Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting and afforestation policy making, and the aspect factors that affect forest stand growth are important to the accuracy of AGB estimation. In this study, aspect was incorporated as a variable into three machi...

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Autores principales:	Quanping Ye, Shichuan Yu, Jinliang Liu, Qingxia Zhao, Zhong Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Aboveground biomass Aspect Machine learning algorithms Black locust planted forests Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/872a76b962b5460ba1bcda3ae8a524fc
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id	oai:doaj.org-article:872a76b962b5460ba1bcda3ae8a524fc
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spelling	oai:doaj.org-article:872a76b962b5460ba1bcda3ae8a524fc2021-12-01T04:56:36ZAboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms1470-160X10.1016/j.ecolind.2021.107948https://doaj.org/article/872a76b962b5460ba1bcda3ae8a524fc2021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21006130https://doaj.org/toc/1470-160XAn accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting and afforestation policy making, and the aspect factors that affect forest stand growth are important to the accuracy of AGB estimation. In this study, aspect was incorporated as a variable into three machine learning algorithms (MLAs) (support vector machine (SVM), artificial neural network (ANN) and random forest (RF)), to estimate the AGB of black locust (Robinia pseudoacacia) planted forests in 96 field plots with four different aspects (sunny slope, semi-sunny slope, semi-shady slope and shady slope). The results showed that in the models incorporating aspect variables, the increase in accuracy varied from 36.72% to 41.23% for 29 validation plots based on R2. The A-RF model (RF with aspect variable), which had the highest R2 (0.8519) and lowest RMSE and rRMSE (12.552 Mg/ha and 0.175) was considered optimal for AGB estimation of black locust planted forests. The overestimation of sunny and shady slopes, and the underestimation of semi-sunny and semi-shady slopes are reduced by incorporating the aspect variable. Areas with lower AGB values mainly occur on sunny and shady slopes, and areas with higher AGB values mainly occur on semi-sunny and semi-shady slopes. Overall, our study demonstrates that the introduction of the aspect variable provided the model with a basis for the effects of different growth conditions of black locust planted forests on different aspects, which can improve the accuracy of AGB estimation.Quanping YeShichuan YuJinliang LiuQingxia ZhaoZhong ZhaoElsevierarticleAboveground biomassAspectMachine learning algorithmsBlack locust planted forestsEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107948- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aboveground biomass Aspect Machine learning algorithms Black locust planted forests Ecology QH540-549.5
spellingShingle	Aboveground biomass Aspect Machine learning algorithms Black locust planted forests Ecology QH540-549.5 Quanping Ye Shichuan Yu Jinliang Liu Qingxia Zhao Zhong Zhao Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
description	An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting and afforestation policy making, and the aspect factors that affect forest stand growth are important to the accuracy of AGB estimation. In this study, aspect was incorporated as a variable into three machine learning algorithms (MLAs) (support vector machine (SVM), artificial neural network (ANN) and random forest (RF)), to estimate the AGB of black locust (Robinia pseudoacacia) planted forests in 96 field plots with four different aspects (sunny slope, semi-sunny slope, semi-shady slope and shady slope). The results showed that in the models incorporating aspect variables, the increase in accuracy varied from 36.72% to 41.23% for 29 validation plots based on R2. The A-RF model (RF with aspect variable), which had the highest R2 (0.8519) and lowest RMSE and rRMSE (12.552 Mg/ha and 0.175) was considered optimal for AGB estimation of black locust planted forests. The overestimation of sunny and shady slopes, and the underestimation of semi-sunny and semi-shady slopes are reduced by incorporating the aspect variable. Areas with lower AGB values mainly occur on sunny and shady slopes, and areas with higher AGB values mainly occur on semi-sunny and semi-shady slopes. Overall, our study demonstrates that the introduction of the aspect variable provided the model with a basis for the effects of different growth conditions of black locust planted forests on different aspects, which can improve the accuracy of AGB estimation.
format	article
author	Quanping Ye Shichuan Yu Jinliang Liu Qingxia Zhao Zhong Zhao
author_facet	Quanping Ye Shichuan Yu Jinliang Liu Qingxia Zhao Zhong Zhao
author_sort	Quanping Ye
title	Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
title_short	Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
title_full	Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
title_fullStr	Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
title_full_unstemmed	Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
title_sort	aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/872a76b962b5460ba1bcda3ae8a524fc
work_keys_str_mv	AT quanpingye abovegroundbiomassestimationofblacklocustplantedforestswithaspectvariableusingmachinelearningregressionalgorithms AT shichuanyu abovegroundbiomassestimationofblacklocustplantedforestswithaspectvariableusingmachinelearningregressionalgorithms AT jinliangliu abovegroundbiomassestimationofblacklocustplantedforestswithaspectvariableusingmachinelearningregressionalgorithms AT qingxiazhao abovegroundbiomassestimationofblacklocustplantedforestswithaspectvariableusingmachinelearningregressionalgorithms AT zhongzhao abovegroundbiomassestimationofblacklocustplantedforestswithaspectvariableusingmachinelearningregressionalgorithms
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Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms

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