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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.

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 1985 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 1975 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 1980 over cells of 100x100 m size.

This data represents the total BU surface (square meters) for year 1985 over cells of 1x1 km size.

This data represents the total BU surface estimates (square meters) for year 1990 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 1995 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 2000 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 2005 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 2010 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 2015 over cells of 100x100 m size.

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

This data represents the total BU surface estimates (square meters) for year 2025 over cells of 100x100 m size.

This data represents the total BU surface estimates (square meters) for year 2030 over cells of 100x100 m size.

This data represents the Non Residential BU surface estimates (square meters) for year 2030 over cells of 100 x100 m size.

This data represents the total BU surface (square meters) for year 1975 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 1980 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 1990 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 1995 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 2000 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 2005 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 2010 over cells of 1x1 km size.

This data represents the total BU surface (square meters) for year 2015 over cells of 1x1 km size.

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

This data represents the total BU surface estimates (square meters) for year 2025 over cells of 1x1 km size.

This data represents the total BU surface estimates (square meters) for year 2030 over cells of 1x1 km size.

This data represents the Non residential BU surface estimates (square meters) for year 2030 over cells of 1x1 km size.

The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpolated from 1975 to 2030 in 5 year intervals. Data were obtained by multiplying the GHS built-up surface versus the GHS building height (ANBH) part of the same GHS collection. The layer available within this portal depicts the 2030 prediction.

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

The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. The data reports about the built-up volume allocated to dominant non-residential (NRES) uses. Data are spatial-temporal interpolated from 1975 to 2030 in 5 year intervals. Data were obtained by multiplying the GHS built-up surface versus the GHS building height (ANBH) part of the same GHS collection. The layer available within this portal depicts the 2030 prediction.

This raster dataset depicts the distribution of the building heights, as Average of the Net Building Height (ANBH), generalized at the resolution of 100m, and referred to the year 2018. The input data used to predict the building heights are the ALOS Global Digital Surface Model "ALOS World 3D - 30m (AW3D30)25, the NASA Shuttle Radar Topographic Mission data - 30m (SRTM30)26 , and the Sentinel-2 global pixel based image composite from L1C data for the period 2017-201827 that is the support of the year 2018 in this release.

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

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

Map showing the average continental surface temperature of the year 2022. This map was created by interpolating raw monthly satellite data to fill holes in the grids and subsequently adding and averaging the monthly surface temperatures of the year 2022. The data is clipped to only show the African continental region. The raw data is collected during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS), an instrument on NASA's Terra and Aqua satellites. Please note that the type of "surface" MODIS measures varies as a function of location. In some places, the measurement represents the skin temperature of the bare land surface. In other places, the temperature represents the skin temperature of whatever is on the land-including snow and ice, or the leafy canopy of forests and crop fields, or human-made structures such as pavement and building rooftops.
Values are expressed in °C.
This dataset is part of the LEAP-RE project collection. For more information visit https://www.leap-re.eu/

Some extraordinary changes have occurred across the globe over the past decades regarding human habitation. Globally, between 1975 and 2015 built-up areas increased by approximately 250 %, while population increased by a factor of 1.8. The most considerable changes occurred in Africa where it has nearly quadrupled. The extent of built-up area poses a number of challenges to global sustainable development. As urban clusters expand, productive land and soil is sealed, and natural ecosystems are replaced by land use to support urban centres. This layer highlights the areas of concern for built-up related issues derived from the convergence of global evidence of human-environment interactions that can have land degradation consequences.

Monitoring of sea surface temperature (SST) provides fundamental information on the global climate system and for the study of marine ecosystems. For example, it helps estimating heat stress conducive to coral bleaching, the process by which they expel the symbiotic algae living in their tissues and become white (bleached) and vulnerable. The NOAA Coral Reef Watch's daily global 5km satellite SST Anomaly (SSTA) compares the daily SST value with the long-term mean SST. A positive anomaly (+1.0 °C or more, warm colours) means the daily SST is warmer than the long-term average for that day; a negative anomaly (-1.0 °C or less, cold colours) means it is cooler than the average.

Ocean temperature is related to ocean heat content (the energy absorbed by the ocean), an important topic in the study of global warming. Monitoring of sea surface temperature (SST) from earth-orbiting infrared radiometers has had a wide impact on oceanographic science. It provides fundamental information on the global climate system and for the study of marine ecosystems. For example, it helps estimating heat stress conducive to coral bleaching, the process by which they expel the symbiotic algae living in their tissues and become white (bleached) and vulnerable. The NOAA Coral Reef Watch Daily Global 5km Satellite Sea Surface Temperature product (a.k.a. CoralTemp) measures the night-time ocean temperature at the sea surface, calibrated to 0.2 meters depth.

Monitoring of sea surface temperature (SST) provides fundamental information on the global climate system and for the study of marine ecosystems. For example, it helps estimating heat stress conducive to coral bleaching, the process by which they expel the symbiotic algae living in their tissues and become white (bleached) and vulnerable. The NOAA Coral Reef Watch's daily global 5km 7-day SST Trend product shows the SST trend for the most recent seven days. Pixels coloured in blue to purple follow a cooling trend while pixels coloured in yellow to red follow a warming trend. Pixels coloured in green have insignificant trends.

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

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).

Higher energy demands in Africa has led to a wide expansion of the number of hydropower sites, mainly between the 1960s and 1980s. The construction of dams causes impoundments of rivers and reservoirs in the regions of dam influence, with higher evaporation and water temperatures due to increased water surfaces. This map shows the he surface area (sqkm) of African reservoirs subject to hydropower production for the year 2016. It includes reservoirs of associated hydropower plants with installed capacities above 5MW. Apart from this, only reservoirs with a detected dam-caused impoundment of water surface are considered.