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Above and Below Ground Terrestrial Carbon Storage (t/ha)

This map represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010. The dataset was constructed by combining the most reliable publicly available datasets and overlaying them with the ESA CCI landcover map for the year 2010 (ESA, 2017), assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell's landcover type. Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see Table 1 in paper cited for further information on datasets selected). After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type. Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above-and-below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer. This dataset has not been validated.


GOAL 15: Life on land


Other SDGs


Environment Biodiversity Forests


Source: EC-JRC

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Biodiversity Hotspots

The terrestrial biodiversity hotspots identified by Conservation International and partners delineate large regions characterized both by exceptional levels of plant endemism and by serious levels of habitat loss. To qualify as a hotspot, a region must meet two strict criteria: it must contain at least 1,500 species of vascular plants (> 0.5 percent of the world's total) as endemics, and it has to have lost at least 70 percent of its original habitat.


GOAL 15: Life on land


Other SDGs


Environment Biodiversity Protected Areas


Source: Critical Ecosystem Partnership Fund (CEPF)

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Biomes

This map shows the Terrestrial Biomes, the eight major terrestrial biomes on Earth are each distinguished by characteristic temperatures and amount of precipitation. Comparing the annual totals of precipitation and fluctuations in precipitation from one biome to another provides clues as to the importance of abiotic factors in the distribution of biomes. Temperature variation on a daily and seasonal basis is also important for predicting the geographic distribution of the biome and the vegetation type in the biome. The distribution of these biomes shows that the same biome can occur in geographically distinct areas with similar climates.


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: EC-JRC

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Biomes protection levels

This map shows the level of protection of the Terrestrial Biomes in Africa. The results are computed using the World Database on Protected Areas (WDPA), June 2020. Cambridge, UK. Available at: www.protectedplanet.net.


GOAL 15: Life on land


Other SDGs


Environment Biodiversity Protected Areas


Source: EC-JRC

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Convergence of Global Change Issues

At any given place on Earth, complex human-environment interactions are at play, which include differing rates and magnitudes of drivers (e.g. overgrazing, climate change, agricultural practices) and consequences (e.g. soil erosion,changes in productivity, loss of biodiversity). Because these are tied to specific places on the ground with their own intertwined biophysical, social, economic and political environments, land degradation is not a phenomenon that can be modelled or mapped at a global scale. WAD3 builds on a systematic framework of providing a convergence of reliable,global evidence of human environment interactions to identify local or regional areas of concern where land degradation processes may be underway. Concerns can be validated or dismissed only by evaluating them within local biophysical, social, economic and political contexts. Local context provides an understanding of causes and consequences of degradation, but also offers guidance for efforts to control or reverse it.


GOAL 15: Life on land


Other SDGs


Environment


Source: EC-JRC

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Country's species richness

This map shows species richness in country assessed by the International Union for the Conservation of Nature (IUCN) and documented in the IUCN Red List of Threatened Species TM (RLTS). Country summary statistics are expert based as reported by the IUCN in their summary tables: https://www.iucnredlist.org/resources/summarystatistics


GOAL 15: Life on land


Other SDGs


Environment Biodiversity


Source: EC-JRC

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Country's Threatened Mammals

This map shows the number of threatened mammals in country assessed by the International Union for the Conservation of Nature (IUCN) and documented in the IUCN Red List of Threatened Species TM (RLTS). Country summary statistics are expert based as reported by the IUCN in their summary tables: https://www.iucnredlist.org/resources/summarystatistics


GOAL 15: Life on land


Other SDGs


Environment Biodiversity


Source: EC-JRC

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Crop land

Each pixel represents the area fraction of the specific cover (i.e. percentage of the pixel with crops/rangeland). Data are scaled between 1 and 200 (50 = 25%, 100 = 50%, 150 = 75%, 200 = 100%): image values V = 0-200, scaling 0.5 - > physical value 0-100%. These layers were generated for ASAP, combining existing data sets.


GOAL 1: No poverty


Other SDGs

GOAL 12: Responsible Consumption and Production, GOAL 2: Zero Hunger


Environment Food and Agriculture Land Use in Agriculture


Source: EC-JRC

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Forest Cover

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2018. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013).


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: Hansen/UMD/Google/USGS/NASA

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Forest Gain

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2018. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013). Year of gross forest cover loss event: Forest gain during the period 2000–2012, defined as the inverse of loss, or a non-forest to forest change entirely within the study period. Encoded as either 1 (gain) or 0 (no gain).


