The Global Land Cover-SHARE (GLC-SHARE) is a new land cover database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions.

It provides a set of major thematic land cover layers resulting by a combination of “best available” high resolution national, regional and/or sub-national land cover databases with the weighted average land cover information derived from large-scale available datasets. The database is produced with a resolution of 30 arc second (1km). The approach implemented is based on the utilization of the Land Cover Classification System (LCCS) and SEEA (System of Environmental-Economic Accounting) legend systems for the harmonization of the various global, regional and national land cover legends.

The major benefit of the GLC-SHARE product is its capacity to preserve the existing and available high resolution land cover information at the regional and country level obtained by spatial and multi-temporal source data, integrating them with the best synthesis of global datasets.

Preliminary validation campaign was performed using 1000 random points statistically distributed over each land cover classes.

The database is distributed in the following eleven layers, in raster format (GeoTIFF ), whose pixel values represent the percentage of density coverage in each pixel of the land cover type. The dominant layer, representing the value of the dominant land cover type, is also available along with a legend in LYR ESRI format. Finally, information on each layer's source is retrievable in sources layer, by joining the raster values with an Excel table.

Global Map of Irrigation Areas - Version 5 Grid with percentage of area equipped for irrigation with a spatial resolution of 5 arc minutes or 0.083333 decimal degrees. This dataset is developed in the framework of the AQUASTAT Programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations and the Rheinische Friedrich-Wilhems University, Germany. The map shows the amount of area equipped for irrigation around the year 2005 in percentage of the total area on a raster with a resolution of 5 minutes. Additional map layers show the percentage of the area equipped for irrigation that was actually used for irrigation and the percentages of the area equipped for irrigation that was irrigated with groundwater, surface water or non-conventional sources of water. In details, the following products have been released and made available for download:

• Area equipped for irrigation expressed as percentage of total area: total=aei, surface water=aeisw, groundwater=aeigw, non-conventional sources of water=aeinc (ASCII-grid);

• Area actually irrigated expressed as percentage of area equipped for irrigation (ASCII-grid);

• Area equipped for irrigation expressed in hectares per cell (ASCII-grid);

• Irrigated areas v.5 (ESRI shapefile);

• High and low resolution images (PDF);

• Quality Assessment (Excel)

Due to the map generation method, the quality of the map can never be uniform. The overall quality of the map depends heavily on the individual quality of the data for the different countries.

Data revision: 2013-10-07

Supplemental Information:

The maps are generated as a grid with a cellsize of 5 arc minutes. For the GIS-users the maps are distributed in two different formats: as a zipped ASCII-grid that can be easily imported in most GIS-software that support rasters or grids; and, to accommodate people who use GIS-software that doesn't support rasters or grids, as a zipped ESRI shape file. The non-GIS-users can download the map as PDF-file in two different resolutions.

Citation:

Users are requested to refer to the map as follows: "Stefan Siebert, Verena Henrich, Karen Frenken and Jacob Burke (2013). Global Map of Irrigation Areas version 5. Rheinische Friedrich-Wilhelms-University, Bonn, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy".

Contact points:

Metadata Contact: AQUASTAT

Data lineage:

Due to the map generation method, the quality of the map can never be uniform. The overall quality of the map depends heavily on the individual quality of the data for the different countries.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO

Online resources:

Global Map of Irrigation Areas (GMIA) on the AQUASTAT website

Download - Global area equipped for irrigation expressed as percentage of total area - Raster (ASCII-grid, 2.3 MB)

Download - Global area equipped for irrigation expressed in hectares per cell - Raster (ASCII-grid, 2.3 MB)

Download - Global Map of Irrigation Areas v.5 - Vector (ESRI shapefile, 4 MB)

Download - Global Map of Irrigation Areas v.5 - All files

Download - Global Map of Irrigation Areas v.5 - High resolution image (PDF, 3.1 MB)

Download - Global Map of Irrigation Areas v.5 - Low resolution image (PDF, 0.9 MB)

