Dataset Open Access

High-resolution inundation dataset for coastal India and Bangladesh

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


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    <subfield code="a">High-resolution inundation dataset for coastal India and Bangladesh</subfield>
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    <subfield code="a">&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;</subfield>
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