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: Hansen/UMD/Google/USGS/NASA

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Forest Loss

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2018. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013). Year of gross forest cover loss event: Forest loss during the period 2000–2018, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 0 (no loss) or else a value in the range 1–17, representing loss detected primarily in the year 2001–2018, respectively.


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: Hansen/UMD/Google/USGS/NASA

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Frequency of ten-daily warnings about rangeland anomalies

The dataset contains warnings about low or delayed vegetation performance at sub-national level for rangeland . The warning classification scheme is applied globally and is based on rainfall estimates (RFE) and NDVI anomalies. The results are a reliable warning of hydrological stress for agricultural production and the warning level ranges from 1 to 4. The historical frequency of ASAP warnings reports the percentage of dekads with a warning for rangeland, out of the total number of active dekads in the period 2004-2019.


GOAL 1: No poverty


Other SDGs

GOAL 12: Responsible Consumption and Production, GOAL 2: Zero Hunger


Natural Disasters Environment Food and Agriculture Crop Health


Source: EC-JRC

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Geo-location of sites with biodiversity funding

This map provides the geolocation of all sites that have received funding in the frame of a project - or an activity within a project- within the last 20 years or so. It specifies if the site is protected or not. A project usually includes several sites. By clicking on a site, further information is provided on (1) the name, total budget, timeframe and status of the project and (2) the donor and implementing agency. Targeted actions can be education/awareness, land/water management, land/water protection, species management, law and policy, livelihood/economic and other incentives, external capacity building. By clicking on the project, the type of action is specified, and the funding.


GOAL 15: Life on land


Other SDGs

GOAL 14: Life Below Water


Environment Biodiversity Protected Areas


Source: EC-JRC

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Key Landscapes for Conservation

Key Landscape for Conservation (KLC) were proposed in the European Commission publication “Larger than Elephants” (EC, 2015). The polygons shown in the report did not always emcompass the target protected areas and their limits were difficult to use in quantitative spatial analyses.


GOAL 15: Life on land


Other SDGs


Environment Biodiversity Protected Areas


Source: EC-JRC

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Land Degradation

Humans need increasingly more biomass for food, fodder, fiber and energy. In Africa, circa 22% of the vegetated land surface showed a decline or unstable land productivity between 1999 and 2013. Persistent reduction of land productivity points to long-term alteration of the health and productive capacity of the land, which are characteristic of land degradation. It has impact on ecosystem services and benefits, thus on the sustainable livelihoods of human communities. This map shows the dynamics of (vegetated) land productivity over a time period, in other terms the trajectories of above-ground biomass. It reflect changes in ecosystem functioning e.g. vegetation growth cycles due to natural variation and/or human intervention, and can be associated with processes of land degradation or recovery. The 5 classes depict two levels of persistent productivity decline, one level of instability or stress in capacity, one level of stable productivity and one level of increased productivity.


GOAL 15: Life on land


Other SDGs

GOAL 5: Gender Equality, GOAL 7: Affordable and Clean Energy


Environment Food and Agriculture Land Use in Agriculture Crop Health


Source: EC-JRC

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Land Fragmentation

The Natural Land Pattern Index (NLPI) assesses the spatial pattern of the natural/semi-natural land by reporting the area (in km2) covered by six spatial pattern classes (core, edge, perforation, islet, margin, core-opening) in which natural/semi-natural land has been classified as of 2015. The Natural Land Pattern Dynamics (NLPD) reports the trends in the area occupied by these pattern classes in the last 20 years (1995-2015). The six pattern classes are determined based on the spatial context and size of the patches of natural/semi-natural land cover, accounting for its proximity to non-natural (agricultural and urban) areas. See below (Use and Interpretation section) for a detailed description of these six classes.


GOAL 15: Life on land


Other SDGs


Environment Biodiversity


Source: EC-JRC

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Mangroves

The data show the total change in mangrove extent, either gain or loss since the baseline (year 2000).


GOAL 6: Clean water and sanitation


Other SDGs


Environment Water


Source: The Global Mangrove Watch (GMW)

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Natural Areas

Natural Areas is calculated using Copernicus Global 100m Land Cover map 2015.


GOAL 15: Life on land


Other SDGs

GOAL 11: Sustainable Cities and Communities, GOAL 13: Climate Action, GOAL 3: Good Health and Well-being


Environment Biodiversity Forests


Source: EC-JRC

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Oil Palm Plantations

Industrial mature oil palm plantation in Country (v1) Smallholder mature oil palm plantation in Country (v2)


GOAL 15: Life on land


Other SDGs


Environment Biodiversity Forests


Source: EC-JRC

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Protected Areas

The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via Protected Planet, the online interface for the WDPA. The WDPA is a joint initiative of the International Union for Conservation of Nature (IUCN) and the UN Environment Programme's World Conservation Monitoring Centre (UNEP-WCMC) to compile spatially referenced information about protected areas. The data are provided as shapefiles and updated monthly.