FAO Major Fishing Areas for Statistical Purposes are arbitrary areas, the boundaries of which were determined in consultation with international fishery agencies on various considerations, including (i) the boundary of natural regions and the natural divisions of oceans and seas; (ii) the boundaries of adjacent statistical fisheries bodies already established in inter-governmental conventions and treaties; (iii) existing national practices; (iv) national boundaries; (v) the longitude and latitude grid system; (vi) the distribution of the aquatic fauna; and (vii) the distribution of the resources and the environmental conditions within an area.The rationale of the FAO Major Fishing Areas has been that the areas should, as far as possible, coincide with the areas of competence of other fishery commissions when existing. This system facilitates comparison of data, and improves the possibilities of cooperation in statistical matters in general.More Information: http://www.fao.org/geonetwork/srv/en/main.home?uuid=ac02a460-da52-11dc-9d70-0017f293bd28

Physiographic maps for the CIS and Baltic States (CIS_BS), Mongolia, China and Taiwan Province of China. Between the three regions (China, Mongolia, and CIS_BS countries) DCW boundaries were introduced. There are no DCW boundaries between Russian Federation and the rest of the new countries of the CIS_BS. The original physiographic map of China includes the Chinese border between India and China, which extends beyond the Indian border line, and the South China Sea islands (no physiographic information is present for islands in the South China Sea). The use of these country boundaries does not imply the expression of any opinion whatsoever on the part of FAO concerning the legal or constitutional states of any country, territory, or sea area, or concerning delimitation of frontiers. The Maps visualize the items LANDF, HYPSO, SLOPE that correspond to Landform, Hypsometry and Slope.

The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the Bill & Melinda Gates Foundation, Agricultural Market Information System (AMIS) and AfricaFertilizer.org projects. The GAUL compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments) and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System and e) distribute data to the users (see TechnicalaspectsGAUL2015.pdf). Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see TechnicalaspectsGAUL2015.pdf and G2015_DisputedAreas.dbf). GAUL is released once a year and the target beneficiary of GAUL data is the UN community and other authorized international and national partners. Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. 5 territories have been updated respect to the previous release. Moreover, the coastline of American countries or other special areas have been updated using Open Street Map (see ReleaseNoteGAUL2015.pdf). GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see ReleaseNoteGAUL2015.pdf). GAUL 2015 is the eighth release of the GAUL Set.

The dataset on fish catches in the European waters by FAO statistical areas was created in 2015 by Cogea for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation of EUROSTAT's fish catches datasets fish_ca_atl 27, fish_ca_atl 34, fish_ca_atl 37, fish_ca_atl271, fish_ca_atl272, fish_ca_atl34_h and fish_ca_atl37_h. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). EUROSTAT data have been related to FAO's georeferenced fishing areas (polygons) for statisticl purposes (FAO, 2020. FAO Statistical Areas for Fishery Purposes. In: FAO Fisheries and Aquaculture Department). Tonnes live weight is provided for each fish species caught (3-alpha code and english or scientific name if the english one is not available), by EUMOFA's larger aggregations such as EUMOFA's Commodity Groups and Main Commercial Species (see 'Species_Eumofa_ASFIS_2023' table), by year of reference and country (code and name). The dataset is updated yearly and it covers a time series from 1950 to 2021, where available. Compared with the previous version this new one's schema have been updated.

Physical suitability for Drip irrigation and low pressure irrigation systems (Californian) has been assessed on the basis of: Travel time to markets: Setting a threshold < 4 hours High suitability, 4-8 hours Medium and >8hours low we refine a bit more the suitability. Input grid: Travel time to the closest city with more than 20.000 inhabitants. (http://www.fao.org/geonetwork/srv/en/main.home?uuid=e0 8b8b0c-8c5f-44b9-bbcf-45a14db88975) Groundwater potential: An estimated Static Groundwater Level Depth has been generated by interpolating a boreholes georeferenced database provided by the Ministère de l’Hydraulique du Mali.A 15 m depth threshold was stablished as the limit pumping height. Proximity to surface water (1km buffer distance)

