What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. This dataset contains raw stressor data from 17 different human activities that directly or indirectly have an impact on the ecological communities in the ocean's ecosystems. For more information on specific dataset, see the methods section. All data are projected in WGS 1984 Mollweide.

This activity uses Map Viewer and is designed for intermediate users. We recommend MapMaker when getting started with maps in the classroom - see this StoryMap for the same activity in MapMaker.ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.AP skills & objectives (CED)Skill 3.B: Describe spatial patterns presented in maps and in quantitative and geospatial data.PSO-2.A: Identify the factors that influence the distribution of human populations at different scales.SPS-2A: Explain the intent and effects of various population and immigration policies on population size and composition.Learning outcomesStudents will be able to visualize and analyze variations in the time-space compression.More activitiesAll Human Geography GeoInquiriesAll GeoInquiries

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. The top level of this dataset contains the raster data for the modeled impacts map, along with a high resolution jpg version. Sub-levels of this dataset include: raw stressor data, transformed stressor data (raw data is log(x+1) transformed and rescaled by dividing by maximum global value so values range from 0-1), and ecosystem data. All spatial data are projected in WGS 1984 Mollweide.

The Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 1993 and 2009. It is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).

See: Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11.

Data can also be downloaded from ""https://datadryad.org/resource/doi:10.5061/dryad.052q5"">Dryad.

DFO Maritimes Region has conducted a cumulative human impact mapping analysis for the Scotian Shelf-Bay of Fundy management area to support ongoing Marine Spatial Planning initiatives (Murphy et al. 2024). Cumulative human impact mapping (CIM) combines spatial information on human activities and habitats with a matrix of vulnerability weights, into an intuitive relative ‘cumulative impact score’ that shows where cumulative human impacts are greatest and least. To map cumulative impacts in DFO’s Maritimes Region, a recently developed ecosystem vulnerability assessment for Atlantic Canadian waters (Murray et al. 2022) was combined with spatial information on 21 different habitat types and 45 human activities across five different sectors (climate change, land-based, marine-based, coastal, commercial fishing) following the methodology from Halpern et al. (2008). An uncertainty analysis of the cumulative impact map was conducted to assess the robustness of results and identify hot and cold spots of cumulative impacts. This dataset provides: 1) cumulative impact maps for the DFO Maritimes Region at 1 km2 resolution: a total cumulative impact map (i.e. including all 45 human activities), as well as cumulative impact maps for each of the five sectors, 2) a layer that identifies which grid cells are considered hot and cold spots of cumulative human impacts, and 3) the habitat layers included in the CIM. For further information concerning specifics of the maps and methods see Murphy et al. (2024) or contact the data provider. References: Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D'Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Perry, M.T., Selig, E.R., Spalding, M., Steneck, R., and Watson, R. 2008. A Global Map of Human Impact on Marine Ecosystems. Science. 319(5865): 948-952. doi:10.1126/science.1149345. Murray, C.C., Kelly, N.E., Nelson, J.C., Murphy, G.E.P., and Agbayani, S. 2022. Cumulative impact mapping and vulnerability of Canadian marine ecosystems to anthropogenic activities and stressors. DFO Can. Sci. Advis. Sec. Res. Doc. 2022/XXX. vi. + 52 p. Murphy, G.E.P., Stock, A., and Kelly, N.E. 2024 (in press). From land to deep sea: A continuum of cumulative human impacts on marine habitats in Atlantic Canada. Ecosphere. Cite this data as: Murphy, Grace; Kelly, Noreen (2023) Cumulative human impact maps for the Bay of Fundy and Scotian Shelf. Published September 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/37b59b8b-1c1c-4869-802f-c09571cc984b

Explore the patterns of world population in terms of total population, arithmetic density, total fertility rate, natural increase rate, life expectancy, and infant mortality rate. The GeoInquiry activity is available here.Educational standards addressed:APHG: II.A. Analyze the distribution patterns of human populations.APHG: II.B. Understand that populations grow and decline over time and space.This map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

Students will explore the patterns of world population in terms of total population, arithmetic density, total fertility rate, natural increase rate, and infant mortality rate. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes:Students will be able to identify and explain the spatial patterns and distribution of world population based on total population, density, total fertility rate, natural increase rate, and infant mortality rate.Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

This archive contains indicative distribution maps and profiles for T4.1 Trophic savannas, a ecosystem functional group (EFG, level 3) of the IUCN Global Ecosystem Typology (v2.0). Please refer to Keith et al. (2020) for details.

