74个北半球树种的功能特征和无人机衍生的表观之间的联系
Simon Kloos1, Marvin Lüpke1, Nicole Estrella1
1TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany.
The Science of the total environment
|August 25, 2024
概括
无人机图像准确地捕捉了许多树叶物种的树木表态 (季节开始和结束). 植物的功能特征,如种子质量和起源,解释了树的现象时间和季节长度的显著变化.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 植物科学 植物科学
背景情况:
- 树木现象学对碳和水循环至关重要,并表明气候变化的影响.
- 了解跨树种的现象变异性是必不可少的,但具有挑战性.
- 植物功能特征在驱动叶树表态中的作用需要进一步研究.
研究的目的:
- 评估无人机监测在捕获多种多叶树种的现象变异性方面的有效性.
- 确定影响植物表观学的跨物种差异的关键植物功能特征.
- 使用无人机图像分析季节开始 (SOS),季节结束 (EOS) 和季节长度 (LOS).
主要方法:
- 通过使用无人机成像,从74种叶树物种中获得3099个个体的现象学指标 (SOS,EOS,LOS).
- 经过验证的无人机衍生的现象学与现场地面观测相比.
- 使用增强回归树 (BRT) 模型,分析了与植物功能特征相关的跨种类现象变异性.
主要成果:
- 来自无人机的SOS和EOS显示与地面观测有很高的一致性 (R2=0.49用于叶子展开,R2=0.79用于叶子变色).
- 观察到显著的内部和跨种类的现象学变异性,LOS差异长达两个月.
- 植物的功能特征 (种子干质量,染色体数量,起源) 显著解释了SOS,EOS和LOS的跨物种变异.
结论:
- 基于无人机的现象监测是一种强大的方法,可以在很少的努力下进行个别树木评估.
- 植物的功能特征是跨物种现象学差异的重要预测因素,解释了大量的变异.
- 这些发现有助于更好地了解树叶树的生长策略,并开辟了现象学领域的新研究途径.
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