Effect of tide level on submerged mangrove recognition index using multi-temporal remotely-sensed data

Ecological Indicators

卷:131

Tingting He¹  Xuemin Xing²  Lingjie Zhu³  Qing Xia³  Mingming Jia⁴ 

[1] Corresponding author.;

[2] China University of Mining and Technology (Beijing), Beijing 100083, China;

[3] Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science and Technology, Changsha 410114, China;

[4] Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

关键词: Mangrove forests;    Tide level;    Submerged mangrove recognition index (SMRI);    GF-1 images;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown