2021
DOI: 10.1111/geb.13270
|View full text |Cite
|
Sign up to set email alerts
|

From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy

Abstract: Aim The majority of work done to gather information on the Earth's biodiversity has been carried out using in‐situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
18
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
7
1

Relationship

3
5

Authors

Journals

citations
Cited by 30 publications
(18 citation statements)
references
References 46 publications
0
18
0
Order By: Relevance
“…The SV_categorical approach is closely related to the spectral-species approach originally suggested by Féret and Asner (2014), where class numbers (typically obtained by a spectral clustering approach) are considered proxies of species numbers (thus relating to the 'species SVH'). Recently, this approach has been scaled up to wider spatial extents and to a higher level of biological organization (e.g., vegetation types or habitats) (Rocchini et al, 2021). The approach has some obvious advantages: (1) consistency over time may increase as even if the optical traits of plant species change over the year, the landcover patches or landscape elements may be more persistent (e.g., a broad-leaved forest stand may look very different in a satellite scene acquired in summer and winter but may be detectable as a spectrally homogeneous spatial unit/patch in both scenes); (2) spectrally extreme pixels or land-cover types will not have unproportionally large influence on the spectral-variation metric but will rather represent individual discrete classes amongst a plethora of other classes; and…”
Section: Discrete Vs Continuous Datamentioning
confidence: 99%
“…The SV_categorical approach is closely related to the spectral-species approach originally suggested by Féret and Asner (2014), where class numbers (typically obtained by a spectral clustering approach) are considered proxies of species numbers (thus relating to the 'species SVH'). Recently, this approach has been scaled up to wider spatial extents and to a higher level of biological organization (e.g., vegetation types or habitats) (Rocchini et al, 2021). The approach has some obvious advantages: (1) consistency over time may increase as even if the optical traits of plant species change over the year, the landcover patches or landscape elements may be more persistent (e.g., a broad-leaved forest stand may look very different in a satellite scene acquired in summer and winter but may be detectable as a spectrally homogeneous spatial unit/patch in both scenes); (2) spectrally extreme pixels or land-cover types will not have unproportionally large influence on the spectral-variation metric but will rather represent individual discrete classes amongst a plethora of other classes; and…”
Section: Discrete Vs Continuous Datamentioning
confidence: 99%
“…Increasing alpha in Eq. (3) will increase the weight of higher distances among different values until reaching the maximum distance value possible (Rocchini et al, 2021a). For this reason, spatio-ecological heterogeneity values of the parametric Rao's Q increase with each alpha progressively added to the calculation constructing a curve for every moving window built around each pixel (Rocchini et al, 2021b).…”
Section: Discussionmentioning
confidence: 99%
“…Both Shannon and Rao's Q indices are point descriptors of heterogeneity, namely they can only show part of the whole heterogeneity spectrum. Recently Rocchini et al (2021a) proposed an implementation of the Rao's Q index by parameterizing the original formula, and allowing the whole continuum of heterogeneity to be measured thanks to Rao's Q continuous profiles (see Section 2).…”
Section: Introductionmentioning
confidence: 99%
“…This approach is supported by the spectral variation hypothesis, which states that the spectral diversity in an RS image originates from the spatial heterogeneity of the environment, which influences the distribution of plant species and their functional traits (Palmer et al, 2002). PFD can be estimated from the variability of (1) spectral signals (spectral diversity) (Rocchini et al, 2021; Wang, Gamon, Cavender‐Bares, et al, 2018) or (2) plant traits (PT) estimated from spectral imagery (e.g. leaf pigments) (Schneider et al, 2017; Torresani et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…PFD can be estimated from the variability of (1) spectral signals (spectral diversity) (Rocchini et al, 2021; or (2) plant traits (PT) estimated from spectral imagery (e.g. leaf pigments) (Schneider et al, 2017;Torresani et al, 2021).…”
Section: Introductionmentioning
confidence: 99%