Global datasets

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lobal maps on our environment (both remote sensing-based and thematic) are nowadays increasingly attractive for environmental modelling. A variety of publicly available maps can be obtained at no cost at resolutions up to 1 km or better. This article reviews some of the most known global datasets of interest for various environmental modeling projects (see also some similar galleries of world maps by Google Earth, FAO's GeoNetwork, UNEP/GRID GEO DataPortal, UNEP/GRID-Arendal, The Environmental Information Portal of the World Resources Institute, Atlas of the Biosphere portal, and/or GeoPortal).

Use this R script to automatically download and browse the maps shown below (the script also includes some small exercises to import/export data, set the correct coordinate system, make displays and run spatial analysis using point-sampled variables). The total size of maps is about 300 MB (62 maps). Each gridded map consists of 7200 columns and 3600 rows; the cell size is 0.05 arcdegrees, which corresponds to about 5 km. All maps are projected in the Latitude-Longitude WGS84 system +proj=longlat +ellps=WGS84. Displays shown below were produced using the SAGA GIS.

Obtain all maps at once.
  • Icon R.png worldmaps.R : R script to automatically download and browse the global maps (includes an data analysis example: mapping species distribution for Sturnella magna over the USA).
  • Icon txt.gif README.txt : A complete description of the directory and the metadata fields used.
  • Ge icon.jpg worldmaps.kml : Browse maps in Google Earth.
  • 7z icon.jpg GlobCover1km.7z : ~1 km resolution (0.1 arc-degree) DEM of the world (16 MB).
  • 7z icon.jpg globedem1km.7z : ~1 km (0.08333) resolution DEM of the world (626 MB!).

A description of how were the maps produced and what was the original source is provided in the attached *.rdc image documentation file (adopted Idrisi's format for metadata). The processing steps are described in the R scripts as indicated in the field lineage:. In most cases I used the GDAL utilities, which I highly recommend for processing large data and for data processing automation. By using a relatively coarse resolution (5 km), many original small features in the input maps have disappeared or their spatial accuracy has been downgraded. In almost all cases, the original data is available at 5 (or more) times better resolution / level of detail, so please consider using the original data for continental and regional studies instead of using these maps.

Some maps listed in the repository are simply reformatted/reprojected version of the original grids. Most of maps listed are in fact original. For example, map PCEVI1.tif shows first Principal Component of the complete monthly time-series of MODIS EVI images. Principal component analysis has shown to be especially attractive techniques to analyze time-series of images and reduce their dimensionality (Eastman and Fulk, 1993). Such data you will not be able to find on the MODIS website.

The compendium of maps shown in this article are intended for non-commercial scientific, conservation, and educational purposes only. The author of this article is not responsible for any loss, damage, liability or expense that resulted from use of this data. The author does not make any warranties as to the accuracy or completeness of any of the maps presented (see also the general disclaimer). To obtain the most up-to-date, most detailed version of each dataset, please follow the links to the original data producers/providers. If you wish to cite some of the derived maps shown here or this article, please cite as:


Administrative/Topographic data

Administrative data

Administrative data can be used to calculate proximity-based parameters and to orient the users geographically. One such global administrative data database is the GADM. It comprises borders of countries and lower level subdivisions such as provinces and counties (more than 100,000 areas). Lower level administrative boundaries can be obtained via the FAO's GeoNetwork server. Another important global dataset is the World Vector Shoreline dataset at scale 1:250,000 (Soluri and Woodson, 1990). This can be, for example, used to derive the global distance from the sea coast line map (see below). Various small scale topographic (vector and raster) maps at scales 1:5M to 1:110M can be obtained from Natural Earth Data website (compiled by Nathaniel Vaughn (KELSO) and volunteers). These raster maps are ideal as background or base maps for visualization of global vector layers at small scales (e.g. at A4 or A3 page formats).

Paul Wessel, from the University of Hawai'i, maintains a similar vector database called "A Global Self-consistent, Hierarchical, High-resolution Shoreline Database", which is described in detail in Wessel and Smith (1997). These vectors can be imported to R by using the maptools package (see Rgshhs command).

