Spectral Field Campaigns: Planning and Data Collection

Schweiger, Anna K.

doi:10.1007/978-3-030-33157-3_15

Cited by 20 publications

(27 citation statements)

References 76 publications

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“…Our results further demonstrated that the cross‐site scalable spectral– V c,max25 relationship can be derived only when leaf samples covering sufficiently wide variability in both V c,max25 and spectra are involved in the model development (Figs 4, 5; Schweiger, 2020). Therefore, the prerequisite for developing a general and broadly applicable spectral model of V c,max25 is to encompass the full trait space associated with both physiological and spectral variation in the training dataset.…”

Section: Discussionsupporting

confidence: 54%

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

et al. 2021

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Summary Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max), an important trait for modelling photosynthesis, can be inferred from other easier‐to‐measure traits. However, whether trait–Vc,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area‐based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type‐specific relationships between Vc,max and the four morphological and biochemical traits (R2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait–Vc,max relationships across three forest biomes into a single robust model for Vc,max (R2 = 0.77), and also accurately estimated the four traits (R2 = 0.75–0.94). These findings challenge the traditional use of the empirical trait–Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

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Section: Discussionsupporting

confidence: 54%

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

et al. 2021

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“…In the Alps, for example, such modifications have accelerated biodiversity loss at unprecedented rates as in the last decades modifications in society, tourism, and agricultural production have led to substantial land use changes and a loss of landscape diversity, particularly for grassland ecosystems [4]. In this context, to address the current decline in biodiversity, novel and efficient methods and tools are required to monitor biodiversity across spatial scales from the leaf level to the canopy, ecosystem, and global scales [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

Imran

Gianelle

Scotton³

et al. 2021

Remote Sensing

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Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.

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“…Importantly, field data must also be collected appropriately to align with airborne observations (Chadwick et al. 2020, Schweiger 2020). In general, field sampling should aim for representing spatial coverage as well as sampling across trait space, which are not mutually inclusive.…”

Section: Resultsmentioning

confidence: 99%

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

Pau

Nippert

Slapikas

et al. 2021

Ecology

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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 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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Spectral Field Campaigns: Planning and Data Collection

Cited by 20 publications

References 76 publications

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

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

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