Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland

Authors: PAU, STEPHANIE - Florida State University; NIPPERT, JESSE - Kansas State University; SLAPIKAS, RYAN - Florida State University; GRIFFITH, DANIEL - Us Geological Survey (USGS); BACHLE, SETON - Kansas State University; HELLIKER, BRENT - University Of Pennsylvania; O'Connor, Rory; RILEY, WILLIAM - Lawrence Berkeley National Laboratory; STILL, CHRISTOPHER - Oregon State University; ZARICOR, MARISSA - Kansas State University

Submitted to: Ecology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/3/2021
Publication Date: 11/17/2021
Citation: Pau, S., Nippert, J.B., Slapikas, R., Griffith, D., Bachle, S., Helliker, B.R., O'Connor, R.C., Riley, W.J., Still, C.J., Zaricor, M. 2021. Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland. Ecology. 103(2). Article e03590. https://doi.org/10.1002/ecy.3590.
DOI: https://doi.org/10.1002/ecy.3590

Interpretive Summary: The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated pre-packaged data products at numerous field sites across different biomes at 1 m spatial resolution. This publicly available data provides opportunities for high-resolution mapping of plant traits. However, the reliability of these data depends on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s product AOP derived data products by comparing them to field measurements from a mesic tallgrass prairie. We found that the correlations with AOP data products showed weak or no relationships with corresponding field measurements. For seven of the nine traits we examined less than 30% of variation was explained in each trait by partial least squares regression. However, woody plant canopy height had the highest variation explained, 84 percent. These results suggest that currently available AOP derived data products may be unreliable, at least at this mesic grassland site. This information is useful to grassland managers and scientists in attempting to use NEON-AOP derived data for conservation management decisions and environmental modelling purposes.

Technical Abstract: 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 R2of more than 0.25. For all vegetation traits, validation R2 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 AOPderived 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.