Dataset Open Access

High-resolution inundation dataset for coastal India and Bangladesh

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


JSON-LD (schema.org) Export

{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "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>\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>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "University of Delaware, USA", 
      "@id": "https://orcid.org/0000-0002-7323-6335", 
      "@type": "Person", 
      "name": "Mondal, Pinki"
    }, 
    {
      "affiliation": "University of Goettingen, Germany", 
      "@type": "Person", 
      "name": "Dutta, Trishna"
    }, 
    {
      "affiliation": "University of Maryland, USA", 
      "@type": "Person", 
      "name": "Qadir, Abdul"
    }, 
    {
      "affiliation": "University of Goettingen, Germany", 
      "@type": "Person", 
      "name": "Sharma, Sandeep"
    }
  ], 
  "url": "https://zenodo.org/record/4390084", 
  "datePublished": "2020-12-23", 
  "version": "1.0.0", 
  "keywords": [
    "Remote sensing", 
    "Cyclone", 
    "Amphan", 
    "Mangrove", 
    "Sundarban", 
    "Inundation"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_L_inundation.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_L_post_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_L_pre_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_U_inundation.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_U_post_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_U_pre_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/OD_inundation.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/OD_post_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/OD_pre_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/WB_inundation.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/WB_post_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/WB_pre_binary.tif", 
      "encodingFormat": "tif", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.4390084", 
  "@id": "https://doi.org/10.5281/zenodo.4390084", 
  "@type": "Dataset", 
  "name": "High-resolution inundation dataset for coastal India and Bangladesh"
}
38
47
views
downloads
All versions This version
Views 3838
Downloads 4747
Data volume 652.0 MB652.0 MB
Unique views 3737
Unique downloads 1515

Share

Cite as