Thumbnail Image

Land Cover Atlas of the Republic of South Sudan












FAO. 2023. Land Cover Atlas of the Republic of South Sudan. Second edition. Rome. 




Also available in:
No results found.

Related items

Showing items related by metadata.

  • Thumbnail Image
    Brochure, flyer, fact-sheet
    Land Cover Mapping of the Republic of South Sudan 2023
    Also available in:
    No results found.

    This brochure represents a simplified and introductory version of the more detailed publication: "Land Cover Atlas of the Republic of South Sudan". Understanding natural resources' utilization, distribution, temporal variations, and human activities is crucial for sustainable land management, especially in areas facing long-standing crises and significant environmental challenges. The competition for natural resources such as water, grassland, and wood among various stakeholders with diverse visions and interests is not only responsible for land degradation but also often a key driver of tensions and violent conflicts. Therefore, obtaining this fundamental information is imperative for promoting sustainable land use and mitigating the negative impacts of resource competition.
  • Thumbnail Image
    Book (stand-alone)
    Land Cover Atlas of the Republic of South Sudan 2011
    Also available in:
    No results found.

    The Land Cover Atlas of the Republic of South Sudan provides information on the land cover distribution by administrative and sub-basin divisions. The dataset was created using the FAO/GLCN methodology and tools. Main data sources include satellite imagery from SPOT and Global Land Survey (GLS) Landsat, existing Africover land cover database and ancillary data. The legend was prepared using the Land Cover Classification System (LCCS): a comprehensive, standardized a priori classification system, designed to meet specific user requirements and created for mapping exercises, independent of the scale or means used to map. The classification uses a set of independent diagnostic criteria that allows the correlation with existing classifications and legends. Satellite images of South Sudan were segmented into homogeneous polygons and they were interpreted according to the FAO/GLCN methodology for the production of a seamless and detailed land cover dataset for the whole country. A field veri fication was completed by national experts who received a customized training on methodology and tools. The final land cover product has around 100,000 polygons, classified into 43 different classes and eventually aggregated into 7 major classes for ease of analysis and display.
  • Thumbnail Image
    Book (stand-alone)
    Land Cover Atlas of Yemen 2024
    Also available in:
    No results found.

    Understanding the utilization, distribution, temporal variations, and human activities related to natural resources is crucial for sustainable land management, especially in Yemen, a country grappling with prolonged conflicts and severe environmental challenges. The competition for natural resources such as water, arable land, and wood among various stakeholders with diverse interests often leads to land degradation and is a key driver of tensions and conflicts. Therefore, obtaining this fundamental information is imperative for promoting sustainable land use and mitigating the negative impacts of resource competition.In Yemen, land cover mapping is essential to support growing concerns about food and nutrition security, improving the resilience of livelihoods to threats and crises in the context of climate change. This atlas contains the land cover dataset for Yemen, prepared as part of a portfolio of projects aimed at enhancing governance and preventing conflicts across the country. The ultimate goal is to reduce displacement and irregular migration by promoting household food security, nutrition, and income.Creating an accurate initial inventory of natural resources is critical for sustaining these achievements over time. The new land cover dataset allows for detailed mapping of natural resources, human settlements, and activities in Yemen. It represents an updated dataset developed for Yemen, integrating high-resolution multi-temporal imagery, machine-learning algorithms, and the Land Cover Meta Language (LCML) to support the Natural Resource Management strategy, land use planning, and other innovative approaches.

Users also downloaded

Showing related downloaded files

No results found.