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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-67024-3 |
| _version_ | 1818722525558865920 |
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| author | Yingchang Li Mingyang Li Chao Li Zhenzhen Liu |
| author_facet | Yingchang Li Mingyang Li Chao Li Zhenzhen Liu |
| author_sort | Yingchang Li |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T20:56:01Z |
| format | Article |
| id | doaj.art-48b07a471e1445d18bc5d1b262e06d87 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T20:56:01Z |
| publishDate | 2020-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-48b07a471e1445d18bc5d1b262e06d872022-12-21T21:32:53ZengNature PortfolioScientific Reports2045-23222020-06-0110111210.1038/s41598-020-67024-3Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithmsYingchang Li0Mingyang Li1Chao Li2Zhenzhen Liu3Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry UniversityCo-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry UniversityCo-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry UniversityCollege of Forestry, Shanxi Agricultural UniversityAbstract 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.https://doi.org/10.1038/s41598-020-67024-3 |
| spellingShingle | Yingchang Li Mingyang Li Chao Li Zhenzhen Liu Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-020-67024-3 |
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