This full resolution land cover is an updated version of the Africover Kenya data base dated circa 1999. The dataset was created using the FAO/GLCN methodology and tools. The land cover mapping was carried out with the visual interpretation of digitally enhanced LANDSAT ETM images acquired mainly in the year 2000. The legend was prepared using the FAO/UNEP international standard 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. The dataset can be analyzed in the GLCN software Advanced Database Gateway (ADG), which provides a user-friendly interface and advanced functionalities to breakdown the LCCS classes in their classifiers for further aggregations and analysis. Source: WRI/FAO/DRSRS List of abbreviations: DRSRS - Kenya Department of Resource Surveys and Remote Sensing FAO - Food and Agriculture Organization of the United Nations GLCN - Global Land Cover Network LCCS - FAO/UNEP Land Cover Classification System UNEP - United Nations Environmental Programme WRI - World Resources Institute

This dataset has been produced from visual interpretation of digitally enhanced LANDSAT ETM images (Bands 4,3,2) acquired mainly in the year 2000-2005. The land cover classes have been developed using the FAO/UNEP international standard LCCS classification system. The land cover legend of Senegal, consisting of 55 classes, was set up using the F.A.O. LCCS methodology. To affine the interpretation, a set of aerial photos donated by USGS and the high resolution images of Google Earth have been used. The mapping scale used for the visual photo-interpretation was 1:100.000. The full resolution version of the Land Cover dataset consists of 23,922 polygons, covering an area of 19,659 thousands ha. There is also an aggregated version generated on the basis of a spatial criteria, which produces about the 11% reduction of the total amount of polygons. The spatially aggregated dataset (available for download) consists of 21,238 polygons. The Senegal Land Cover mapping was carried out in the framework of Global Land Cover Network (GLCN) activities.

This dataset is a thematic reaggregated version of the original national Africover landcover multipurpose database. It contains all cultivated land. The original full resolution land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1999 (see the "Multipurpose Landcover Database" metadata for more details). This dataset is intended for free public access. Thematic aggregation is the way that the end user customizes the Africover database to fulfil his/her specific requirements. The Africover database gives equal level of detail to Agriculture as well as Natural vegetation or Bare Areas etc. Generally a single user does not need this level of detail for each class type; therefore he/she will enhance the information of one land cover type and will generalize or erase the information related to other land cover aspects. The most powerful way to conduct an aggregation exercise is to use the classifiers as basic elements of the exercise. This gives the user the maximum flexibility on the use of data. The shape main attributes correspond to the following fields: -ID -HECTARES -WOODY_ID -WOODY_DESC You can download a zip archive containing: -the er-woody-agg (.shp) -the Eritrea Classifiers Used (.pdf) -the Eritrea legend (.pdf and .xls) -the Eritrea Legend - LCCS Import file (.xls) -the LCCSglossary_eritrea (.pdf) -the thematic-aggregation-procedure (.pdf) -the thematic-aggregation-annex1 (.pdf) -the thematic-aggregation-annex2 (.pdf) -the Userlabel Definitions (.pdf)

The full resolution land cover has been produced from visual interpretation of digitally enhanced high-resolution LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1999. The land cover classes have been developed using the FAO/UNEP international standard LCCS classification system. This database can be analyzed in the GLCN software Advanced Database Gateway (ADG), which provides a user-friendly interface and advanced functionalities to breakdown the LCCS classes in their classifiers for further aggregations and analysis. The ADG software is available for download on the GLCN web site at http://www.glcn.org/sof_7_en.jsp. The shape main attributes correspond to the following fields: -ID -USERLABER -LCCCODE (unique LCCS code) You can download a zip archive containing: -the dataset er-landcover-ge (.shp) -the Eritrea Classifiers Used (.pdf) -the Eritrea legend (.pdf and .xls) -the Eritrea Legend - LCCS Import file (.xls) -the Userlabel Definitions (.pdf) Note: the document Eritrea Classifiers Used.pdf, is a list of all the LCCS classifiers used in the study area. They are grouped under the 8 major land cover types. In addition to the standard classifiers contained in LCCS the user may find “user defined” classifiers used by the map producer to add additional information to a specific class, not available in LCCS. The user-defined attributes are always coded with the letter “Z”.

