In order to provide rapid, scalable, and consistent insights, Restor delivers globally available data products grouped into thematic data chapters (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 35+ data insights on biodiversity, carbon, water, and land use. Here are the data insights that are available on Restor today:

This resource contains information about the global unrealized potential for above ground carbon storage in forest ecosystems. The map provides an estimate for the tonnes of carbon per hectare that would be stored in forests if fully mature native plants were allowed to recover in a given area. Areas unsuitable for supporting forest cover – including grassland and dryland habitats – are displayed in black. Areas unable to support natural forest cover are masked out.

This dataset provides information on which of ~10,000 well-studied tree, shrub, and woody vine species are predicted to be able to grow in the wider region containing this site (based on a machine learning approach). This can potentially aid in choosing species for restoration work. However, please note that the list is likely to include more species than are typically found in a given location (see Caveats and assumptions below), and local restoration expertise should always be sought prior to planning or implementation.
Species are segregated into separate lists based on whether they are determined to be common, threatened (i.e. those classified into categories other than “Least concern” on the IUCN’s Red List) or invasive (those known to be invasive anywhere globally according to the IUCN’s Global Register of Introduced and Invasive Species).

This dataset provides an assessment of the conservation status of each species featured in Restor’s plant lists.

This dataset provides information on the invasive status of species featured in Restor’s plant lists.

This dataset provides information on plant species richness (a simple measure of diversity based on total species counts) for wider geographical areas.

This dataset provides information on ecological traits of each tree species. These can help inform species selection based on factors including biomass potential (tree height, stem diameter, wood density), drought tolerance (root depth, stem conduit diameter) and resistance to mechanical damage (bark thickness). In general, species with higher tree height, stem diameter and woody density values will ultimately support more aboveground biomass at maturity; species with greater root depth and lower stem conduit diameter will likely be more drought tolerant, on average; and species with greater bark thickness will be more resistant to mechanical damage (e.g. from pests).

This dataset provides information on whether or not a plant species produces products which may be suitable for human consumption. This can assist with species selection in cases where economic decisions related to restoration are being considered.

This dataset provides information on the elevation and its variation for a given area of interest.

This dataset provides information on the mean annual temperature at a given location. This can aid in site selection and management. If data are available on thermal tolerances of native plant species, it can also be used to select appropriate taxa for use in active restoration schemes.

This dataset provides information on the total annual precipitation at a given location. This can aid in site selection and management. If data are available on moisture tolerances of native plant species, it can also be used to select appropriate taxa for use in active restoration schemes.

This dataset provides information on the pH of the top five centimeters of soil. This can aid in site selection and management. If data are available on pH tolerances of native plant species, it can also be used to help select appropriate taxa for use in active restoration schemes.

This dataset provides information on the aridity (dryness) at a given location (where values <0.2 indicate arid to hyper-arid conditions, whilst values >0.65 indicate humid conditions). This can aid in site selection and management. If data are available on drought tolerances of native plant species, it can also be used to select appropriate taxa for use in active restoration schemes.

This dataset allows for estimation of current* (ca. 2010, soil; ca. 2016, woody) and potential mean carbon density in above and below ground woody biomass, along with organic carbon contained within the top two meters of soil.
​Caveats and assumptions
Above ground and below ground woody carbon
This dataset provides estimates of current and potential carbon storage in woody plant biomass, with information for both above and below ground components included. Estimates are derived from a combination of satellite reflectance data and environmental and anthropogenic covariates, with a tree-based regression modeling approach then adopted to infer patterns of storage globally. It is therefore important to note that reported carbon values are approximations only, will be less accurate than those obtained from direct on-the-ground measurements, and should not be used for carbon accounting purposes.
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Soil organic carbon
This dataset estimates current carbon storage in the top two meters of soil at any given location on the terrestrial land surface. Estimates are derived from a combination of direct on-the-ground measurements and environmental and anthropogenic covariates, with a machine learning approach then adopted to infer patterns of storage globally. It is therefore important to note that reported carbon values are approximations only, will be less accurate than those obtained from direct on-the-ground measurements, and should not be used for carbon accounting purposes. A further caveat associated with this dataset is its relatively coarse resolution (~10km), meaning it will likely fail to capture local scale heterogeneity, especially across contrasting habitat types and adjacent areas with differential land use histories.
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​Citation and attribution 2

This dataset provides information on how productive an area of interest is, and is directly measured as the rate at which carbon is currently accumulating in live plants, based on estimates derived from satellite observation.

This resource allows users to remotely assess aspects of hydrological cycling at their area of interest. Specifically, evapotranspiration indicates the amount of water moving from a site into the atmosphere; increasing values are likely if the site has an increasing excess water, whilst decreasing values indicate that the site is likely becoming drier over time.

