Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms

Abstract Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle and climate change in the global terrestrial ecosystem. AGB estimation based on remote sensing is an effective method for regional scale. In this study, Landsat 8 Operational Land Imager and Sentinel-1...

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Autores principales:	Yingchang Li, Mingyang Li, Chao Li, Zhenzhen Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/48b07a471e1445d18bc5d1b262e06d87
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spelling	oai:doaj.org-article:48b07a471e1445d18bc5d1b262e06d872021-12-02T17:40:44ZForest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms10.1038/s41598-020-67024-32045-2322https://doaj.org/article/48b07a471e1445d18bc5d1b262e06d872020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-67024-3https://doaj.org/toc/2045-2322Abstract Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle and climate change in the global terrestrial ecosystem. AGB estimation based on remote sensing is an effective method for regional scale. In this study, Landsat 8 Operational Land Imager and Sentinel-1A data and China’s National Forest Continuous Inventory data in combination with three algorithms, either the linear regression (LR), random forest (RF), or the extreme gradient boosting (XGBoost), were used to estimate biomass of the subtropical forests in Hunan Province, China. XGBoost is a scalable tree boosting system that is widely used by data scientists and provides state-of-the-art results for many problems. It can process an entire dataset with billions of examples using a minimal amount of computational resources through the particular way of cache access patterns, data compression, and data fragmentation. The results include: (1) The combination of Landsat 8 and Sentinel-1A images as predictor variables in the XGBoost model provided the best AGB estimation. (2) In contrast to the LR method, the F-test results indicated that a significant improvement in AGB estimation was achieved with the RF and XGBoost algorithms. (3) The effect of parameter optimization was found to be more significant on XGBoost than on RF. (4) The XGBoost model is an effective method for AGB estimation and can reduce the problems of overestimation and underestimation. This research provides a new way of estimating AGB for the subtropical forest based on remote sensing through the synergy of different sensors datasets and modeling algorithms.Yingchang LiMingyang LiChao LiZhenzhen LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yingchang Li Mingyang Li Chao Li Zhenzhen Liu Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
description	Abstract Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle and climate change in the global terrestrial ecosystem. AGB estimation based on remote sensing is an effective method for regional scale. In this study, Landsat 8 Operational Land Imager and Sentinel-1A data and China’s National Forest Continuous Inventory data in combination with three algorithms, either the linear regression (LR), random forest (RF), or the extreme gradient boosting (XGBoost), were used to estimate biomass of the subtropical forests in Hunan Province, China. XGBoost is a scalable tree boosting system that is widely used by data scientists and provides state-of-the-art results for many problems. It can process an entire dataset with billions of examples using a minimal amount of computational resources through the particular way of cache access patterns, data compression, and data fragmentation. The results include: (1) The combination of Landsat 8 and Sentinel-1A images as predictor variables in the XGBoost model provided the best AGB estimation. (2) In contrast to the LR method, the F-test results indicated that a significant improvement in AGB estimation was achieved with the RF and XGBoost algorithms. (3) The effect of parameter optimization was found to be more significant on XGBoost than on RF. (4) The XGBoost model is an effective method for AGB estimation and can reduce the problems of overestimation and underestimation. This research provides a new way of estimating AGB for the subtropical forest based on remote sensing through the synergy of different sensors datasets and modeling algorithms.
format	article
author	Yingchang Li Mingyang Li Chao Li Zhenzhen Liu
author_facet	Yingchang Li Mingyang Li Chao Li Zhenzhen Liu
author_sort	Yingchang Li
title	Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
title_short	Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
title_full	Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
title_fullStr	Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
title_full_unstemmed	Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
title_sort	forest aboveground biomass estimation using landsat 8 and sentinel-1a data with machine learning algorithms
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/48b07a471e1445d18bc5d1b262e06d87
work_keys_str_mv	AT yingchangli forestabovegroundbiomassestimationusinglandsat8andsentinel1adatawithmachinelearningalgorithms AT mingyangli forestabovegroundbiomassestimationusinglandsat8andsentinel1adatawithmachinelearningalgorithms AT chaoli forestabovegroundbiomassestimationusinglandsat8andsentinel1adatawithmachinelearningalgorithms AT zhenzhenliu forestabovegroundbiomassestimationusinglandsat8andsentinel1adatawithmachinelearningalgorithms
_version_	1718379758835728384

Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms

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