Effect of tide level on submerged mangrove recognition index using multi-temporal remotely-sensed data
Mangrove forests are intertidal wetland with a diverse assemblage of trees, shrubs and palms growing along tropical and subtropical coastlines. Effective mapping of mangrove forests has not yet been achieved due to the periodicity of tidal dynamics. Our previous studies showed that a submerged mangr...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/3cda14e51cf149efbd6346b9a4ced70a |
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| Sumario: | Mangrove forests are intertidal wetland with a diverse assemblage of trees, shrubs and palms growing along tropical and subtropical coastlines. Effective mapping of mangrove forests has not yet been achieved due to the periodicity of tidal dynamics. Our previous studies showed that a submerged mangrove recognition index (SMRI), which was proposed based on the differential spectral signature of mangrove forests from high and low tides, has potential advantages in mangrove discrimination and classification. However, the effect of tide level on the performance of SMRI is still unclear. In this study, GaoFen-1 images with various tide heights were acquired, and SMRI images from low tide to high tide were obtained. Then, the resulting SMRI images were compared in detail, and the relationship between tide level and SMRI values was analyzed. This experiment was accomplished via a case study in Yunlin, Guangxi Province in China. The results showed that an increased difference in tide level led to an increase in the number of pixels of high SMRI values, indicating that more undetected submerged mangrove forests could be distinguished using SMRI. Furthermore, an exponential relationship was observed between SMRI and tide level. It suggests that SMRI effectively helps to distinguish submerged mangrove forests from multi-tide remotely-sensed imagery, and also benefits accurate mapping of mangrove forests. |
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