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Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia.

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Title: Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia.
Authors: Gumma, Murali Krishna1,2 (AUTHOR) m.gumma@cgiar.org, Amede, Tilahun1 (AUTHOR), Getnet, Mezegebu1 (AUTHOR), Pinjarla, Bhavani2 (AUTHOR), Panjala, Pranay2 (AUTHOR), Legesse, Gizachew1 (AUTHOR), Tilahun, Gebeyaw3 (AUTHOR), Van den Akker, Elisabeth4 (AUTHOR), Berdel, Wolf4 (AUTHOR), Keller, Christina4 (AUTHOR), Siambi, Moses1 (AUTHOR), Whitbread, Anthony M.5 (AUTHOR)
Source: Renewable Agriculture & Food Systems. 2022 Supplement 1, Vol. 37, pS28-S42. 15p.
Subject Terms: *Floods, *Precision farming, *Rainfall, *Storm surges, Remote-sensing images, Geographic information systems
Geographic Terms: Ethiopia
Company/Entity: Sentinel-1 (Artificial satellite)
Abstract: The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20 m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90 m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially >0.55 m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of <2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities. [ABSTRACT FROM AUTHOR]
Copyright of Renewable Agriculture & Food Systems is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia.
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  Data: The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20 m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90 m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially &gt;0.55 m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of &lt;2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Renewable Agriculture &amp; Food Systems is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1017/S1742170519000516
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 15
        StartPage: S28
    Subjects:
      – SubjectFull: Floods
        Type: general
      – SubjectFull: Precision farming
        Type: general
      – SubjectFull: Rainfall
        Type: general
      – SubjectFull: Storm surges
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: Geographic information systems
        Type: general
      – SubjectFull: Ethiopia
        Type: general
      – SubjectFull: Sentinel-1 (Artificial satellite)
        Type: general
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              Text: 2022 Supplement 1
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