The Biodiversity Intactness Index shows the modelled average abundance of originally-present species in a grid cell, as a percentage, relative to their abundance in an intact ecosystem. Originally available for year 2015, the data is now available in a time series covering the period 2000-2015 - here we provide a bi-decade subset of the index.

Biodiversity hotspots are the Earth’s most biologically rich—yet heavily threatened—terrestrial regions. These are regions where success in conserving species can have an enormous impact in securing our global biodiversity. To qualify as a biodiversity hotspot, an area must meet two strict criteria: it must contain at least 1,500 species of vascular plants found nowhere else on Earth (known as "endemic" species), and it must have lost at least 70% of its primary native vegetation. 36 regions are identified as hotspots by Conservation International and partners, 9 of which lay (partially or fully) in Africa. This dataset shows their location.

The Copernicus Global Land Service - Burnt Area products depict burn scars, surfaces which have been sufficiently affected by fire to display significant changes in the vegetation cover (destruction of dry material, reduction or loss of green material) and in the ground surface (temporarily darker because of ash). The monthly products are available at global scale in the spatial resolution of 300 m and cover the period from January 2019 to April 2022. The pixel bears the value of the day of the year in which the burn scars were visible while, if selected for mapping in this platform, the dataset shows the burn scars detected in the selected month.

''Intact Forest Landscapes (IFLs) are defined as those with an unfragmented area of at least 500 km2 and which are minimally influenced by human economic activity''.(Thies et al. 2011) IFLs are critical for stabilizing terrestrial carbon storage, harboring biodiversity, regulating hydrological regimes, and providing other ecosystem functions. Researchers, firstly, created a global IFL map using existing fine-scale maps and
a global coverage of high spatial resolution satellite imagery (Potapov et al. 2008). Moreover they assessed the distribution and dynamics of IFLs within the extent of present-day forest ecosystems tracking loss of intact forest landscapes from 2000 to 2020. In this layer we show the reduction of the IFL extent for African, Caribbean and Pacific (ACP)countries that decreased by 16,6% since the year 2000. Countries that experienced the largest reduction in intact forest landscape area over the past two decades are Solomon Islands with 66,3 %, Central African Republic with 57% and Equatorial Guinea with 50,4%. Democratic Republic of the Congo still  has the highest proportion of intactness of ACP countries with 59,7Mha. IFL in Democratic Republic of the Congo has an extent of 64,3Mha as the 27,7% of its forest cover. 

An intact forest landscape (IFL) is a seamless mosaic of forest and naturally treeless ecosystems with no remotely detected signs of human activity and a minimum area of 500 km2. (Potapov et al.2017) Intact forests are complex and diverse ecosystems that if lost, are irreplaceable. Research shows that designating intact forest landscapes as protected areas has proven effective at limiting their fragmentation. Since 2000, around 100 conserved and protected areas were created in intact forests areas in ACP countries, increasing the percentage of IFL protected from 11% in 2000 to 26% in 2020.

Differences in countries in terms of IFL area reduction: Cuba has not experienced any reduction in IFLs and nowadays its intact forests are fully protected by PAs (544sqkm). At the contrary Angola still not has any kind of protection for its IFLs and experienced a reduction of 39,83% of its IFL (1160 sqkm) in country.The reduction of IFL area in ACP countries was higher outside PAs (19%) than within PAs (5%).Madagascar is the country where the reduction of IFL areas was very high inside protected areas (2420 sqkm). The IFL loss inside PAs has been more than 40% between 2000 and 2020. Central African Republic experienced 23% of reduction inside PAs (938,13 sqkm). This layer shows the percentage of IFLs protected by country

Analysis performed by Simona Lippi

This dataset is an index that estimates the relative value provided by coral reefs that protect coastlines through reduction of wave height and wave energy. The value as of 2014 is modelled as a function of exposed populations and infrastructure that received some level of protection from coastal and barrier reefs, and is described in relative terms, classified by decile (i.e., grouped into the most valuable ten percent of reefs for protection, the second-most valuable tenth of reefs, etc.). It was calculated at 1 kilometre (km) resolution globally, encompassing all countries and territories containing coral reefs.

Poverty affects billions of people around the globe. On a daily basis, they face low wages and substandard health, education, and living standards. Because of this, poverty must be understood and approached as a multidimensional issue. The Multidimensional Poverty Index (MPI) acknowledges that poverty has many faces. The second dimension in the MIP is the Education dimension. This includes years of schooling and child school attendance. This map shows the percentage contribution of the education dimension to overall poverty. The lower percentages are shown in darker blues while the higher percentages are shown as brighter, lighter blues.

This layer provides the Effective Leaf Area Index (LAIe), a critical parameter for modeling evapotranspiration and carbon fluxes between the biosphere and the atmosphere. Monitoring the distribution and seasonal evolution of LAI is essential for assessing vegetation health and ecosystem dynamics across Africa.

The Effective Leaf Area Index is a quantitative measure of the amount of live green leaf material present per unit ground surface. Unlike "True LAI," which represents the total physical leaf area, the Effective LAI is derived from canopy gap fraction measurements and assumes a random distribution of foliage. It is a non-dimensional quantity, typically expressed in units of m²/m².

