通过远程传感和机器学习技术估计厄瓜多尔高安第斯地区的初级生产率
Cindy Urgilés1,2, Johanna Orellana-Alvear3,4, Patricio Crespo3,5
1Departamento de Recursos Hídricos y Ciencias Ambientales, Universidad de Cuenca, Cuenca, 010207, Ecuador. cindyurgiles1996@gmail.com.
International journal of biometeorology
|November 27, 2024
概括
机器学习模型准确地估计了安第斯帕拉莫生态系统的总初级生产率 (GPP). 随机森林模型表现出色,确定太阳辐射是GPP的关键驱动因素.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 气候变化研究 气候变化研究
背景情况:
- 估计总初级生产率 (GPP) 对碳循环建模和气候变化缓解至关重要.
- 直接GPP测量超出叶子尺度是有限的,需要间接估计方法.
- 遥感和建模方法是提高GPP估计的关键.
研究的目的:
- 通过机器学习 (ML) 和传统模型,在湿的安第斯帕拉莫生态系统中估计GPP.
- 分析影响GPP的复杂,非线性关系,并评估气候预测模型的不确定性.
- 将ML模型 (随机森林,支持向量回归) 的性能与传统方法进行比较.
主要方法:
- 使用机器学习模型,特别是随机森林 (RF) 和支持向量回归 (SVR),用于GPP估计.
- 将ML模型的准确性和性能与传统的GPP估计技术进行了比较.
- 根据GPP估计,对未来气候预测进行不确定性分析.
主要成果:
- 基于ML的模型在估计GPP方面表现优于传统方法.
- 模型性能因季节性而异,相关系数 (R) 从0.24到0.86.8不等.
- 射频模型在较低湿度的季节显示出最高准确度 (R=0.86),RMSE和偏差较低.
- 太阳辐射被确定为帕拉莫生物群中GPP的主要预测因素,超过了水的影响.
结论:
- 机器学习模型提供了一种可靠的方法来估计复杂生态系统中的每日GPP流量.
- 了解GPP驱动因素,如帕拉莫的太阳辐射,对于准确的植被建模至关重要.
- 这项研究的结果支持开发改善的植被预测模型,以适应气候变化.
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