Global Forest Change dataset, also known as Hansen Global Forest Change or simply Global Forest Change, is a global remote sensing dataset of tree cover extent and change produced by researchers at the University of Maryland and collaborators. Derived from time-series analysis of the Landsat program, it provides near-global coverage at roughly 30 metres per pixel. The maintained release covers 2000-2024 and includes layers for tree canopy cover in 2000, annual tree-cover loss and year of loss, cumulative tree-cover gain for 2000–2012, reference image composites from the first and last available years, and a data mask. First described in a 2013 paper in Science, the dataset was created to provide a globally consistent annual record of tree-cover change using one methodology. The initial study reported 2.3 million square kilometres of tree-cover loss and 0.8 million square kilometres of tree-cover gain between 2000 and 2012 and found increasing loss in the tropics despite declines elsewhere. Its development depended on the opening of the Landsat archive and cloud-based computation, allowing the product to evolve from a one-time publication into a maintained data series. The dataset became widely used in forest monitoring, conservation, environmental accountability and national reporting. It broadened access to high-resolution forest-change data beyond specialist mapping agencies and became an important input to downstream platforms such as Global Forest Watch. Its interpretation has also been contested. Because it measures biophysical tree-cover change rather than land-use change, mapped loss may reflect harvesting, plantation cycles, fire, storms, pests or permanent conversion to another land use. Critics argued soon after publication that it could blur natural forests and plantations, while later validation studies found that default canopy thresholds often require calibration before the data can be used reliably in national statistics or as a proxy for deforestation.
History and development The dataset addressed a long-standing problem in global forest monitoring: forest change mattered for climate, biodiversity and land-use governance, but existing measurements were often inconsistent across countries and too coarse for comparable year-by-year analysis. Matthew Hansen and colleagues set out to produce a single global record from Landsat imagery using one methodology and a common definition of tree-cover change. The resulting 2013 study analyzed 654,000 Landsat images representing 143 billion pixels to map changes between 2000 and 2012. It reported aggregate loss and gain as well as strong regional contrasts, including declining deforestation in Brazil, sharply rising loss in Indonesia, and intensive patterns of harvest and regrowth in parts of the southeastern United States. A key precondition was the opening of the Landsat archive, which made systematic global image analysis possible at no direct data cost. Researchers at the University of Maryland developed the models used to process and characterize the imagery, while cloud-based processing by Google Earth Engine made it possible to reproduce those models at planetary scale. With that infrastructure, a processing task that would once have taken years was reduced to days. At the time, many global land-cover and forest products had spatial resolutions of about 250 to 300 metres, so the new dataset represented a major increase in detail as well as a major improvement in cross-country comparability. The initial scientific publication was followed by a public online demo and download service that allowed users to explore the maps directly. The 2013 demo site evolved from a fixed 2000-2012 interval described in the paper to a maintained product within the Global Land Analysis and Discovery ecosystem, with later releases extending annual loss detection beyond the original study period. The current official catalog lists the maintained release as version 1.12 and gives its coverage as 2000–2024. It also notes that annual loss continues to be updated while the gain layer remains cumulative only for 2000–2012. This shift from a landmark study to a regularly updated data product made Global Forest Change a long-lived reference dataset for later forest-monitoring systems and analyses.
Dataset structure The maintained product is organized as raster layers describing baseline tree cover, change and contextual imagery. The treecover2000 layer records the percentage of canopy closure for vegetation taller than five metres in the year 2000 on a scale from 0 to 100. The loss layer records whether tree-cover loss was detected during the study period, while lossyear identifies the first year in which that loss was detected. The gain layer records cumulative tree-cover gain, but only for 2000–2012, so the maintained product combines an annually updated loss record with a gain layer fixed to the original interval. A datamask layer distinguishes mapped land surface from permanent water and areas with no data. The downloadable country statistics are available at several minimum canopy-cover thresholds, and global aggregates derived from those tables change substantially depending on which threshold is chosen.
The dataset also includes reference image composites commonly described as first and last. These are multispectral composites from the first and last available years, using Landsat bands in the red, near-infrared and shortwave infrared ranges. In the current release, lossyear is encoded from 1 to 24, corresponding to loss detected primarily in calendar years 2001 through 2024, while 0 indicates no mapped loss. Each band has a nominal pixel size of 30.92 metres. Together, these layers let users compare baseline canopy cover, the timing of detected loss and representative imagery from the beginning and end of the observation period.
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