Poor relationships between NEON Airborne Observation Platform data and field‐based vegetation traits at a mesic grassland
Poor relationships between NEON Airborne Observation Platform data and field‐based vegetation traits at a mesic grassland
复制标题
NEON 机载观测平台数据与湿地草原实地植被特征之间的关系较差
DOI：
10.1002/ecy.3590
复制
发表时间：
2021
期刊：
影响因子：
4.8
通讯作者：
Zaricor, Marissa
中科院分区：
文献类型：
--
作者：
Pau, Stephanie;Nippert, Jesse B.;Slapikas, Ryan;Griffith, Daniel;Bachle, Seton;Helliker, Brent R.;O’Connor, Rory C.;Riley, William J.;Still, Christopher J.;Zaricor, Marissa
了解植物性状的时空变化是准确预测群落和生态系统如何应对全球变化的必要条件。国家生态观测网络（氖）的机载观测平台（AOP）提供了高光谱图像和相关的数据产品，在许多领域的网站在1米的空间分辨率，可能允许高分辨率的性状映射。我们测试了氖的AOP，如叶面积指数（LAI），总生物量，生态系统结构（冠层高度模型[CHM]），和冠层氮，通过比较他们的空间广泛的实地测量从一个mesic tallgrass草原的准确性。与AOP数据产品的相关性表现出一般弱或没有相应的现场测量的关系。最强的关系是AOP LAI和地面测量的LAI（r= 0.32）和AOP总生物量和地面测量的生物量（r= 0.23）。我们还研究了全反射光谱（380- 2500 nm）与衍生产品相比，如何使用偏最小二乘回归（PLSR）模型预测植被特征。在所有的8个性状中，只有氮的有效度大于0.25。对于所有植被性状，验证范围为0.08 - 0.29，预测的均方根误差（RMSEP）范围为14- 64%。我们的研究结果表明，目前可用的AOP衍生数据产品不应该在没有广泛的地面验证的情况下使用。使用全反射光谱的关系可能更有希望，尽管需要仔细考虑空间和/或时间上的场和AOP数据不匹配，基于场的测量或AOP算法的偏差以及模型的不确定性。最后，草原网站可能是特别具有挑战性的空中光谱，因为它们的高物种多样性在一个小区域内，混合功能类型的植物群落，和异质马赛克的干扰和资源的可用性。遥感观测是了解跨空间和时间的生态模式的最有前途的方法之一。但是，要想有机会让不同的氖数据用户社区参与进来，就必须与不同地点的现场测量建立严格的联系。
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high‐resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), 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 strongest relationships were between AOP LAI and ground‐measured LAI (r= 0.32) and AOP Total Biomass and ground‐measured biomass (r= 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least‐squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validationof more than 0.25. For all vegetation traits, validationranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP‐derived data products should not be used without extensive ground‐based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field‐based measurements or AOP algorithms, and model uncertainty are needed. 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 heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in‐situ field measurements across a diversity of sites.