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The implementation of global initiatives towards sustainable development, climate change mitigation, and maintaining biodiversity and ecosystem functions depends on the timely provision of relevant data on land cover and land use change (LCLUC) at global, national, and local scales. The growing and diverse user community requires LCLUC data that is globally consistent yet locally relevant, freely and openly accessible, operationally updated, and has customization capabilities to support a wide range of practical applications. The Landsat data archive is the only tool that enables global multidecadal forest monitoring at medium (30 m) spatial resolution. Recent advances in Landsat data processing into analysis-ready data enhanced our capacity to map LCLUC globally with higher precision and thematic detail. Our team at the Global Land Analysis and Discovery Lab (GLAD) employed the Landsat analysis-ready data to map changes in forest extent and height, intact forest landscapes, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020 at 30-m spatial resolution. Each thematic product was independently derived using state-of-the-art 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 global bitemporal maps portray dramatic changes in the Earth’s land cover and land use during the first 20 years of the century. The results show the reduction of global tree cover extent and expansion of cropland and settlements. LCLUC dynamics have distinct regional patterns reflecting the expansion and abandonment of intensive land management. Surface water dynamics have pronounced regional variation linked to hydropower projects and depletion of natural lakes in dry climates. The area of perennial snow and ice declined dramatically in a warming climate. The individual datasets and the global LCLUC maps are publicly available from the dedicated web portal https://glad.umd.edu/dataset/GLCLUC2020/ and serve as inputs for the comprehensive global land monitoring system developed by the WRI Land & Carbon Lab.

Keywords:

Landsat, forest, cropland, urbanization, remote sensing, land use, land cover

Peter Potapov, Svetlana Turubanova, Matthew Hansen, Amy Pickens, Alexandra Tyukavina, Viviana Zalles, Xinyuan Li, Ahmad Khan, Andres Hernandez-Serna

Presentation within symposium:

S-23 Emerging uses of large-scale remote sensing in tropical forest monitoring

Landsat Data Archive: Global Applications for Natural Resource Management and Conservation

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