DOI: 10.5061/dryad.ttdz08m1f
title: Global 100m Terrestrial Human Footprint (HFP-100)

description: Maps depicting the intensity of human pressure on the environment have
 become a critical tool for spatial planning and management, monitoring the
 extent of human influence across Earth, and identifying critical remaining
 intact habitat. Yet, these maps are often years out of date by the time
 they are available to scientists and policy-makers. Here we provide an
 updated Human Footprint methodology to run on an annual basis to monitor
 changing anthropogenic pressures. Software and methods are parameterized
 to enable regular updates in the future. In addition, we release a
 100-meter global dataset for the years 2015–2019 and 2020 based on land
 use, population, infrastructure, and accessibility data. Results show high
 levels of agreement in validation against expert-interpreted satellite
 imagery and improved performance compared to previous iterations of
 similar datasets. These maps are directly relevant to measuring progress
 towards national and international targets related to biodiversity
 conservation and sustainable development.
description: This dataset was created by combining data on human pressures
 across the period 2015 to 2019 and for 2020 to map: 1) Land cover change
 (built environments, crop lands, and pasture lands), 2) population
 density, 3) electric infrastructure, 4) roadways, 5) railways, and 6)
 navigable waterways. Each pressure layer is assigned a score relative to
 its level of human pressure, then computed into a standardized scale of
 0–50 as the sum of all pressure layers. Pressures are not mutually
 exclusive, rather the co-occurrence of pressures is intended to identify
 the greatest levels of human impact. The majority of layers cover the
 complete time period of 2015–2020, however, pressures from pasture, roads,
 and railways are treated as static in the Human Footprint maps due to
 limitations in the input datasets. Scripts used to
 produce this data are available at: https://gitlab.com/impactobservatory/dwi-humanfootprint Overall methodology is based on the following:  --B. A. Williams, O. Venter, J. R. Allan, S. C. Atkinson, J. A. Rehbein, M. Ward, M. Di Marco, H. S. Grantham, J. Ervin, S. J. Goetz, A. J. Hansen, P. Jantz, R. Pillay, S. Rodríguez-Buriticá, C. Supples, A. L. S. Virnig, J. E. M. Watson, Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems. One Earth. 3, 371–382 (2020). --E. W. Sanderson, M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, G. Woolmer, The Human Footprint and the Last of the Wild: The human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience. 52, 891–904 (2002). --O. Venter, E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, H. P. Possingham, W. F. Laurance, P. Wood, B. M. Fekete, M. A. Levy, J. E. M. Watson, Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data. 3, 160067 (2016). Please see the following for more detail: <br><br>Gassert F, Venter O, Watson JEM, Brumby SP, Mazzariello JC, Atkinson SC and Hyde S, An operational approach to near real-time global high-resolution mapping of the terrestrial human footprint. Front. Remote Sens. 4:1130896. doi: 10.3389/frsen.2023.1130896 (2023)
description: HFP scores range from 0 to 50, with 0 representing virtually no
 human pressure and 50 representing severe human pressure on the
 environment. Data files are divided into tiles of 8192x8192 pixels. The
 data available here is multiplied by 1000 and converted to uint16 data
 type to reduce file sizes such that a value of 50000 corresponds to a
 score of 50. No data regions are coded as 65535.
