Search

Your search keyword '"Thouverai, Elisa"' showing total 21 results
Start Over Author "Thouverai, Elisa"
21 results on '"Thouverai, Elisa"'

4. Integrals of life: Tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao’s Q index

Author

Thouverai, Elisa, Marcantonio, Matteo, Lenoir, Jonathan, Galfré, Mariasole, Marchetto, Elisa, Bacaro, Giovanni, Cazzolla Gatti, Roberto, Da Re, Daniele, Di Musciano, Michele, Furrer, Reinhard, Malavasi, Marco, Moudrý, Vítězslav, Nowosad, Jakub, Pedrotti, Franco, Pelorosso, Raffaele, Pezzi, Giovanna, Šímová, Petra, Ricotta, Carlo, Silvestri, Sonia, Tordoni, Enrico, Torresani, Michele, Vacchiano, Giorgio, Zannini, Piero, and Rocchini, Duccio

Published
2022
Full Text
View/download PDF

6. Under the mantra: 'Make use of colorblind friendly graphs'.

Author

Rocchini, Duccio, Chieffallo, Ludovico, Thouverai, Elisa, D'Introno, Rossella, Dagostin, Francesca, Donini, Emma, Foody, Giles, Garnier, Simon, Mazzochini, Guilherme G., Moudry, Vitezslav, Rudis, Bob, Simova, Petra, Torresani, Michele, and Nowosad, Jakub

Subjects
OPEN source software, PALETTE (Color range)
Abstract

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

8. Scientific maps should reach everyone: a straightforward approach to let colour blind people visualise spatial patterns

Author

Pezzi, Giovanna, Furrer, Reinhard, Lovei, Gabor, Chieffallo, Ludovico, Nowosad, Jakub, Moudrý, Vı́tězslav, Ricotta, Carlo, Rocchini, Duccio, Gábor, Lukáš, Malavasi, Marco, Torresani, Michele, DIntrono, Rossella, Marcantonio, Matteo, Šı́mová, Petra, Marchetto, Elisa, Thouverai, Elisa, Foody, Giles, Bacaro, Giovanni, Gatti, Roberto, Rocchini, Duccio, Nowosad, Jakub, D'Introno, Rossella, Chieffallo, Ludovico, Bacaro, Giovanni, Cazzolla Gatti, Roberto, Foody, Giles M., Furrer, Reinhard, Gabor, Luka, L Lovei, Gabor, Malavasi, Marco, Marcantonio, Matteo, Marchetto, Elisa, Moudry, Vı́tězslav, Pezzi, Giovanna, Ricotta, Carlo, Šı́mova, Petra, Torresani, Michele, Thouverai, Elisa, and University of Zurich

Subjects
bepress|Physical Sciences and Mathematics, computational ecology, bepress|Physical Sciences and Mathematics|Earth Sciences, R software, ecological informatics, remote sensing, colour blind, maps, R package, spatial pattern, 510 Mathematics, colours, map, Physical Sciences and Mathematics, bepress|Physical Sciences and Mathematics|Environmental Sciences, spatial statistics Themen: Computer Sciences, bepress|Physical Sciences and Mathematics|Mathematics, bepress|Physical Sciences and Mathematics|Computer Sciences, 10123 Institute of Mathematics, 10231 Institute for Computational Science, Earth Sciences, Schlagwörter: colour blind deficienty, Environmental Sciences, Mathematics
Abstract

Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present a function in R - \texttt{cblind.plot} - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colur blind friendly ramp palette. The function described in this paper (\ttt{cblind.plot}), on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.

