Scientific News | Scientists are using new techniques to identify lakes and reservoirs around the world


Minneapolis [St. Paul]July 19 (ANI): A group of researchers has released the first comprehensive global dataset of Earth’s lakes and reservoirs, which shows how they have changed over the past 30 years.

The study highlighting the Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset was recently published in Scientific Data, an open-access peer-reviewed journal published by Nature.

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The research was conducted by data scientists at the University of Minnesota Twin Cities with funding from NASA and the US National Science Foundation.

The data will provide environmental researchers with new insights into land and freshwater use as well as the impact of humans and climate change on lakes and reservoirs. Research is also a major advancement in machine learning techniques.

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A paper highlighting the Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset was recently published in Scientific Data, an open access peer-reviewed journal published by Nature.

Highlights of the study include:

The ReaLSAT dataset contains the location and area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (south of 50 degrees north latitude). The previous most comprehensive database, called HydroLAKES, had identified only 245,420 lakes and reservoirs for the part of the world and the minimum size considered in this study.

ReaLSAT provides data on the area of ​​each body of water for each month from 1984 to 2015. This allows quantification of changes in the area of ​​lakes and reservoirs over time, which is essential for understanding how and land use alter water bodies. fresh water. The HydroLAKES data contains only one static shape for each water body.

The ReaLSAT dataset is the culmination of eight years of research. It represents a major step in the application of new knowledge-driven machine learning for use in environmental science. Unlike other existing efforts, this dataset can now be extended almost automatically via machine learning and can be rapidly replicated for a wide variety of Earth observation data that are becoming available at increasingly higher resolution. better.

“Around the world, we find that lakes and reservoirs are changing rapidly with seasonal rainfall patterns, long-term changes in climate, and human management decisions,” said Vipin Kumar, lead author of the study and Regents Professor and William Norris Endowed Professor in the Twin Cities Department of Computer Science and Engineering at the University of Minnesota. “This new dataset greatly improves scientists’ ability to understand the impact of climate change and human actions on our fresh water around the world.”

Creating a global dataset of lakes and reservoirs and their evolution has required a new type of machine learning algorithms that combine knowledge of the physical dynamics of water masses with satellite imagery.

“ReaLSAT is a shining example where environmental challenges have driven a new class of knowledge-driven machine learning algorithms that are now used in many scientific applications,” Kumar said.

Scientists who study the environment agree that ReaLSAT will improve their work.

“The availability and quality of fresh surface water is critical to the sustainable use of our planet,” said Paul C. Hanson, research professor emeritus at the University of Wisconsin-Madison Center for Limnology and co- author of the study. “Because ReaLSAT shows changes in lakes and their boundaries, rather than just water pixels across the landscape, we can now connect ecosystem process on water quality with hundreds of thousands of lakes across the world.” (ANI)

(This is an unedited and auto-generated story from syndicated newsfeed, LatestLY staff may not have edited or edited the body of the content)


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