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JORDAN - Land Cover Atlas











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    Book (stand-alone)
    The Land Cover Atlas of Sudan 2012
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    The Land Cover Atlas of Sudan provides information on the land cover distribution by administrative divisions. The dataset has been created using the FAO/GLCN methodology and tools. Main data sources include high resolution satellite imagery from SPOT, Landsat, IRS (Indian Satellite), Aster, 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 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. Field verification was complet ed by national experts who received a customized training on methodology and tools. The final land cover product has around 490,000 polygons, classified into 83 different classes and eventually aggregated into 7 major classes for ease of analysis and display. The Land Cover Atlas is organized into two main sections: country and states. Each section provides information on the distribution of aggregated land cover as map and table. These products provide the user with valuable information on the availability and distribution of land resources through a multifaceted approach.
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    Book (stand-alone)
    Land Cover Atlas of the Republic of South Sudan 2011
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    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.
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    Book (stand-alone)
    Atlas of Malawi Land Cover and Land Cover Change 1990-2010 2012
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    The Atlas of Malawi, land cover and land cover change (1990s-2010s) provides information on the land cover resources, their distribution and changes over time, at national, regional and district levels. The Atlas is published in 2013. The administrative unit layer as well as the water basin layer and a number of ancillary datasets was provided by the Land Resources and Development Department of the Ministry of Agriculture of Malawi. The land cover change database was prepared according to the FA O, Land and Water Division www.fao.org and Global Land Cover Network (GLCN) www.glcn.org land cover change mapping methodology; underpinned by the use of FAO/ISO standards and the Land Cover Mapping Toolbox. The national land cover legend was prepared using the Land Cover Classification System (LCCS): a FAO 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 c lassification uses a set of independent diagnostic criteria that allows the correlation with existing classifications and legends.

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