The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Use this global model layer when performing analysis across continents. This layer displays predictions globally of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create this prediction.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Summary: Creating the world’s first open-source, high-resolution, land cover map of the worldStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) K: Standard K-ESS3-1 - Earth and Human Activity - Use a model to represent the relationship between the needs of different plants or animals (including humans) and the places they liveGrade level(s) K: Standard K-ESS3-3 - Earth and Human Activity - Communicate solutions that will reduce the impact of humans on the land, water, air, and/or other living things in the local environmentGrade level(s) 2: Standard 2-ESS2-1 - Earth’s Systems - Compare multiple solutions designed to slow or prevent wind or water from changing the shape of the landGrade level(s) 2: Standard 2-ESS2-2 - Earth’s Systems - Develop a model to represent the shapes and kinds of land and bodies of water in an areaGrade level(s) 3: Standard 3-LS4-1 - Biological Evolution: Unity and Diversity - Analyze and interpret data from fossils to provide evidence of the organisms and the environments in which they lived long ago.Grade level(s) 3: Standard 3-LS4-1 - Biological Evolution: Unity and Diversity - Analyze and interpret data from fossils to provide evidence of the organisms and the environments in which they lived long ago.Grade level(s) 3: Standard 3-LS4-4 - Biological Evolution: Unity and Diversity - Make a claim about the merit of a solution to a problem caused when the environment changes and the types of plants and animals that live there may changeGrade level(s) 4: Standard 4-ESS1-1 - Earth’s Place in the Universe - Identify evidence from patterns in rock formations and fossils in rock layers to support an explanation for changes in a landscape over timeGrade level(s) 4: Standard 4-ESS2-2 - Earth’s Systems - Analyze and interpret data from maps to describe patterns of Earth’s featuresGrade level(s) 5: Standard 5-ESS2-1 - Earth’s Systems - Develop a model using an example to describe ways the geosphere, biosphere, hydrosphere, and/or atmosphere interact.Grade level(s) 6-8: Standard MS-ESS2-2 - Earth’s Systems - Construct an explanation based on evidence for how geoscience processes have changed Earth’s surface at varying time and spatial scalesGrade level(s) 6-8: Standard MS-ESS2-6 - Earth’s Systems - Develop and use a model to describe how unequal heating and rotation of the Earth cause patterns of atmospheric and oceanic circulation that determine regional climates.Grade level(s) 6-8: Standard MS-ESS3-3 - Earth and Human Activity - Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.Grade level(s) 9-12: Standard HS-ESS2-1 - Earth’s Systems - Develop a model to illustrate how Earth’s internal and surface processes operate at different spatial and temporal scales to form continental and ocean-floor features.Grade level(s) 9-12: Standard HS-ESS2-7 - Earth’s Systems - Construct an argument based on evidence about the simultaneous coevolution of Earth’s systems and life on EarthGrade level(s) 9-12: Standard HS-ESS3-4 - Earth and Human Activity - Evaluate or refine a technological solution that reduces impacts of human activities on natural systems.Grade level(s) 9-12: Standard HS-ESS3-6 - Earth and Human Activity - Use a computational representation to illustrate the relationships among Earth systems and how those relationships are being modified due to human activityMost frequently used words:areaslandclassesApproximate Flesch-Kincaid reading grade level: 9.7. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

MAPS OverviewThe MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry.Instrument The MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data.SRL-1 Mission GoalsThe MAPS SRL-1 mission took place during Northern Hemisphere Spring when global biomass burning does not typically occur. Some burning may occur for the purpose of clearing the damaged and felled trees in the forests of North America after the rather severe winter. The goals of the MAPS SRL-1 mission are to provide a validated, near-global atlas of the distribution of tropospheric Carbon Monoxide during the mission, and to assess the health status of the MAPS instrument as the mission progresses. SL1 SummaryHigh concentrations of carbon monoxide over the Northern Hemisphere can be seen in measurements made by the Measurement of Air Pollution from Space(MAPS) instrument. These April 1994 measurements, made from the Space Shuttle Endeavour(STS-59), show large sources of air pollution in the lower atmosphere (2 to 10 miles above the surface) over the industrialized Northern Hemisphere.The data that are available from MAPS SRL1 include a 5 by 5 degree gridded box (MAPS_SRL1_5X5_HDF) and a second by second data product (MAPS_SRL1_COSEC_HDF). These data sets are available from the Langley DAAC.

