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A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia. / Kantamaneni , Komali; Christie, David; Lyddon, Charlotte et al.
Yn: Remote Sensing, Cyfrol 14, Rhif 12, 2857, 15.06.2022.

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

HarvardHarvard

Kantamaneni , K, Christie, D, Lyddon, C, Huang, P, Nizar, M, Balasubramani, K, Ravichandran, V, Prasad, KA, Pushparaj, RRB, Robins, P & Panneer, S 2022, 'A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia', Remote Sensing, cyfrol. 14, rhif 12, 2857. https://doi.org/10.3390/rs14122857

APA

Kantamaneni , K., Christie, D., Lyddon, C., Huang, P., Nizar, M., Balasubramani, K., Ravichandran, V., Prasad, K. A., Pushparaj, R. R. B., Robins, P., & Panneer, S. (2022). A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia. Remote Sensing, 14(12), Erthygl 2857. https://doi.org/10.3390/rs14122857

CBE

Kantamaneni K, Christie D, Lyddon C, Huang P, Nizar M, Balasubramani K, Ravichandran V, Prasad KA, Pushparaj RRB, Robins P, et al. 2022. A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia. Remote Sensing. 14(12):Article 2857. https://doi.org/10.3390/rs14122857

MLA

VancouverVancouver

Kantamaneni K, Christie D, Lyddon C, Huang P, Nizar M, Balasubramani K et al. A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia. Remote Sensing. 2022 Meh 15;14(12):2857. doi: 10.3390/rs14122857

Author

Kantamaneni , Komali ; Christie, David ; Lyddon, Charlotte et al. / A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia. Yn: Remote Sensing. 2022 ; Cyfrol 14, Rhif 12.

RIS

TY - JOUR

T1 - A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia

AU - Kantamaneni , Komali

AU - Christie, David

AU - Lyddon, Charlotte

AU - Huang, Peng

AU - Nizar, Muhammad

AU - Balasubramani, Karuppusamy

AU - Ravichandran, Venkatesh

AU - Prasad, Kumar Arun

AU - Pushparaj, Robert Ramesh Babu

AU - Robins, Peter

AU - Panneer, Sigamani

PY - 2022/6/15

Y1 - 2022/6/15

N2 - 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

AB - 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

U2 - 10.3390/rs14122857

DO - 10.3390/rs14122857

M3 - Article

VL - 14

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 12

M1 - 2857

ER -