Dataset associated with "A machine-learning approach to human footprint index estimation with applications to sustainable development"
Date
2020
Authors
Keys, Patrick
Barnes, Elizabeth
Carter, Neil
Journal Title
Journal ISSN
Volume Title
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.
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.
School of Global Environmental Sustainability
Department of Atmospheric Science
School of Global Environmental Sustainability
Department of Atmospheric Science
Rights Access
Subject
biodiversity conservation
sustainable development
human impacts
human footprint index
machine learning
neural network
remote sensing
Citation
Associated Publications
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