Oceanic index variability and its role in mangrove dieback events in East Lampung

¹ Marine Environmental Science Study Program, Institut Teknologi Sumatera (ITERA), South Lampung, Lampung 35365, Indonesia
² Data Science Study Program, Institut Teknologi Sumatera (ITERA), South Lampung, Lampung 35365, Indonesia

^* Corresponding author: rizki.permana@sll.itera.ac.id

Abstract

A significant dieback event of Avicennia marina was observed in the coastal area of East Lampung, beginning in 2020, with its cause remaining unidentified. This study aimed to assess the rate of mangrove degradation and investigated the influence of the ocean index on mangrove dieback event. Sequential satellite imagery analysis (2019–2022) was performed to evaluate changes in mangrove cover, focusing on a 200-hectare area within the total 528.69-hectare mangrove forest, utilizing using Normalized Difference Vegetation Index (NDVI) and Mangrove Health Index (MVI). Field data were collected at 30 research stations, encompassing live mangroves, partially affected areas, and fully dieback sites, both before and during the dieback event (2020 and 2021). These efforts provide critical insights into the extent and drivers of mangrove decline in the region. Based on the results, the mangrove dieback caused a rapid and significant loss of healthy stands (results of the MVI) and very-high-density stands (results of the NDVI). With reduction in mangrove area was 117.64 ha during 2020-2021. A pronounced decline in 2021 coincided with a prolonged La Niña phase, leading to excessive freshwater influx and prolonged inundation, which likely decreased the salinity and sediment dynamics required for mangrove survival. Additionally, a negative ENSO phase (-1.2) and a concurrent positive IOD phase (+1.5) were associated with increased drought conditions, elevated salinity, and reduced freshwater availability, further stressing Avicennia marina populations. These findings underscore the strong link between oceanic variability and mangrove health, highlighting the need for continued monitoring and improved predictive models to assess the long-term impacts of climate change on coastal ecosystems.