| An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data | |
| P. Lou; B. Fu; H. He; J. Chen; T. Wu; X. Lin; L. Liu; D. Fan; T. Deng | |
| 2021-05-18 | |
| Source Publication | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Impact Factor & Quartile |
| ISSN | 2151-1535 |
| Volume | 14Pages:5311-5325 |
| Abstract | High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This article proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multiscale marsh vegetation CCC sample data. A random forest (RF) regression algorithm was used to evaluate the application performance of GF-1 wide field view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 multispectral instrument (MSI) satellite remote sensing data in marsh vegetation CCC inversion. In addition, parameter optimization of the RF regression model was used to construct an optimization algorithm suitable for marsh vegetation, and the importance of input variables was quantitatively evaluated. The results showed that the UAV multispectral images assisted in the acquisition of marsh vegetation CCC sample data, as the method expanded the number of CCC samples while quantifying the CCC sample data collection accuracy [R2 鈮?0.86, root mean square error (RMSE) 鈮?6.98 SPAD], which improved the CCC inversion accuracy compared with traditional sampling methods. Extracting pure vegetation pixels through binary classification reduces the uncertainty of the UAV-scale CCC inversion results. Parameter optimization of the RF regression model further improves the CCC inversion accuracy at GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI scales. Among the three satellite remote sensing data, Sentinel-2 MSI achieved the highest CCC inversion accuracy for marsh vegetation (R2 = 0.79, RMSE = 10.96 SPAD) due to the inclusion of red-edge bands that are more sensitive to vegetation properties. Red-edge Chlorophyll Index (Clred-edge) and Green Chlorophyll Index (Clgreen) have the highest influence on the CCC inversion accuracy among input variables. |
| Keyword | Canopy chlorophyll content (CCC) multiscale remote sensing data random forest (RF) regression scale matching unmanned aerial vehicle (UAV) |
| Publisher | IEEE |
| DOI | 10.1109/JSTARS.2021.3081565 |
| Indexed By | IEEE |
| Language | 英语 |
| Original Document Type | IEEE Journals |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/451382 |
| Collection | 兰州大学 |
| Affiliation | 1.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China 2.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China 3.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China 4.College of Geomatics and Geoinformation, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, China 5.Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, China 6.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China 7.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China 8.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China 9.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China |
| Recommended Citation GB/T 7714 | P. Lou,B. Fu,H. He,et al. An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021,14:5311-5325. |
| APA | P. Lou.,B. Fu.,H. He.,J. Chen.,T. Wu.,...&T. Deng.(2021).An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,14,5311-5325. |
| MLA | P. Lou,et al."An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021):5311-5325. |
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