Global terrestrial Human Footprint maps for 1993 and 2009 Oscar Venter Eric W. Sanderson Ainhoa Magrach James R. Allan Jutta Beher Kendall R. Jones Hugh P. Possingham William F. Laurance Peter Wood Balázs M. Fekete Marc A. Levy James E.M. Watson

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 Global terrestrial Human Footprint maps for 1993 and 2009 2016 Scientific Data 10.1038/sdata.2016.67 venter2016-global-terrestrial-human-footprint-maps venter2016-global-terrestrial-human-footprint-maps.txt, the project's staged copy 05c06a10d977807a13104e68d9ec877e67db8fd6 cc-by ecology

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 197 spans located by searching the stored text for the quote's exact words, allowing any whitespace between them the spatial distribution and temporal change of cumulative human pressures across non-Antarctic terrestrial environments how eight direct and indirect pressure layers can be standardized, combined, and validated as temporally comparable global Human Footprint maps the first temporally consistent 1993 and 2009 Human Footprint map pair and the first visual validation of a cumulative pressure map method cumulative human pressure, disturbance, wilderness, and their macro-ecological and conservation consequences computational-modelcomputational-model Remote land-cover sensing is combined with systematically surveyed and modelled pressures to detect features either source alone misses.top-down and bottom-up coupling Each input is placed on a common zero-to-ten scale before weighted summation.pressure standardization Standardized co-occurring pressure layers are summed into a zero-to-fifty Human Footprint score.weighted summation Pixels with calibrated digital number above twenty are treated as built environments.DMSP-OLS threshold Population pressure rises logarithmically with density and saturates at one thousand people per square kilometre.logarithmic scaling Night-light digital numbers are binned and rescaled into comparable zero-to-ten scores.equal quintile approach Road and water access pressures decline exponentially to fifteen kilometres from the feature.exponential access decay Independent image interpretation supplies reference pressure scores for random validation plots.high-resolution visual interpretation Cloud-obscured and insufficient-resolution plots are discarded before agreement analysis.certainty filter Mapped and visual scores are categorical matches when they differ by no more than twenty percent.20% match threshold Publishing components separately lets users construct region- or species-specific alternative scoring schemes.individual pressure layers Consistent 1993 and 2009 layers make changes in cumulative pressure measurable through time.temporal comparison Remote sensing alone has difficulty detecting low-intensity linear infrastructure and pasture and can confuse natural with anthropogenic cover. The original Human Footprint was a static and dated early-1990s view. Three pressure layers lacked a second time point and were treated as static. DMSP-OLS over-glow and gas flaring can be falsely detected as built environments. No alternative global settlement dataset was temporally comparable across the study horizon. gROADS omits minor roads and inconsistently mapped private roads and trails. Comparable high-resolution imagery was unavailable for 1993, so only the 2009 map was validated. The map sometimes records pressures as absent where visual validation finds them, making it conservative. Kappa agreement depends strongly on the chosen match threshold. The mapping does not include every human pressure, including some invasive species and pollution. Static layers can underestimate expansion when those pressures grow faster than average. Human Footprint measures pressure rather than realized ecosystem state or biodiversity impact. Responses may be nonlinear or exhibit thresholds that accelerate impacts, which the dataset does not determine. uncertain validation plotseliminates: cloud-obscured or insufficient-resolution visual interpretations · outcome: held 15% and 25% match thresholdseliminates: dependence of the agreement verdict on the default 20% threshold · outcome: partial Landsat imagery for 10 citieseliminates: unsupported selection of the built-environment brightness threshold Human pressures on the environmentHuman pressures on the environment cumulative pressure mappingcumulative pressure mapping top-down remote sensingtop-down remote sensing bottom-upbottom-up Human FootprintHuman Footprint Human Footprint mapsHuman Footprint maps human disturbancehuman disturbance macro-ecological patternsmacro-ecological patterns remote sensingremote sensing direct and indirect human pressuresdirect and indirect human pressures visually interpret human pressuresvisually interpret human pressures WildernessWilderness standardized pressuresstandardized pressures built environmentsbuilt environments pressure scorepressure score DMSP-OLSDMSP-OLS digital numbersdigital numbers over-glowover-glow gas flaringgas flaring human population densityhuman population density Gridded Population of the WorldGridded Population of the World human pressureshuman pressures pressures people induce on their local natural systemspressures people induce on their local natural systems electric infrastructureelectric infrastructure equal quintile approachequal quintile approach crop landscrop lands intensive agricultureintensive agriculture pasture landspasture lands direct human pressuredirect human pressure roadsroads population sinkspopulation sinks habitat conversionhabitat conversion humidityhumidity fire frequencyfire frequency gROADSgROADS railwaysrailways direct pressuredirect pressure navigable waterwaysnavigable waterways navigable riversnavigable rivers RiversRivers naturenature Dryad Digital RepositoryDryad