GOAL 15: Life on land


Other SDGs

GOAL 14: Life Below Water


Environment Biodiversity Protected Areas


Source: UNEP-WCMC/IUCN

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Relative likelihood of hydro-political interactions (Ranking in 1997–2012)

Competition over limited water resources is one of the main concerns for the coming decades. Although water issues alone have not been the sole trigger for warfare in the past, tensions over freshwater management and use represent one of the main concerns in political relations between riparian states and may exacerbate existing tensions, increase regional instability and social unrest. Previous studies made great efforts to understand how international water management problems were addressed by actors in a more cooperative or confrontational way. In this study, we analyze what are the pre-conditions favoring the insurgence of water management issues in shared water bodies, rather than focusing on the way water issues are then managed among actors. We do so by proposing an innovative analysis of past episodes of conflict and cooperation over transboundary water resources (jointly defined as “hydro-political interactions”). On the one hand, we aim at highlighting the factors that are more relevant in determining water interactions across political boundaries. On the other hand, our objective is to map and monitor the evolution of the likelihood of experiencing hydro-political interactions over space and time, under changing socioeconomic and biophysical scenarios, through a spatially explicit data driven index. Historical cross-border water interactions were used as indicators of the magnitude of corresponding water joint-management issues. These were correlated with information about river basin freshwater availability, climate stress, human pressure on water resources, socioeconomic conditions (including institutional development and power imbalances), and topographic characteristics. This analysis allows for identification of the main factors that determine water interactions, such as water availability, population density, power imbalances, and climatic stressors. The proposed model was used to map at high spatial resolution the probability of experiencing hydro-political interactions worldwide. This baseline outline is then compared to four distinct climate and population density projections aimed to estimate trends for hydro-political interactions under future conditions (2050 and 2100), while considering two greenhouse gases emission scenarios (moderate and extreme climate change). The combination of climate and population growth dynamics is expected to impact negatively on the overall hydro-political risk by increasing the likelihood of water interactions in the transboundary river basins, with an average increase ranging between 74.9% (2050 – population and moderate climate change) to 95% (2100 - population and extreme climate change). Future demographic and climatic conditions are expected to exert particular pressure on already water stressed basins such as the Nile, the Ganges/Brahmaputra, the Indus, the Tigris/Euphrates, and the Colorado. The results of this work allow us to identify current and future areas where water issues are more likely to arise, and where cooperation over water should be actively pursued to avoid possible tensions especially under changing environmental conditions. From a policy perspective, the index presented in this study can be used to provide a sound quantitative basis to the assessment of the Sustainable Development Goal 6, Target 6.5 “Water resources management”, and in particular to indicator 6.5.2 “Transboundary cooperation”


GOAL 6: Clean water and sanitation


Other SDGs

GOAL 10: Reduced Inequality


Climate Growth & Inequality Environment Water


Source: EC-JRC

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Soil Map

At the African Union and European Union Commission College meeting in Addis Abeba, Ethiopia (April 25-26, 2013) this map contained in the Soil Atlas of Africa was introduced by EU Commissioner Hedegaard (Climate Action) on behalf of the European Commission President José Manuel Barroso. The atlas is available for download at https://esdac.jrc.ec.europa.eu/content/soil-map-soil-atlas-africa


GOAL 2: Zero hunger


Other SDGs

GOAL 13: Climate Action, GOAL 15: Life on Land


Environment Land Use in Agriculture Crop Health


Source: EC-JRC

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Terrestrial Priority Ecoregions

WWF’s Global 200 project analyzed global patterns of biodiversity to identify a set of the Earth's terrestrial, freshwater, and marine ecoregions that harbor exceptional biodiversity and are representative of its ecosystems. This process yielded 238 ecoregions--the Global 200--comprised of 142 terrestrial, 53 freshwater, and 43 marine priority ecoregions.


GOAL 15: Life on land


Other SDGs


Environment


Source: WWF

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Total Carbon

The above-ground carbon (AGC) is expressed in Mg (megagrams or tonnes) of carbon per km2. It corresponds to the carbon fraction of the oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees, excluding stump and roots, as estimated by the GlobBiomass project (globbiomass.org) with 2010 as the reference year.


