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用无人机衍生的多谱指数和机器学习回归预测欧洲春的现象相

Stuart Krause^1,2, Tanja Sanders³

¹Thünen Institute of Forest Ecosystems, Alfred-Möller-Str. 1, Haus 41/42, 16225, Eberswalde, Germany.

Scientific reports

|July 9, 2024

概括

这项研究表明,无人机上的低成本RGB传感器可以使用绿色染色坐标 (GCC) 和机器学习准确预测森林现象学. 这推动了气候变化影响研究,使可靠的,高分辨率的现象学绘图成为可能.

科学领域:

生态生态学生态生态学
遥感遥感遥感遥感
林业林业林业林业林业

背景情况:

气候变化影响森林动态,需要准确的现象学事件数据.
地球观测 (EO) 数据提供了森林现象学的大规模绘图,但地面真实性仍然是一个挑战.
了解现象时间对于评估气候变化风险至关重要,例如早期叶子发作.

研究的目的:

探索预测欧洲 (Fagus sylvatica) 的高分辨率现象相数据的可行性.
为了利用无人驾驶飞行器 (UAV) 基于多谱指数和机器学习的现象预测.
为了确定最佳的传感器,植被指数,训练数据和机器学习模型,以准确地绘制现象地图.

主要方法:

采用了基于无人机的多谱指数和机器学习模型的全面特征选择过程.
利用绿色色色坐标 (GCC) 和通用添加模型 (GAM) 促进现象相预测.
从校准的视频频段获得GCC训练数据,并使用未校准的RGB传感器数据进行预测.

主要成果:

该GCC/GAM增强模型在未见数据集 (RMSE <0.5) 上预测现象学阶段方面取得了高准确性.
展示了常见无人机上装的传感器的潜在互操作性,特别是低成本的RGB传感器.
由于过度和,鉴定了近红外频段指数的限制.

结论:

基于无人机的多谱指数和机器学习,特别是GCC和GAM提升,对于高分辨率的森林现象学预测是有效的.
低成本的RGB传感器为获取现象学数据提供了一个可行的替代方案,提高了可访问性.
未来的研究应该专注于将模型与已确定的现象学阶段 (如ICP森林冲洗阶段) 结合起来.

关键词:

进行密集的森林监测.机器学习是机器学习.现象学现象学是指现象学.无人机无人驾驶飞行器 (UAV) 是一个

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