Data scientists at the University of Minnesota Twin Cities led an interdisciplinary team that developed a first-of-its kind comprehensive global dataset of the Earth's lakes and reservoirs.
The Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset shows changes in lakes and reservoirs over time, including land and fresh water use and the impact of humans and climate change.
The dataset features the location and surface area variations of 681,137 lakes and reservoirs, with monthly data on each from 1984 to 2015.
The dataset was developed using machine learning algorithms that combine information on the physical dynamics of bodies of water with satellite imagery.
Said the University of Wisconsin-Madison's Paul C. Hanson, "Because ReaLSAT shows changes in lakes and their boundaries, rather than just water pixels across the landscape, we can now connect ecosystem processes about water quality with hundreds of thousands of lakes around the world."
From University of Minnesota College of Science & Engineering
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