Rainfall Variability and Tidal Inundation Influences on Mangrove Greenness in Karimunjawa National Park, Indonesia

Abstract

Mangroves, which are vulnerable to natural threats and human activities on small islands in the tropics, play an essential role as carbon sinks, helping to mitigate climate change. In this study, we discussed the effect of natural factors on mangrove sustainability by analyzing the impact of rainfall, land surface temperature (LST), and tidal inundation on the greenness of mangroves in Karimunjawa National Park (KNP), Indonesia. We used Sentinel-2 image data to obtain the normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) during the dry season to determine the effect of inundation on mangrove greenness and soil moisture. The tidal inundation area was calculated using topographic data from the KNP and tidal observations from the area adjacent to it. Unmanned autonomous vehicles and topographic data were used to estimate mangrove canopy height. We also calculated mangrove greenness phenology and compared it to rainfall from satellite data from 2019–2021. Results show that the intertidal area is dominated by taller mangroves and has higher NDVI and NDMI values than non-intertidal areas. We also observed that mangroves in intertidal areas are mostly evergreen, and optimum greenness in KNP occurs from February to October, with maximum greenness in July. Cross-correlation analysis suggests that high rainfall affects NDVI, with peak greenness occurring three months after high rainfall. The LST and NDVI cross-correlation showed no time lag. This suggests that LST was not the main factor controlling mangrove greenness, suggesting tides and rainfall influence mangrove greenness. The mangroves are also vulnerable to climate variability and change, which limits rainfall. However, sea-level rise due to climate change might positively impact mangrove greenness.

Python Programming for Degree Heating Weeks Estimation using Sea Surface Temperature Data from The Google Earth Engine Dataset as Coral Bleaching Analysis Tools

“…marine protected areas (MPA) in Indonesia, particularly where coral reefs grow). The advantage of using GEE is that extraction processes of SST data in an area to time series data are carried out using cloud computing, so it performs quickly and free of charge [18][19][20]. Thus, estimating DHW using GEE can be achieved quickly and easily compared to conventional methods, which have to download images individually, make corrections, and extract them using a PC [21].…”

Effects of climate variability and hydrological rehabilitation measures on long‐term mangrove trajectories: From reproduction to recruitment and landscape cover changes

“…In turn, porewater salinity would control responses in reproduction and tree recruitment at the landscape level, affecting projected restoration targets in CGSM. Climate variability is a driver of porewater salinity associated with the strength of the ENSO phase that produces changes in river catchments, river discharge flows and time lags between climatic factors, porewater salinity and plant performance (Prihantono et al., 2022; Restrepo & Kjerfve, 2000; Wu et al., 2015).…”