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High-resolution inundation dataset for coastal India and Bangladesh

Mondal, Pinki; Dutta, Trishna; Qadir, Abdul; Sharma, Sandeep


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  <identifier identifierType="DOI">10.5281/zenodo.4390084</identifier>
  <creators>
    <creator>
      <creatorName>Mondal, Pinki</creatorName>
      <givenName>Pinki</givenName>
      <familyName>Mondal</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7323-6335</nameIdentifier>
      <affiliation>University of Delaware, USA</affiliation>
    </creator>
    <creator>
      <creatorName>Dutta, Trishna</creatorName>
      <givenName>Trishna</givenName>
      <familyName>Dutta</familyName>
      <affiliation>University of Goettingen, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Qadir, Abdul</creatorName>
      <givenName>Abdul</givenName>
      <familyName>Qadir</familyName>
      <affiliation>University of Maryland, USA</affiliation>
    </creator>
    <creator>
      <creatorName>Sharma, Sandeep</creatorName>
      <givenName>Sandeep</givenName>
      <familyName>Sharma</familyName>
      <affiliation>University of Goettingen, Germany</affiliation>
    </creator>
  </creators>
  <titles>
    <title>High-resolution inundation dataset for coastal India and Bangladesh</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Remote sensing</subject>
    <subject>Cyclone</subject>
    <subject>Amphan</subject>
    <subject>Mangrove</subject>
    <subject>Sundarban</subject>
    <subject>Inundation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-12-23</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4390084</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4390083</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Input data:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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 (&amp;sigma;&amp;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Methods:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;We then used a decision rule to identify areas that changed from &amp;lsquo;non-water&amp;rsquo; to &amp;lsquo;water&amp;rsquo; after the cyclone. The decision rule generated the &amp;lsquo;inundation layer&amp;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data set format:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data set for download:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A. Three data layers for Odisha, India:&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;OD_pre_binary.tif&lt;/li&gt;
	&lt;li&gt;OD_post_binary.tif&lt;/li&gt;
	&lt;li&gt;OD_inundation.tif&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.&lt;/p&gt;

&lt;p&gt;B. Three data layers for West Bengal, India:&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;WB_pre_binary.tif&lt;/li&gt;
	&lt;li&gt;WB_post_binary.tif&lt;/li&gt;
	&lt;li&gt;WB_inundation.tif&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.&lt;/p&gt;

&lt;p&gt;C. Six data layers for Bangladesh &amp;ndash; three each for lower (L) region and upper (U) region.&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;BNG_L_pre_binary.tif&lt;/li&gt;
	&lt;li&gt;BNG_L_post_binary.tif&lt;/li&gt;
	&lt;li&gt;BNG_L_inundation.tif&lt;/li&gt;
	&lt;li&gt;BNG_U_pre_binary.tif&lt;/li&gt;
	&lt;li&gt;BNG_U_post_binary.tif&lt;/li&gt;
	&lt;li&gt;BNG_U_inundation.tif&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.&lt;/p&gt;

&lt;p&gt;The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.&lt;/p&gt;</description>
  </descriptions>
</resource>
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