biodivMapR: An <scp>r</scp> package for α‐ and β‐diversity mapping using remotely sensed images

Féret, Jean‐Baptiste; Boissieu, Florian de

doi:10.1111/2041-210x.13310

Cited by 46 publications

(45 citation statements)

References 14 publications

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“…In this paper, we made use of an 8-bit NDVI layer rescaled from Copernicus data. However, a multispectral system reduced to one single layer through the first component of a Principal Component Analysis, or similar multidimensionality reduction techniques, would also be useful (Féret and Boissieu 2020). In fact, NDVI assumes a biomass-grounded reflectance model, while the direct use of the original spectral data (digital numbers) does not generally require any assumptions about the biology of objects being sensed.…”

Section: Discussionmentioning

confidence: 99%

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

et al. 2021

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The variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new package - - to calculate diversity indices based on remotely sensed data, by discussing the theory behind the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and biochemical), taxonomic, phylogenetic and genetic diversity. Making use of the package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

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Section: Discussionmentioning

confidence: 99%

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

et al. 2021

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“…This simplified translation of the BC dissimilarity matrix can then be displayed as a colored map. More details can be found in Féret and de Boissieu (in press).…”

Section: The Algorithmmentioning

confidence: 99%

“…Strictly speaking, the method is a clustering approach which (i) divides the subspaces in spectral units and (ii) assigns it to spectral species from which (iii) different diversity maps can be obtained. Box 1 focuses in detail on the main steps of the algorithm, while the dedicated R package biodivMapR is now available (https://github.com/jbferet/biodivMapR) and fully described in Féret and de Boissieu (in press).…”

Section: The Algorithmmentioning

confidence: 99%

From local spectral species to global spectral communities: a benchmark for ecosystem diversity estimate by remote sensing

Rocchini

Salvatori

Beierkuhnlein

et al. 2020

Preprint

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In the light of unprecedented change in global biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use of remote sensing approaches. Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called "spectral species". Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method to MODIS imagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a single plant species in the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to derive alpha- (local relative abundance and richness of spectral species) and beta-diversity (turnover of spectral species) maps over wide geographical areas.

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“…Furthermore, their intrinsic transparency, community-vetoing options, sharing and rapid availability are also valuable additions and reasons to move towards open source options. Among the different open source software options, the software is certainly one of the most used statistical and computational environment worldwide and different packages have already been devoted to remote sensing data processing for: (i) raster data management ( package, Hijmans and van Etten (2020)); (ii) remote sensing data analysis ( package, Leutner et al (2019)); (iii) spectral species diversity ( package, Féret and Boissieu (2020)); (iv) Sparse Generalized Dissimilarity Modelling based on remote sensing data ( package, Leitao et al (2017)); (v) entropy-based local spatial association ( package, Naimi et al (2019)); or (vi) landscape metrics calculation ( package, Hesselbarth et al (2019)), to name just a few. Readers can also refer to https://cran.r-project.org/web/views/Spatial.html for the CRAN Task View on analysis of spatial data.…”

Section: Introductionmentioning

confidence: 99%

rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

Rocchini

Thouverai

Marcantonio³

et al. 2021

Preprint

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Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.

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biodivMapR: An r package for α‐ and β‐diversity mapping using remotely sensed images

Cited by 46 publications

References 14 publications

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

From local spectral species to global spectral communities: a benchmark for ecosystem diversity estimate by remote sensing

rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

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