Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR
文献类型：期刊论文
| 作者 | Sun, Xiaofang1; Li, Guicai2; Wang, Meng1; Fan, Zemeng3
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-03-02 |
| 卷号 | 11期号:6页码:16 |
| 关键词 | aboveground biomass prediction model number of samples cross-validation remotely sensed data |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs11060722 |
| 通讯作者 | Wang, Meng(wangmeng@qfnu.edu.cn) |
| 英文摘要 | Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R-2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R-2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model. |
| WOS关键词 | TROPICAL FOREST ; PREDICTION ; PROVINCE ; STOCK ; GLAS |
| 资助项目 | National Key Research and Development Program of China[2016YFA0600204] ; National Natural Science Foundation of China[41501428] ; National Natural Science Foundation of China[41371400] ; Natural Science Foundation of Shandong Province, China[ZR2017BD010] ; Project of Shandong Province Higher Educational Science and Technology Program[J16LH01] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000465615300080 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province, China ; Project of Shandong Province Higher Educational Science and Technology Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/59745]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Meng |
| 作者单位 | 1.Qufu Normal Univ, Sch Geog & Tourism, Rizhao 276800, Peoples R China 2.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Xiaofang,Li, Guicai,Wang, Meng,et al. Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR[J]. REMOTE SENSING,2019,11(6):16. |
| APA | Sun, Xiaofang,Li, Guicai,Wang, Meng,&Fan, Zemeng.(2019).Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR.REMOTE SENSING,11(6),16. |
| MLA | Sun, Xiaofang,et al."Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR".REMOTE SENSING 11.6(2019):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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