Note that some basic (and slightly out-dated) vector maps are available in the R's package "maps". This contains data map of political borders, world cities, CIA World Data Bank II data, administrative units from the NUTS III (Tertiary Administrative Units of the European Community) and similar. To obtain these vector maps for your GIS, you can run:


The original shape files of World political borders can be obtained from Wikipedia has a repository of blank maps of the World (in SVG and PNG formats), that you can use either as background maps for plotting, or as mask maps. Eight general purpose thematic layers: boundaries, transportation, drainage, population centres, elevation, vegetation, land use and land cover (al at scale 1:1,000,000) can be obtained via the Global Map Data project.

Height/geomorphology data

Global SRTM Digital Elevation Model is probably the most well-known global environmental dataset (Rabus et al. 2003). The area covered is between 60° North and 58° South. It was recorded by X-Band Radar (NASA and MIL, covering 100% of the total global area) and C-Band Radar (DLR and ASI, covering 40%). The non-public DLR-ASI data is available with a resolution of approximately 30 m (1 arcsec). A complete land surface model ETOPO1 Global Relief Model (includes bathymetry data) is available at resolution of 1 km and can be obtained from the NOAA's National Geophysical Data Center (Amante and Eakins, 2008). An updated 1 km resolution global topography map (SRTM30 PLUS; used by Google Earth) has been prepared by Becker et al. (2009). On the worldclim website, you can download global DEMs at different resolutions from 1 km to 2.5, 5 and 10 arc-minutes. The 90 m SRTM DEMs can be obtained from the CGIAR - Consortium for Spatial Information. From June 2009, ASTER-based Global Digital Elevation Model (GDEM) at resolution of 30 m has been made available to the worldwide public. The GDEM was created by stereo-correlating the 1.3 million-scene ASTER archive of optical images, covering almost 98% of Earth's land surface. The one-by-one- degree tiles can be downloaded from NASA's EOS data archive and/or Japan's Ground Data System.

Figure: Slope map (based on SRTM 30+ and ETOPO DEM).

If you are interested to see how the world (continents) looked like 50, 100 and more millions ago, look at the global paleogeography maps contributed by Ron Blakey (2008).

Socio-economic data

The most important global socio-economic data layers are the population density maps and attached socio-economic variables. The Socioeconomic Data and Applications Center (SEDAC) distributes the global population density maps at resolution of 1 km for periods from 1990 up to 2015 (projected density). The two maps (principal components) shown down-below were derived from a set of 6 maps (1990-2015 period; the maps for 2010 and 2015 are projected population densities). The second component shows high values in China and India, some parts of Europe and USA, but then low values over large areas of African continent and Middle East. This map can be literary interpreted as the anticipated population change (positive or negative) and could be of interest to various environmental impact studies.

Another 0.5-degree gridded dataset with population density (and estimated GDP) is the SRES gridded global population dataset (Bengtsson et al., 2006). It is based on the SRES scenarios developed for the IPCC climate modeling framework, and covers the period 1990-2100.

Water resources

The most detailed and the most accurate inventory of the global water resources is the Global Lakes and Wetlands Database (GLWD), which comprises lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-arcsec resolution (Lehner and Doll, 2004). Shape files of the World basins and similar vector data can be best obtained via the RS GIS Unit of the International Water Management Institute (IWMI). Gabriel Bowen from Purdue University prepared global maps of H- and O-isotopes in the precipitation using data from some 340 stations (Bowen and Revenaugh, 2003). The original maps can be obtained from the

Geo-data via a web-service

If you wish to obtain similar type of geographic information but only for specific point location, you should consider using some of the free web-services such as the GeoNames (also available via the R package GeoNames). For example, to obtain elevation, name of the closest city and/or actual weather at some point location, we can run:


Another alternative is the Google's maps service, which allows you to obtain similar type of information (Google's service also allows you to geocode postal addresses).

