Dataset Open Access
High-resolution inundation dataset for coastal India and Bangladesh
Mondal, Pinki;
Dutta, Trishna;
Qadir, Abdul;
Sharma, Sandeep
MARC21 XML Export
<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
<leader>00000nmm##2200000uu#4500</leader>
<datafield tag="041" ind1=" " ind2=" ">
<subfield code="a">eng</subfield>
</datafield>
<datafield tag="653" ind1=" " ind2=" ">
<subfield code="a">Remote sensing</subfield>
</datafield>
<datafield tag="653" ind1=" " ind2=" ">
<subfield code="a">Cyclone</subfield>
</datafield>
<datafield tag="653" ind1=" " ind2=" ">
<subfield code="a">Amphan</subfield>
</datafield>
<datafield tag="653" ind1=" " ind2=" ">
<subfield code="a">Mangrove</subfield>
</datafield>
<datafield tag="653" ind1=" " ind2=" ">
<subfield code="a">Sundarban</subfield>
</datafield>
<datafield tag="653" ind1=" " ind2=" ">
<subfield code="a">Inundation</subfield>
</datafield>
<controlfield tag="005">20201224002711.0</controlfield>
<controlfield tag="001">4390084</controlfield>
<datafield tag="700" ind1=" " ind2=" ">
<subfield code="u">University of Goettingen, Germany</subfield>
<subfield code="a">Dutta, Trishna</subfield>
</datafield>
<datafield tag="700" ind1=" " ind2=" ">
<subfield code="u">University of Maryland, USA</subfield>
<subfield code="a">Qadir, Abdul</subfield>
</datafield>
<datafield tag="700" ind1=" " ind2=" ">
<subfield code="u">University of Goettingen, Germany</subfield>
<subfield code="a">Sharma, Sandeep</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">7514698</subfield>
<subfield code="z">md5:9c2977c4695113b3edf9d5eeadd7b1c7</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/BNG_L_inundation.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">9761137</subfield>
<subfield code="z">md5:441a51d0d25dc55bcb4e39b28ac65cf0</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/BNG_L_post_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">8080027</subfield>
<subfield code="z">md5:c50daedc697caaf71c6837f304c1f31d</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/BNG_L_pre_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">7006580</subfield>
<subfield code="z">md5:9798d6158a7acb75ca9e48b098365587</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/BNG_U_inundation.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">12942785</subfield>
<subfield code="z">md5:d3064ceeb60493524a1e24cfad7c4479</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/BNG_U_post_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">14430054</subfield>
<subfield code="z">md5:e44ec454ccfd0a541c8ee815669325e2</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/BNG_U_pre_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">20974966</subfield>
<subfield code="z">md5:15470a51f722c78e20aaf6cb25ee93d9</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/OD_inundation.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">24475936</subfield>
<subfield code="z">md5:cf2879e8a55f30ae33cafb2cb39b8cab</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/OD_post_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">27370218</subfield>
<subfield code="z">md5:ef0327867ceebe24b709a5b22cc61abb</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/OD_pre_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">12578612</subfield>
<subfield code="z">md5:477f4111cffb371bee209d0b814a89ad</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/WB_inundation.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">13502185</subfield>
<subfield code="z">md5:b89889b52f6f1a78b8e0043e53006db5</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/WB_post_binary.tif</subfield>
</datafield>
<datafield tag="856" ind1="4" ind2=" ">
<subfield code="s">9670305</subfield>
<subfield code="z">md5:458460ebd9c4dfa87d61eb12a212044e</subfield>
<subfield code="u">https://zenodo.org/record/4390084/files/WB_pre_binary.tif</subfield>
</datafield>
<datafield tag="542" ind1=" " ind2=" ">
<subfield code="l">open</subfield>
</datafield>
<datafield tag="260" ind1=" " ind2=" ">
<subfield code="c">2020-12-23</subfield>
</datafield>
<datafield tag="909" ind1="C" ind2="O">
<subfield code="p">openaire_data</subfield>
<subfield code="o">oai:zenodo.org:4390084</subfield>
</datafield>
<datafield tag="100" ind1=" " ind2=" ">
<subfield code="u">University of Delaware, USA</subfield>
<subfield code="0">(orcid)0000-0002-7323-6335</subfield>
<subfield code="a">Mondal, Pinki</subfield>
</datafield>
<datafield tag="245" ind1=" " ind2=" ">
<subfield code="a">High-resolution inundation dataset for coastal India and Bangladesh</subfield>
</datafield>
<datafield tag="540" ind1=" " ind2=" ">
<subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
<subfield code="a">Creative Commons Attribution 4.0 International</subfield>
</datafield>
<datafield tag="650" ind1="1" ind2="7">
<subfield code="a">cc-by</subfield>
<subfield code="2">opendefinition.org</subfield>
</datafield>
<datafield tag="520" ind1=" " ind2=" ">
<subfield code="a"><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></subfield>
</datafield>
<datafield tag="773" ind1=" " ind2=" ">
<subfield code="n">doi</subfield>
<subfield code="i">isVersionOf</subfield>
<subfield code="a">10.5281/zenodo.4390083</subfield>
</datafield>
<datafield tag="024" ind1=" " ind2=" ">
<subfield code="a">10.5281/zenodo.4390084</subfield>
<subfield code="2">doi</subfield>
</datafield>
<datafield tag="980" ind1=" " ind2=" ">
<subfield code="a">dataset</subfield>
</datafield>
</record>
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