KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Remote sensing estimation of suspended sediment concentration based on Random Forest Regression Model | |
Fang, Xinrui1,2; Wen, Zhaofei1 ; Chen, Jilong1; Wu, Shengjun1,2; Huang, Yuanyang1; Ma, Maohua1,2
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| 2019 | |
| 摘要 | Since 2003 when the Three-Gorge Dam (TGD) was in impoundment, the dam abundantly blocks suspended sediment and cause clear water flowing through the dam, which induces scouring effect on the beds and banks of the Yangtze river below the dam.Furthermore, the altered Suspended Sediment Concentration (SSC) has adversely affected the downstream coastal environment. In this study, the random forest model was applied for SSC estimation. The model is flexible and robust, and can be used for regression analysis of ecological environment variables. Yet, its ability in estimating SSC in aquatic environment has not been fully understood. On the basis of the monitoring data of SSC and satellite remote sensing reflectance data from 2002 to 2015, this study estimated the SSC in Yichang-Chenglingji downstream reach of the TGD by constructing a non-parametric regression model using random forest. The results showed that:(1) the random forest model could effectively monitor SSC, and the correlation coefficient and prediction accuracy were significantly improved from those of other models (linear regression, support vector machine, and artificial neural network model).(2) the red band is a suitable predictor for SSC in the random forest model, but cannot be independently used for forecasting. SSC remote sensing prediction requires multivariate co-participation. (3)By using the random forest model, the average root mean square error of the seasonal division was 0.46 mg/L, and the average relative root mean square error was 12.33%. These values met the needs of high-precision SSC estimation. In conclusion, this study reveals that the season shall be considered as temporal factors to estimate SSC and then prepare for the subsequent SSC spatiotemporal inversion. Which is of great help to reveal the TGD's downstream river sediment evolution, and understand the regional distribution of sediment and sediment variation process in the future. © 2019, Science Press. All right reserved. |
| DOI | 10.11834/jrs.20197498 |
| 发表期刊 | Yaogan Xuebao/Journal of Remote Sensing
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| ISSN | 10074619 |
| 卷号 | 23期号:4页码:756-772 |
| 收录类别 | EI |
| 语种 | 中文 |
| EISSN | 20959494 |
