. Furthermore, boosting machine learning regression algorithms (CatBoost and GBoost) outperformed RFR (bagging) and LR (traditional linear regression) models, both in terms of goodness-of-fit (R<sup>2</sup>) and stability (variations in training and testing samples).Fernando J. AguilarAbderrahim NemmaouiManuel A. AguilarAlberto PeñalverMDPI AGarticleterrestrial laser scanningallometric modelsmachine learning regressionteak plantationsforest inventoryTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10139, p 10139 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	terrestrial laser scanning allometric models machine learning regression teak plantations forest inventory Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	terrestrial laser scanning allometric models machine learning regression teak plantations forest inventory Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Fernando J. Aguilar Abderrahim Nemmaoui Manuel A. Aguilar Alberto Peñalver Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
description	Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (<i>Tectona grandis</i> Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that allometric models involving both H and CD to estimate DBH performed better than those based solely on H. Furthermore, boosting machine learning regression algorithms (CatBoost and GBoost) outperformed RFR (bagging) and LR (traditional linear regression) models, both in terms of goodness-of-fit (R<sup>2</sup>) and stability (variations in training and testing samples).
format	article
author	Fernando J. Aguilar Abderrahim Nemmaoui Manuel A. Aguilar Alberto Peñalver
author_facet	Fernando J. Aguilar Abderrahim Nemmaoui Manuel A. Aguilar Alberto Peñalver
author_sort	Fernando J. Aguilar
title	Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
title_short	Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
title_full	Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
title_fullStr	Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
title_full_unstemmed	Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
title_sort	building tree allometry relationships based on tls point clouds and machine learning regression
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/63d156813db946388d991daa73f8f2b3
work_keys_str_mv	AT fernandojaguilar buildingtreeallometryrelationshipsbasedontlspointcloudsandmachinelearningregression AT abderrahimnemmaoui buildingtreeallometryrelationshipsbasedontlspointcloudsandmachinelearningregression AT manuelaaguilar buildingtreeallometryrelationshipsbasedontlspointcloudsandmachinelearningregression AT albertopenalver buildingtreeallometryrelationshipsbasedontlspointcloudsandmachinelearningregression
_version_	1718436653787250688

Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression

Ejemplares similares