Physical suitability for Shallow wells and boreholes has been assessed on the basis of: Travel time to markets: Setting a threshold < 4 hours High suitability, 4-8 hours Medium and >8hours low we refine a bit more the suitability. Input grid: Travel time to the closest city with more than 20.000 inhabitants. (http://www.fao.org/geonetwork/srv/en/main.home?uuid=e08b8b0c-8c5f-44b9-bbcf-45a14db88975) Groundwater potential: An estimated Static Groundwater Level Depth has been generated by interpolating a boreholes georeferenced database provided by the Ministère de l’Hydraulique du Niger.

The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change. The Harmonized World Soil Database (HWSD) was established in 2008 by the International Institute for Applied Systems Analysis (IIASA) and FAO, and in partnership with International Soil Reference and Information Centre (ISRIC), the European Soil Bureau Network (ESBN) and the Institute for Soil Sciences Chinese Academy of Sciences (CAS). The data entry and harmonization within a Geographic Information System (GIS) was carried out at IIASA, with verification of the database undertaken by all partners. HWSD was then updated in 2013 (HWSD v1.2) and in 2023 (HWSD v2.0). This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database. The GIS raster image file is linked to the soil attribute database. The HWSD v2.0 soil attribute database provides information on the soil unit composition for each of the near 30 000 soil association mapping units. The HWSD v2.0 Viewer, provided with the database, creates this link automatically and provides direct access to the soil attribute data and the soil association information. Note: A tutorial for accessing HWSD ver. 2.0 using R (prepared by David Rossiter, June 2023) has been added as an 'associated resource' (NOTE: Needs the SQLite version of HWSD v2 as provided below).

This data-set shows the most recent global model of the chickens distribution. It is the first update (version 2.01) of the recently published Gridded Livestock of the World (GLW) 2.0 (May 2014). More information and access to the data of the GLW version 2.0 are in the dedicated web-site: http://livestock.geo-wiki.org/ The GLW 2007 remains available for download in FAO Geonetwork. However, a quantitative assessment of change is not possible between the GLW 2007 and the GLW 2.0 (and its updates) due to different modeling techniques, spatial resolution, predicting variables and training data. GLW 2.0 and its updates map separately the chickens and the ducks distributions whereas GLW 2007 mapped a single poultry distribution. The bibliographic reference to the GLW 2.0 and its updates is: Robinson TP, Wint GRW, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, Cinardi G, D’Aietti L, Hay SI, and Gilbert M. (2014) Mapping the Global Distribution of Livestock. PLoS ONE 9(5): e96084. doi:10.1371/journal.pone.0096084 The supplementary information includes a list of the observed data used to train this version of the chickens model.

This data set is a re-aggregated version of the full resolution Libyan land cover database. The original full resolution data has been concentrated in few generalized classes highlighting the most relevant features mapped without loosing information. The result is an aggregation of 10 generalized classes listed below, which keeps a good level of information, giving at the same time an exact estimation of the areas covered by each aggregated class.

The 10 Generalized Classes aggregation Land Cover map of Libya:

AGRICULTURE AI - Irrigated Agriculture AR - Rainfed Agriculture

NATURAL VEGETATION NF - Natural Forest and Reforestations NV - Rangeland

BARE AREAS BC - Bare Soil Consolidated BU - Bare Soil Unconsolidated BSn - Loose and Shifting Sand BW - Bare Soil in Wadi Environment

SABKHAS SK - Terrestrial and Aquatic Sabkha Environment and Waterbodies

URBAN AREAS UB - Urban areas, Quarries and Dump Sites

The land cover products were developed by FAO as part of the LIB/00/04 'Mapping of Natural Resources for Agriculture Use and Planning Project in Libya'. This project was initiated by the Government of Libya, FAO and the United Nations Development Programme (UNDP) to strengthen the capacity of the General People's Committee for Agriculture, Animal and Marine Wealth (GAAAMW) to manage land resources at national and sub-national levels through the establishment of a strategy, and a spatially based operational decision support system - the Land Resources Information Management System (LRIMS).

