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| Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass | |
| Huang, Weiye1,2; Li, Wenlong1,2; Xu, Jing3; Ma, Xuanlong4; Li, Changhui5; Liu, Chenli1,2 | |
| 2022-05-01 | |
| 发表期刊 | Remote Sensing
 |
| 卷号 | 14期号:9 |
| 摘要 | Above-ground biomass (AGB) is a key indicator for studying grassland productivity and evaluating carbon sequestration capacity; it is also a key area of interest in hyperspectral ecological remote sensing. In this study, we use data from a typical alpine meadow in the Qinghai–Tibet Plateau during the main growing season (July–September), compare the results of various feature selection algorithms to extract an optimal subset of spectral variables, and use machine learning methods and data mining techniques to build an AGB prediction model and realize the optimal inversion of above-ground grassland biomass. The results show that the Lasso and RFE_SVM band filtering machine learning models can effectively select the global optimal feature and improve the prediction effect of the model. The analysis also compares the support vector machine (SVM), least squares regression boosting (LSB), and Gaussian process regression (GPR) AGB inversion models; our findings show that the results of the three models are similar, with the GPR machine learning model achieving the best outcomes. In addition, through the analysis of different data combinations, it is found that the accuracy of AGB inversion can be significantly improved by combining the spectral characteristics with the growing season. Finally, by constructing a machine learning interpretable model to analyze the specific role of features, it was found that the same band plays different roles in different records, and the related results can provide a scientific basis for the research of grassland resource monitoring and estimation. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| 关键词 | Adaptive boosting Biomass Data mining Feature extraction Remote sensing Aboveground biomass Alpine grasslands Features selection Gaussian process regression Growing season HyperSpectral Interpretability Machine learning methods Machine learning models Support vectors machine |
| DOI | 10.3390/rs14092086 |
| 收录类别 | EI ; SCIE |
| ISSN | 2072-4292 |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000795412300001 |
| 出版者 | MDPI |
| EI入藏号 | 20221912092579 |
| EI主题词 | Support vector machines |
| EI分类号 | 723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing |
| 原始文献类型 | Journal article (JA) |
| EISSN | 2072-4292 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.lzufe.edu.cn/handle/39EH0E1M/31968 |
| 专题 | 兰州财经大学 |
| 作者单位 | 1.State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou; 730000, China; 2.Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, Lanzhou University, Lanzhou; 730020, China; 3.School of Agriculture and Forestry Economic and Management, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; 4.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou; 730020, China; 5.Agriculture and Animal Husbandry Collage, Qinghai University, Xining; 810016, China |
| 推荐引用方式 GB/T 7714 | Huang, Weiye,Li, Wenlong,Xu, Jing,et al. Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass[J]. Remote Sensing,2022,14(9). |
| APA | Huang, Weiye,Li, Wenlong,Xu, Jing,Ma, Xuanlong,Li, Changhui,&Liu, Chenli.(2022).Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass.Remote Sensing,14(9). |
| MLA | Huang, Weiye,et al."Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass".Remote Sensing 14.9(2022). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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