Published
2023

9. Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

Author

Moudrý, Vítězslav, Keil, Petr, Gábor, Lukáš, Lecours, Vincent, Zarzo-Arias, Alejandra, Barták, Vojtěch, Malavasi, Marco, Rocchini, Duccio, Torresani, Michele, Gdulová, Kateřina, Grattarola, Florencia, Leroy, François, Marchetto, Elisa, Thouverai, Elisa, Prošek, Jiří, Wild, Jan, Šímová, Petra, Moudrý, Vítězslav, Keil, Petr, Gábor, Lukáš, Lecours, Vincent, Zarzo-Arias, Alejandra, Barták, Vojtěch, Malavasi, Marco, Rocchini, Duccio, Torresani, Michele, Gdulová, Kateřina, Grattarola, Florencia, Leroy, François, Marchetto, Elisa, Thouverai, Elisa, Prošek, Jiří, Wild, Jan, and Šímová, Petra

Abstract

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resol

Published
2023

10. Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns

Author

Rocchini, Duccio; https://orcid.org/0000-0003-0087-0594, Nowosad, Jakub; https://orcid.org/0000-0002-1057-3721, D’Introno, Rossella, Chieffallo, Ludovico, Bacaro, Giovanni, Gatti, Roberto Cazzolla, Foody, Giles M, Furrer, Reinhard; https://orcid.org/0000-0002-6319-2332, Gábor, Lukáš, Malavasi, Marco; https://orcid.org/0000-0002-9639-1784, Marcantonio, Matteo, Marchetto, Elisa, Moudrý, Vítězslav, Ricotta, Carlo, Šímová, Petra, Torresani, Michele, Thouverai, Elisa, Rocchini, Duccio; https://orcid.org/0000-0003-0087-0594, Nowosad, Jakub; https://orcid.org/0000-0002-1057-3721, D’Introno, Rossella, Chieffallo, Ludovico, Bacaro, Giovanni, Gatti, Roberto Cazzolla, Foody, Giles M, Furrer, Reinhard; https://orcid.org/0000-0002-6319-2332, Gábor, Lukáš, Malavasi, Marco; https://orcid.org/0000-0002-9639-1784, Marcantonio, Matteo, Marchetto, Elisa, Moudrý, Vítězslav, Ricotta, Carlo, Šímová, Petra, Torresani, Michele, and Thouverai, Elisa

Abstract

Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present the cblindplot R package and its founding function - cblind.plot() - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colour blind friendly ramp palette. The function described in this paper, on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.

Published
2023

11. Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

Author

Moudrý, Vítězslav, primary, Keil, Petr, additional, Cord, Anna F, additional, Gábor, Lukáš, additional, Lecours, Vincent, additional, Zarzo-Arias, Alejandra, additional, Barták, Vojtěch, additional, Malavasi, Marco, additional, Rocchini, Duccio, additional, Torresani, Michele, additional, Gdulová, Kateřina, additional, Grattarola, Florencia, additional, Leroy, François, additional, Marchetto, Elisa, additional, Thouverai, Elisa, additional, Prošek, Jiří, additional, Wild, Jan, additional, and Šímová, Petra, additional

Published
2023
Full Text
View/download PDF

12. rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back

Author

Rocchini, Duccio, Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, Da Re, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Elisa, Messori, Filippo, Montaghi, Alessandro, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael E., Schneider, Fabian D., Schuh, Leila, Silvestri, Sonia, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, Wegmann, Martin, and Goslee, Sarah

Subjects
Ecological Modelling, Ecology, Evolution, Behavior and Systematics
Abstract

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, 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.

Published
2021

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

Author

UCL - SST/ELI/ELIC - Earth & Climate, UCL - SST/ELI/ELIB - Biodiversity, Thouverai, Elisa, Marcantonio, Matteo, Bacaro, Giovanni, Da Re, Daniele, Iannacito, Martina, Marchetto, Elisa, Ricotta, Carlo, Tattoni, Clara, Vicario, Saverio, Rocchini, Duccio, UCL - SST/ELI/ELIC - Earth & Climate, UCL - SST/ELI/ELIB - Biodiversity, Thouverai, Elisa, Marcantonio, Matteo, Bacaro, Giovanni, Da Re, Daniele, Iannacito, Martina, Marchetto, Elisa, Ricotta, Carlo, Tattoni, Clara, Vicario, Saverio, and Rocchini, Duccio

Abstract

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 R package - rasterdiv - 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 rasterdiv package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

