世界初のデータセットにより、土地や水の利用、気候変動に関する新たな知見がもたらされる First-of-its-kind dataset will provide new insights into land and water use and climate change
2022-07-19 ミネソタ大学
このデータは、土地や淡水の利用、湖や貯水池が人間や気候変動によってどのような影響を受けているかについて、環境研究者に新たな情報を提供するものです。また、この研究は機械学習技術を大きく前進させるものです。
ReaLSATは、地形全体の水ピクセルではなく、湖とその境界の変化を示すので、水質に関する生態系プロセスを世界中の何十万もの湖と結びつけることができるようになった
＜関連情報＞
- https://cse.umn.edu/college/news/data-scientists-use-new-techniques-identify-lakes-and-reservoirs-around-world
- https://www.nature.com/articles/s41597-022-01449-5
貯水池・湖沼の表面積変化のグローバルデータセット「ReaLSAT」 ReaLSAT, a global dataset of reservoir and lake surface area variations
Ankush Khandelwal,Anuj Karpatne,Praveen Ravirathinam,Rahul Ghosh,Zhihao Wei,Hilary A. Dugan,Paul C. Hanson & Vipin Kumar
Scientific Data Published:21 June 2022
DOI:https://doi.org/10.1038/s41597-022-01449-5
Abstract
Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.
