Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation

Hauser, Leon T.; Timmermans, J.; Windt, Niels van der; Sil, Ângelo; Sá, Nuno César de; Soudzilovskaia, Nadejda A.; Bodegom, Peter M. van

doi:10.1016/j.rse.2021.112684

Cited by 47 publications

(19 citation statements)

References 113 publications

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“…While the Spectral Variation Hypothesis has been validated for functional diversity (Pacheco‐Labrador et al, 2022; Torresani et al, 2019), we do not know whether spectral diversity should equal PFD. GN allows us to hypothesize that they might not be due to non‐linearities and saturating absorption or scattering (Pacheco‐Labrador et al, 2022), background (Hauser, Timmermans, et al, 2021; Wang, Gamon, Schweiger, et al, 2018) or directional effects. We also found that species richness and β ‐diversity affect the correlations between PT and RS metrics.…”

Section: Discussionmentioning

confidence: 99%

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%

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

et al. 2023

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“…Indeed, coarse spatial and spectral resolution imagery has been used for a long time, hindering local-scale interpretation of biodiversity (Anderson, 2018). However, with increasingly detailed information, continued advances in satellite or airborne sensors provide objective, time-efficient, and reliable proxies for biological diversity (Mura et al, 2015;Hauser et al, 2021) at both local and large scales (Ustin, 2016;Udali et al, 2021). These approaches can be used for wall-to-wall mapping of biodiversity (Mura et al, 2016;Mao et al, 2018) and provide essential guidance for monitoring biodiversity in forest ecosystems, supporting forest planning and adaptive management (Groves, 2003;Hoekstra et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests

Parisi

Vangi

Francini

et al. 2023

Front. For. Glob. Change

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Biodiversity monitoring represents a major challenge to supporting proper forest ecosystem management and biodiversity conservation. The latter is indeed shifting in recent years from single-species to multi-taxon approaches. However, multi-taxonomic studies are quite rare due to the effort required for performing field surveys. In this context, remote sensing is a powerful tool, continuously providing consistent and open access data at a different range of spatial and temporal scales. In particular, the Sentinel-2 (S2) mission has great potential to produce reliable proxies for biological diversity. In beech forests of two Italian National Parks, we sampled the beetle fauna, breeding birds, and epiphytic lichens. First, we calculated Shannon’s entropy and Simpson’s diversity. Then, to produce variables for biodiversity assessment, we exploited S2 data acquired in the 4 years 2017–2021. S2 images were used to construct spectral bands and photosynthetic indices time series, from which 91 harmonic metrics were derived. For each taxon and multi-taxon community, we assessed the correlation with S2 harmonic metrics, biodiversity indices, and forest structural variables. Then, to assess the potential of the harmonic metrics in predicting species diversity in terms of Shannon’s and Simpson’s biodiversity indices, we also fit a random forests model between each diversity index and the best 10 harmonic metrics (in terms of absolute correlation, that is, the magnitude of the correlation) for each taxon. The models’ performance was evaluated via the relative root mean squared error (RMSE%). Overall, 241 beetle, 27 bird, and 59 lichen species were recorded. The diversity indices were higher for the multi-taxon community than for the single taxa. They were generally higher in the CVDA site than in GSML, except for the bird community. The highest correlation values between S2 data and biodiversity indices were recorded in CVDA for multi-taxon and beetle communities (| r| = 0.52 and 0.38, respectively), and in GSML for lichen and beetle communities (| r| = 0.34 and 0.26, respectively). RMSE% ranged between 2.53 and 9.99, and between 8.1 and 16.8 for the Simpson and Shannon index, respectively. The most important variables are phase and RMSE of red-Edge bands for bird and lichen communities, while RMSE and time of tassel cap and from EVI indices for beetles and multi-taxon diversity. Our results demonstrate that S2 data can be used for identifying potential biodiversity hotspots, showing that the herein presented harmonic metrics are informative for several taxa inhabiting wood, giving concrete support to cost-effective biodiversity monitoring and nature-based forest management in complex mountain systems.

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“…However, selective feeding by large herbivores can lead to a homogeneous community structure and composition (Zhu et al, 2012). These asynchronous and/or synchronous influences associated with fluctuating precipitation and grazing practices reveal unpredictable variations in species diversity (Hauser et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Diminishing influence of negative relationship between species richness and evenness on the modeling of grassland α-diversity metrics

Yan

Li²,

Liu³

2023

Front. Ecol. Evol.

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Species richness and evenness have been widely used to investigate the spatiotemporal variation of α-diversity. However, some studies have indicated that a negative relationship exists between species richness and evenness. The question is how the differing sensitivity of α-diversity metrics and interactive behavior between richness and evenness affect the modeling of α-diversity variation. Here, we explored the response of species diversity, represented by three Hill numbers (i.e., species richness, exponential of Shannon index – expShannon, and inverse of Simpson index – invSimpson) focusing on the abundance of rare and common species, and Pielou index underlining the evenness of a community, to α-diversity variation through structural equation modeling (SEM). The model scheme integrated three categories of variables, spectral variation hypothesis (SVH), community pattern, and vertical structure, along the precipitation gradient spanning three steppes, including meadow steppe, typical steppe, and desert steppe. Our results showed that there were large differences in species richness across the three steppes, with v-shaped patterns emerging along the gradient (low-point in the typical steppe). Differences between steppes were diminished in the expShannon or invSimpson indices, though the v-shaped patterns persisted. The Pielou index showed the opposite pattern, with the peak in the typical steppe. Accordingly, a negative relationship between species richness and Pielou index was found across the three steppes. The concurrent increases in annual species number and dominant species abundance in response to precipitation variations led to the negative relationship. As a result, the SEM fitness on expShannon and invSimpson indices over the region was substantially diminished by the negative relationship. Overall, community pattern better explained the variation in species richness, invSimpson and Pielou indices. The performance of SVH differed among α-diversity metrics due to the collinearity with the variables of community pattern and vertical structure. This study emphasizes the variability of α-diversity metrics in response to environmental change. Particularly, distinguishing the asynchronous behaviors between species richness and evenness is paramount to account for α-diversity variation over heterogeneous ecosystems.

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Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation

Cited by 47 publications

References 113 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

Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests

Diminishing influence of negative relationship between species richness and evenness on the modeling of grassland α-diversity metrics

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