Digital Repository GeoTIFF datasetsGeoTIFF datasets individual pressure layersindividual pressure layers high resolution imageshigh resolution images visual scorevisual score uncertainuncertain RMSERMSE error metricerror metric Kappa statisticKappa statistic Human Footprint performanceHuman Footprint performance strong agreementstrong agreement conservative estimatesconservative estimates geographic variationgeographic variation priority areaspriority areas alternate scoring schemesalternate scoring schemes realized ‘state’realized ‘state’ ‘impacts’ on natural systems‘impacts’ on natural systems invasive speciesinvasive species pollutionpollution non-linearitynon-linearity thresholdsthresholds staticstatic Human Footprint mapHuman Footprint map Sanderson and colleaguesSanderson and colleagues cumulative pressure mapcumulative pressure map cumulative pressure mapping → remote sensingCoupling remote sensing with surveyed and modelled data circumvents the limits of remote sensing alone. Human Footprint maps → human disturbanceThe maps are intended as proxies for human disturbance. Human Footprint maps → WildernessPressure-free map cells identify candidate wilderness sites. DMSP-OLS → over-glowSensor hypersensitivity detects over-glow adjacent to built environments. DMSP-OLS → gas flaringBright gas-flaring signals can be confused with built environments. pasture lands → direct human pressurePasture is the most extensive direct pressure and degradation combines fencing, browsing, compaction, invasive species, and altered fire. roads → population sinksRoads can act as population sinks through traffic-induced mortality. railways → direct pressureRailways exert direct pressure but do not enable access along most borders. Rivers → natureNavigable rivers act as access conduits into nature. individual pressure layers → alternate scoring schemesPublished component layers allow developers to construct alternative scores. uncertain → visual scoreDiscarding uncertain plots protects the visual score from cloud and coarse-resolution errors. Human Footprint maps → conservative estimatesMissing present pressures makes the maps conservative estimates. thresholds → Kappa statisticChanging the match threshold changes the Kappa statistic substantially. static → Human FootprintStatic pressure layers can underestimate Human Footprint expansion. Human Footprint → non-linearityThe dataset leaves open whether natural-system responses are nonlinear or thresholded. Human Footprint → thresholdsThe dataset leaves open whether thresholds produce accelerated impacts. Human Footprint maps → priority areasMapping pressures is a first step toward identifying protection or restoration priorities. Human Footprint map → Human Footprint mapsThe authors reserve the singular name for unaltered distributed data, not user-created alternatives. Human Footprint as cumulative pressure rather than ecological state or impact temporally consistent as comparable methodology across 1993 and 2009 built environment as urban buildings, paving, and parks night lights as sparse electric infrastructure outside the built threshold roads as direct, edge-effect, and access pressures railways as direct pressure without broad access pressure visual validation as high-resolution independent plot interpretation conservative map as more likely to omit a present pressure than invent one pressure layers as reusable ingredients for alternate local scores Human Footprint map name reserved for the unaltered distributed data builds-on → Sanderson and colleaguesnote: The map explicitly adopts the earlier Human Footprint method. extends → Human Footprintnote: The paper updates the static early-1990s release into a comparable time series. validates → cumulative pressure mapnote: The article reports the first validation of this map class. built environments —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact assigned score. · the relation stated: Built environments are assigned a pressure score of 10.built environmentspressure score human population density —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact thresholded scoring rule. · the relation stated: Population density is mapped into a pressure score that reaches 10 above 1000 people per square kilometre.human population densitypressure score crop lands —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact assigned score. · the relation stated: Intensive crop land is assigned a pressure score of 7. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'intensive agriculture', not 'crop lands' (named earlier in the section).crop landspressure score pasture lands —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact scaled score. · the relation stated: Pasture is assigned a pressure score up to 4 according to percent pasture.pasture landspressure score roads —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact distance-dependent score. · the relation stated: Road pressure is scored 8 directly and 4 for access before exponential decay.roadspressure score railways —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact distance-dependent score. · the relation stated: Railway direct pressure is assigned a score of 8 within half a kilometre.railwayspressure score navigable waterways —operationalised-as→ pressure scorethe relation itself is separately evidenced · strength of the relation: 1 · note: Exact distance-dependent score. · the relation stated: Navigable-water access pressure is assigned a score of 4 and decays to 15 km. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'awarded a score of 4', not 'pressure score'.navigable waterwayspressure score human population density —causes→ pressures people induce on their local natural systemsdirection on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.7 · note: Explicitly stated as an assumption rather than a measured causal result. · the relation stated: The scoring assumes pressures increase logarithmically with increasing population density. