Work

The phenology and water level time-series mangrove index for improved mangrove monitoring

Year: 2024

Type: article

Abstract: • A robust and efficient method for rapid mangrove mapping. • A novel index considering phenology and water level time-series characteristics. • Integrates spectral and temporal characteristics from dense time-series data. • Outperforms existing multispectral-based mangrove indices. Mangroves face decline and degradation due to human activities and natural forces, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices that rely on multispectral image spectral characteristics suffer from limitations in terms of recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2024.104188