A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia
Research output: Contribution to journal › Article › peer-review
Electronic versions
Documents
- remotesensing-14-02857
Final published version, 9.88 MB, PDF document
Licence: CC BY Show licence
DOI
Climate-change-induced hazards are negatively affecting the small islands across Indonesia. Sabang Island is one of the most vulnerable small islands due to the rising sea levels and increasing coastal inundation which threaten the low-lying coastal areas with and without coastal defences. However, there is still a lack of studies concerning the long-term trends in climatic variables and, consequently, sea level changes in the region. Accordingly, the current study attempts to comprehensively assess sea level changes and coastal inundation through satellite-derived datasets and model-based products around Sabang Island, Indonesia. The findings of the study show that the temperature (both minimum and maximum) and rainfall of the island are increasing by ~0.01 °C and ~11.5 mm per year, respectively. The trends of temperature and rainfall are closely associated with vegetative growth; an upward trend in the dense forest is noticed through the enhanced vegetation index (EVI). The trend analysis of satellite altimeter datasets shows that the sea level is increasing at a rate of 6.6 mm/year. The DEM-based modelling shows that sea level rise poses the greatest threat to coastal habitations and has significantly increased in recent years, accentuated by urbanisation. The GIS-based model results predict that about half of the coastal settlements (2.5 sq km) will be submerged completely within the next 30 years, provided the same sea level rise continues. The risk of coastal inundation is particularly severe in Sabang, the largest town on the island. The results allow regional, sub-regional, and local comparisons that can assess variations in climate change, sea level rise, coastal inundation, and associated vulnerabilities
Original language | English |
---|---|
Article number | 2857 |
Journal | Remote Sensing |
Volume | 14 |
Issue number | 12 |
DOIs | |
Publication status | Published - 15 Jun 2022 |
Total downloads
No data available