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span states this as the modelling ASSUMPTION behind the score.human population densitypressures people induce on their local natural systems roads —causes→ habitat conversiondirection on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.95 · note: Flat positive causal claim. · the relation stated: Roads are an important direct driver of habitat conversion.roadshabitat conversion roads —causes→ humiditydirection on the measure: reduces · the relation itself is separately evidenced · strength of the relation: 0.95 · note: Flat negative effect with the sign preserved. · the relation stated: Road edge effects reduce humidity beyond the immediate footprint.roadshumidity roads —causes→ fire frequencydirection on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.95 · note: Flat positive effect with the sign preserved. · the relation stated: Road edge effects increase fire frequency.roadsfire frequency RMSE —is-a→ error metricthe relation itself is separately evidenced · strength of the relation: 1 · note: Direct categorical definition. · the relation stated: RMSE is a dimensioned error metric for numerical predictions.RMSEerror metric Human Footprint maps —distinct-from→ realized ‘state’the relation itself is separately evidenced · strength of the relation: 0.95 · note: Flat explicit distinction. · the relation stated: The Human Footprint measures human pressure, not the realized state of natural systems. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'the Human Footprint', not 'Human Footprint maps'.Human Footprint mapsrealized ‘state’ Human Footprint maps —distinct-from→ ‘impacts’ on natural systemsthe relation itself is separately evidenced · strength of the relation: 0.95 · note: Flat explicit distinction. · the relation stated: The Human Footprint measures pressure, not impacts on natural systems. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'the Human Footprint', not 'Human Footprint maps'.Human Footprint maps‘impacts’ on natural systems The maps are the first temporally consistent global Human Footprint series for 1993 and 2009. Visual scores from 3,114 one-square-kilometre plots strongly agree with the mapped Human Footprint. The original Human Footprint remained widely used but was a static and dated view of pressure. The new maps support change-over-time analysis and supply the first validation of a cumulative pressure map. Recent economic and population growth greatly exceeded Human Footprint growth, while biodiverse regions were disproportionately affected. Eight standardized pressure layers were summed, with overlapping pressures allowed to co-occur. The non-Antarctic terrestrial analysis contains about 134.1 million one-square-kilometre pixels. DMSP-OLS is temporally consistent but over-detects glow and gas flaring near settlements. Pasture is the most extensive direct human pressure and covers 22 percent of Earth's land base. Roads drive conversion, mortality, fragmentation, edge effects, and human access to nature. Railways exert direct pressure but do not provide broad access along their borders. The retained visual validation plots excluded cloud-obscured and insufficient-resolution imagery. Validation produced RMSE 0.125, Kappa 0.737, and 88.5 percent of plots within 20 percent agreement. The map tends to miss pressures that are present and should be treated as a conservative estimate. Agreement varies geographically and is weakest in temperate broadleaf/mixed forest and boreal biomes. The dataset measures human pressure rather than realized ecosystem state or biodiversity impacts. human actions with the potential to harm naturehuman pressures on the environment coupling remote sensing of land-cover change with surveyed and modelled pressure datacumulative pressure mapping a standardized cumulative measure formed from eight direct and indirect human-pressure layersHuman Footprint human-produced urban settings including buildings, paved land, and urban parksbuilt environments gridded local human population used as a pressure proxypopulation density DMSP-OLS brightness used to represent sparse electric infrastructurenight-time lights intensively managed agricultural areas treated as direct environmental pressurecrop lands grazed lands affected by fencing, browsing, compaction, invasive species, and altered firepasture lands rivers deep enough for vessels and connected to lights, coasts, or inland lakesnavigable rivers a dimensioned numerical-prediction error metric that heavily penalizes large errorsRMSE agreement between categorical datasets corrected for expected random agreementKappa statistic the authors' requested name for only the unaltered data in the distributed archiveHuman Footprint map eight — direct and indirect human-pressure variables 1993 and 2009 — two temporally comparable map years 0–10 — standardized scale for each individual pressure 0–50 — possible total Human Footprint range per grid cell ~134.1 million pixels — one-square-kilometre cells covering non-Antarctic land 22% — share of Earth's land base covered by pasture 3114 — retained validation plots 0.125 — global normalized RMSE 0.737 — Kappa at the 20-percent match threshold 88.5% — validation plots within 20-percent agreement 0.565 — Kappa at the stricter 15-percent threshold 0.856 — Kappa at the looser 25-percent threshold built environmentsbuilt environments population densitypopulation density electric infrastructureelectric infrastructure crop landscrop lands pasture landspasture lands roadsroads railwaysrailways navigable waterwaysnavigable waterways pressure scorepressure score visual scorevisual score RMSERMSE Kappa statisticKappa statistic