A high quantity of maps can be also obtained via some of the many commercial WCS's. A list of Open Geospatial Consortium (OGC) WMS's is available here. A popular WMS that allows download of the original vector data is the Openstreetmap. The original data come in the OSM format, but can be easily sorted and converted to e.g. ESRI shape files using OpenJUMP GIS. Another extensive WMS is the NASA's OnEarth. NASA also maintains the Global Change Master Directory that can be used to locate and download a variety of global environmental maps and field data.

Remote sensing imagery

Remote sensing images are increasingly the main source of data for any national/continental scale mapping projects. The amount of field and remote sensing data in the world is rapidly increasing. To get an idea about how many sensors/imagery is currently available, see for example the ITC's database. Down-below I list only the imagery with global coverage (and available at no cost) and of interest for global modelling projects.


The most attractive source of global high-temporal resolution imagery is the NASA's MODIS system. MODIS contains a number of products ranging from raw multispectral images, to various vegetation and atmospheric indices; all at resolution of 250 m (also available at coarser resolutions of 500 m and 1 km), and at very high temporal resolution. To import the MODIS images to your GIS, you will first need to obtain the MODIS resampling tool, then reproject/mosaic the images to your grid of interest and more compatible data format (e.g. geoTiff).

If you only wish to use already processed MODIS images, then look at the NASA's Earth Observation (NEO) portal, which distributes global time-series of MODIS-derived parameters such as: snow cover and ice extent, leaf area index, land cover classes, net primary production, chlorophyll concentration in the sea and sea surface temperature, cloud water content, carbon monoxide in the atmosphere and many more. All the global maps on NEO are freely available for public use. You can simply download the 0.1 arcdegrees (~10 km) GeoTiff's and load them to your GIS. Please credit NASA as the source.

Down-below are shown Principal Components derived using 120 monthly MODIS EVI images. The first component shows the mean biomass over the period of 20 years, the second, thirds and fourth components are assumed to depict the vegetation seasonality, but also different land use practice, vegetation succession and degradation (Eastman and Fulk, 1993).

The European version of MODIS TERRA is the ENVISAT MERIS. The ENVISAT satellite is a platform for several instruments adjusted for monitoring of the environmental resources: ASAR, MERIS, AATSR, MWR and similar. The MEdium Resolution Image Spectrometer (MERIS) is used to obtain images of the Earth’s surface at temporal resolution of 3–days. The images comprise of 15 bands, all at resolution of 300 m. To obtain MERIS images, one needs to register and receive an access to the repository (unlike the MODIS images that are available directly via an FTP).


SPOT and Landsat provide satellite imagery at about 10-30 times finer resolution than MODIS, but at commercial price. A number of their products is also available at no cost. High resolution (15 m) Landsat images for nearly all of the world (years 1990 and 2000) can be downloaded from the NASA’s Seamless Server.

Another excellent repository of free global imagery is the GLCF geoportal operated by the University of Maryland. GLCF provides not only access to raw data but also distribute a number of global products: land cover classification maps, estimated tree cover continuous fields, inventory of burned and deforested areas and similar (Zhan et al, 2002). All these data can be accessed also via their ftp.

SPOT vegetation offers a relatively coarse vegetation-based 10–day images of the whole Earth collected in the period from 1998 until today. Only two bands are available at the moment: NDVI and radiometry images.

Google Earth imagery

You can also consider obtaining the color composites of the high resolution imagery (QuickBird, Ikonos) that are used in Google Earth (this has some copyright restrictions). The fastest way to download a mosaic for an area of interest is to use the Universal maps downloader shareware. You only need to define the zoom level and Left Longitude, Right Longitude, Top Latitude, Bottom Latitude, and then you can sellect if you wish to obtain satellite, terrain or topo-maps (from Google, Microsoft Virtual Earth or Yahoo maps). With RgoogleMaps package you can also automate retrieval and mosaicking of images. For example, to obtain a hybrid satellite image of the Netherlands, it is enough to define the position of the center and the zoom level (scale):