This dataset is a thematic reaggregated version of the original national Africover landcover multipurpose database. It contains all natural vegetation with a herbaceous component. The original full resolution land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1997 (see the "Multipurpose Landcover Database" metadata for more details). This dataset is intended for free public access. Thematic aggregation is the way that the end user customizes the Africover database to fulfil his/her specific requirements. The Africover database gives equal level of detail to Agriculture as well as Natural vegetation or Bare Areas etc. Generally a single user does not need this level of detail for each class type; therefore he/she will enhance the information of one land cover type and will generalize or erase the information related to other land cover aspects. The most powerful way to conduct an aggregation exercise is to use the classifiers as basic elements of the exercise. This gives the user the maximum flexibility on the use of data. The shape main attributes correspond to the following fields: -ID -HECTARES -GRASS_ID -GRASS_DESC You can download a zip archive containing: -the tz-grass-agg (.shp) -the Tanzania Classifiers Used (.pdf) -the Tanzania legend (.pdf and .xls) -the Tanzania Legend - LCCS Import file (.xls) -the LCCSglossary_tanzania (.pdf) -the thematic-aggregation-procedure (.pdf) -the thematic-aggregation-annex1 (.pdf) -the thematic-aggregation-annex2 (.pdf) -the Userlabel Definitions (.pdf)

This dataset is a thematic reaggregated version of the original national Africover landcover multipurpose database. It contains all cultivated land. The original full resolution land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1999 (see the "Multipurpose Landcover Database" metadata for more details). This dataset is intended for free public access. Thematic aggregation is the way that the end user customizes the Africover database to fulfil his/her specific requirements. The Africover database gives equal level of detail to Agriculture as well as Natural vegetation or Bare Areas etc. Generally a single user does not need this level of detail for each class type; therefore he/she will enhance the information of one land cover type and will generalize or erase the information related to other land cover aspects. The most powerful way to conduct an aggregation exercise is to use the classifiers as basic elements of the exercise. This gives the user the maximum flexibility on the use of data. The shape main attributes correspond to the following fields: -ID -HECTARES -GRASS_ID -GRASS_DESC You can download a zip archive containing: -the er-grass-agg (.shp) -the Eritrea Classifiers Used (.pdf) -the Eritrea legend (.pdf and .xls) -the Eritrea Legend - LCCS Import file (.xls) -the LCCSglossary_eritrea (.pdf) -the thematic-aggregation-procedure (.pdf) -the thematic-aggregation-annex1 (.pdf) -the thematic-aggregation-annex2 (.pdf) -the Userlabel Definitions (.pdf)

This dataset is a raster format GeoTIFF representing the percentage of density in each pixel of the trees coverage. It is part of the Global Land Cover-SHARE (GLC-SHARE) database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions.

This dataset is a thematic reaggregated version of the original national Africover landcover multipurpose database. It contains all natural vegetation with a herbaceous component. The original full resolution land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1999 (see the "Multipurpose Landcover Database" metadata for more details). This dataset is intended for free public access. Thematic aggregation is the way that the end user customizes the Africover database to fulfil his/her specific requirements. The Africover database gives equal level of detail to Agriculture as well as Natural vegetation or Bare Areas etc. Generally a single user does not need this level of detail for each class type; therefore he/she will enhance the information of one land cover type and will generalize or erase the information related to other land cover aspects. The most powerful way to conduct an aggregation exercise is to use the classifiers as basic elements of the exercise. This gives the user the maximum flexibility on the use of data. The shape main attributes correspond to the following fields: -ID -HECTARES -WOODY_ID -WOODY_DESC You can download a zip archive containing: -the rw-woody-agg (.shp) -the Rwanda Classifiers Used (.pdf) -the Rwanda legend (.pdf and .xls) -the Rwanda Legend - LCCS Import file (.xls) -the LCCS glossary_rwanda(.pdf) -the thematic-aggregation-procedure (.pdf) -the thematic-aggregation-annex1 (.pdf) -the thematic-aggregation-annex2 (.pdf) -the Userlabel Definitions (.pdf)

This dataset is a raster format GeoTIFF representing the percentage of density in each pixel of the herbaceous vegetation coverage. It is part of the Global Land Cover-SHARE (GLC-SHARE) database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions.

This dataset is a thematic reaggregated version of the original national Africover landcover multipurpose database. It contains all natural vegetation with a herbaceous component. The original full resolution land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1999 (see the "Multipurpose Landcover Database" metadata for more details). This dataset is intended for free public access. Thematic aggregation is the way that the end user customizes the Africover database to fulfil his/her specific requirements. The Africover database gives equal level of detail to Agriculture as well as Natural vegetation or Bare Areas etc. Generally a single user does not need this level of detail for each class type; therefore he/she will enhance the information of one land cover type and will generalize or erase the information related to other land cover aspects. The most powerful way to conduct an aggregation exercise is to use the classifiers as basic elements of the exercise. This gives the user the maximum flexibility on the use of data. The shape main attributes correspond to the following fields: -ID -HECTARES -GRASS_ID -GRASS_DESC You can download a zip archive containing: -the rw-grass-agg (.shp) -the Rwanda Classifiers Used (.pdf) -the Rwanda legend (.pdf and .xls) -the Rwanda Legend - LCCS Import file (.xls) -the LCCS glossary_rwanda(.pdf) -the thematic-aggregation-procedure (.pdf) -the thematic-aggregation-annex1 (.pdf) -the thematic-aggregation-annex2 (.pdf) -the Userlabel Definitions (.pdf)