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进化趋势和Ulva prolifera绿潮驱动因素的分析:基于随机森林算法和多源遥感图像的研究

Wenlong Hou¹, Jinyue Chen¹, Maoxia He²

¹Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao, 266237, China; Shenzhen Research Institute of Shandong University, Shenzhen, 518057, China.

Marine environmental research

|April 30, 2024

概括

卫星遥感有效监测了南黄海的Ulva prolifera绿色潮,显示2011-2022年每年减少3%. 影响生长的关键因素包括海面温度,风和营养物质的流失.

科学领域:

海洋生态海洋生态学
遥感遥感遥感遥感
环境科学环境科学

背景情况:

绿色潮对海洋生态系统构成重大挑战,需要有效的监测和管理策略.
卫星遥感对于大规模,长期监测诸如绿色潮之类的海洋现象至关重要.

研究的目的:

开发和验证用于监测Ulva prolifera分布的遥感模型.
为了分析时间空间进化,趋势和推动因素的Ulva prolifera在南黄海的绿色潮.
提出减轻绿潮事件的管理策略.

主要方法:

使用GOCI I/II图像开发了一种混合遥感提取模型 (ACI-RF),获得F1评分>0.95.
使用气象,流水和水质数据 (SST,电流,风,降水,DO,PAR,Si,NO3-,PO43-,N/P) 分析了时空演变.
采用机器学习算法和统计分析来确定驱动因素.

主要成果:

乌尔瓦繁殖的绿色潮在2011-2022年间 (不包括2021年) 显著下降 (平均每年3%).
超过75%的绿色潮向北漂移,受到电流和风场的影响.
海洋表面温度,风速,降水量,PO43-,N/P/Si,沿海土地使用,以及6月份的下水对增长产生了重大影响.

结论:

该ACI-RF模型为Ulva prolifera监测提供了一个强大的方法.
有效的管理需要减轻污染物输入和精确的环境监测在关键领域.
该方法和因素选择标准可应用于其他海洋生态灾害管理工作.

关键词:

原因分析原因分析机器学习是机器学习.南黄海南黄海是什么意思时间空间分布的分布.乌尔瓦繁殖的绿色潮.

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