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

   https://doi.org/10.1002/ecy.3590  

   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 (December 2021, Ecology)

   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 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. 

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2. Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance

   https://doi.org/10.5194/gmd-14-2603-2021  

   Shiklomanov, Alexey N.; Dietze, Michael C.; Fer, Istem; Viskari, Toni; Serbin, Shawn P. (January 2021, Geoscientific Model Development)

   null (Ed.)

   Abstract. Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks.In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty.Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified.Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state.We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States.The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types.Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure.However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations.Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance.In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”).In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products.We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data. 

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3. Evaluation of Small-Footprint Full-Waveform Airborne Lidar Instrument Requirements Using DIRSIG Simulations of Forests

   https://doi.org/10.1109/IGARSS39084.2020.9323232  

   Krause, Keith (September 2020, International Geoscience and Remote Sensing Symposium)

   The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) provides long-term, quantitative information on land use, vegetation structure and canopy chemistry over the NEON sites. AOP flies a suite of integrated remote sensing instruments consisting of a hyperspectral imager, a waveform lidar, and a color digital camera. Small-footprint full-waveform airborne lidar provides an enhanced capability beyond discrete return lidar for capturing and characterizing canopy structure. Due to high data rates/volumes, a common practice is to truncate waveforms. Very little research exists to determine how much data should be saved. In this study, simulations are run in Rochester Institute of Technology’s DIRSIG software. The resulting output waveforms are analyzed to assess three lidar system requirements: the total number of bins with a detected signal, the number of segments, and the max number of bins in a single segment. Recommendations for the values of these requirements are provided. 

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4. Field and LiDAR-based estimates of above-ground biomass in the Yukon River Watershed, Alaska (2023)

   https://doi.org/10.18739/A23B5W95N  

   Geyman, Emily; Anadu, Joshua; Cushman, KC; Douglas, Madison; Dunne, Kieran; Fischer, Woodward; Ke, Yutian; Lamb, Michael; Magyar, John; Nghiem, Justin; et al (January 2023, NSF Arctic Data Center)

   This dataset includes field measurements of above-ground biomass made between May and October, 2023 in three locations within the Yukon River Watershed: Huslia, Alaska(AK) (65.700 N, 156.387W), Beaver, AK (66.362 N, 147.398W), and Alakanuk, AK (62.685N, 164.644W). We measured a total of 11,335 trees, distributed in 190 field plots (approximately 10 meter (m) x 10 m). We apply allometric scaling relations to convert measurements of tree diameter to kilograms of dry biomass. We then link these filed measurements of above-ground biomass density to the mean forest canopy height (MCH), derived from airborne Light Detection and Ranging (LiDAR) data. We derive empirical regressions linking MCH to above-ground biomass in each of the field sites, and then apply these empirical relationships to the LiDAR datasets to obtain maps of above-ground biomass density. This dataset includes both the field observations (coordinates, tree type, and tree diameter of the 11,335 inventoried trees) and the processed above-ground biomass maps (georeferenced TIFF files, with a spatial resolution of 10 m). 

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5. Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site

   https://doi.org/10.1002/ecs2.70257  

   Pau, Stephanie; Slapikas, Ryan; Ho, Che‐Ling; Bayliss, Shannon_L J; Donnelly, Ryan C; Abdullahi, Adam; Helliker, Brent R; Nippert, Jesse B; Riley, William J; Still, Christopher J; et al (April 2025, Ecosphere)

   Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C₃or C₄), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjustedR² = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjustedR² = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjustedR² = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies (R² = 0.65 to <0.01), butR²was >0.80 for certain lineages and sites. The relationship between Vc_max, a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients. 

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