Mangrove recovery in the Nicobar archipelago after the 2004 tsunami and coastal subsidence

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Nehru Prabakaran ◽  
Sharad Bayyana ◽  
Kai Vetter ◽  
Hauke Reuter
Keyword(s):  
2004 Tsunami

Lessons for long-term social recovery following the 2004 tsunami: Community, livelihoods, tourism and housing

2012 ◽  
Vol 11 (1) ◽  
pp. 38-51 ◽  
Author(s):  
Martin Mulligan ◽  
Iftekhar Ahmed ◽  
Judith Shaw ◽  
Dave Mercer ◽  
Yaso Nadarajah
Keyword(s):  
Social Recovery ◽  
2004 Tsunami

The 2004 Tsunami in Sri Lanka: Destruction and recovery

Geography ◽  
2007 ◽  
Vol 92 (3) ◽  
pp. 285-293
Author(s):  
Daya Gunatillake
Keyword(s):  
Sri Lanka ◽  
2004 Tsunami

Aspergillosis of the gorgonian sea fan Annella sp. after the 2004 tsunami at Mu Ko Similan National Park, Andaman Sea, Thailand

Coral Reefs ◽  
2006 ◽  
Vol 25 (2) ◽  
pp. 296-296 ◽  
Author(s):  
S. Phongpaichit ◽  
S. Preedanan ◽  
N. Rungjindama ◽  
J. Sakayaroj ◽  
C. Benzies ◽  
...  
Keyword(s):  
National Park ◽  
Andaman Sea ◽  
Sea Fan ◽  
2004 Tsunami

scholarly journals GIS Based Approach for Vulnerability Assessment of the Karnataka Coast, India

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Akshaya Beluru Jana ◽  
Arkal Vittal Hegde
Keyword(s):  
Shoreline Change ◽  
Vulnerability Index ◽  
Tidal Range ◽  
Coastal Zones ◽  
Remotely Sensed Data ◽  
Coastal Vulnerability Index ◽  
Karnataka State ◽  
Administrative Units ◽  
2004 Tsunami ◽  
Very High

The coastal zones are highly resourceful and dynamic. In recent times, increased events of tropical cyclones and the devastating impact of the December 2004 tsunami have brought forth the importance of assessing the vulnerability of the coast to hazard-induced flooding and inundation in coastal areas. This study intends to develop coastal vulnerability index (CVI) for the administrative units, known astalukasof the Karnataka state. Seven physical and geologic risk variables characterizing the vulnerability of the coast, including rate of relative sea level change, historical shoreline change, coastal slope, coastal regional elevation, mean tidal range, and significant wave height derived using conventional and remotely sensed data, along with one socioeconomic parameter “population,” were used in the study. A total of 298 km of shoreline are ranked in the study. It was observed that about 68.65 km of the shoreline is under very high vulnerable category and 79.26 km of shoreline is under high vulnerable category. Of the remaining shoreline, 59.14 km and 91.04 km are of moderate and low vulnerable categories, respectively.

What Is Tsunami Earthquake?

2021 ◽  
Author(s):  
Toshikazu Ebisuzaki
Keyword(s):  
Surface Waves ◽  
Seismic Waves ◽  
Submarine Landslides ◽  
Lower Efficiency ◽  
Tsunami Waves ◽  
Tsunami Earthquakes ◽  
Seismic Surface Waves ◽  
Tsunami Earthquake ◽  
Volcanic Islands ◽  
2004 Tsunami

Abstract A tsunami earthquake is defined as an earthquake which induces abnormally strong tsunami waves compared with its seismic magnitude (Kanamori 1972; Kanamori and Anderson 1975; Tanioka and Seno 2001). We investigate the possibility that the surface waves (Rayleigh, Love, and tsunami waves) in tsunami earthquakes are amplified by secondly submarine landslides, induced by the liquefaction of the sea floor due to the strong vibrations of the earthquakes. As pointed by Kanamori (2004), tsunami earthquakes are significantly stronger in longer waves than 100 s and low in radiation efficiencies of seismic waves by one or two order of magnitudes. These natures are in favor of a significant contribution of landslides. The landslides can generate seismic waves with longer period with lower efficiency than the tectonic fault motions (Kanamori et al 1980; Eissler and Kanamori 1987; Hasegawa and Kanamori 1987). We further investigate the distribution of the tsunami earthquakes and found that most of their epicenters are located at the steep slopes in the landward side of the trenches or around volcanic islands, where the soft sediments layers from the landmass are nearly critical against slope failures. This distribution suggests that the secondly landslides may contribute to the tsunami earthquakes. In the present paper, we will investigate the rapture processes determined by the inversion analysis of seismic surface waves of tsunami earthquakes can be explained by massive landslides, simultaneously triggered by earthquakes in the tsunami earthquakes which took place near the trenches.

Impact of the 2004 tsunami on the macrofauna of the continental slope of the southeast coast of India

2018 ◽  
Vol 39 (5) ◽  
pp. e12527 ◽  
Author(s):  
Syed Ajmal Khan ◽  
Balusamy Ravikumar ◽  
Somasundharanair Lyla ◽  
Seerangan Manokaran
Keyword(s):  
Continental Slope ◽  
Southeast Coast Of India ◽  
Southeast Coast ◽  
2004 Tsunami

scholarly journals Using remote sensing to assess tsunami-induced impacts on coastal forest ecosystems at the Andaman Sea coast of Thailand

2010 ◽  
Vol 10 (4) ◽  
pp. 729-745 ◽  
Author(s):  
H. Roemer ◽  
G. Kaiser ◽  
H. Sterr ◽  
R. Ludwig
Keyword(s):  
Remote Sensing ◽  
Forest Ecosystems ◽  
Ground Truth ◽  
Andaman Sea ◽  
Coastal Forest ◽  
Coastal Forests ◽  
Change Detection Algorithms ◽  
Damage Maps ◽  
2004 Tsunami ◽  
Sea Coast

Abstract. The December 2004 tsunami strongly impacted coastal ecosystems along the Andaman Sea coast of Thailand. In this paper tsunami-induced damage of five different coastal forest ecosystems at the Phang-Nga province coast is analysed with a remote sensing driven approach based on multi-date IKONOS imagery. Two change detection algorithms, change vector analysis (CVA) and direct multi-date classification (DMC), are applied and compared regarding their applicability to assess tsunami impacts. The analysis shows that DMC outperforms CVA in terms of accuracy (Kappa values for DMC ranging between 0.947 and 0.950 and between 0.610–0.730 for CVA respectively) and the degree of detail of the created change classes. Results from DMC show that mangroves were the worst damaged among the five forests, with a 55% of directly damaged forest in the study area, followed by casuarina forest and coconut plantation. Additionally this study points out the uncertainties in both methods which are mainly due to a lack of ground truth information for the time between the two acquisition dates of satellite images. The created damage maps help to better understand the way the tsunami impacted coastal forests and give basic information for estimating tsunami sensitivity of coastal forests.

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