This dataset is widely utilized in agro-meteorology, biogeochemical modeling, and General Circulation Models (GCMs) to parameterize vegetation cover and its complex interactions with the atmosphere.

This layer is part of the Earth Land Information System (ELIS).

This layer presents the Effective Leaf Area Index (LAIe) Anomalies, representing the deviation of current vegetation density from the historical average. Monitoring the change of LAI is essential for assessing the evolution of the vegetation over Africa.

LAI anomalies are calculated relative to the average values between 2003 and 2010. The dataset captures LAI anomalies every 10 days, reflecting the high variability and rapid changes in African vegetation cover. Increases in temperature and precipitation deficits are the primary drivers for negative anomalies (reduced foliage density). Beyond climatic factors, human activities or animal grazing may also locally impact the state and density of leaves.

This layer is part of the a href="https://fapar.jrc.ec.europa.eu/_www/index.php">Earth Land Information System (ELIS).

Mapping ongoing project activities in the field of conservation in protected areas is essential to identify the various actors and to identify the areas where information and actors are scarce. The aim is to better understand who is funding what and where, with a view to improve decision-making on biodiversity conservation. This map provides the geolocation of all sites that have received funding in the frame of a biodiversity conservation project -or an activity within a project- within approx. the last 20 years. It specifies whether the site is protected or not. One project usually includes several sites.

The Global Gridded Relative Deprivation Index (GRDI) characterizes the relative levels of multidimensional deprivation and poverty, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDI is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final GRDI layer.

The INFORM Risk Index 2021 is a composite model structured into a hierarchy of dimensions, categories, and components. All scores are normalized on a scale of 0 to 10 (10 being the highest risk).

Below is the clear breakdown of the index's architecture based on your descriptions:

1. The Composite Index

The top-level score representing a country's overall risk of humanitarian crisis. It is used to assign countries into Risk Classes:

• Low: ≤3.5
• Medium: 3.5−5.0
• High: 5.0−6.5
• Very High: ≥6.5

2. Dimension: Vulnerability

This dimension measures the predisposition of a population to be affected by a hazard based on economic, political, and social characteristics.

Sub-Index: Socio-Economic Vulnerability

An aggregate index covering three main components of systemic instability:

• Development Deprivation: Measures poverty using the Human Development Index (HDI) and Multidimensional Poverty Index (MPI).
• Inequality: Captures the dispersion of human development, including the Gender Inequality Index and the Gini Coefficient.
• Aid Dependency: Measures economic instability through reliance on public aid, development assistance, and remittances.

Sub-Index: Vulnerable Groups

Captures social groups with limited access to care and heightened susceptibility:

• Uprooted People: Tracks refugees, IDPs, and returnees.
• Children Under 5: Specifically monitors child malnutrition and mortality rates.
• Recent Shocks: Factors in populations already affected by natural disasters in the last 3 years.
• Food Security: Captures trends in chronic hunger and nutritional balance.
• Health Conditions: Monitors the prevalence of pandemics (HIV, TB, Malaria) and neglected tropical diseases.

3. Dimension: Lack of Coping Capacity

This dimension measures the ability of a country to manage and recover from disasters through its infrastructure and government effort.

Sub-Index: Institutional Capacity

Measures the "soft" infrastructure of a country's disaster management:

• Governance: Captures government effectiveness, resilience-building, and political integrity (lack of corruption).
• DRR Implementation: Quantifies how well Disaster Risk Reduction activities are actually put into practice.

Sub-Index: Infrastructure

Measures the "hard" assets and systems available during a crisis:

• Communication: Evaluates the efficiency of early warning dissemination and the literacy/education of the recipients.
• Physical Connectivity: Assesses the accessibility of transport and the quality/availability of water and sanitation services.
• Access to Healthcare: Captures health system strength, including physician density and maternal/infant health services.

In 2020, a group of researchers carried out analysis to examine the importance of Indigenous Peoples’ lands for preserving Intact Forest Landscapes.(Fa et al.2020)They used geospatial data on the extent of Indigenous Peoples’ lands reported by Garnett et al. (2018), and Geospatial data for IFLs were sourced from the Intact Forest Landscapes website (www.intactforests.org) for the years 2000, 2013, and 2016 (Potapov et al. 2017)In the paper they have shown that the proportion of Indigenous Peoples’ lands mapped as IFLs was considerably higher (10.9%) than the proportion of other lands (defined here as all land outside Indigenous Peoples’ lands) mapped as IFLs (6.8%).

In this map we reported the percentage of Intact Forest Lansdcapes reduction in Indigenous People Lands for the period 2000-2016. IN the table we reported also the percentage of IFLs reduction in other lands. To explore more IFLs conservation strategies for African, Caribbean and Pacific countries you can check BIOPAMA Geonode Layers

Sources:

Fa JE, Watson JE, Leiper I, Potapov P, Evans TD, Burgess ND, Molnár Z, Fernández‐Llamazares Á, Duncan T, Wang S, Austin BJ. Importance of Indigenous Peoples’ lands for the conservation of Intact Forest Landscapes. Frontiers in Ecology and the Environment. 2020 Apr;18(3):135-4

The International Wealth Index is an asset-based wealth index that runs from 0 (no assets) to 100 (all assets) and is comparable across place and time. The lower the level of the Index, the greater the potential of electricity access to reduce poverty and foster development.

