Dataset associated with "A machine-learning approach to human footprint index estimation with applications to sustainable development"

Keys, Patrick; Barnes, Elizabeth; Carter, Neil

Dataset associated with "A machine-learning approach to human footprint index estimation with applications to sustainable development"

dc.contributor.author	Keys, Patrick
dc.contributor.author	Barnes, Elizabeth
dc.contributor.author	Carter, Neil
dc.coverage.spatial	Global	en_US
dc.date.accessioned	2020-11-02T21:26:44Z
dc.date.available	2020-11-02T21:26:44Z
dc.date.issued	2020
dc.description	The data are a set of gridded raster maps corresponding to the Human Footprint Index (HFI) for the years 2000 and 2019. The data presented here are based on two other openly accessible datasets: 1. The human footprint index data by Williams et al. https://github.com/scabecks/humanfootprint_2000-2013; 2. Global Forest Cover data https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html.	en_US
dc.description	School of Global Environmental Sustainability
dc.description	Department of Atmospheric Science
dc.description.abstract	Fundamental to the success of sustainable development is a foundation of intact ecosystems. While the United Nations Sustainable Development Goal 15, “Life on Land”, seeks to protect biodiversity in terrestrial ecosystems, accelerating human-driven changes across the Earth system are undermining efforts to preserve biodiversity. Understanding this tension has never been more urgent and requires tools that reveal pathways for development that also support biodiversity. Here we introduce a near-present, global-scale machine learning-based human footprint index which is capable of routine update. By comparing global changes in the machine learning human footprint index between 2000 and 2019 to national-scale biodiversity metrics for Goal 15, we find that some countries are experiencing increases in their human footprint while biodiversity metrics are improving as well. We further examine development and policy dynamics to uncover enabling mechanisms for balancing increased human pressure with biodiversity gains. This has immediate policy relevance, since the majority of countries globally are not on track to achieve Goal 15 by the declared deadline of 2030. Moving forward, the machine learning human footprint index can be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system.	en_US
dc.description.sponsorship	This research was funded, in part, by the United States National Aeronautics and Space Administration (NASA) under grant #18-SLSCVC18-0006.	en_US
dc.format.medium	PDF
dc.format.medium	HDF5
dc.identifier.uri	https://hdl.handle.net/10217/216207
dc.identifier.uri	http://dx.doi.org/10.25675/10217/216207
dc.language	English	en_US
dc.language.iso	eng	en_US
dc.publisher	Colorado State University. Libraries	en_US
dc.relation.ispartof	Research Data
dc.relation.isreferencedby	Keys, P.W., Barnes, E.A., Carter, N. (2021) A machine-learning approach to human footprint index estimation with applications to sustainable development. Environmental Research Letters 16 044061. https://doi.org/10.1088/1748-9326/abe00a	en_US
dc.rights.license	The material is open access and distributed under the terms and conditions of the Creative Commons Attribution International License (https://creativecommons.org/licenses/by/4.0/).
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	biodiversity conservation	en_US
dc.subject	sustainable development	en_US
dc.subject	human impacts	en_US
dc.subject	human footprint index	en_US
dc.subject	machine learning	en_US
dc.subject	neural network	en_US
dc.subject	remote sensing	en_US
dc.title	Dataset associated with "A machine-learning approach to human footprint index estimation with applications to sustainable development"	en_US
dc.type	Dataset	en_US