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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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4390084", 
  "language": "eng", 
  "title": "High-resolution inundation dataset for coastal India and Bangladesh", 
  "issued": {
    "date-parts": [
      [
        2020, 
        12, 
        23
      ]
    ]
  }, 
  "abstract": "<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>\n\n<p><strong>Input data:</strong></p>\n\n<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>\n\n<p><strong>Methods:</strong></p>\n\n<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>\n\n<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>\n\n<p><strong>Data set format:</strong></p>\n\n<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>\n\n<p><strong>Data set for download:</strong></p>\n\n<p>A. Three data layers for Odisha, India:</p>\n\n<ol>\n\t<li>OD_pre_binary.tif</li>\n\t<li>OD_post_binary.tif</li>\n\t<li>OD_inundation.tif</li>\n</ol>\n\n<p>These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.</p>\n\n<p>B. Three data layers for West Bengal, India:</p>\n\n<ol>\n\t<li>WB_pre_binary.tif</li>\n\t<li>WB_post_binary.tif</li>\n\t<li>WB_inundation.tif</li>\n</ol>\n\n<p>These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.</p>\n\n<p>C. Six data layers for Bangladesh &ndash; three each for lower (L) region and upper (U) region.</p>\n\n<ol>\n\t<li>BNG_L_pre_binary.tif</li>\n\t<li>BNG_L_post_binary.tif</li>\n\t<li>BNG_L_inundation.tif</li>\n\t<li>BNG_U_pre_binary.tif</li>\n\t<li>BNG_U_post_binary.tif</li>\n\t<li>BNG_U_inundation.tif</li>\n</ol>\n\n<p>The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.</p>\n\n<p>The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.</p>", 
  "author": [
    {
      "family": "Mondal, Pinki"
    }, 
    {
      "family": "Dutta, Trishna"
    }, 
    {
      "family": "Qadir, Abdul"
    }, 
    {
      "family": "Sharma, Sandeep"
    }
  ], 
  "version": "1.0.0", 
  "type": "dataset", 
  "id": "4390084"
}
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