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

• Published in: bioRxiv 2021-01
• Main Authors: Keys, Patrick W, Barnes, Elizabeth A, Carter, Neil H
• Format: Article
• Language: English
• Subjects: Biodiversity; Ecology; High density lipoprotein; Learning algorithms; Machine learning; Sustainable development
• ISSN: 2692-8205
• DOI: 10.1101/2020.09.06.284414

Abstract The human footprint index is an extensively used tool for interpreting the accelerating pressure of humanity on Earth. Up to now, the process of creating the human footprint index has required significant data and modeling, and updated versions of the index often lag the present day by many years. Here we introduce a near-present, global-scale machine learning-based human footprint index (ml-HFI) which is capable of routine update using satellite imagery alone. We present the most up-to-date map of the human footprint index, and document changes in human pressure during the past 20 years (2000 to 2019). Moreover, we demonstrate its utility as a monitoring tool for the United Nations Sustainable Development Goal 15 (SDG15), “Life on Land”, which aims to foster sustainable development while conserving biodiversity. We identify 43 countries that are making progress toward SDG15 while also experiencing increases in their ml-HFI. We examine a subset of these in the context of conservation policies that may or may not enable continued progress toward SDG15. 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 ml-HFI may be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system. Competing Interest Statement The authors have declared no competing interest. Footnotes * The primary revisions in this version include modification of the Abstract and a new paragraph in the Introduction section. * https://hdl.handle.net/10217/216207