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1. Characterizing canopy complexity of natural and restored intertidal oyster reefs ( Crassostrea virginica ) with a novel laser‐scanning method

   https://doi.org/10.1111/rec.13973  

   Cannon, David J. ; Kibler, Kelly M. ; Taye, Jyotismita ; Medeiros, Stephen C. ( July 2023 , Restoration Ecology)

   The structural complexity of oyster reef canopy plays a major role in promoting biodiversity, balancing the sediment budget, and modulating hydrodynamics in estuarine systems. Although oyster canopy structure is both spatially and temporally heterogeneous, oyster canopies are generally characterized using simple first‐order quantities, like oyster density, which may lack the ability to sufficiently parameterize reef roughness. In this study, a novel laser‐scan approach was used to map the surface of intact reference and restored reefs (restoration age: 1–4 years) during low tide, when the oyster canopy was fully exposed. Measurements were used to estimate hydrodynamically relevant roughness characteristics over the entire reef surface (>140 m²; 0.50 m resolution), providing estimates of the canopy height (h_c), standard deviation (), rugosity index (R), and fractal dimension (D). Average canopy heights ranged from 3.6 to 4.9 cm, with canopy height standard deviations between 1.4 and 2.0 cm. Mean rugosity indices and fractal dimensions were relatively low on the youngest (1 year) restored reef (R = 1.28;D = 2.67), with substantial increases observed for more mature reef canopies (4 years:R = 1.56;D = 2.71). Structural complexity was consistently greater on reef margins than in reef interiors. Increases in complexity were linked to restoration age, with older reefs exhibiting more complex oyster canopies. The highest fractal dimension was observed on the intact reference reef, highlighting the importance of sustained reef growth for maintaining higher‐order structural complexity. Results provide spatially explicit surface roughness characterizations for healthy, intertidal oyster reefs, with applications in both restoration science and natural and nature‐based feature design.

    

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2. Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

   https://doi.org/10.1111/geb.13516  

   Kamoske, Aaron G. ; Dahlin, Kyla M. ; Read, Quentin D. ; Record, Sydne ; Stark, Scott C. ; Serbin, Shawn P. ; Zarnetske, Phoebe L. ( May 2022 , Global Ecology and Biogeography)

   Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).

   Eastern USA.

   2017–2018.

   Trees.

   With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.

   Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.

   We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

    

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3. Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness

   https://doi.org/10.1111/geb.13158  

   Marselis, Suzanne M. ; Abernethy, Katharine ; Alonso, Alfonso ; Armston, John ; Baker, Timothy R. ; Bastin, Jean‐Francois ; Bogaert, Jan ; Boyd, Doreen S. ; Boeckx, Pascal ; Burslem, David F. R. P. ; et al ( July 2020 , Global Ecology and Biogeography)

   Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness.

   Tropics.

   Present.

   Trees.

   First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression.

   The results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R²ranging from .44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R² = .39 and RMSD% = 43%, 0.25‐ha resolution).

   Our results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics.

    

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4. 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)

   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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5. Contrasting Development of Canopy Structure and Primary Production in Planted and Naturally Regenerated Red Pine Forests

   https://doi.org/10.3390/f10070566  

   Hickey, Laura J. ; Atkins, Jeff ; Fahey, Robert T. ; Kreider, Mark T. ; Wales, Shea B. ; Gough, Christopher M. ( July 2019 , Forests)

   Globally, planted forests are rapidly replacing naturally regenerated stands but the implications for canopy structure, carbon (C) storage, and the linkages between the two are unclear. We investigated the successional dynamics, interlinkages and mechanistic relationships between wood net primary production (NPPw) and canopy structure in planted and naturally regenerated red pine (Pinus resinosa Sol. ex Aiton) stands spanning ≥ 45 years of development. We focused our canopy structural analysis on leaf area index (LAI) and a spatially integrative, terrestrial LiDAR-based complexity measure, canopy rugosity, which is positively correlated with NPPw in several naturally regenerated forests, but which has not been investigated in planted stands. We estimated stand NPPw using a dendrochronological approach and examined whether canopy rugosity relates to light absorption and light–use efficiency. We found that canopy rugosity increased similarly with age in planted and naturally regenerated stands, despite differences in other structural features including LAI and stem density. However, the relationship between canopy rugosity and NPPw was negative in planted and not significant in naturally regenerated stands, indicating structural complexity is not a globally positive driver of NPPw. Underlying the negative NPPw-canopy rugosity relationship in planted stands was a corresponding decline in light-use efficiency, which peaked in the youngest, densely stocked stand with high LAI and low structural complexity. Even with significant differences in the developmental trajectories of canopy structure, NPPw, and light use, planted and naturally regenerated stands stored similar amounts of C in wood over a 45-year period. We conclude that widespread increases in planted forests are likely to affect age-related patterns in canopy structure and NPPw, but planted and naturally regenerated forests may function as comparable long-term C sinks via different structural and mechanistic pathways. 

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