将近距离和遥感与机器学习集成在一起,用于牧场生物质估计
Bernardo Cândido1, Ushasree Mindala1, Hamid Ebrahimy2
1Division of Plant Science and Technology, University of Missouri, Columbia, MO 65201, USA.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
使用近距离传感,遥感和机器学习准确估计牧场生物量. 使用Landsat 7数据的XGBoost模型实现了高精度,为精准农业和牧场监测提供了可扩展的解决方案.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 准确的牧场生物质估计对于有效的畜牧管理和牧场可持续性至关重要.
- 整合不同的数据来源对精确的生物质量量定量提出了挑战.
研究的目的:
- 开发和评估牧场生物量估计的综合方法.
- 为了比较不同机器学习模型和遥感数据的性能.
主要方法:
- 结合地面植被高度 (PaddockTrac) 与卫星衍生的植被指数 (Landsat 7,Sentinel-2).
- 使用Boruta算法进行特征选择并评估线性回归,决策树,随机森林和XGBoost模型.
- 使用R2,平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估预测准确性.
主要成果:
- XGBoost模型表现出卓越的性能,使用Landsat 7数据实现了0.86的R2.
- 基于地面的高度和植被指数是生物质估计的关键预测指标.
- 哨兵-2的红边指数在均的牧场环境中提供了有限的改善.
结论:
- 结合近距离传感,遥感和机器学习的综合方法为牧场生物量估计提供了可扩展和准确的方法.
- XGBoost模型对于牧场生态系统中的生物质预测非常有效.
- 通过加强监测能力,研究结果支持精准农业和可持续牧场管理的进步.
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