![]() ![]() The global acquisition strategy has improved over time, with acquisitions increasing from under 150k per year in the early 2000s to over 250k per year in recent years. Data richness, or the number of viable land observations available as inputs to analysis.The increased signal enables better detection capabilities in mapping land change. The result is a signal to noise ratio that is a magnitude greater than that of Landsat 7’s Enhanced Thematic Mapper Plus sensor. For example, the Operational Land Imager (2013-onward) onboard the Landsat 8 spacecraft employs a pushbroom sensor technology that increases per observation dwell time compared to past whiskbroom systems. Differences in Landsat sensor technology, whether Thematic Mapper, Enhanced Thematic Mapper Plus, or Operational Land Image data.However, inconsistencies exist due to the following factors: These data, available here, are a relative indicator of spatiotemporal trends in forest loss dynamics globally. The Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, in partnership with Global Forest Watch (GFW), provides annually updated global-scale forest loss data, derived using Landsat time-series imagery. To keep up to date with the latest updates, and to help us better understand how these data are used, please register as a user. We anticipate releasing updated versions of this dataset. Please use that URL when linking to this dataset. Web-based visualizations of these results are also available at our main site: For additional information about these results, please see the associated journal article (Hansen et al., Science 2013). Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2020. Global Forest Change Global Forest Change 2000–2020
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