Global terrestrial Human Footprint maps for 1993 and 2009

Abstract

Remotely-sensed and bottom-up survey information were compiled on eight variables measuring the direct and indirect human pressures on the environment globally in 1993 and 2009. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. A validation analysis using scored pressures from 3114×1 km 2 random sample plots revealed strong agreement with the Human Footprint maps. We anticipate that the Human Footprint maps will find a range of uses as proxies for human disturbance of natural systems. The updated maps should provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application.

Subject terms: Conservation biology, Environmental chemistry

Background & Summary

Human pressures on the environment are the actions taken by humans with the potential to harm nature 1 , 2 . Cumulative pressure mapping measures the breadth of these pressures by coupling top-down remote sensing of land cover change with data on additional human pressures collected ‘bottom-up’ through systematic surveys and modelling 3 , 4 . The method circumvents the limitations of using remote sensing alone, which has difficulty in detecting low intensity pressures 5 , such as linear infrastructures 6 and pasture lands 7 , and often confounds natural and anthropogenic land covers in arid and mosaic environments 8 .

Cumulative pressure maps have been developed at regional 9 , 10 and global scales 11 , 12 . The ‘Human Footprint’ was first released in 2002 using data primarily from the early 1990s (approximately 1993) on eight human pressures globally, making it the most complete, highest resolution and globally-consistent terrestrial dataset on cumulative human pressures on the environment 13 . It has been used in a large number of ecological and conservation analyses, and still receives around 100 citations each year, particularly from its data users. However, the Human Footprint is a static and dated view of human pressures on the environment. With many of Earth’s systems experiencing pressures close to or beyond safe levels 14 , there is a strong need for an up-to-date understanding of the spatial and temporal trends in human pressures.

Here we use the Human Footprint methodology 13 to compile remotely-sensed and bottom-up survey information on eight variables measuring the direct and indirect human pressures on the environment in 1993 and 2009. This synthesis represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps, allowing for analyses of change over time. We also provide the first validation of a cumulative pressure map by adopting methods from remote sensing 15 to visually interpret human pressures in high resolution (median=0.5 m) imagery from 3114 1 km 2 random sample plots globally ( Supplementary Appendix 1 ). We then determine the level of agreement between these visually interpreted pressures and those mapped by the Human Footprint.

The Human footprint maps provide information on where humans are exerting pressure on natural systems, altering them from their natural states. They also provide information on where these pressures are absent, and ecosystems are likely to be operating in a more natural state. These pressure-free lands represent candidate sites for consideration as ‘Wilderness’ 16 , 17 . The new Human Footprint maps have already been used to show that recent economic and population growth has far outstripped increases in the Human Footprint, yet the most biologically diverse regions of Earth have been disproportionately impacted 18 . We anticipate that the 1993 and 2009 Human Footprint maps will find a range of additional uses, such as serving as proxies for human disturbance and wilderness, including understanding the role of human pressures in driving macro-ecological patterns 19 , 20 , species extinction risk and distribution analyses 21 , dispersal ecology 22 , conservation science and decision making 23 , and tracking progress toward policy commitments to conservation 23 , among others.

