通过环境和无人机遥感变量评估田的甲排放.
Andres Felipe Velez1, Cesar Ivan Alvarez2, Fabian Navarro1
1Alliance of Bioversity International and CIAT, A.A. 6713, Cali, Colombia.
Environmental monitoring and assessment
|May 23, 2024
概括
准确量化来自田的甲 (CH4) 排放对于减缓气候变化至关重要. 这项研究使用机器学习和无人机遥感来开发一种具有成本效益的方法来预测CH4排放,实现高精度.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 米种植是甲 (CH4) 的主要来源,甲是一种有助于气候变化的有力温室气体.
- 准确量化来自大米田的CH4排放对于减缓气候变化战略至关重要.
- 测量CH4排放的传统方法是劳动密集型和昂贵的.
研究的目的:
- 开发和验证一种新的,具有成本效益的方法来量化来自田的甲 (CH4) 排放.
- 将机器学习 (ML) 算法与遥感数据集成,以改进CH4排放预测.
- 挑战传统的闭室方法在米CH4排放评估中的局限性.
主要方法:
- 使用了配备Micasense Altum摄像头的无人机进行数据收集.
- 集成地面传感器以捕捉环境变量.
- 采用并评估了20多种回归模型,包括随机森林回归器,用于CH4排放预测.
- 利用遥感衍生的植被指数和环境数据作为预测指标.
主要成果:
- 实现了高预测准确度,训练数据为0.98的R平方值,测试数据为0.95.
- 确定了,GRVI中位数和累积土壤和水温作为关键预测变量.
- 随机森林回归器显示出对CH4排放的优越预测能力.
结论:
- 开发的ML和遥感方法为量化米CH4排放提供了一种创新,高效和具有成本效益的替代方案.
- 这种技术驱动的方法为大米生长参数和植被指数评估提供了宝贵的见解.
- 这些发现代表了监测农业系统温室气体排放的重大进展.
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