scholarly journals Inundation mapping using SENTINEL-1 data in the aftermath of super cyclone Amphan : A case study

MAUSAM ◽  
2021 ◽  
Vol 72 (1) ◽  
pp. 253-264
Author(s):  
AMIT KUMAR ◽  
ANIL KUMAR SINGH ◽  
R. K. GIRI ◽  
J. N. TRIPATHI
Keyword(s):  
Inundation Mapping ◽  
Super Cyclone

Impact of FORMOSAT-3/COSMIC radio occultation data on the prediction of super cyclone Gonu (2007): a case study

2013 ◽  
Vol 70 (2) ◽  
pp. 1209-1230 ◽  
Author(s):  
S. K. A. V. Prasad Rao Anisetty ◽  
Ching-Yuang Huang ◽  
Shu-Ya Chen
Keyword(s):  
Radio Occultation ◽  
Cosmic Radio ◽  
Super Cyclone ◽  
Occultation Data ◽  
Radio Occultation Data

scholarly journals Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

2021 ◽  
Vol 7 (3) ◽  
pp. 267
Author(s):  
Pollen Chakma ◽  
Aysha Akter
Keyword(s):  
Synthetic Aperture Radar ◽  
Coastal Region ◽  
Flood Damage ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
Radar Images ◽  
Super Cyclone ◽  
Aperture Radar

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165  gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.

Comparative Flood Inundation Mapping Utilizing Multi-Temporal Optical and SAR Satellite Data Over North Bihar Region

2020 ◽  
pp. 149-168 ◽  
Author(s):  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Bikash Ranjan Parida ◽  
Achala Shakya
Keyword(s):  
River Basins ◽  
Flood Inundation ◽  
Flood Events ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Multi Temporal ◽  
Inundation Extent ◽  
Present Case Study ◽  
Gandak River

Floods are investigated to be the utmost frequent and destructive phenomena among all other types of natural calamities worldwide. Thus, flood events need to be mapped to understand their impact on the affected region. The present case study is intended to examine and analyze the flood events occurred in July-August 2019 over the Northern Bihar region situated in Kosi and Gandak river basins. Furthermore, a comparative study was carried out to map the satellite based near real time flood inundation using multi-temporal Sentinel–1A (SAR) and MODIS NRT Flood data (optical and 3-day composite). Optical (MODIS) and Sentinel-1 SAR data were acquired to compare their flood inundation extent and the result shows overestimation in MODIS flood data due to varying spatial resolutions.

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