> library(RgoogleMaps)
# Get a satellite image of the Netherlands:
> MyMap <- GetMap.bbox(center=c(52.1551723,5.3872035), zoom=7, destfile="netherlands.png", maptype ="hybrid")
Read 1 item
[1] ",5.3872035&zoom=7&size=640x640
+   &maptype=hybrid&format=png32&key=****&sensor=true"
trying URL ',5.3872035&zoom=7&size=640x640
+   &maptype=hybrid&format=png32&key=****=true'
Content type 'image/png' length 703541 bytes (687 Kb)
opened URL
downloaded 687 Kb

netherlands.png has GDAL driver PNG 
and has 640 rows and 640 columns
> PlotOnStaticMap(MyMap, lat=52.1551723, lon=5.3872035)

An alternative is to obtain the Google Earth tiles directly from the server (e.g. using this python script), then attach the right georeference (east, west, north, south coordinates) to each tile, and then mosaic them to create a complete GIS layer. Again, you need to be aware that these maps/images are copyrighted, so you should really use them only for your personal purpose, non-commercial use.

Barry Rowlingson is developing a package called webmaps that can be used to load OpenStreetMap tiles into R graphics devices (this has no copyright restrictions). It can also be used to create HTML pages that show your points, lines, and polygons on world map data.


Although outdated, AVHRR was the one of the main global environmental monitoring systems in the 80's. A set of 232 monthly NDVI images can be obtained via the International Water Management Institute. The original images are available at resolution of 300 arcseconds (cca 10 km), and cover the period July, 1981 through September, 2001.

Aviso Altimetry data

A range of satellites (Topex/Poseidon-ERS, Jason-Envisat) are used to track the sea altimetry, tides, significant wave height and wind speed, and alike oceanographic features. Most of these images are available only at course resolution of 0.125-1 arcdegree. Merged data products are available for download via the AVISO website. One needs to register to gain the FTP access to auxiliary products such as the long term Mean Sea Surface, Global tide (heights of tidal constituents) and similar.

Meteorological images

The Meteosat Second Generation (MSG) satellites (from Meteosat-8 onwards) produce SEVIRI 15–minutes images at resolution of 1 km. The most attractive data set for environmental applications is the High Rate SEVIRI, which consists of 12 spectral channels including: visible and near infrared light, water vapour band, carbon dioxide and ozone bands. MODIS produces monthly estimates of the global Land Surface Temperature, which are supposedly highly accurate (Wan et al. 2004). The time series of MODIS LST images can be used to analyze seasonal variation and compare differences between the temperatures at day and night time. The Japan Aerospace Exploration Agency generated a list of precipitation products based on a combination of meteorological images. These images are available in resolutions of 0.1 and 0.25 arcdegrees, and can be used to get a more global and consistent estimate of the precipitation patterns (Kubota et al. 2007).

Figure: Mean Day-time MODIS Land Surface Temperature based on the monthly LST images.
Figure: Mean annual precipitation in mm/month based on the meteorological images (GSMaP).

Lights at night images

Images of lights at night have shown to be highly correlated with industrial activity and Gross Domestic Product (Small et al., 2005; Doll et al. 2007). A time-series of (1 km resolution) annual global night light images is available via the NOAA's National Geophysical Data Center. The lights at night map contains the lights from cities, towns, and other sites with persistent lighting, including gas flares. The filtered annual composites are available from 1992 until 2009 (Elvidge et al, 2009). Here I show the mean value of the lights at night and the second PCA component derived from the original 1 km maps and resampled to 5 km resolution. The second component indicates the increase/decrease of lights at night over the period.

Thematic (derived) maps

Numerous organizations generate and distribute thematic maps that come as a result of complex GIS modeling and/or manual interpretation. These maps differ from the raw remote sensing imagery or topographic data in a sense that they can imply interpreted information (classes, complex features etc). They are also often generalized by a surveyor/cartographer, so that they are more appropriate for display, orientation, and/or planning. Their biggest limitation is that they often come as a result of subjective interpretation, thus many features are hard to reproduce. Regardless of this limitation, thematic maps carry irreplaceable information about the environment. The following section lists some of the most known global thematic datasets maintained by various teams.