Methods

Overview of methods for mapping the Human Footprint

To create the Human Footprint maps we adopted the methods developed by Sanderson and colleagues 13 . Data on human pressures in 1993 and 2009 were collected or developed for: 1) the extent of built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways, which are described in detail below ( Fig. 1 , step 1). To facilitate comparison across pressures we placed each human pressure within a 0–10 scale ( Fig. 1 , step 2), weighted within that range according to estimates of their relative levels of human pressure following Sanderson et al. 13 . The resulting standardized pressures were then summed together to create the standardized Human Footprint maps for all non-Antarctic land areas ( Fig. 1 , step 3). Pressures are not intended to be mutually exclusive, and many will co-occur in the same location. Three pressures only had data from a single time period, and these are treated as static in the Human Footprint maps.

Workflow of the Human Footprint approach to mapping cumulative human pressures on the environment.

We used ArcGIS 10.1 to integrate spatial data on human pressures. Analyses were conducted in Mollowedie equal area projection at the 1 km 2 resolution, yielding ~134.1 million pixels for Earth’s non-Antarctic terrestrial surface. For any grid cell, the Human Footprint can range between 0–50. The following sections and Table 1 (available online only) describe in detail the source data for each pressure, the processing steps applied, and the rationale behind the pressure weighting, and the output datasets created.

Summary of data inputs, manipulations and outputs in the Human Footprint workflow

Built environments

Built environments are human produced areas that provide the setting for human activity. In the context of the human footprint, we take these areas to be primarily urban settings, including buildings, paved land and urban parks. Built environments do not provide viable habitats for many species of conservation concern, nor do they provide high levels of ecosystem services 24–27 . As such, built environments were assigned a pressure score of 10.

To map built environments, we used the Defence Meteorological Satellite Program Operational Line Scanner (DMSP-OLS) composite images which gives the annual average brightness of 30 arc second (~1 km at the equator) pixels in units of digital numbers (DN) 28 . These data are provided for each year from 1992 to 2012. We extracted data for the years 1994 (1993 was excluded due to anomalies in the data), and 2009, and both datasets were then inter-calibrated to facilitate comparison 29 . Using the DMSP-OLS datasets, we considered pixels to be ‘built’ if they exhibited a calibrated DN greater than 20. We selected this threshold based on a global analyses of the implications of a range of thresholds for mapped extent of cities 30 , and visual validation against Landsat imagery for 10 cities spread globally.

The DMSP-OLS has limitations for the purpose of mapping human settlements, including hyper sensitivity of the sensors causing detection of over-glow adjacent to built environments 30 and bright lights associated with gas flaring from oil production facilities 29 . However, no other data exist to map built environments in a consistent way globally over our time horizon. While other datasets provide a one year snap shot of urban extent, they cannot be compared across time due to large differences in the methodologies used 31–33 , and the wildly contrasting extents in mapped built environments.

Population density

Many of the pressures humans impose on the environment are proximate to their location, such as human disturbance, hunting and the persecution of non-desired species 34 . Moreover, even low-density human populations with limited technology and infrastructure developments can have significant impacts on biodiversity, as evidenced by the widespread loss of various taxa, particularly mega fauna, following human colonization of previously unpopulated areas 35 , 36 .

Human population density was mapped using the Gridded Population of the World dataset developed by the Centre for International Earth Science Information Network (CIESEN) 37 . The dataset provides a ~4 km×~4 km gridded summary of population census data for the years 1990 and 2010, which we downscaled using bilinear sampling in ArcGIS 10.1 to match the 1 km 2 resolution of the other datasets. For all locations with more than 1000 people·km −2 , we assigned a pressure score of 10 ( Table 2 ). For more sparsely populated areas with densities lower than 1000 people·km −2 , we logarithmically scaled the pressure score using,

Pressure scheme used to assign weights to the eight individual pressures in the Human Footprint maps.

Human population density is scored in this way under the assumption that the pressures people induce on their local natural systems increase logarithmically with increasing population density, and saturate at a level of 1000 people per km 2 .

Night-time lights

The high sensitivity of the DMSP-OLS 28 dataset provides a means for mapping the sparser electric infrastructure typical of more rural and suburban areas. In 2009, 79% of the lights registered in the DMSP-OLS dataset had a Digital Number less than 20, and are therefore not included in our ‘built environments’ layers. However, these lower DN values are often important human infrastructures, such as rural housing or working landscapes, with associated pressures on natural environments.

