A robust and efficient approach to estimating the age of secondary mangrove forests employing time-series Landsat images and the CCDC model

Abstract

Secondary mangrove forests are ecosystems that regenerate in areas where original mangrove stands have been degraded or removed as a result of natural disturbances or anthropogenic activities. Compared to mature mangrove forests, secondary stands exhibit enhanced carbon accumulation, increased sediment trapping efficiency, and intensified nitrogen fixation, contributing significantly to coastal eutrophication mitigation. Accurately mapping secondary mangroves and determining their age is essential for sustainable ecosystem management and assessing their services. However, reliably determining mangrove forest age using remote sensing has been hindered by the complex dynamics of intertidal environments. To overcome these challenges, we developed a robust and efficient approach for estimating the age of secondary mangrove forests (ASMF) by integrating Landsat time-series data and the Continuous Change Detection and Classification (CCDC) algorithm. We implemented this method in the Dongzhaigang National Nature Reserve (DNNR), which is the first mangrove nature reserve established in China, achieving a coefficient of determination (R²) of 0.723. Key findings include: (1) the ASMF estimates exhibited high accuracy (R² = 0.723), with optimal performance for forests aged 9–10 years; (2) secondary mangrove forests comprised 47 % (823.87 ha) of the total mangrove area within the DNNR; and (3) younger stands (1–9 years) represented 32 % of all secondary mangrove forests. This approach offers an effective solution for regional-scale mangrove age estimation and provides a critical basis for evaluating the carbon sequestration potential of secondary mangroves in the DNNR.