An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data
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 n...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9435019/ |
| _version_ | 1818942848798556160 |
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| author | Peiqing Lou Bolin Fu Hongchang He Jianjun Chen Tonghua Wu Xingchen Lin Lilong Liu Donglin Fan Tengfang Deng |
| author_facet | Peiqing Lou Bolin Fu Hongchang He Jianjun Chen Tonghua Wu Xingchen Lin Lilong Liu Donglin Fan Tengfang Deng |
| author_sort | Peiqing Lou |
| collection | DOAJ |
| description | 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 [<italic>R</italic><sup>2</sup> ≥ 0.86, root mean square error (RMSE) ≤ 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 (<italic>R</italic><sup>2</sup> = 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 (Cl<sub>red-edge</sub>) and Green Chlorophyll Index (Cl<sub>green</sub>) have the highest influence on the CCC inversion accuracy among input variables. |
| first_indexed | 2024-12-20T07:17:57Z |
| format | Article |
| id | doaj.art-17a6e80a3e8b46089970531bd19b4a57 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-20T07:17:57Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-17a6e80a3e8b46089970531bd19b4a572022-12-21T19:48:45ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01145311532510.1109/JSTARS.2021.30815659435019An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing DataPeiqing Lou0https://orcid.org/0000-0001-5463-2909Bolin Fu1https://orcid.org/0000-0002-3469-1861Hongchang He2Jianjun Chen3Tonghua Wu4https://orcid.org/0000-0002-5084-3570Xingchen Lin5Lilong Liu6Donglin Fan7Tengfang Deng8College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, ChinaCryosphere 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, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaHigh-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 [<italic>R</italic><sup>2</sup> ≥ 0.86, root mean square error (RMSE) ≤ 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 (<italic>R</italic><sup>2</sup> = 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 (Cl<sub>red-edge</sub>) and Green Chlorophyll Index (Cl<sub>green</sub>) have the highest influence on the CCC inversion accuracy among input variables.https://ieeexplore.ieee.org/document/9435019/Canopy chlorophyll content (CCC)multiscale remote sensing datarandom forest (RF) regressionscale matchingunmanned aerial vehicle (UAV) |
| spellingShingle | Peiqing Lou Bolin Fu Hongchang He Jianjun Chen Tonghua Wu Xingchen Lin Lilong Liu Donglin Fan Tengfang Deng 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 Canopy chlorophyll content (CCC) multiscale remote sensing data random forest (RF) regression scale matching unmanned aerial vehicle (UAV) |
| title | An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data |
| title_full | An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data |
| title_fullStr | An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data |
| title_full_unstemmed | An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data |
| title_short | An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data |
| title_sort | effective method for canopy chlorophyll content estimation of marsh vegetation based on multiscale remote sensing data |
| topic | Canopy chlorophyll content (CCC) multiscale remote sensing data random forest (RF) regression scale matching unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/9435019/ |
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