Characterizing the spatiotemporal of near surface seawater intrusion and its association with mangrove distribution in east Lampung-Indonesia

Numerous research studies have discussed the significance of mangrove forest coverage in protecting coastal regions from seawater intrusion (SWI). Nonetheless, its coverage does not encompass the entire coastline, resulting in the variability of SWI impacts across different coastal regions. This study aims to generate the actual mangrove forest coverage, its change, and the SWI prediction. Therefore, we can quantitatively examine the correlation between changes in mangrove cover and SWI. The Sentinel 2 Multispectral Imaging (MSI), Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM + ), and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) offered brief and extended analysis durations from 2018 to 2022 and 1990 to 2023, respectively. The SWI is determined by integrating the salt concentration observed in soil and water, using linear and multiple regression models, and the random forest for the near-surface salinity index (NSSI). The predictors for effectively detecting surface salt concentration include the green, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, as well as spectral indices such as the vegetation soil salinity index (VSSI), normalized difference salinity index (NDSI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The findings indicated a linear relationship between SWI and mangrove forest change, since the salinity increases by approximately 5 to 10 % for every hectare of mangrove forest lost. A comprehensive observation from 1990 to 2023 proved that the mangrove forest reduced salinity levels from high to moderate and low, achieving around 49 %, as indicated by a correlation coefficient (R 2 ) of 0.82. This study agreed that mangroves naturally control the SWI in coastal areas.