Mapping Invasive <italic>Spartina alterniflora</italic> Using Phenological Information and Red-Edge Bands of Sentinel-2 Time-Series Data
The accurate mapping of <italic>Spartina alterniflora</italic> (<italic>S. alterniflora</italic>) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for <italic>S. alterniflora</ital...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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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/10748369/ |
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| Summary: | The accurate mapping of <italic>Spartina alterniflora</italic> (<italic>S. alterniflora</italic>) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for <italic>S. alterniflora</italic> invasion monitoring; however, there are still several issues that need to be addressed. The spectral similarities between <italic>S. alterniflora</italic> and surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifying <italic>S. alterniflora</italic>, current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) for <italic>S. alterniflora</italic> extraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed for <italic>S. alterniflora</italic> extraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detecting <italic>S. alterniflora</italic>. <italic>S. alterniflora</italic> extraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies of <italic>S. alterniflora</italic> than the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features for <italic>S. alterniflora</italic> extraction studies and serve as an effective control and management of <italic>S. alterniflora</italic>. |
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| ISSN: | 1939-1404 2151-1535 |