Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China
Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study,...
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MDPI AG
2020-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/3/393 |
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| author | Shuai Wang Jinhu Gao Qianlai Zhuang Yuanyuan Lu Hanlong Gu Xinxin Jin |
| author_facet | Shuai Wang Jinhu Gao Qianlai Zhuang Yuanyuan Lu Hanlong Gu Xinxin Jin |
| author_sort | Shuai Wang |
| collection | DOAJ |
| description | Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R<sup>2</sup>), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m<sup>−2</sup>, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region. |
| first_indexed | 2024-04-11T18:43:44Z |
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| id | doaj.art-0655222dcbce47f9b5893da5adf87844 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:43:44Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0655222dcbce47f9b5893da5adf878442022-12-22T04:08:54ZengMDPI AGRemote Sensing2072-42922020-01-0112339310.3390/rs12030393rs12030393Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in ChinaShuai Wang0Jinhu Gao1Qianlai Zhuang2Yuanyuan Lu3Hanlong Gu4Xinxin Jin5College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, ChinaInstitute of Cash Crops, Shanxi Academy of Agricultural Sciences, Taiyuan 030031, ChinaDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USANanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaCollege of Land and Environment, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Land and Environment, Shenyang Agricultural University, Shenyang 110866, ChinaAccurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R<sup>2</sup>), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m<sup>−2</sup>, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.https://www.mdpi.com/2072-4292/12/3/393soil organic carbon stocksmultispectral remote sensingforestry ecologyspatial variation |
| spellingShingle | Shuai Wang Jinhu Gao Qianlai Zhuang Yuanyuan Lu Hanlong Gu Xinxin Jin Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China Remote Sensing soil organic carbon stocks multispectral remote sensing forestry ecology spatial variation |
| title | Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China |
| title_full | Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China |
| title_fullStr | Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China |
| title_full_unstemmed | Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China |
| title_short | Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China |
| title_sort | multispectral remote sensing data are effective and robust in mapping regional forest soil organic carbon stocks in a northeast forest region in china |
| topic | soil organic carbon stocks multispectral remote sensing forestry ecology spatial variation |
| url | https://www.mdpi.com/2072-4292/12/3/393 |
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