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用历史GOCI和MODIS图像来预测绿藻开花的空间分布的框架

Zhongyuan Wang¹, Zhixiang Fang², Rui Feng²

¹School of Geomatics Science and Technology, Nanjing Tech University, Nanjing, China.

Marine environmental research

|February 21, 2025

概括

这项研究引入了一种细胞自动机模型,用于预测卫星数据不可用时的绿藻开花 (GAB). 该CA-GAB预测模型 (CA-GPM) 有效地预测GAB,帮助海洋环境管理.

科学领域:

海洋生态海洋生态学
遥感遥感遥感遥感
计算建模计算建模

背景情况:

绿藻繁殖 (GABs) 构成一个重大的全球环境挑战,特别是在南黄海 (SYS).
基于卫星的GAB探测受到诸如云层覆盖等限制的阻碍,需要使用替代预测方法.
了解影响GAB动态的环境因素的复杂相互作用,对于有效管理至关重要.

研究的目的:

开发和验证使用蜂自动机 (CA-GPM) 预测GAB的有效框架,以克服遥感数据的局限性.
分析海洋环境数据对GAB增长和漂移模式的影响.
调查影响因素 (竞争,物理和生物) 在SYS中的GAB.

主要方法:

使用了历史的地球静止海洋彩色成像仪 (GOCI) 和中等分辨率成像光谱辐射仪 (MODIS) 图像.
采用结构方程建模来确定海洋环境数据对GAB的影响重量.
开发了一种细胞自动机模型 (CA-GPM),用于GAB预测的竞争,漂移和增长和衰退规则.

主要成果:

与2011-2022年的遥感数据相比,CA-GPM的精度为0.6909 ± 0.5001,缺失警报率为0.2980 ± 0.6975,虚假警报率为0.3091 ± 0.5001.与远程传感数据相比,CA-GPM的精度为0.6909 ± 0.5001,缺失警报率为0.2980 ± 0.6975,虚假警报率为0.3091 ± 0.5001.
揭示了生物和物理条件对于2011-2020年的GAB同样重要.
自2021年以来,确定了物理条件对GAB的负面影响.

结论:

CA-GPM为GAB检测和预测提供了可行的解决方案,特别是当远程传感数据无法访问时.
该模型有助于GAB的紧急管理,并提供了对花动态驱动因素的见解.
该研究强调了环境因素对SYS的GAB的影响的不断变化.

关键词:

蜂自动机是一个自动机.绿藻的开花绿藻的开花绿藻的开花海洋环境条件海洋环境条件

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