Land cover / land use maps

Land cover maps are categorical-type maps, commonly derived using semi-automated methods and remote sensing images as the main input. There are at least four global land cover mapping projects in the world where such data can be found (they differ in legends, resolution, temporal coverage etc). A Global Land Cover map for the year 2000 (GLC2000) at 1 km resolution is distributed by the Joint Research Centre in Italy (Bartholome et al., 2002). A slightly outdated (1998) global map of land cover is provided by the AVHRR Global Land Cover Classification, provided at resolutions of 1 and 8 km (Hansen et al. 2000). International Steering Committee for Global Mapping provides access to the Global Land Cover by National Mapping Organizations (GLCNMO) map, produced using MODIS data observed in 2003. European Space Agency has recently released the GlobCover Land Cover version V2 dataset, produced using the ENVISAT MERIS images. So far, this is the highest resolution (300 meters) Global Land Cover product in the world. The forth important source of land cover data is the MODIS12C1 Land Cover Type Yearly L3 Global product (available in resolution from 500 m to 0.05 arcdegrees). The advantage of using the MODIS Land cover maps (17 land cover classes defined by the International Geosphere Biosphere Programme - IGBP) is that this is a temporal dataset so that one can also derive various change indices and quantify the land cover dynamics (Friedl et al. 2002).

In addition to standard land cover maps, Ellis and Ramankutty (2008) prepared the first global map of the anthropogenic biomes (18 classes; read more) showing dense settlements, villages, croplands, rangelands, forested lands and wildlands. FAO distributes a number of global thematic maps (usually at 5 arcmin resolution) including: suitability for various type of land use, soil and water resources, maps of environmental conditions, and similar. This can be downloaded directly from the FAO's GeoNetwork server.

The International Water Management Institute also produced the Global map of Irrigated Areas (GMIA; 28 classes) and the Global map of Rainfed Cropped Areas (GMRCA), both at 10 km resolution, and based on the twenty years of AVHRR images, augmented with higher resolution SPOT and JERS-1 imagery. A Global Map of Irrigation Areas is also available for download (ArcInfo ASCII grids and shape files) via the FAO's Information System on Water and Agriculture website.

Figure: Global map of area equipped for irrigation expressed as percentage of total area.

Center for Sustainability and the Global Environment (SAGE), a research center of the Nelson Institute for Environmental Studies at the University of Wisconsin-Madison, distributes a number of global Land Use products: (1) Crop Calendar Dataset, (2) harvested area and yields of 175 crops, (3) historical and current cropland maps and similar. 1 arcmin resolution global yield estimates for 175 crops (year 2000) can be obtained directly from the Department of Geography, McGill University website. A number of human impacts, land use, water resources and ecosystem maps can be browsed more systematically via the SAGE's AtlasBiosphere server.

Climatic maps provides global maps of some 18 bioclimatic parameters derived (thin plate smoothing splines) using >15,000 weather stations (Hijmans et al., 2005). The climatic parameters include: mean, minimum and maximum temperatures, monthly precipitation and bioclimatic variables. All at ground resolution of 1 km. Coarse 1 degree global monthly and daily precipitation data (1979-present) can be obtained via the NASA/GSFC. Climatic Research Unit of the University of East Anglia prepared a repository of high resolution maps (10 arcseconds) representing mean monthly surface climate over global land areas, excluding Antarctica (New et al. 2003). Even more detailed climatic images can be obtained via the Global Energy and Water Cycle Experiment project and British Atmospheric Data Centre (BADC).

The National Geophysical Data Centre (NGDC) provides free access to numerous remote sensing based global maps - from solar parameters to cloud imagery, energetic particle measurements and similar (collectively called "Solar-terrestrical physics" products).

