Challenging the link between functional and spectral diversity with radiative transfer modeling and data

Pacheco‐Labrador, Javier; Migliavacca, Mirco; Ma, Xuanlong; Mahecha, Miguel D.; Weber, Ulrich; Benavides, Raquel; Bouriaud, Olivier; Bărnoaiea, Ionuț; Coomes, David A.; Bohn, Friedrich J.; Kraemer, Guido; Heiden, Uta; Huth, Andreas; Wirth, Christian

doi:10.1016/j.rse.2022.113170

Cited by 23 publications

(18 citation statements)

References 105 publications

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“…Furthermore, while local normalization dependence on the maximum dissimilarity observed can increase the uncertainty of the normalized metrics, the data‐agnostic GN prevents further uncertainty propagation. We evaluated the new approach using a simulation framework similar to the one developed and validated by Pacheco‐Labrador et al (2022), who also discussed the use of specific radiative transfer models (i.e. SCOPE) and the computation of PFD from their inputs.…”

Section: Discussionmentioning

confidence: 99%

“…We also found that species richness and β ‐diversity affect the correlations between PT and RS metrics. Pacheco‐Labrador et al (2022) simulated a maximum of 30 species, and FRic and Rao Q indices presented r 2 ~ 0.61 and 0.75, respectively. Here, S reg = 300 led to lower r 2 (0.04 and 0.47, Figure 3); repeating the simulations with S reg = 30 increased r 2 to 0.22 and 0.66, respectively (Figure S3).…”

Section: Discussionmentioning

confidence: 99%

“…As in Pacheco-Labrador et al (2022), we defined synthetic plant species as sets of radiative transfer model parameters (Table 1) that enable simulating the corresponding reflectance factors. These are used as surrogates of plant functional traits that could be sampled in field surveys (see Pacheco-Labrador et al (2022) discussion on the suitability of these parameters). We randomly sampled foliar and structural PT within plausible biological bounds ordinary in RS literature and avoided unrealistic combinations by accounting for known covariance between foliar traits (Supporting Information S1).…”

Section: Simulation Of Synthetic Vegetation Communitiesmentioning

confidence: 99%

“…field surveys) but not in operational RS, where datasets are enormous and continuously grow. Pacheco‐Labrador et al (2022) showed that (1) pre‐processing is necessary to link FDMs computed from RS and PT datasets, and (2) separately pre‐processing RS images allows comparing some FDMs across images (Rao's quadratic entropy ( Q ), functional dispersion and functional richness (FRic)). However, differences in the data structure (dimensionality—the number of traits—and collinearity) lead to differences in the FMD values calculated from different datasets (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing

et al. 2023

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Remote sensing (RS) increasingly seeks to produce global‐coverage maps of plant functional diversity (PFD) across scales. PFD can be quantified with metrics assessing field or RS data dissimilarity. However, their comparison suffers from the lack of normalization approaches that (1) correct for differences in the number and correlation of traits and spectral variables and (2) do not require comparing all the available samples to estimate the maximum trait's dissimilarity (unfeasible in RS). We propose a generalizable normalization (GN) based on the maximum potential dissimilarity for the traits and spectral data considered and compare it to more traditional approaches (e.g. the maximum dissimilarity within datasets). To do so, we simulated plant communities with radiative transfer models and compared RS‐based diversity measurements across spatial scales (α‐ and β‐diversity components). Specifically, we assessed the capability of different normalization approaches (GN, local, none) to provide PFD estimates comparable between (1) RS and plant traits and (2) estimates from different RS missions. Unlike the other approaches, GN provides diversity component estimates that are directly comparable between field data and RS missions with different spectral configurations by removing the effect of differences in the number of traits or bands and the maximum dissimilarity across datasets. Therefore, GN enables the separated analysis of RS images from different sensors to produce comparable global‐coverage cartography. We suggest GN is necessary to validate RS approaches and develop interpretable maps of PFD using different RS missions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Simulation Of Synthetic Vegetation Communitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing

et al. 2023

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“…Such Additionally, such data can also be used in DA contexts (Section 3) much more easily than irregular, spot measurements. One application of note on the horizon is capturing patterns of functional diversity, which can be validated in the field (Pacheco-Labrador et al, 2022) and through EME explore causative mechanisms behind changes in environment, changes in function and changes in species or functional diversity. As models move more towards representing species diversity (Kauwe et al, 2015) and new traits based demographic models become more widespread (Xu & Trugman, 2021), such measurements in the context of EMEs can provide an invaluable source of process understanding.…”

Section: New Data For Eme-model Synthesismentioning

confidence: 99%

Ideas and perspectives: Beyond model evaluation – combining experiments and models to advance terrestrial ecosystem science

Caldararu

Rolo

Stocker

et al. 2023

Preprint

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Abstract. Ecosystem manipulative experiments are one of the most powerful tools to understand terrestrial ecosystem responses to global change because they measure real responses in real ecosystems. However, their scope is limited in space and time due to cost and labour intensity. This makes generalising results from such experiments difficult, which creates a conceptual gap between local scale process understanding and global scale future predictions. Recent efforts have seen results from such experiments used in combination with dynamic global vegetation models, most commonly to evaluate model predictions under global change drivers. However, there is much more potential in combining models and experiments. Here, we discuss the value and potential of a workflow for using ecosystem experiments together with process-based models to enhance the potential of both. We suggest that models can be used prior to the start of an experiment to generate hypotheses, identify data needs and in general guide experimental design. Models, when adequately constrained with observations, can also predict variables which are difficult to measure frequently or at all, and together with the data can provide a more complete picture of ecosystem states. Finally, models can be used to help generalise the experimental results in space and time, by providing a framework in which process understanding derived from site-level experiments can be incorporated. We also discuss the potential for using manipulative experiments together with models in formalised model-data integration frameworks for parameter estimation and model selection, a path made possible by the increasing number of ecosystem experiments and diverse observation streams. The ideas presented here can provide a roadmap to future experiment - model studies.

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Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing

Torresani,

Rossi,

Perrone

et al. 2024

Ecological Informatics

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Challenging the link between functional and spectral diversity with radiative transfer modeling and data

Cited by 23 publications

References 105 publications

A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing

A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing

Ideas and perspectives: Beyond model evaluation – combining experiments and models to advance terrestrial ecosystem science

Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing

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