The United States Geological Survey has published a new global ecosystems map of unprecedented detail that lets you explore a tapestry of World ecosystems. The map was produced by a team led by Roger Sayre, Ph.D., Senior Scientist for Ecosystems at the USGS Land Change Science Program. It is a mosaic of almost 4,000 unique ecological areas called Ecological Land Units (ELUs) based on four factors that are key in determining the makeup of ecosystems. Three of these—bioclimate, landforms, and rock type—are physical phenomena that drive the formation of soils and the distribution of vegetation. The fourth, land cover, is the vegetation that is found in a location as a response to the physical factors. This Story Map Journal has two main features, an ecosystems browser and an ecosystem tour. In the ecosystem browser, point and click at any location on the map and the name of that ecosystem appears in a pop-up box. In general, tans are deserts, yellows and light greens are savannas, darker greens are forests, mountainous regions have texture, reddish is warm and bluish is cold. The browser includes pan and zoom functions. ​The ecosystem tour starts on the next page of this map journal. It features places on Earth where the diversity of Ecological Facets (EFs), the building blocks of ELUs, is highly concentrated in an area. The world is divided up into 3.5 billion cells, each one 250 meters on a side, and each of these cells represents one of 47,500 types of EFs. The areas described in the following pages are all locations with relatively high numbers of EFs. While these are areas of high ecological landscape diversity, they are not necessarily areas of high biodiversity. Many EFs have naturally low species diversity, or have been heavily modified by human activity. The areas in the ecosystem tour below include many interesting and beautiful locations that are widely scattered across our hugely diverse planet.

About the dataLand use land cover (LULC) maps are an increasingly important tool for decision-makers in many industry sectors and developing nations around the world. The information provided by these maps helps inform policy and land management decisions by better understanding and quantifying the impacts of earth processes and human activity.ArcGIS Living Atlas of the World provides a detailed, accurate, and timely LULC map of the world. The data is the result of a three-way collaboration among Esri, Impact Observatory, and Microsoft. For more information about the data, see Sentinel-2 10m Land Use/Land Cover Time Series.About the appOne of the foremost capabilities of this app is the dynamic change analysis. The app provides dynamic visual and statistical change by comparing annual slices of the Sentinel-2 10m Land Use/Land Cover data as you explore the map.Overview of capabilities:Visual change analysis with either 'Step Mode' or 'Swipe Mode'Dynamic statistical change analysis by year, map extent, and classFilter by selected land cover classRegional class statistics summarized by administrative boundariesImagery mode for visual investigation and validation of land coverSelect imagery renderings (e.g. SWIR to visualize forest burn scars)Data download for offline use

OverviewHuman modificationThe degree of human modification across the globe for each terrestrial cell of 110 km by 110 km is shown. The human modification ranges from 0 (no modification) to 1 (high modification). For each cell, we provide the proportion of modification belonging to four different types of human activity: urban, rangeland, rainfed and irrigated agriculture. The degree of modification of the land can be used as a proxy of the cost of its restoration: the higher the modification, the higher the degradation and therefore the higher the cost of restoration. ProtectionSeveral global projects are proposing to increase the proportion of protected terrestrial areas to 30-50%. The proportion of area protected is provided for each cell. Also the proportion for two types of protection: community protected and non-community protected. Community-based Conservation Areas are territories that are managed in a way that allows for the maintenance of biodiversity, environmental services and cultural values. They may operate under different types of governance (e.g., indigenous communities, local communities, private). MethodsHuman modificationThe mean Human modification (Kennedy et al., 2019) is dimensionless and ranges from 0 (no modification) to 1 (complete modification). Kennedy et al. (2019) categorise it as:low (between 0 and 0.1), moderate (between 0.1 and 0.4), high (between 0.4 and 0.7) and very high (over 0.7). Then, four proportion values of human modification are provided: Urban, Irrigated Agriculture, Rainfed Agriculture and Rangelands. The four classes have been derived using the Anthrome Classification Algorithm from Ellis et al. (2010) using the following key:Urban: 11: Urban/12: Mixed settlementsRainfed agriculture: 21: Rice villages/23: Rainfed villages/32: Residential rainfed croplands/33: Populated croplands/34: Remote croplandsIrrigated agriculture: 22: Irrigated villages/31: Residential irrigated croplandsRangeland: 24: Pastoral villages/41: Residential rangelands/42: Populated rangelands/43: Remote rangelandsProtectionThree proportion values of protection are provided: 'Non Community', 'Community' and 'Total'. The protection has been calculated using the WDPA (UNEP and IUCN, version December 2019) and the RAISG data (Amazonia 2019). Community protected areas encompass RAISG and those protected areas from the WDPA designated with any of the following wild cards: Aborig*, Indigen*, Commun*, Conservanc*, Local*, Region*, Trust*, Conservator*, Private*, Nature Center*. Non Community protected areas refer to those not belonging to the previous selection in the WDPA. Total protected areas is the combination of both Community and Non Community protected areas described above.The cell id references the grid from Jetz et al. (2012).SourcesAmazonia 2019 – Protected Areas and Indigenous Territories Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography 19(5):589-606.Jetz, W., McPherson, J. M., and Guralnick, R. P. (2012). Integrating biodiversity distribution knowledge: toward a global map of life. Trends in Ecology and Evolution 27:151-159. DOI:10.1016/j.tree.2011.09.007Kennedy, M. C., Oakleaf, J., Theobald, D.M., Baruch-Mordo, S., Kiesecker, J. (2018). Global Human Modification. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7283087.v1UNEP-WCMC and IUCN (2019), Protected Planet: The World Database on Protected Areas (WDPA), Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.