To include these pressures, we used the inter-calibrated DMSP-OLS layers 28 used for the built environments mapping. The equations for intercalibrating across years are second order quadratics trained using data from Sicily, which was chosen as it had negligible infrastructure change over this period and where DN average roughly 14 (ref. 28 ). For our purposes, DN values of six or less where excluded from consideration prior to calibration of data, as the shape of the quadratic function leads to severe distortion of very low DN values. The inter-calibrated DN data from 1994 were then rescaled using an equal quintile approach into a 0–10 scale ( Table 2 ). To scale the data, we divided the calibrated night light data into 10 equal sample bins (each bin with a DN greater than 1 contains the same number of pixels) based on the DN values and then assigned them scores of 1 through 10, starting with the lowest DN bin. DN values of 0 were assigned a score of 0. The thresholds used to bin the 1994 data where then used to convert the 2009 data into a comparable 0–10 scale.

Crop and pasture lands

Crop lands vary in their structure from intensely managed monocultures receiving high inputs of pesticides and fertilizers, to mosaic agricultures such as slash and burn methods that can support intermediate levels of natural values 38 , 39 . For the purposes of the human footprint, we focused only on intensive agriculture because of its greater direct pressure on the environment, as well as to circumvent the shortcomings of using remotely sensed data to map mosaic agriculture globally, namely the tendency to confound agriculture mosaics with natural woodland and savannah ecosystems 8 .

Spatial data on remotely sensed agriculture extent in 1992 were extracted from the UMD Land Cover Classification 40 , and for 2009 from GlobCover 41 . Although intensive agriculture often results in whole-scale ecosystem conversion, we gave it a pressure score of 7 ( Table 2 ), which is lower than built environments because of their less impervious cover.

Pasture lands cover 22% of the Earth’s land base or almost twice that of agricultural crops 42 , making them the most extensive direct human pressure on the environment. Land grazed by domesticated herbivores is often degraded through a combination of fencing, intensive browsing, soil compaction, invasive grasses and other species, and altered fire regimes 43 . We mapped grazing lands for the year 2000 using a spatial dataset that combines agricultural census data with satellite derived land cover to map pasture extent 42 . We assigned pasture a pressure score of 4, which was then scaled from 0–4 using the percent pasture for each 1 km 2 pixel.

Roads and railways

As one of humanity’s most prolific linear infrastructures, roads are an important direct driver of habitat conversion 44 . Beyond simply reducing the extent of suitable habitat, roads can act as population sinks for many species through traffic induced mortality 45 . Roads also fragment otherwise contiguous blocks of habitat, and create edge effects such as reduced humidity 6 and increased fire frequency that reach well beyond the roads immediate footprint 46 . Finally, roads provide conduits for humans to access nature, bringing hunters and nature users into otherwise wilderness locations 47 .

We acquired data on the distribution of roads from gROADS 48 , and excluded all trails and private roads, which were inconsistently mapped, with only a subset of countries mapping their linear infrastructure to this resolution. The dataset is the most comprehensive publicly available database on roads, which compiles nationally mapped road data spanning the period 1980–2000 and has a spatial accuracy of around 500 m. The gROADS data do not include all minor roads, and therefore should be viewed as a map of the major roadways. We mapped the direct and indirect influence of roads by assigning an pressure score of 8 for 0.5 km out for either side of roads, and access pressures were awarded a score of 4 at 0.5 km and decaying exponentially out to 15 km either side of the road ( Table 2 ).

While railways are an important component of our global transport system, their pressure on the environment differs in nature from that of our road networks. By modifying a linear swath of habitat, railways exert direct pressure where they are constructed, similar to roads. However, as passengers seldom disembark from trains in places other than rail stations, railways do not provide a means of accessing the natural environments along their borders. To map railways we used the same dataset as was used in the original footprint 31 , as no update of this dataset or alternate source has been developed. The direct pressure of railways where assigned a pressure score of 8 for a distance of 0.5 km on either side of the railway.

Navigable waterways

Like roads, coastlines and navigable rivers act as conduits for people to access nature. While all coastlines are theoretically navigable, for the purposes of the human footprint we only considered coasts 31 as navigable for 80 km either direction of signs of a human settlement, which were mapped as a night lights signal with a DN 28 greater than 6 within 4 km of the coast. We chose 80 km as an approximation of the distance a vessel can travel and return during daylight hours. As new settlements can arise to make new sections of coast navigable, coastal layers were generated for the years 1994 and 2009.