description: # Global 100m Terrestrial Human Footprint (HFP-100)
 [https://doi.org/10.5061/dryad.ttdz08m1f](https://doi.org/10.5061/dryad.ttdz08m1f) This readme.txt file was generated on 2023-10-25 by corresponding author Joe Mazzariello, Impact Observatory Inc. Corresponding Author Information Name: Joe Mazzariello Institution: Impact Observatory, Inc Address: 900 19th St NW, Squad 18, Washington, DC 20006, USA Email: joe@impactobservatory.com ## General Information **Date of data collection:** 2015-01-01 through 2020-12-31 **Geographic location of data collection:** Global **Data Description:** This file contains the global results for the Human Footprint 100m product for 2015, 2016, 2017, 2018, and 2019. The Human Footprint (HFP) is used to monitor changing anthropogenic pressures on a yearly cadence based on land cover change, population density, nighttime lights, roads, railways, and navigable rivers. Land cover for this set of data is based on the Copernicus Global Land Service Dynamic Land Cover Map at 100m resolution version 3 (CGLS-LC100). Please see the following for detail to any subsequent section: Gassert F., Venter O., Watson J.E.M., Brumby S.P., Mazzariello J.C., Atkinson S.C. and Hyde S., An Operational Approach to Near Real Time Global High Resolution Mapping of the Terrestrial Human Footprint. Front. Remote Sens. 4:1130896 doi: 10.3389/frsen.2023.1130896 (2023) ## Description of the data and file structure **DATA-SPECIFIC INFORMATION** HFP scores range from 0 to 50, with 0 representing virtually no human pressure and 50 representing severe human pressure on the environment. Data files are divided into tiles of 8192x8192 pixels. The data available here is multiplied by 1000 and converted to uint16 data type to reduce file sizes such that a value of 50000 corresponds to a score of 50. No data regions are coded as 65535. **File List:** HFP-100m-2015.tgz HFP-100m-2016.tgz HFP-100m-2017.tgz HFP-100m-2018.tgz HFP-100m-2019.tgz HFP-100m-2020.tgz **Number of objects/files within:** 891 **Relationship between files:** All files are results for the Global 100m Terrestrial Human Footprint (HFP-100), grouped by year. Each set of data were calculated with the exact same methodology except for the input land cover. Years 2015-2019 utilize the Copernicus Global Land Service Dynamic Land Cover Map at 100m resolution version 3 (CGLS-LC100). Year 2020 utilizes the ESRI 2020 Global Land Use Land Cover from Sentinel-2 (ESRI LC), rescaled from 10m to 100m. **METHODOLOGICAL INFORMATION** **Description of methods used for collection/generation of data:** This dataset was created by combining data on human pressures across the period 2015 to 2019 and for 2020 to map: 1) Land cover change (built environments, crop lands, and pasture lands), 2) population density, 3) electric infrastructure, 4) roadways, 5) railways, and 6) navigable waterways. Each pressure layer is assigned a score relative to its level of human pressure, then computed into a standardized scale of 0-50 as the sum of all pressure layers. Pressures are not mutually exclusive, rather the co-occurrence of pressures is intended to identify the greatest levels of human impact. The majority of layers cover the complete time period of 2015-2020, however pressures from pasture, roads, and railways are treated as static in the Human Footprint maps due to limitations in the input datasets. **Overall methodology is based on the following:** --B. A. Williams, O. Venter, J. R. Allan, S. C. Atkinson, J. A. Rehbein, M. Ward, M. Di Marco, H. S. Grantham, J. Ervin, S. J. Goetz, A. J. Hansen, P. Jantz, R. Pillay, S. Rodríguez-Buriticá, C. Supples, A. L. S. Virnig, J. E. M. Watson, Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems. One Earth. 3, 371–382 (2020). --E. W. Sanderson, M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, G. Woolmer, The Human Footprint and the Last of the Wild: The human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience. 52, 891–904 (2002). --O. Venter, E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, H. P. Possingham, W. F. Laurance, P. Wood, B. M. Fekete, M. A. Levy, J. E. M. Watson, Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data. 3, 160067 (2016). **Methods for processing the data:** Data was produced using open source Python scripts and processed in the Microsoft Planetary Computer infrastructure. See "Code/Software" section below. **Quality-assurance procedures:** Data was validated against a set of expert-interpreted satellite imagery for 3114 points randomly sampled from global land area. **People involved with sample collection, processing, analysis and/or submission:** J. Mazzariello, F. Gassert ## Sharing/Access information **Licenses/restrictions placed on the data:** CC0 1.0 Universal (CC0 1.0) **Links to publications that cite or use the data:** DOI: 10.3389/frsen.2023.1130896 **Links to other publicly accessible locations of the data:** Currently none **Links/relationships to ancillary data sets:** None **Recommended citation for this dataset:** F. Gassert, O. Venter, J. E.M. Watson, S.P. Brumby, J.C. Mazzariello, S.C. Atkinson, S. Hyde 2023: "Global 100m Terrestrial Human Footprint (HFP-100)" ## Code/Software Scripts used to produce this data are available at https://gitlab.com/impactobservatory/dwi-humanfootprint The data can be accessed in any GIS software such as ArcGIS Desktop (any version), ArcGIS Pro (any version), QGIS (any version). The data can also be read in Python, R, or any other script that has libraries that can interpret geospatial data (such as folium)