In order to provide rapid, scalable, and consistent insights, Restor delivers globally available data products grouped into thematic data categories (e.g. land cover, biodiversity, carbon, etc.) for any terrestrial area on earth.
These data products show changes across a range of variables and spatial & temporal scales in order to serve projects ranging in size and start date/duration. Each data source and analytic technique (whether derived externally or in-house) is visible on Restor; we believe this transparency is key to building trust and understanding any discrepancies between data sources.
Upload or draw your site to access 45+ data insights on biodiversity, carbon, water, and land use.
Here are the data insights that are available on Restor today:
| Layer | Category | Resolution | Frequency | Description |
1. | Risk | 30 m | Every 32 days | A satellite-derived index that reveals how burned or stressed an area of vegetation is. Healthy dense vegetation scores high; recently burned land scores low or negative. | |
2. | Risk | ~375 m–1 km | Daily | Active fire detections and thermal anomalies (volcanoes, gas flares). Suited for fire management support. | |
3. | Risk | ~10km at the equator | Static | Relative drought risk. Higher values indicate need for attentive water management. | |
4. | Risk | ~1km at the equator | Static | Global rainfall erosivity from 3,625 stations and ~60,000 years of precipitation records. | |
5. | Carbon | ~1 km | Static | Tonnes of carbon/ha that would be stored if fully mature native forest recovered. Unsuitable areas shown in black. | |
6 | Carbon | ~500m | Static | Current (~2010/2016) and potential mean carbon density in above/below-ground woody biomass and top 2 m of soil. | |
7. | Carbon | 500 m | Annual | Measures how fast plants are pulling carbon from the atmosphere and converting it to biomass each year — the ecosystem's annual 'growth rate'. From NASA Terra satellite, 2001–present. Declining NPP is an early warning sign of ecosystem degradation. | |
8. | Biodiversity | Regional | Static | A machine-learning model predicts which of ~10,000 tree, shrub and woody vine species are likely to be able to grow in the wider region. Species are tagged as common, threatened (IUCN Red List) or invasive. | |
9. | Biodiversity | Regional | Static | Same ML approach as the tree layer but for ~17,000 herbaceous plant species. Lists tend to be broader than what's actually present at a specific site, so use as a first filter rather than a final species list. | |
10. | Biodiversity | ~50 km | Static | A count of how many plant species are typically found in the broader geographic area. Higher numbers signal greater diversity and often a more resilient ecosystem. Useful for comparing sites. | |
11. | Biodiversity | ~50 km | Static | Estimated number of mammal species expected locally, based on species range maps. Gives a sense of the area's wildlife value and what animal communities could be supported after restoration. | |
12. | Biodiversity | ~50 km | Static | Estimated number of bird species expected in the area. Birds are among the best-studied biodiversity indicators, so this layer gives a reliable sense of ecological potential and restoration targets. | |
13. | Biodiversity | ~50 km | Static | Estimated number of amphibian species expected locally. Amphibians are sensitive to water quality and habitat health — their predicted presence is a useful indicator of ecosystem condition. | |
14. | Biodiversity | Per species | Periodically | Conservation status for plant species in Restor's plant lists. | |
15. | Biodiversity | Per species | periodically | Invasive status from the Global Register of Introduced and Invasive Species. | |
16. | Biodiversity | Per species | Static | Ecological traits: biomass potential, drought tolerance, bark thickness. Informs species selection. | |
17. | Biodiversity | Per species | Static | Whether a species produces products suitable for human consumption. | |
18. | Land cover | 30 m | Annual | NDVI is a simple 'greenness index' from Landsat satellites. Negative = water; near zero = bare rock or snow; 0.2–0.4 = shrubs/grassland; approaching 1 = dense tropical forest. A quick, long-running record (from 1984) of vegetation health and density over time. | |
19. | Land cover | 10 m | Annual | Classifies every 10 m pixel on Earth's land surface into categories: trees, shrubs, crops, built-up areas, bare ground, water, etc. One of the highest-resolution global land cover maps available. Useful for understanding baseline land use at a site. | |
20. | Biodiversity | ~1 km | Static | Assigns each location to one of 14 major terrestrial biomes (e.g. Tropical Forest, Boreal Forest, Grassland, Desert). Sets the ecological context for a site — what kind of ecosystem naturally belongs there. | |
21. | Biodiversity | ~1 km | Static | A finer-grained version of the biome layer: assigns each location to one of 846 distinct ecoregions worldwide, each with a unique mix of species and ecology. Valuable for understanding local biodiversity baselines and choosing appropriate restoration goals. | |
22. | Land cover | ~ 30 m | Baseline (year 2000) | Per-pixel estimate of tree canopy cover at the year 2000, derived from Landsat. This is the baseline against which all forest loss since then is measured. Values are 0–100% canopy cover. | |
23. | Land cover | 30 m | Snapshot (year 2010) | Same as the 2000 layer but representing peak-season canopy cover around 2010. Useful for comparing how tree cover changed in the first decade of the 21st century. | |
