This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 100x100 m size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

The GHS-BUILT-S spatial raster dataset depicts the distribution of the built-up (BU) surfaces estimates between 1975 and 2030 in 5 year intervals and two functional use components a) the total BU surface and b) the non-residential (NRES) BU surface. The data is made by spatial-temporal interpolation of five observed collections of multiple-sensor, multiple-platform satellite imageries: Landsat (MSS, TM, ETM sensor) data supports the 1975, 1990, 2000, and 2014 epochs, while a Sentinel-2 (S2) image composite (GHS-composite-S2 R2020A) supports the 2018 epoch.

This data represents the total BU surface (square meters) for year 2020 over cells of 1x1 km size.

The GHS-BUILT-S spatial raster dataset depicts the distribution of the built-up (BU) surfaces estimates between 1975 and 2030 in 5 year intervals and two functional use components a) the total BU surface and b) the non-residential (NRES) BU surface. The data is made by spatial-temporal interpolation of five observed collections of multiple-sensor, multiple-platform satellite imageries: Landsat (MSS, TM, ETM sensor) data supports the 1975, 1990, 2000, and 2014 epochs, while a Sentinel-2 (S2) image composite (GHS-composite-S2 R2020A) supports the 2018 epoch.

This data represents the total BU surface estimates (square meters) for year 2020 over cells of 100x100 m size.

This data represents the total built-up volume between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

It derives from the GHS-BUILT-V - R2023A spatial raster dataset, that depicts the distribution of built-up volumes, expressed as number of cubic metres. The data report about the total built-up volume and the built-up volume allocated to dominant non-residential (NRES) uses.

The dataset is part the Global Human Settlement Layer , Global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 100x100 m size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 100x100 m size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

The EC JRC global map of forest cover provides a spatially explicit representation of forest presence and absence for the year 2020 at 10m spatial resolution.

The year 2020 corresponds to the cut-off date of the Regulation from the European Union "on the making available on the Union market and the export from the Union of certain commodities and products associated with deforestation and forest degradation" (EUDR, Regulation (EU) 2023/1115). In the context of the EUDR, the global forest cover map can be used as a non-mandatory, non-exclusive, and not legally binding source of information. Further information about the map and its use can be found on the EU Observatory on Deforestation and Forest Degradation (EUFO) in the section on Frequently Asked Questions.

Forest means land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach those thresholds in situ, excluding land that is predominantly under agricultural or urban land use. Agricultural use means the use of land for the purpose of agriculture, including for agricultural plantations (i.e. tree stands in agricultural production systems such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems) and set- aside agricultural areas, and for rearing livestock. All plantations of relevant commodities other than wood, that is cattle, cocoa, coffee, oil palm, rubber, soya, are excluded from the forest definition.

The global map of forest cover was created by combining available global datasets (wall-to-wall or global in their scope) on tree cover, tree height, land cover and land use into a single harmonized globally-consistent representation of where forests existed in 2020.

The workflow consisted in first mapping the global maximum extent of tree cover circa the year 2020 from the combination of ESA World Cover 2020 and 2021, WRI Tropical Tree Cover 2020, UMD Global land cover and land use 2019, Global Mangrove Watch 2020, and JRC Tropical Moist Forest 2020 datasets. In the second step, a series of overlays and decision rules were applied to reduce this maximum extent of tree cover and align it with the Forest definition using datasets covering cropland and commodity expansion (ESA World Cereal, UMD Global land cover and land use 2019, UMD Global Cropland Expansion, High-resolution global map of smallholder and industrial oil palm plantations, and WRI Spatial Database of Planted Trees), land use change (UMD global forest cover loss, JRC Tropical Moist Forest, IIASA Global Forest Management), built-up (JRC Global Human Settlement), and water (JRC Global Surface Water).

The detailed mapping approach will be described in a separate technical report expected to be released by March 2024. The accuracy of this map has not been yet assessed but will be reported as soon as available.

Please also refer to the list of known issues and to the JRC Data Catalogue entry.

From the new available dataset in GFW on tree cover change between 2000 and 2020, here we are displaying the net change in tree cover by percent for country.The net change in tree cover corresponds to the area of gross gain minus the area of gross loss to show the overall change (negative or positive). The attribute table provides also statistics on net change in tree cover  such stable forest or disturbed forest(in hectars).

This dataset is accessed through Global Forest Watch on 04/10/2022. www.globalforestwatch.org.

Potapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. https://doi.org/10.3389/frsen.2022.856903; summarized by administrative area at WRI

The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population change related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation. It reflects the dynamics of increasing number of people in a certain area.

This layer is part of the World Altas of Desertification

According to UN estimates, the global population will increase by 2.4 billion between 2015 and 2050. Of this, an overwhelming 50 % will be concentrated in Africa (1.3 billion). There could be up to four times as many people in sub-Saharan Africa by the end of the century. The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population density related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation.

Constrained estimates of the total number of people per spatial unit, derived from high-resolution population distribution data produced by the WorldPop Global Demographic Data project. These estimates are based on a 30-arcsecond (~1 km) resolution modeling framework and incorporate official census counts, administrative boundaries, and a suite of geospatial covariates.

Percentage of undernourished people. The higher the incidence of undernourished people the more beneficial decentralised renewable energy solutions may be, in terms of improving both cooking facilities within households and agricultural productivity with positive impacts on nutrition. Thus electricity can be used to make agricultural practices more efficient and refrigerate food produce to store for longer.

This data represents the total built-up volume between 1975 and 2030 in 5 year intervals over cells of 1x1 km size.

It derives from the GHS-BUILT-V - R2023A spatial raster dataset, that depicts the distribution of built-up volumes, expressed as number of cubic metres. The data report about the total built-up volume and the built-up volume allocated to dominant non-residential (NRES) uses.

The dataset is part the Global Human Settlement Layer , Global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report

These layers present the application of the Degree of Urbanisation stage I methodology recommended by UN Statistical Commission to the global population grid generated by the JRC in the epochs 1975-2030 (5 years timestep).

They derive from the GHS-SMOD - R2023A.

The layers have been generated by integration of built-up surface extracted from Landsat and Sentinel-2 image data processing (GHS-BUILT-S R2023), and population data derived from the CIESIN GPW v4.11 (GHS-POP R2023).

The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.