Large lakes can act essentially as inland seas, with their coasts frequently plied by trade and harvest vessels. Based on their size and visually identified shipping traffic and shore side settlements, we treated the great lakes of North America, Lake Nicaragua, Lake Titicaca in South America, Lakes Onega and Peipus in Russia, Lakes Balkash and Issyk Kul in Kazakhstan, and Lakes Victoria, Tanganyika and Malawi in Africa as we did navigable marine coasts.

Rivers were considered as navigable if their depth was greater than 2 m and there were signs of nighttime lights (DN>=6) within 4km of their banks, or if contiguous with a navigable coast or large inland lake, and then for a distance of 80 km or until stream depth is likely to prevent boat traffic ( Table 2 ). To map rivers and their depth we used the hydrosheds (hydrological data and maps based on shuttle elevation derivatives at multiple scales) 49 dataset on stream discharge, and the following formulae from 50 , 51 :

and

and

and

Assuming second order parabola as channel shape.

Navigable rivers layers were created for the years 1994 and 2009, and combined with the navigable coasts and inland seas layers to create the final navigable waterways layers. The access pressure from navigable water bodies were awarded a score of 4 adjacent to the water body, decaying exponentially out to 15 km.

Data Records

The 1 km 2 resolution, temporally-comparable Human Footprint maps [Data Citation 1] are stored in the Dryad Digital Repository, and may also be freely accessed through the Socioeconomic Data and Applications Center website ( www.worldpop.org/data/ ). From Dryad the files may be downloaded as a single 7-zip file archive (7-Zip.org) which contains individual GeoTIFF datasets, an excel file with the validation data and a PDF with the validation key. The GeoTIFFs include the Human Footprint maps for 1993 and 2009 ( Fig. 2 ), as well 14 additional GeoTIFFs of the processed data for each of the eight pressures ( Fig. 1 , step 2) from each time step ( Tables 1 , 3 (available online only)). The individual pressure layers are provided should data users wish to rework these data to create alternate maps of human pressure for their particular needs or region.

The Human Footprint map for 2009, with panels showing regional overlays with the results of the validation plots.

The name, description and type of data included in the HumanFooptrint.zip file

Technical Validation

High resolution images were used to visually interpret human pressures in 3460×1 km 2 sample plots randomly located across the Earth’s non-Antarctic land areas ( Fig. 3a ). Images for these plots were obtained from World Imagery 52 , which provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map features 0.3 m resolution imagery across the continental United States and parts of Western Europe, as well as many parts of the world, with concentrations in South America, Eastern Europe, India, Japan, the Middle East and Northern Africa, Southern Africa, Australia, and New Zealand. The imagery used for the validation plots had a median resolution of 0.5 meters and a median acquisition year of 2010. Comparable imagery was not available for the 1993 time period, and therefore only the 2009 map underwent validation.

Results from 3114×1 km 2 validation plots interpreted and scored following Supplementary Appendix 1 . ( a ) The location and visually interpreted pressure score for plots, and ( b ) the disagreement between the Human Footprint score and the visual validation score on a normalized 0–1 scale.

For the visual interpretation, the extent of built environments, crop lands, pasture lands, roads, human settlements, infrastructures and navigable waterways, were recorded using a standard key for identifying these features ( Supplementary Appendix 1 ). Shape, size, texture and colour of features in the imagery were important characteristics for identifying human pressures on the environment. Interpretations were also marked as ‘certain’ or ‘uncertain’, and the year and resolution of the interpreted image was recorded. The 346 ‘uncertain’ plots were discarded, leaving 3114 validation plots ( Fig. 3a ). In general, plots were classified as ‘uncertain’ for two reasons; either because cloud cover obscured the image, or because only medium resolution (15 m) imagery was available for the plot, preventing accurate interpretation of the image. The human footprint score for each plot was determined in ArcGIS, and the visual and Human Footprint scores were then normalized to a 0–1 scale. As we only retained plots for which visual interpretations of the images were determined to be ‘certain’, we consider the visual score to be the true state of in-situ pressures for the plots.

Two statistics were used to determine Human Footprint performance, root mean squared error (RMSE) 53 and the Cohen kappa statistic of agreement 54 . The RMSE is a dimensioned (expresses average error in the units of variable of interest) error metric for numerical predictions, and tends to heavily punish large errors. The Kappa statistic expresses the agreement between two categorical datasets corrected for the expected agreement, which is based on a random allocation given the relative class sizes. When calculating the kappa statistic, the Human Footprint score was considered as a match to the visual score if they were within 20% of one another on the 0–1 scale.