The National Snow and Ice Data Center maintains a number of global datasets - frozen ground maps, monthly satellite-derived snow water equivalent (SWE) climatologies and similar. These data can be obtained for norhtern and southern hemisphere at resolution of 25 km.

Ecoregions/Biogeographic regions

Ecoregions are terrestrial, freshwater and/or marine areas with characteristic combinations of soil and landform that characterize that region. Olson et al. (2001) produced the Terrestrial Ecoregions global data set, which shows some 867 distinct eco-units, including the relative richness of terrestrial species by ecoregion. The legend for the map shown down-below is attached to the original grid map. A somewhat more generalized is the FAO's map of Eco-floristic regions (e.g. boreal coniferous forest, tropical rainforest, boreal mountain system etc.).

NOAA's NGDC prodives a free access to several global ecosystem maps including the Bailey (1993) Ecoregions of the Continents. From their website, you can either choose to browse the maps interactively via a WMS or using KML files, or access it directly via ftp.

Figure: Terrestrial ecoregions: 867 distinct eco-units.

Soil/geology maps

Soil and geology maps are especially important for SDM of vascular plants. One harmonized global soil-type map is the USGS Global Soil Regions map at resolution of 60 arcsec (FAO-UNESCO, 2005). The USDA global soil data is also available via ftp. FAO, IIASA, ISRIC, ISSCAS, JRC have recently produced a 1 km resolution gridded map, by merging various national soil maps, which is also known as the Harmonized World Soil Database (v 1.1). Global HWSD-derived soil property maps for top- and sub-soil can be download as geotifs from here. A list of seven gridded soil property maps (at resolution of 5 arc-minutes) - soil-carbon density, total nitrogen density, field capacity, wilting point, profile available water capacity, thermal capacity, and bulk density - is available via the IGBP-DIS data set. Some additional soil property maps such as pH and soil moisture, can be also obtained from the Atlas of Biosphere project. Status of the soil information in the world can be also followed via David G. Rossiter's compendium of On-Line Soil Survey Information.

USDA Soil Survey Division also distributes the global map of wetlands (includes: upland, lowland, organic, permafrost and salt affected wetlands). ISRIC maintains a global soil profile database with over 12,000 profiles and over 50 analytical and descriptive parameters (Batjes, 2008). Global Carbon storage in soils is available from the WRI website.

The only global geological map available at the moment is the 1:25M Geological Map of the World (Bouysse, 2009; maps not available publicly). The geological maps are now being integrated via the OneGeology project which aims at producing a consistent Geological map of the world in approximate scale 1:1M; progress can be followed via their interactive portal. USGS has several data portals, e.g. that allow browsing of the International Surace Geology (split into South Asia, South America, Iran, Gulf of Mexico, Former Soviet Union, Europe, Carribean, Bangladesh, Asia Pacific, Artic, Arabian Peninsula, Africa and Afganistan).

Figure: Major FAO soil groups based on the Harmonized World Soil Database (download soil property maps).

Natural Hazards

A number of institutions have jointly produced a Global Seismic Hazard map (Giardini, 1999). This map, although slightly outdated and of limited detail, can be obtained directly from the project webite. From the NOAA's National Geophysical Data Center one can obtain a point map with all major earth quakes (Significant Earthquake Database; cca 5000 quakes), and generate a (kernel density) map for Earthquake magnitude (shown below). University of Hawaii maintains a Global Hazards Information Network, which contains a number of global layers including a map of Global Airports, locations of significant earthquakes and earthquake zones.

NASA's Climate Center Lightning Team maintains a global map of Lightning Activity. The average yearly counts of lightning flashes per square kilometer based on data collected by NASA satellites between 1995 and 2002 can be obtained from here.

World forest/wildlife resources

FAO periodically (every 5 years) organizes the so called Forest Resources Assessment (FRA) - an international compilation of forest resource assessment (forest maps, heatlh and vitality status, forest functions and policies connected with forest management). This assessment typically results in a comprehensive report that includes both graphical and tabular data (see e.g. FRA2005 report). Global maps (projected in the Goode Homolosine Interrupted projection) for year 2000 (FRA2000) can be obtained from the USGS website. The most recent FRA2005 can be ordered on a CD (not for public distribution).

