陆地卫星数据显示,全世界的湖泊缺氧
Ziwen Tu1, Yibo Zhang2, Kun Shi2
1Nanjing University of Information Science and Technology, Nanjing 210044, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
Water research
|September 29, 2024
概括
全球湖泊溶解氧 (DO) 正在减少,威胁到水生生态系统. 这项研究开发了机器学习模型来绘制长期的DO变化,揭示了全球湖泊中广泛的脱氧和增加的DO压力.
科学领域:
- 环境科学 环境科学
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
背景情况:
- 溶解氧 (DO) 对水生生物和生态系统健康至关重要.
- 由于数据的局限性,全球长期湖泊DO趋势的了解很少.
研究的目的:
- 开发和应用机器学习模型来估计全球湖泊中长期溶解的氧气.
- 分析 DO 变化的空间和时间模式,并确定处于压力下的区域.
主要方法:
- 整合Landsat数据和地理特征以构建DO估计模型.
- 利用了机器学习算法,包括随机森林 (RF),人工神经网络 (ANN),支持矢量机器回归 (SVR) 和极端梯度增强 (XGBoost).
- 应用了表现最好的射频模型来分析1984-2021年全球35万多个湖泊的DO趋势.
主要成果:
- 与其他测试模型相比,射频模型实现了更高的性能 (R2 = 0.72,RMSE = 1.24 mg/L).
- 平均膜学DO为9.72±1.07 mg/L,在极地地区度较高,在赤道地区度较低.
- 观察到每十年的广泛脱氧趋势为-0.036 mg/L,越来越多的湖泊面积经历了DO压力.
结论:
- 机器学习,特别是射频,使用遥感数据有效地估计了全球湖泊DO.
- 全球湖泊正在经历显著的脱氧,对水生生态系统构成风险.
- 这些发现为水生生态系统管理和保护工作提供了关键数据.
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