The World Atlas of Desertification was published by UNEP in 1992 as the result of a cooperative effort between UNEP's Desertification Control Programme Activity Centre (DC/PAC), the Global Environment Monitoring System (GEMS) and the Global Resource Information Database (GRID). GRID compiled and/or derived most of the global and regional databases, produced the maps and carried out the data analyses and tabulations for the Atlas, assisted by a Technical Advisory Group on Desertification Assessment and Mapping composed of various international experts. The Atlas includes information and many maps derived from the Global Assessment of Human-Induced Soil Degradation (GLASOD), as conducted in 1990 by the International Soil Reference and Information Centre (ISRIC) at Wageningen, The Netherlands, on behalf of UNEP.

Aside from GLASOD's data on soil degradation, and in order to capture the multi-dimensional nature of global desertification processes, other data layers relating to global climate and vegetation were compiled by GRID for inclusion in the 1992 World Atlas of Desertification. The NOAA/GVI data set described herein was created by GRID-Nairobi as a unique product for the Desertification Atlas, in order to represent baseline or "normal" conditions of global vegetation, and to be used in combination with the climate, GLASOD and land degradation data sets.

The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

This spatial raster dataset depicts the distribution and density of residential population, expressed as the number of people per cell. Resident population from censuses for year 2011 provided by Eurostat were disaggregated from source zones to grid cells, informed by land use and land cover from Corine Land Cover Refined 2006 and by the distribution and density of built-up as mapped in the European Settlement Map 2016 layer.

MAPS OverviewThe MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere. The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry. InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. SRL2 GoalsThe MAPS SRL-2 mission took place during the Northern Hemisphere summer when global biomass burning is nearing its maximum. The southern hemispheric burning of savanna and agricultural grasslands can be extensive in central and southern South America and in nearly all of Africa, south of the equator. The tundra regions of the northern boreal zone also are approaching the peak burning season. Other regions may experience scattered fire events as a result of lightning strikes during severe thunderstorms. The primary goal of the MAPS experiment on SRL-2 is to provide a near global survey of the distribution of tropospheric carbon monoxide during northern hemisphere summer. The secondary goal is to determine how the global distribution of carbon monoxide changes over the course of the mission.SL2 SummaryThe high values of carbon monoxide are associated with extensive areas of smoke and haze that have been observed by the Endeavour (STS-68) flight crew. The smoke results from fires that are burning in the continental regions. The carbon monoxide is carried by tropical thunderstorms to the altitudes (2 to 10 miles above the surface) at which it is measured by the MAPS instrument. The data that are available from MAPS SRL2 include a 5 by 5 degree gridded box (MAPS_SRL2_5X5_HDF) and a second by second data product (MAPS_SRL2_COSEC_HDF). These data sets are available from the Langley DAAC.

Use this country model layer when performing analysis within a single country. This layer displays predictions within each country of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.

Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Aims: The rapid increase in the number of species that have naturalized beyond their native range is among the most apparent features of the Anthropocene. How alien species will respond to other processes of future global changes is an emerging concern and remains largely misunderstood. We therefore ask whether naturalized species will respond to climate and land-use change differently than those species not yet naturalized anywhere in the world.

Location: Global

Methods: We investigated future changes in the potential alien range of vascular plant species endemic to Europe that are either naturalized (n = 272) or not yet naturalized (1,213) outside of Europe. Potential ranges were estimated based on projections of species distribution models using 20 future climate-change scenarios. We mapped current and future global centres of naturalization risk. We also analyzed expected changes in latitudinal, elevational and areal extent of species' potential alien ranges.

Results: We showed a large potential for more worldwide naturalizations of European plants currently and in the future. The centres of naturalization risk for naturalized and non-naturalized plants largely overlapped, and their location did not change much under projected future climates. Nevertheless, naturalized plants had their potential range shifting poleward over larger distances, whereas the non-naturalized ones had their potential elevational ranges shifting further upslope under the most severe climate change scenarios. As a result, climate and land-use changes are predicted to shrink the potential alien range of European plants, but less so for already naturalized than for non-naturalized species.

Main conclusions: While currently non-naturalized plants originate frequently from mountain ranges or boreal and Mediterranean biomes in Europe, the naturalized ones usually occur at low elevations, close to human centres of activities. As the latter are expected to increase worldwide, this could explain why the potential alien range of already naturalized plants will shrink less.