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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Methods in Ecology and Evolution

ISSN

2041-210X

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

2123-2136

Kód UT WoS článku

001011196700001

EID výsledku v databázi Scopus

2-s2.0-85161985885