There is strong agreement between the Human Footprint measure of pressure and pressures scored by visual interpretation of high resolution imagery. The RMSE for the 3114 validation plots was 0.125 on the normalized 0–1 scale, indicating an average error of approximately 13%. The Kappa statistic was 0.737 ( P <0.01), also indicating good agreement between the Human Footprint and the validation dataset. Of the 3114×1 km 2 validation plots, the Human Footprint scored 94 of them 20% higher than the visual score and 263 of them 20% lower. The remaining 2757 plots (88.5%) were within 20% agreement. While this represents good agreement, it appears that the Human Footprint is to some extent susceptible to mapping pressures as absent in locations where they are actually present. The maps should therefore be considered as conservative estimates of human pressures on the environment. The Kappa statistic measure of agreement is sensitive to the threshold used to consider plots as a ‘match’. If we apply a more stringent threshold for agreement of within 15% of one another, the Kappa statistic falls to 0.565 (moderate agreement), and if we apply a less stringent threshold of within 25%, the Kappa statistic increases to 0.856 (very high agreement).

While agreement is generally strong, there is some geographic variation in the RMSE results comparing the Human Footprint scores and those derived from visual interpretation ( Fig. 3b ). By calculating RMSE for all biomes that contain at least 100 of the 3114 sample plots, we found that agreement was strongest in the Tundra biome and the Temperate grasslands, savannas and shrublands biomes ( Table 4 ). Agreement was weakest in the Temperate broadleaf and mixed forest biome and the Boreal.

Root Mean Square Errors results comparing the Human Footprint scores with 3114 validation plots globally, and for biomes with at least 100 plots within them.

Usage Notes

Mapping human pressures to the environment is an essential first step to identifying priority areas for protection or restoration of natural systems. Understanding the spatial distribution of pressures, as well as their change through time, also provides insights for studies on macro-ecological patterns. The Human Footprint maps for 1993 and 2009 represent the first temporally-consistent maps of the human footprint, as well as much more up-to-date information on cumulative pressures than is currently available. Moreover, the 2009 Human Footprint map is the first cumulative pressure map to have undergone an accuracy assessment.

The individual pressure maps were developed to be globally consistent, using a scoring approach originally developed by Sanderson and colleagues 13 . However, in some regions and for some species groups, alternate scores may be better suited for reflecting the pressures exerted by humans on nature. We therefore provide the individual pressure layers that compose the Human Footprint maps, thereby allowing data developers to create alternate scoring schemes that better suit their purposes, as well facilitating the addition of new or alternate data sources.

Moreover, our work is subject to three primary limitations. First, like all attempts to map cumulative pressures we did not fully account for all human pressures. Some of the missing and static pressures, such as invasive species and pollution, may be closely associated with pressures we did consider, and therefore their inclusion may not affect our overall results. Second, a lack of available data resulted in three of our pressures being static through time, which would cause an underestimation of Human Footprint expansion if these pressures expanded at a higher than average rate. Third, the Human Footprint measures the pressure humans place on nature, not the realized ‘state’ or ‘impacts’ on natural systems or their biodiversity. Significant scope exists to determine how natural systems respond to cumulating human pressures, and if non-linearity or thresholds exist where pressures lead to accelerated impacts.

While we welcome the opportunity to contribute intellectually and as co-authors to research projects that incorporate our datasets into their work, we make the data freely available without restriction for non-commercial use and redistribution. The data may be altered from their original form, and redistributed if done so free of charge and with a full description of any alterations to the original data. We do however ask that term ‘Human Footprint map’ be used only when referring to the unaltered data in the Human Footprint 7-zip file, and not to alternative versions of the data created by data users, and that the data be cited following the template at the end of this manuscript.

Additional information

How to cite this article: Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3:160067 doi: 10.1038/sdata.2016.67 (2016).

Supplementary Material

Acknowledgments

This work was supported by grants from the Wildlife Conservation Society, James Cook University and the Australian Research Council DP110102872 and DE140101624. We thank Neil Burgess, Jonas Geldman, Michelle Venter, Todd Stevens, Dan Segan, Joe Walston, John Robinson, Kim Fisher for their thoughtful feedback on this work. We thank Christopher Small for help with the nightlights data, and Alex De Sherbinin for help with roads and population data.

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References

Associated Data

Data Citations

Supplementary Materials