This dataset provides up-to-date (circa 2020) information on satellite-derived predicted land cover at very high spatial resolution (10m). (This dataset is based on the dataset produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.)
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This dataset provides information on which terrestrial biome(s) a given location belongs to, allowing you to understand the basic ecology typical of an area of interest.

This dataset provides information on which terrestrial ecoregion(s) a given location belongs to, allowing you to understand the basic biogeography and ecology typical of the local geographic area.

Global tree cover data (treecover2010) are per pixel estimates of circa 2010 percent maximum (peak of growing season) tree canopy cover derived from cloud-free annual growing season composite Landsat 7 ETM+ data. A regression tree model estimating per pixel percent tree canopy cover was applied to annual composites from 2000 to 2012 inclusive (Hansen and others, 2013). Data gaps and noise from individual years were replaced using multi-year median values. First, a median from annual tree canopy cover values from 2009-2011 was used to estimate 2010 tree cover. For pixels still lacking an estimate, the median calculation was expanded to include tree cover values from 2008-2011, then from 2008-2012. Any remaining gaps were filled with tree canopy cover values derived from a regression tree model using all growing season Landsat ETM+ data as inputs. The resulting layer represents estimated maximum tree canopy cover per pixel, 1-100% for the year 2010 in integer values (1-100).

This series of geospatial layers is derived from global Landsat reflectance data at a 30-meter spatial resolution and characterize tree cover extent and forest loss from 2000 to 2020 (where a tree is defined as vegetation taller than 5m in height). This is accomplished by comparing differential reflectance values through time to quantify areas that have experienced deforestation relative to the 2000 baseline, allowing for monitoring of recent forest cover change globally. Outputs per pixel include annual percent tree cover, and annual forest loss (as well as a rough estimate of forest gain) from 2000 to 2020.

This series of geospatial layers is derived from global Landsat reflectance data at a 30-meter spatial resolution and characterizes tree cover extent and forest loss from 2000 to 2019 (where a tree is defined as vegetation taller than 5m in height). This is accomplished by comparing differential reflectance values through time to quantify areas that have experienced deforestation relative to the 2000 baseline, allowing for monitoring of recent forest cover change globally. Outputs per pixel include annual percent tree cover, and annual forest loss (as well as a rough estimate of forest gain) from 2000 to 2019.

This resource contains information about which areas of the planet could theoretically support tree cover, based on prevailing environmental conditions. The resulting map reveals Earth’s full tree cover capacity at a spatial resolution ~1km. When areas currently supporting tree cover or occupied by croplands and human settlements are subtracted, it also provides a global map of areas theoretically available for tree cover restoration.

ESRI Wayback provides imagery of high spatial resolution (up to 30cm in selected locations) across multiple time points between 2014 and the present, enabling you to monitor change on the ground using data that was captured over the years. Note that the data availability may differ in various regions of the world. In a small number of cases, the date of the imagery varies from the actual date due to issues in the metadata.

This dataset provides information on which of ~17,000 well-studied herbaceous plant species are predicted to be able to grow in the wider region containing this site (based on a machine learning approach). This can potentially aid in choosing species for restoration work. However, please note that the list is likely to include more species than are typically found in a given location (see caveats and assumptions, below), and local restoration expertise should always be sought prior to planning or implementation.
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Species are segregated into separate lists based on whether they are determined to be common, threatened (i.e. those classified into categories other than “Least concern” on the IUCN’s Red List) or invasive (those known to be invasive anywhere globally according to the IUCN’s Global Register of Introduced and Invasive Species).

NDVI is a simple indicator assessing whether and to what extent a given area contains live green vegetation.Negative values (values approaching - 1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow.Lastly, low, positive values represent shrub and grassland(approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). Landsat-5, -7 and -8 imagery used to derive NDVI values provided courtesy of the United States Geological Survey

This dataset allows for estimation of typical mammalian diversity expected within the local area. Citation and attribution 2

This dataset allows for estimation of typical avian diversity expected within the local area. Citation and attribution 2

This dataset allows for the estimation of typical amphibian diversity expected within the local area.

This resource provides estimates of mean annual depth of the water table on the terrestrial land surface (in meters below land surface). Water table depth can be a major factor determining survival of different plant species.

This dataset allows you to discern estimated human population density within your area of interest.

The global Human Modification dataset provides a cumulative measure of human modification of terrestrial lands globally. Values range from 0.0-1.0 and are calculated by estimating the proportion of a given pixel that is modified (where 0.0 = no modification; 1.0 = total modification).

Map of global wetland prevalence. Shows the absence of wetlands (0) or the presence of four different kinds.

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