The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

作者：Peter Potapov, Matthew C. Hansen, Amy Pickens, Andrés Hernández-Serna, Alexandra Tyukavina, Svetlana Turubanova, Viviana Zalles, Xinyuan Li, Ahmad Khan, Fred Stolle, Nancy L. Harris, Xiao‐Peng Song, Antoine Baggett, Indrani Kommareddy, Anil Kommareddy · 发表于：Frontiers in Remote Sensing · 年份：2022 · DOI：10.3389/frsen.2022.856903 · 被引用次数：609 · 研究领域：Cryospheric studies and observations、Remote Sensing in Agriculture、Climate change and permafrost

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system.