湿地生态系统中营养循环的混合时空建模使用先进的绘图技术和机器学习方法
Eric Ariel L Salas1, Kellsie Schrack2, Sakthi S Kumaran2
1Agricultural Research Development Program (ARDP), Central State University, Wilberforce, OH, 45384, USA. eric_asalas@yahoo.com.
Scientific reports
|February 19, 2026
概括
这项研究引入了一种新的两阶段模型,使用卫星数据和谷歌地球引擎来监测湿地营养. 该框架准确地预测了和,有助于保护工作.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 准确的湿地营养物质监测对于保护至关重要,但受到传统方法的挑战.
- 现有的现场技术很难有效地捕捉湿地范围内的动态.
- 开发先进的监测解决方案对于生态系统健康至关重要.
研究的目的:
- 开发和验证一种新的双阶段混合随机森林回归框架,用于湿地空间时间营养监测.
- 使用谷歌地球引擎将现场水质数据与湿地特征和卫星图像 (哨兵-1,哨兵-2) 集成.
- 预测和的度,并分析它们在比弗克里克湿地绿道中的驱动因素.
主要方法:
- 采用了两阶段的随机森林回归模型,整合了离散的湿地特征和连续的卫星衍生的预测因素.
- 该框架利用谷歌地球引擎处理Sentinel-1和Sentinel-2图像以及现场水质数据.
- 对于比弗克里克湿地绿道的四季度和度预测为四年 (2021-2024年).
主要成果:
- 两阶段模型实现了对 (R2 = 0.90) 和 (R2 = 0.89) 的高预测精度.
- 度驱动因素包括景观因素和遥感衍生的生物物理条件.
- 的度主要受到发达土地利用的来源负荷的影响.
结论:
- 开发的框架提供了精确的时空营养监测,克服了传统方法的局限性.
- 变量重要性分析揭示了和的独特驱动因素,为目标管理提供了信息.
- 时空地图显示了被研究的湿地系统中的季节性应力和损害的恢复.
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