24. | Land cover | 30 m | Annual (2000-2019) | Tracks where tree cover (trees taller than 5 m) has been permanently lost each year from 2000 to 2019, using Landsat. A pixel that 'lost' cover in a given year went from forested to cleared that year. The gold standard for global deforestation monitoring. | |
25. | Annual tree cover loss | Land cover | 30 m | Annual | A year-by-year breakdown of tree cover loss, allowing you to see exactly when clearing events happened at a site. Useful for understanding disturbance history and recovery timelines. |
26. | Land cover | ~1 km | Static | Models where trees could naturally grow based on current climate, soil, and topography — ignoring human land use. | |
27. | Environment | ~90 m | Static (2000) | Height above sea level from NASA's Shuttle Radar Topography Mission. Elevation affects temperature, rainfall, species ranges, and water flow. | |
28. | Climate | ~1 km | Static (1979–2013 avg) | The long-term average annual temperature at each location, derived from a high-resolution climate model. Useful for understanding what plant species can survive there and how the site compares to a species' thermal tolerance range. | |
29. | Climate | ~1 km | Static (1979–2013 avg) | The long-term average total annual rainfall at each location. One of the most important drivers of what can grow where. Helps match species to site moisture conditions and plan for water availability. | |
30. | Climate | ~250 m | Static | The acidity or alkalinity of the top 5 cm of soil. Most plants prefer pH 5.5–7.5; extremes outside this range dramatically limit what can grow. Useful for species selection and for deciding if soil amendment is needed before planting. | |
31. | Climate | ~1 km | Static | A ratio of rainfall to potential evaporation. Values below 0.2 = hyper-arid or arid (think Sahara); 0.2–0.5 = semi-arid; 0.5–0.65 = dry sub-humid; above 0.65 = humid. A single number summarising how dry or wet a location fundamentally is. | |
32. | Water | ~1 km | Static | How deep underground the water table sits (in metres below the surface). Many trees need their roots to reach groundwater. Shallow water tables support wetland plants; deep ones limit which species can establish and survive. | |
33. | Land cover | ~250 m | Static | Maps the presence and type of wetland (bog, fen, marsh, swamp, etc.) at each location. Wetlands store enormous amounts of carbon and support unique biodiversity — essential to identify before planning any land intervention. | |
34. | Socio-economic | ~1 km | Periodically updated | Estimated number of people per km² in the area. High density can indicate pressure on land and resources; low density may mean lower management intensity. Relevant for planning community engagement and access logistics. | |
35. | Socio-economic | ~1 km | Static (ca. 2016) | A 0–1 score showing how much human activity (agriculture, roads, urban areas, mining, etc.) has altered each pixel of land. 0 = pristine; 1 = completely modified. A key metric for assessing baseline degradation and restoration potential. | |
36. | Map | Varies | Periodically updated | Standard Google satellite and street map imagery used as the background map. Provides spatial context for all other layers — not an analytical dataset itself. | |
37. | Esri Wayback imagery | Imagery | Up to 30 cm | Multiple snapshots 2014–present | High-resolution historical satellite and aerial imagery at multiple time points since 2014. Lets you visually compare what a site looked like at different dates — useful for tracking change on the ground at very fine detail. |
38. | Sentinel timelapse | Imagery | 10 m | Every 5–12 days | Time-lapse imagery from the European Space Agency's Sentinel-2 satellites, updated every 5–12 days at 10 m resolution. Great for watching seasonal vegetation cycles and detecting recent disturbances like fires or clearing. |
39. | Map | Vector | Static (2015) | Official country boundary polygons from the UN Food and Agriculture Organization. Used to show which country a site falls in and to clip analyses by country. | |
40. | Map | Vector | Static (2015) | Sub-national boundaries at the first administrative level (states, provinces, regions). Useful for filtering sites by region and for reporting at a sub-national scale. | |
41. | Map | Vector | Static (2015) | Second-level administrative boundaries (districts, counties, municipalities). | |
42. | Environment | ~1 km | Static (1979–2013 avg) | The long-term average annual temperature at each location, derived from a high-resolution climate model. Useful for understanding what plant species can survive there and how the site compares to a species' thermal tolerance range. | |
43. | Land cover | 30m | Static (year 2000 snapshot) | The mapped distribution of mangrove forests in the year 2000, showing where mangroves were present at that time. | |
44. | Land Cover | 30m | Static (year 2010 snapshot) | The mapped distribution of mangrove forests in 2010, useful for tracking changes in mangrove area over time.The amount of living plant material above the ground in mangrove forests, indicating how much carbon is stored in trees. | |
45. | Land cover | 30m | Static | The amount of living plant material above the ground in mangrove forests, indicating how much carbon is stored in trees. | |
46. | Land cover | 30m | Static | The average height of mangrove trees in each area, which reflects forest maturity and structure. | |
47. | Land cover | 30m | Static | A measure of canopy height that gives more importance to thicker (larger diameter) trees, providing a better representation of dominant forest structure and biomass. |