IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/233343.html
   My bibliography  Save this book chapter

Remote Sensing Based Quantification of Forest Cover Change in Somalia for the Period 2000 to 2019

In: Deserts and Desertification

Author

Listed:
  • Sylus Musei
  • Justine Nyaga
  • Abdi Zeila Dubow

Abstract

Deforestation is a driver of land degradation and a major environmental problem in Somalia, and has been linked to frequent incidences of drought over the years. Monitoring of changes in forest cover is therefore critical for the country's environment. The problem of land degradation has been worsened by the large scale charcoal production that is witnessed in the country. This study aimed at estimating forest cover change between 2000 and 2019 in Somalia using Landsat-based forest cover datasets. Google Earth Engine (GEE), a cloud based computing system was used to provide a platform for this analysis. Based on the 30% threshold recommended by International Geosphere Biosphere Program for differentiating forest from non-forest trees, approximately 23% forest cover loss was found, from 87, 294 hectares in 2000 to 67, 199 hectares in 2019. Most of the country's forest is within the southern and central parts of the country, and significant forest cover losses occurred mainly around Mogadishu and Kismayo port throughout the study period. There is therefore a need for the Federal Ministry of Environment and environment ministries in the federal member states to design mechanisms and strategies for restoration of the degraded forests and to curb deforestation.

Suggested Citation

  • Sylus Musei & Justine Nyaga & Abdi Zeila Dubow, 2022. "Remote Sensing Based Quantification of Forest Cover Change in Somalia for the Period 2000 to 2019," Chapters, in: Yajuan Zhu & Qinghong Luo & Yuguo Liu (ed.), Deserts and Desertification, IntechOpen.
  • Handle: RePEc:ito:pchaps:233343
    DOI: 10.5772/intechopen.99365
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/77788
    Download Restriction: no

    File URL: https://libkey.io/10.5772/intechopen.99365?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    remote sensing; land degradation; Somalia; Google earth engine; charcoal production;
    All these keywords.

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ito:pchaps:233343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.