There are two additional important global forest/wildlife datasets: (1) The world map of intact forest landscapes (hardly touched by mankind) at scale 1:1,000,000 (includes four classes of intact forests: 1. intact closed forests; 2. intact open forests, 3. woodlands and savannas, closed forests; and 4. open forests, woodlands and savannas) --- maintained by the Greenpeace (2006) organization (Potapov, et al. 2008), and (2) World Wilderness Areas at scale 1:1,000,000 --- distributed via the UNEP GEO Data Portal (McClosey, and Spalding, 1998).

Figure: World wilderness areas (hardly touched by mankind).

Biodiversity / human impacts maps

GBIF provides global maps showing the distribution of all flora and fauna species. The density maps are available only at resolution of 1 arcdegree (about 100 km). Global maps of biodiversity measures for various groups of taxa (e.g. vascular plants, birds and mammals) can be browsed using the World Atlas of Biodiversity viewer (showing mainly the data published in Groombridge and Jenkins, 2002). Similar type of maps can be browsed via the UNEP's World Conservation Monitoring Centre. National and international protected sites and attributes can be downloaded (ESRI Shapefiles; after registering on the website) via the World Database On Protected Areas. International Union for Conservation of Nature and Natural Resources (IUCN) has recently released a Red List of Threatened Species that contains assessments for 49,000 species. Distribution maps (presence) for these species (with limited coverage) can be downloaded from the IUCN website.

A shape file showing location of hotspot regions is distributed by the Conservation International. The NEES Institute for Plants in Bonn has produced a number of global biodiversity maps including the map of plant species richness zones and floristic knowledge (Kier et al, 2005). Unfortunately the GIS data is not available publicly from the Institute's website, but only high resolution figures. Kreft and Jetz (2007) recently produced a global map of plant species diversity (number of plant species) by using field records from 1,032 locations (map is available only in coarse resolution of 120 km). BirdLife International publishes a number of global maps indicating so called "Endemic and Important Bird Areas" (IBAs and EBAs).

Figure: Areas of human impacts on the Biosphere (roads, railways and settlement density).

Partners in the GLOBIO consortium created a World Map of Human Impacts on the Biosphere for various time periods. This is basically a map showing a current status of the roads, railways and settlement density (UNEP, 2002). Human impact maps can be browsed via the UNEP Grid Arendal. Andy Nelson (2008) has computed a global map of accessibility, i.e. a map showing travel time to major cities, using a variety of GIS layers (all these are publicly available). The International Biosphere-Geosphere Programme, the Stockholm Environment Institute, the Stockholm Resilience Center, the CSIRO in Australia and the International Human Dimensions Programme on Global Environmental Change are producing qan outreach project on the Anthropocene and planetary boundaries. One of the objectives of this project is to make a detailed inventory of the Global Transportation System.

A comprehensive global assessment of the human impacts to marine ecosystems can be followed via the work of the National Center for Ecological Analysis and Synthesis in Santa Barbara. This group have produced a Global Map of Human Impacts to Marine Ecosystems by using a number of connected input GIS layers (Halpern et al. 2008). These layers are available for download as GeoTiff and/or ESRI grids. Distribution of global airports and flight routes can be freely accessed from the (maintained by Conentshare company). A global map of flight routes can be ordered via the LX97 company. Chris Harrison produced the world map of internet connectivity and traffic.

The Carbon Dioxide Information Analysis Center provides access to numerous ecological layers and data of interest to global ecologists. One such product is the Global Biomass Carbon Map (Carbon density tones of C / ha), prepared for year 2000 (Ruesch et al. 2008). From their ftp, you can download a number of GIS layers of interest for ecological modelling (e.g. map of Eco-floristic zones, GLC2000 dataset etc).

Figure: Global Biomass Carbon Map: Carbon density tones of C / ha.


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