Data publication: 2021-01-13

Supplemental Information:

The original full resolution Land Cover (33,551 polygons covering an interpreted area of 166,560,000 hectares) database has been produced from a visual interpretation of satellite images covering the period 2001-02. The land cover classes (108 LC classes) and the 755 mixed units, deriving from the combination of the single classes, have been standardized using the LCCS methodology.

Contact points:

Resource Contact: Matieu Henry

Resource Contact: Fatima Mushtaq

Metadata Contact: FAO GIS Unit

Online resources:

Download: Aggregated Land Cover Database for Libya (2006)

This layer shows soil type, based on the result of a classification established from Kalimantan RePPProT data on 'SL_ORDER' field (1990, 1:250,000 scale) . This data was provided and processed by Daemeter Consulting. Soil categories from RePPProT were then re-classified by the World Resources Institute according to the FAO Digital Soil Map of the World, for use in the Suitability Mapper (2012). The FAO data is available at http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 . Data separated into categories: Inceptisol; Oxisol; Alfisol; Ultisol; Spodosol; Entisol; Histosol.

This full resolution land cover is an updated version of the landcover 2000. 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 images acquired mainly in the period 2005-2010 (ASTER 2005-2010 and LANDSAT ETM 2005-2007). 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

Data publication: 2011-04-20

Supplemental Information:

FAO, Land and Water Division (NRL) has completed the new land cover of Kenya through the finalcial support of the World resources Institute (WRI) and with the technical assistance (field validation) of Kenya Meteorological Department of Resources Surveys and Remote Sensing (DRSRS).

Contact points:

Resource Contact: DRSRS - Kenya Department of Resource Surveys and Remote Sensing - Meteorological Department

Resource Contact: Florence Landsberg

Resource Contact: Antonio Di Gregorio

Metadata Contact: FAO GIS Unit

Resource constraints:

The data remains full property of the owners. It can be accessed, reproduced and distributed given that the owner information is explicitly acknowledged and displayed in the copyright information (I.E. Produced by FAO - Africover). The Authors do not assume any responsibilities for improper use of the data.

Online resources:

Africover

Land cover of Kenya 2008

The HWSD is a 30 arc-second raster database with over 16000 different soil mapping units that combines existing regional and national updates of soil information worldwide (SOTER, ESD, Soil Map of China, WISE) with the information contained within the 1:5 000 000 scale FAO-UNESCO Soil Map of the World (FAO, 19711981).

The raster database consists of 21600 rows and 43200 columns, which are linked to harmonized soil property data. The use of a standardized structure allows for the linkage of the attribute data with the raster map to display or query the composition in terms of soil units and the characterization of selected soil parameters (organic Carbon, pH, water storage capacity, soil depth, cation exchange capacity of the soil and the clay fraction, total exchangeable nutrients, lime and gypsum contents, sodium exchange percentage, salinity, textural class and granulometry).

Acronyms: ESDB - European Soil Database CHINA - China soil map SOTER - Soil and Terrain database SOTWIS - Regional SOTER databases WISE - World Inventory of Soil Emission Potential database DSMW - Digital Soil Map of the World

This dataset is the definitive set of locality boundaries for the state of Victoria as defined by Local Government and registered by the Registrar of Geographic Names. The boundaries are aligned to Vicmap Property. This dataset is part of the Vicmap Admin dataset series.

This dataset divides the Asian continent in major hydrological basins and their sub-basins according to its hydrological characteristics. It was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data (WWF HydroSHEDS and Hydro1K).