Key Biodiversity Areas (KBAs) are the most important places in the world for species and their habitats. Faced with a global environmental crisis we need to focus our collective efforts on conserving the places that matter most. The KBA Programme supports the identification, mapping, monitoring and conservation of KBAs to help safeguard the most critical sites for nature on our planet – from rainforests to reefs, mountains to marshes, deserts to grasslands and to the deepest parts of the oceans. By providing the precise location of places that contribute significantly to the global persistence of biodiversity, KBAs can accelerate efforts to reverse the loss of nature, by ensuring conservation efforts are focussed in the places that matter most, and by enabling entities that may have negative impacts on nature to avoid or reduce those impacts in the places they would be most damaging. This layer shows the location of the KBAs, identified and mapped by the KBA partnership.

Poverty affects billions of people around the globe. On a daily basis, they face low wages and substandard health, education, and living standards. Because of this, poverty must be understood and approached as a multidimensional issue. The Multidimensional Poverty Index (MPI) acknowledges that poverty has many faces. The third and last dimension in the MPI is the Living Standard dimension. This includes access to electricity, improved sanitation services and safe drinking water, flooring, cooking fuel, and assets ownership. This map shows the percentage contribution of the Living Standards Dimension to the overall poverty index. The lower percentages are shown in darker greens while the higher percentages are shown as brighter, lighter greens.

The Rule of Law Index, generated by the World Justice Project, illustrates the perceived adherence to the Rule of Law per country. Rule of law is defined here as a durable system of laws, institutions, norms, and community commitment that delivers accountability, just laws, open government, and accessible justice. These principles are measured across eight factors:

1. Constraints on Government Powers
2. Absence of Corruption
3. Open Government
4. Fundamental Rights
5. Order and Security
6. Regulatory Enforcement
7. Civil Justice
8. Criminal Justice

The Rule of Law Index aggregate the scores across factors. A high ranking (a low numerical rank) means that perceived adherence to the Rule of Law is higher. 0=weakest, 1=strongest. 

This dataset is part of the LEAP-RE project collection. For more information visit https://www.leap-re.eu/

Droughts affect millions of people in the world each year and have long-lasting socioeconomic impacts. They can occur over most parts of the world, even in wet and humid regions, and can profoundly impact agriculture, basic household welfare, tourism, ecosystems and the services they provide. The Standardized Precipitation Index (SPI) is the most commonly used indicator worldwide for detecting and characterizing meteorological droughts, which are prolonged periods of less than average rainfall in a given region. It measures precipitation anomalies at a given location, based on a comparison of observed total precipitation amounts for an accumulation period of interest (in this case, 3 months), with the long-term rainfall record for that period. SPI values below ‒1.0 indicate rainfall deficits (drier than normal – yellow to red), while SPI values above 1.0 indicate excess rainfall (wetter than normal – purple to blue). The lower the SPI, the more intense is the drought. The layer show the SPI-3 from the month second to last and is updated monthly.

Where are the best places to spot the most requested wildlife-watching species? This grid layer presents a richness index of top wildlife watching species/groups, including rhinos, elephants, lions, leopards, buffalos, giraffes, lowland and mountain gorillas, chimpanzees, bonobos, sifakas, ringtails, aye-aye, mouse lemurs and colobus.

This dataset maps regions across Africa with high geothermal energy exploration potential, identifying areas where further research and investment are most likely to yield significant returns. The mapping represents a synthesis of the Indicator Analysis performed by TNO (Hofstra, 2023), specifically selecting the most and least favorable technical outcomes under the assumption of a clay-poor sandstone (CP) compaction model.

To ensure structural robustness and account for geological uncertainty, the indicator results have been projected onto four distinct geological basin models:

• BGS TARGET Model: Projecting indicator values onto basin shapefiles defined by the British Geological Survey.
• Evenick Model: Utilizing sedimentary basin structures characterized by surface porosity and thickness according to Evenick’s framework.
• Getech Model: Integrating regional basin delineations to refine spatial prospectivity based on gravity and crustal structure data.
• Robertson CGG Model: Applying indicator analysis to high-resolution basin geometries to evaluate technical performance.

Key Indicators & Methodology

The favorable results displayed in these maps are derived from a multi-criteria evaluation which includes:

• Thermal Performance: Integration of the 3D conductive thermal model to identify viable reservoir temperatures at depth.
• Reservoir Quality: Assessment of potential hydraulic transmissivity based on depth-dependent porosity and permeability for sedimentary aquifers.
• Economic Viability: Calculations involving the Levelized Cost of Energy (LCOE) to distinguish areas where energy extraction is most cost-effective.

This layer is part of the Geothermal Atlas for Africa developed within the LEAP-RE project.