GOAL 15: Life on land


Other SDGs


Environment Biodiversity Forests


Source: EC-JRC

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Tropical Moist Forests (TMF) - Deforestation year map

The deforestation year is the year when the TMF has been deforested for the first time (followed or not by a regrowth).


GOAL 15: Life on land


Other SDGs


Environment Forests


Source: EC-JRC

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Tropical Moist Forests (TMF) - Degradation year map

The degradation year is the year when the TMF has been degraded for the first time (and remained degraded up to 2019).


GOAL 15: Life on land


Other SDGs


Environment Forests


Source: EC-JRC

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Tropical Moist Forests (TMF) - Transition map

The transition map captures the sequential dynamics of changes by providing transition stages from the initial observation period to the end of the year 2019. It depicts five main land cover types with a few sub-types: (i) remaining undisturbed moist forests (including the mangroves), (ii) degraded forests with two sub-types corresponding mostly to either logged or burned forests, (iii) young forest regrowth, (iv) deforested land that includes three subcategories of converted land cover: (a) water bodies (new dams and river flow changes); (b) tree plantations; and (c) other land cover that includes infrastructure, agriculture and mining, and (v) non-TMF cover (including afforestation).


GOAL 15: Life on land


Other SDGs


Environment Forests


Source: EC-JRC

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Water Occurrence (1984-2018)

The Water Occurrence dataset shows where surface water occurred between 1984 and 2018 and provides information concerning overall water dynamics. This product captures both the intra and inter-annual variability and changes. The occurrence is a measurement of the water presence frequency (expressed as a percentage of the available observations over time actually identified as water). The provided occurrence accommodates for variations in data acquisition over time (i.e. temporal deepness and frequency density of the satellite observations) in order to provide a consistent characterization of the water dynamic over time.


GOAL 6: Clean water and sanitation


Other SDGs


Environment Water Energy


Source: EC-JRC

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Water Quality – Trophic State

Trophic State refers to the degree at which organic matter accumulates in the water body and is most commonly used in relation to monitoring eutrophication (process of excessive growth of algae resulting in oxygen depletion, it is commonly caused by human activities, it can be occasional or frequent). The data show the total percentage deviation, from a baseline for trophic state. A five year baseline (2006- 2010), per lake, has been produced for both parameters. This is used to measure change against recent years (2017-2019). The data represent the number of lakes impacted by a degradation of their environmental conditions (i.e. showing a deviation in turbidity and trophic state from the baseline) compared to the total number of lakes within a country. The values produced account for different sized lakes.


GOAL 6: Clean water and sanitation


Other SDGs

GOAL 6: Clean Water and Sanitation


Environment Water


Source: EC-JRC

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Water Quality – Turbidity

Turbidity is an indicator of water clarity, quantifying the haziness of the water and acting as an indicator of underwater light availability. Light penetration may or may not be sufficient to support the growth of aquatic plants and adversely affect fish and shellfish populations. Mangroves are known to reduce the turbidity of waters. The data show the total percentage deviation, from a baseline, for turbidity and trophic state. A five year baseline (2006- 2010), per lake, has been produced for both parameters. This is used to measure change against recent years (2017-2019). The data represent the number of lakes impacted by a degradation of their environmental conditions (i.e. showing a deviation in turbidity and trophic state from the baseline) compared to the total number of lakes within a country. The values produced account for different sized lakes.


GOAL 6: Clean water and sanitation


Other SDGs


Environment Water


Source: EC-JRC

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Water Transitions (1984-2018)

The data show the total change in extent of permanent and seasonal surface water area, measured against a historical reference period. Change is either gain or loss. Total change in extent of surface water area is calculated by comparing the most recent five years of data against a five year reference period (2000-2004). Permanent water is defined as being present all 12 months per year. Seasonal water is defined as being present less than 12 months per year.


GOAL 6: Clean water and sanitation


Other SDGs


Environment Water


Source: EC-JRC

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Water Transitions in Reservoir (1984-2018)

Reservoir dynamics: Annual extent of reservoir surface water area.


GOAL 6: Clean water and sanitation


Other SDGs


Environment Water


Source: EC-JRC

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Wetlands

The data show the total area of wetlands extent. Inland vegetated wetlands include areas of marshes, peatlands, swamps, bogs and fens, the vegetated parts of floodplains as well as rice paddies and flood recession agriculture.


GOAL 6: Clean water and sanitation


Other SDGs


Environment Water


Source: Third Party Source: DHI-GRAS

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