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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F23%3A43907129" target="_blank" >RIV/60076658:12310/23:43907129 - isvavai.cz</a>

Result on the web

<a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14163" target="_blank" >https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14163</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/2041-210X.14163" target="_blank" >10.1111/2041-210X.14163</a>

Result language

angličtina

Original language name

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

Original language description

1. 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&apos;s dissimilarity (unfeasible in RS). 2. 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 (a-and ss-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. 3. 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. 4. 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10618 - Ecology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Methods in Ecology and Evolution

ISSN

2041-210X

e-ISSN

—

Volume of the periodical

14

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

2123-2136

UT code for WoS article

001011196700001

EID of the result in the Scopus database

2-s2.0-85161985885