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
Department of Geography, Florida State University, Tallahassee, Florida, 32306, USA.
Division of Biology, Kansas State University, Manhattan, Kansas, 66506-4901, USA.
Ecology. 2022 Feb;103(2):e03590. doi: 10.1002/ecy.3590. Epub 2021 Dec 16.
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 validation of more than 0.25. For all vegetation traits, validation ranged 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.
要准确预测群落和生态系统将如何应对全球变化，需要了解植物性状的时空变化。美国国家生态观测站网络（NEON）的机载观测平台（AOP）在众多野外站点以1米的空间分辨率提供高光谱图像和相关数据产品，这有可能实现高分辨率的性状测绘。我们通过将NEON的AOP现成的数据产品，如叶面积指数（LAI）、总生物量、生态系统结构（冠层高度模型[CHM]）和冠层氮含量，与来自一个湿润高草草原的空间广泛的实地测量数据进行比较，测试了这些数据产品的准确性。与AOP数据产品的相关性通常显示出与相应实地测量的关系较弱或无关系。最强的关系存在于AOP的LAI与地面测量的LAI之间（r = 0.32）以及AOP的总生物量与地面测量的生物量之间（r = 0.23）。我们还研究了与派生产品不同的全反射光谱（380 - 2500纳米）使用偏最小二乘回归（PLSR）模型预测植被性状的效果如何。在所研究的所有八个性状中，只有氮含量的验证值超过0.25。对于所有植被性状，验证值范围为0.08至0.29，预测均方根误差（RMSEP）范围为14%至64%。我们的结果表明，在没有广泛的地面验证的情况下，目前可用的AOP派生数据产品不应被使用。使用全反射光谱的关系可能更有前景，尽管需要仔细考虑野外和AOP数据在空间和/或时间上的不匹配、野外测量或AOP算法中的偏差以及模型的不确定性。最后，由于草地在小面积内具有高物种多样性、植物群落功能类型混合以及干扰和资源可用性的异质镶嵌，对于机载光谱学来说可能特别具有挑战性。遥感观测是理解时空生态模式最有前景的方法之一。但是让不同的NEON数据用户群体参与进来的机会将取决于在不同站点建立与实地测量的严格联系。