The dataset consists of the following information:- numerical code and name of the major basin (MAJ_BAS and MAJ_NAME); - area of the major basin in square km (MAJ_AREA); - numerical code and name of the sub-basin (SUB_BAS and SUB_NAME); - area of the sub-basin in square km (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea)

Supplemental Information:

This dataset is developed as part of a GIS-based information system on water resources for the Asian continent. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

Contact points:

Metadata contact: AQUASTAT FAO-UN Land and Water Division

Contact: Jippe Hoogeveen FAO-UN Land and Water Division

Data lineage:

The majority of the linework of the map was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data with a resolution of 15 arc-seconds. The elevation dataset was part of a mapping product, HydroSHEDS, developed by the Conservation Science Program of World Wildlife Fund. Original input data had been obtained during NASA's Shuttle Radar Topography Mission (SRTM). Areas north of the SRTM extent, 60 degrees N, were obtained by merging with the HYDRO1k basin layer.

Online resources:

Download - Hydrological basins in Southeast Asia (ESRI shapefile)

General information regarding the HydroSHEDS data product

HydroSHEDS dataset download and technical information

Rivers of South and East Asia

The Actual Evapotranspiration estimated from remote sensing energy balance model (SEBAL) on Landsat 8 OLI and meteo data, aggregated over main growing seasons (Maha and Yala) for the period 2014-2018. The Actual Evapotranspiration is computed for the irrigated command area identified by the ADB project in Mi Oya and Deduru Oya basins in the North Western province of Sri Lanka.

Data publication: 2020-01-01

Supplemental Information:

Unit: mm/season

Contact points:

Metadata Contact: IWMI-HQs

Resource Contact: Karthi Matheswaran

Data lineage:

The seasonal data is obtained by adding all the monthly ETa maps from Maha season 2014-2017 and Yala season 2014, 2017 and 2018.

Resource constraints:

copyright

Online resources:

Download - Water Productivity data - Sri Lanka

IWMI_Final_Report_Sri-Lanka_PartA.pdf

IWMI_WP_Sri_Lanka_Final_Report_PartB.pdf

Physical suitability for Wells, boreholes and solar pumping devices Travel time to markets: Setting a threshold < 4 hours High suitability, 4-8 hours Medium and >8hours low we refine a bit more the suitability. Input grid: Travel time to the closest city with more than 20.000 inhabitants. (http://www.fao.org/geonetwork/srv/en/main.home?uuid=e0 8b8b0c-8c5f-44b9-bbcf-45a14db88975) Groundwater potential: An estimated Static Groundwater Level Depth has been generated by interpolating a boreholes georeferenced database provided by the Ministère de l’Hydraulique du Mali. This factor is directly related with the suitability from an economical approach, we assume that the deeper is the static level the higher are the investment costs.

The ICES Statistical Areas delineates the divisions and subdivisions of FAO Major Fishing area 27

The map accompanies the SOLAW report 14 “Where are the poor and where are the land and water resources†. The analysis addressed in this report is aimed at identifying areas where land, water resources and farming systems pose potential threats to livelihoods. The map shows an analysis of poverty and access to land and water (Land Management). The analysis is conducted by spatially combining the Poverty Index and the Land management-Access to Land and Water Index. The Poverty Index was developed by FAO while the Land Management - Access to Land and Water Index was developed by the Geodata Institute - University of Southampton. The data for the analysis and the generation of the two indexes comes from the FAO GeoNetwork data archive. The methodology draws from the main hypotheses set in the analysis that are to: • test if per capita share of land and water resources have a significant association with poverty • if land suitability and farming systems modify that relationship. The key element of the methodology developed is referred to as the “Principal Component Analysis†(PCA), which is used to derive a single resource management index based on land and water resources, land suitability and farming systems. PCA involves a mathematical procedure that transforms a number of correlated variables into a smaller number of uncorrelated variables called Principal Components (PC). The new sets of variables (PCs) are a linear combination of the original variables which are derived in decreasing order of importance, with the first principal component accounting for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability. The first PC, which is a measure of variability in access to land and water resources and in farming systems, is chosen as the resource management index. The results from the analysis are presented in the report in the form of graphs and maps.