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卫星Sentinel-2的图像与机器学习相结合,模拟了巴西多斯河流域的水度

Felipe Carvalho Santana¹, Márcio Rocha Francelino¹, Rafael Gomes Siqueira²

¹Departamento de Solos, Universidade Federal de Viçosa, Av. PH Rolfs, S/N, Viçosa, MG, Brazil.

Environmental monitoring and assessment

|March 22, 2025

概括

遥感和机器学习准确地模拟了大破裂后巴西多斯河流域的水. 使用Sentinel-2数据的立体模型确定了特定的光谱指数和频段,这些指数和频段对于预测度水平至关重要.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
机器学习机器学习

背景情况:

芬达奥大破裂释放了大量的矿石尾矿进入多斯河流域,影响了水质.
传统的水质监测技术在空间覆盖和频率方面存在局限性.
度是受沉积物污染影响的关键水质指标.

研究的目的:

用遥感和机器学习来建模和空间预测多斯河流域的水度.
评估不同机器学习算法的有效性,用于度建模.
确定重要的光谱波段和指数,用于监测水尾矿影响的度.

主要方法:

利用了10米分辨率的Sentinel-2卫星图像.
应用并比较各种机器学习算法,包括立体模型.
分析了单个光谱带和光谱指数对度预测的重要性.

主要成果:

立体模型在预测水的度方面表现最高.
铁3指数 (红/蓝波段比) 和红/近红外波段是非常重要的预测指标.
在雨季和上游地区 (Gualaxo do Norte和Carmo河) 的度水平显著更高.

结论:

机器学习与遥感相结合,提供了一种强大的方法来监测灾后的度.
光谱特征,特别是与氧化铁和沉积物负载相关的特征,对于度评估至关重要.
堤尾矿显著增加了度,特别是在特定的支流和高降雨期间.

关键词:

人工智能的人工智能环境监测环境监测环境监测在Fundao大上嵌套-离开一个出去交叉验证.遥感是一种远程传感.水的质量水的质量

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