Phenology-based classification of Sentinel-2 data to detect coastal mangroves

Abstract

Precise categorization of mangrove forests with medium spatial resolution satellite data is challenging and occasionally yields mixed outcomes. The available methods to estimate mangrove vegetation cover using moderately high-resolution images lack differentiation between mangrove and homestead vegetation. Mangrove vegetation displays a range of responses across the phenological cycle at different wavelengths of an optical sensor. Taking advantage of this principle, this study utilized some mangrove and non-mangrove vegetation indices (VIs) as predictor variables sourced from monthly Sentinel-2 data into the random forest algorithm to derive a phenology-based classification outcome. It also ascertained a suitable month for thresholding mangroves across different VIs. Results indicated that phenology-based classification with three classes was more accurate (95% overall accuracy) than threshold-based or WorldCover v100 classifications. MI and MVI layers from December image performed better in discerning mangroves. Findings have important implications in separating mangroves from other coastal vegetations.

Keywords:

• Mangrove
• Sentinel-2
• random forest
• image classification
• vegetation index

Acknowledgements

The authors are thankful to the Science and Technology Ministry of the Government of Bangladesh for providing funding for this study. Gratefulness to the Beat Officer of Nijhum Dwip National Park and the accountant of Nijhum Dwip Co-management Committee for their assistance during the field work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Declaration of conflict of interest

The authors state that they have no known competing financial interests or personal ties that could have compromised the research presented in this study.

Data availability statement

Data of this research can be obtained on request from the corresponding author.

Correction statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was funded by Ministry of Science and Technology, Government of the People’s Republic of Bangladesh.