AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing empirical relationships with field measurements across a diversity of sites.

准确预测群落与生态系统对全球变化的响应，需理解植物性状的时空变异规律。美国国家生态观测网络（National Observatory Ecological Network，NEON）的空基观测平台（Airborne Observation Platform，AOP）在多个野外站点提供空间分辨率为1米的高光谱图像及相关数据产品，可实现高分辨率的性状映射。然而，这些数据的可靠性依赖于与原位实地测量建立严谨关联。我们通过将NEON现成的AOP衍生数据产品——叶面积指数、总生物量、生态系统结构（冠层高度模型；CHM）及冠层氮含量——与中生型高草草原的大范围空间实地测量数据进行对比，测试了其准确性。结果显示，AOP数据产品与对应实地测量值的相关性普遍较弱或无显著关联，其中冠层氮含量与地面氮测量值、CHM与地面冠层高度测量值的相关性最弱。我们还利用偏最小二乘回归模型，检验了全反射光谱（380-2500 nm）相较于衍生产品对植被性状的预测能力。在所考察的8个性状中，仅氮含量的决定系数（R²）超过0.25；所有植被性状的R²范围为0.08-0.29，预测均方根误差范围为14%-64%。研究结果表明，目前可用的AOP衍生数据产品至少在该草原站点不可靠，未经大量地面验证不宜使用。尽管全反射光谱的预测关系可能更具前景，但仍建议进一步评估实地与AOP数据的空间尺度差异，以及通过校正和数据预处理提升数据质量。最后，草原站点因其小范围内的高物种多样性、植物群落功能类型的混合性，以及干扰与资源可获得性的异质镶嵌性，可能为空基光谱学带来特殊挑战。遥感观测是理解跨时空生态模式的最具前景的方法之一，但NEON数据用户社区的广泛参与，将依赖于在多样站点建立与实地测量的经验关联。