Dataset Open Access

High-resolution inundation dataset for coastal India and Bangladesh

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


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_L_inundation.tif"
      }, 
      "checksum": "md5:9c2977c4695113b3edf9d5eeadd7b1c7", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "BNG_L_inundation.tif", 
      "type": "tif", 
      "size": 7514698
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_L_post_binary.tif"
      }, 
      "checksum": "md5:441a51d0d25dc55bcb4e39b28ac65cf0", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "BNG_L_post_binary.tif", 
      "type": "tif", 
      "size": 9761137
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_L_pre_binary.tif"
      }, 
      "checksum": "md5:c50daedc697caaf71c6837f304c1f31d", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "BNG_L_pre_binary.tif", 
      "type": "tif", 
      "size": 8080027
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_U_inundation.tif"
      }, 
      "checksum": "md5:9798d6158a7acb75ca9e48b098365587", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "BNG_U_inundation.tif", 
      "type": "tif", 
      "size": 7006580
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_U_post_binary.tif"
      }, 
      "checksum": "md5:d3064ceeb60493524a1e24cfad7c4479", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "BNG_U_post_binary.tif", 
      "type": "tif", 
      "size": 12942785
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/BNG_U_pre_binary.tif"
      }, 
      "checksum": "md5:e44ec454ccfd0a541c8ee815669325e2", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "BNG_U_pre_binary.tif", 
      "type": "tif", 
      "size": 14430054
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/OD_inundation.tif"
      }, 
      "checksum": "md5:15470a51f722c78e20aaf6cb25ee93d9", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "OD_inundation.tif", 
      "type": "tif", 
      "size": 20974966
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/OD_post_binary.tif"
      }, 
      "checksum": "md5:cf2879e8a55f30ae33cafb2cb39b8cab", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "OD_post_binary.tif", 
      "type": "tif", 
      "size": 24475936
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/OD_pre_binary.tif"
      }, 
      "checksum": "md5:ef0327867ceebe24b709a5b22cc61abb", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "OD_pre_binary.tif", 
      "type": "tif", 
      "size": 27370218
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/WB_inundation.tif"
      }, 
      "checksum": "md5:477f4111cffb371bee209d0b814a89ad", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "WB_inundation.tif", 
      "type": "tif", 
      "size": 12578612
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/WB_post_binary.tif"
      }, 
      "checksum": "md5:b89889b52f6f1a78b8e0043e53006db5", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "WB_post_binary.tif", 
      "type": "tif", 
      "size": 13502185
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add/WB_pre_binary.tif"
      }, 
      "checksum": "md5:458460ebd9c4dfa87d61eb12a212044e", 
      "bucket": "31313ce6-7e2e-4374-81d0-e7728bc84add", 
      "key": "WB_pre_binary.tif", 
      "type": "tif", 
      "size": 9670305
    }
  ], 
  "owners": [
    176751
  ], 
  "doi": "10.5281/zenodo.4390084", 
  "stats": {
    "version_unique_downloads": 15.0, 
    "unique_views": 37.0, 
    "views": 38.0, 
    "version_views": 38.0, 
    "unique_downloads": 15.0, 
    "version_unique_views": 37.0, 
    "volume": 652009709.0, 
    "version_downloads": 47.0, 
    "downloads": 47.0, 
    "version_volume": 652009709.0
  }, 
  "links": {
    "thumb250": "https://zenodo.org/api/iiif/v2/31313ce6-7e2e-4374-81d0-e7728bc84add:c6436b96-0780-4c01-8d6d-7a9d4b660288:BNG_L_inundation.tif/full/250,/0/default.jpg", 
    "doi": "https://doi.org/10.5281/zenodo.4390084", 
    "thumbs": {
      "10": "https://zenodo.org/record/4390084/thumb10", 
      "750": "https://zenodo.org/record/4390084/thumb750", 
      "50": "https://zenodo.org/record/4390084/thumb50", 
      "1200": "https://zenodo.org/record/4390084/thumb1200", 
      "100": "https://zenodo.org/record/4390084/thumb100", 
      "250": "https://zenodo.org/record/4390084/thumb250"
    }, 
    "conceptdoi": "https://doi.org/10.5281/zenodo.4390083", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.4390083.svg", 
    "latest_html": "https://zenodo.org/record/4390084", 
    "bucket": "https://zenodo.org/api/files/31313ce6-7e2e-4374-81d0-e7728bc84add", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.4390084.svg", 
    "html": "https://zenodo.org/record/4390084", 
    "latest": "https://zenodo.org/api/records/4390084"
  }, 
  "conceptdoi": "10.5281/zenodo.4390083", 
  "created": "2020-12-23T21:12:18.404029+00:00", 
  "updated": "2020-12-24T00:27:11.884165+00:00", 
  "conceptrecid": "4390083", 
  "revision": 2, 
  "id": 4390084, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.4390084", 
    "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>", 
    "language": "eng", 
    "title": "High-resolution inundation dataset for coastal India and Bangladesh", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "4390083"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "4390084"
          }
        }
      ]
    }, 
    "version": "1.0.0", 
    "keywords": [
      "Remote sensing", 
      "Cyclone", 
      "Amphan", 
      "Mangrove", 
      "Sundarban", 
      "Inundation"
    ], 
    "publication_date": "2020-12-23", 
    "creators": [
      {
        "orcid": "0000-0002-7323-6335", 
        "affiliation": "University of Delaware, USA", 
        "name": "Mondal, Pinki"
      }, 
      {
        "affiliation": "University of Goettingen, Germany", 
        "name": "Dutta, Trishna"
      }, 
      {
        "affiliation": "University of Maryland, USA", 
        "name": "Qadir, Abdul"
      }, 
      {
        "affiliation": "University of Goettingen, Germany", 
        "name": "Sharma, Sandeep"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.4390083", 
        "relation": "isVersionOf"
      }
    ]
  }
}
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