Dataset Open Access
High-resolution inundation dataset for coastal India and Bangladesh
Mondal, Pinki;
Dutta, Trishna;
Qadir, Abdul;
Sharma, Sandeep
DCAT Export
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<dct:title>High-resolution inundation dataset for coastal India and Bangladesh</dct:title>
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<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
<dcat:keyword>Remote sensing</dcat:keyword>
<dcat:keyword>Cyclone</dcat:keyword>
<dcat:keyword>Amphan</dcat:keyword>
<dcat:keyword>Mangrove</dcat:keyword>
<dcat:keyword>Sundarban</dcat:keyword>
<dcat:keyword>Inundation</dcat:keyword>
<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-12-23</dct:issued>
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<dct:description><p>This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh.</p> <p><strong>Input data:</strong></p> <p>These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (&sigma;&deg;) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH.</p> <p><strong>Methods:</strong></p> <p>We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water.</p> <p>We then used a decision rule to identify areas that changed from &lsquo;non-water&rsquo; to &lsquo;water&rsquo; after the cyclone. The decision rule generated the &lsquo;inundation layer&rsquo; with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions.</p> <p><strong>Data set format:</strong></p> <p>The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Data set for download:</strong></p> <p>A. Three data layers for Odisha, India:</p> <ol> <li>OD_pre_binary.tif</li> <li>OD_post_binary.tif</li> <li>OD_inundation.tif</li> </ol> <p>These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.</p> <p>B. Three data layers for West Bengal, India:</p> <ol> <li>WB_pre_binary.tif</li> <li>WB_post_binary.tif</li> <li>WB_inundation.tif</li> </ol> <p>These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.</p> <p>C. Six data layers for Bangladesh &ndash; three each for lower (L) region and upper (U) region.</p> <ol> <li>BNG_L_pre_binary.tif</li> <li>BNG_L_post_binary.tif</li> <li>BNG_L_inundation.tif</li> <li>BNG_U_pre_binary.tif</li> <li>BNG_U_post_binary.tif</li> <li>BNG_U_inundation.tif</li> </ol> <p>The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.</p> <p>The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.</p></dct:description>
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38
47
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 38 | 38 |
| Downloads | 47 | 47 |
| Data volume | 652.0 MB | 652.0 MB |
| Unique views | 37 | 37 |
| Unique downloads | 15 | 15 |
Indexed in
- Publication date:
- December 23, 2020
- DOI:
-
- Keyword(s):
- Remote sensing Cyclone Amphan Mangrove Sundarban Inundation
- License (for files):
- Creative Commons Attribution 4.0 International