Percentage or surface (km2) of a terrestrial (land and inland waters) and Exclusive Economic Zones (EEZ) covered by protected areas.
Terrestrial Protected area coverage
Percentage or surface (km2) of a terrestrial (land and inland waters) covered by protected areas.
Marine Protected area coverage
Percentage or surface (km2) of the country's marine area covered by protected areas.
Percentage of an area covered by protected connected lands.
The indicator considers the spatial arrangement, size and coverage of protected areas (PAs), and accounts for both the land area that can be reached within PAs and that which is reachable through the connections between different PAs.
The analysis includes all designated PAs in the WDPA (polygons and buffered points) not smaller than 1 km2, except UNESCO Biosphere Reserves, and is conducted for a range of median dispersal distances (1 to 100 km) observed for most terrestrial vertebrates.
The indicator is calculated through network analysis, with the Probability of Connectivity and the Equivalent Connected Area as the underlying metrics.
For more details see Saura et al. (2017, 2018, 2019).
Tree canopy cover for year 2000
This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the year 2000 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket.
Data in this layer were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.
The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.
Global forest cover loss 2000–2018
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 1 (loss) or 0 (no loss).
Global forest cover gain 2000–2012
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).
Natural areas derived from the Copernicus Global 100m Land Cover (CGLC) map for the baseline year 2015 excluding manmade classes such as urban areas and agricultural areas.
Net change of permanent surface water
Net change (%) of permanent surface water (1984 - 2018).
The figure is derived from the global surface water was mapped at 30 m resolution using the full 34-year
history of Landsat data between 1984 and 2018 (Pekel et al. 2016). The long
temporal extent of the product allowed to distinguish between permanent and
seasonal water, and to assess the net change of water inside and outside areas
that are currently protected. Note however that water under vegetation cover,
such as swamp forests, is not detectable from optical remote sensing and hence
is not included in this assessment.
Percentage of degraded land in the country area derived using the Land Productivity Dynamics (LPD) dataset.
Land productivity is calculated from satellite observations of photosynthetically active vegetation as the above-ground biomass production
accumulated during the annual growing season.
Number of Species
Number of Species in country derived using IUCN Red List data.
Number of Endemic Species
Number of Endemic Species in country derived using IUCN Red List data.
Number of Threatened Endemic Species
Number of Threatened Endemic Species in country derived using IUCN Red List data.
Number of Threatened Species
Number of Threatened Species in country derived using IUCN Red List data.
Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.
Source: The World Bank.
Population density is midyear population divided by land area in square kilometers. This ratio can be calculated for any territorial unit for any point in time, depending on the source of the population data.
Source: The World Bank.
Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
Source: The World Bank.
Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops.
Source: The World Bank.
Above and Below Ground Carbon Stock
Above and Below Ground Carbon Stock map is computed by summing above-ground carbon (AGC), Belowground Biomass Carbon (BBCI) and Soil Organic 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.
The Soil Organic Carbon is the amount of carbon stored in the soil (0 to 30 cm depth), expressed in Mg (megagrams or tonnes) per km2.
The Belowground Biomass Carbon (BBCI)is expressed in Mg (Megagrams or Tonnes) of carbon per km2. It represents an estimation of the carbon stored in
the roots of all living trees. This carbon pool is calculated as a fraction of the aboveground biomass carbon stock using root-to-shoot ratios (R). It is derived
from two main data sources: the global aboveground biomass map produced by the GlobBiomass project (globbiomass.org) and the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2019).
Fires Near Real-Time Data
Active fires data are provided by the Global Wildfire Information System (GWIS, https://gwis.jrc.ec.europa.eu/ ). Active fires are located on the basis of the so-called thermal anomalies produced by them. The algorithms compare the temperature of a potential fire with the temperature of the land cover around it; if the difference in temperature is above a given threshold, the potential fire is confirmed as an active fire or "hot spot."
GWIS uses the active fire detections provided by the NASA FIRMS (Fire Information for Resource Management System).
Floods Near Real-Time Data
Global historical and current flood events derived from news, governmental, instrumental, and remote sensing sources from the Dartmouth Flood Observatory and Flood hazard 100 year return period Layer from Global Flood Awareness System.
Sea Surface Temperature Near Real-Time Data
The NOAA Coral Reef Watch (CRW) twice-weekly 50-km Sea Surface Temperature (SST) product SST is defined as the skin temperature of the ocean surface water.
Sea Surface Temperature Anomalies Near Real-Time Data
The NOAA Coral Reef Watch (CRW) twice-weekly 50-km Sea Surface Temperature (SST) product displays the difference between today's SST and the long-term average. The scale goes from -5 to +5 °C.
Sea Surface Temperature Trends Near Real-Time Data
The NOAA Coral Reef Watch (CRW) daily global 5km 7-day Sea Surface Temperature (SST) Trend product, updated daily, provides information on the pace and direction of the SST variation, and thus coral bleaching heat stress, if present, over the past seven days. Seven daily global 5km SST measurements, based on CRW's Version 3.1 daily global 5km 'CoralTemp' SST product, are included in the calculation used to derive the above product images. Pixels colored in green have insignificant trends; this is due either to small SST trends (within the range -0.2 to 0.2 °C) or trends that failed the two-tailed Student's-t test for the 20% significance level with five degrees of freedom.
Daily Coral Bleaching Heat Stress Alert Near Real-Time Data
Level of stress of the Global Coral Reefs derived from NOAA Alerts Bleaching Alerts.
Coral Bleaching HotSpot Near Real-Time Data
The twice-weekly global 50km Coral Bleaching HotSpot product presented here was used to measure the occurrence and magnitude of instantaneous coral bleaching-inducive heat stress. See the 'Coral Reef Watch Operational 50km Satellite Nighttime SST Climatologies' table above to access the Maximum Monthly Mean (MMM) SST climatology, used before February 1, 2016 for this product.
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