Published
2021

14. rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back

Author

UCL - SST/ELI - Earth and Life Institute, UCL - SST/ELI/ELIC - Earth & Climate, Rocchini, Duccio, Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, Da Re, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Elisa, Messori, Filippo, Montaghi, Alessandro, Moudrý, Vítězslav, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael E., Schneider, Fabian D., Schuh, Leila, Silvestri, Sonia, Ŝímová, Petra, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, Wegmann, Martin, UCL - SST/ELI - Earth and Life Institute, UCL - SST/ELI/ELIC - Earth & Climate, Rocchini, Duccio, Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, Da Re, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Elisa, Messori, Filippo, Montaghi, Alessandro, Moudrý, Vítězslav, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael E., Schneider, Fabian D., Schuh, Leila, Silvestri, Sonia, Ŝímová, Petra, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, and Wegmann, Martin

Abstract

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, 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.

Published
2021

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

Author

UCL - SST/ELI/ELIC - Earth & Climate, Rocchini, Duccio, Marcantonio, Matteo, Da Re, Daniele, Bacaro, Giovanni, Feoli, Enrico, Foody, Giles M., Furrer, Reinhard, Harrigan, Ryan J., Kleijn, David, Iannacito, Martina, Lenoir, Jonathan, Lin, Meixi, Malavasi, Marco, Marchetto, Elisa, Meyer, Rachel S., Moudry, Vítězslav, Schneider, Fabian D., Šímová, Petra, Thornhill, Andrew H., Thouverai, Elisa, Vicario, Saverio, Wayne, Robert K., Ricotta, Carlo, Gillespie, Thomas, UCL - SST/ELI/ELIC - Earth & Climate, Rocchini, Duccio, Marcantonio, Matteo, Da Re, Daniele, Bacaro, Giovanni, Feoli, Enrico, Foody, Giles M., Furrer, Reinhard, Harrigan, Ryan J., Kleijn, David, Iannacito, Martina, Lenoir, Jonathan, Lin, Meixi, Malavasi, Marco, Marchetto, Elisa, Meyer, Rachel S., Moudry, Vítězslav, Schneider, Fabian D., Šímová, Petra, Thornhill, Andrew H., Thouverai, Elisa, Vicario, Saverio, Wayne, Robert K., Ricotta, Carlo, and Gillespie, Thomas

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 diversity of ecosystems are based on Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions The proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.

Published
2021

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

Author

Rocchini, Duccio, Marcantonio, Matteo, Da Re, Daniele, Bacaro, Giovanni, Feoli, Enrico, Foody, Giles M., Furrer, Reinhard, Harrigan, Ryan J., Kleijn, David, Iannacito, Martina, Lenoir, Jonathan, Lin, Meixi, Malavasi, Marco, Marchetto, Elisa, Meyer, Rachel S., Moudry, Vítězslav, Schneider, Fabian D., Šímová, Petra, Thornhill, Andrew H., Thouverai, Elisa, Vicario, Saverio, Wayne, Robert K., Ricotta, Carlo, Rocchini, Duccio, Marcantonio, Matteo, Da Re, Daniele, Bacaro, Giovanni, Feoli, Enrico, Foody, Giles M., Furrer, Reinhard, Harrigan, Ryan J., Kleijn, David, Iannacito, Martina, Lenoir, Jonathan, Lin, Meixi, Malavasi, Marco, Marchetto, Elisa, Meyer, Rachel S., Moudry, Vítězslav, Schneider, Fabian D., Šímová, Petra, Thornhill, Andrew H., Thouverai, Elisa, Vicario, Saverio, Wayne, Robert K., and Ricotta, Carlo

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 diversity of ecosystems are based on Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation: The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions: The proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.

Published
2021

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

Author

Rocchini, Duccio, primary, Marcantonio, Matteo, additional, Da Re, Daniele, additional, Bacaro, Giovanni, additional, Feoli, Enrico, additional, Foody, Giles M., additional, Furrer, Reinhard, additional, Harrigan, Ryan J., additional, Kleijn, David, additional, Iannacito, Martina, additional, Lenoir, Jonathan, additional, Lin, Meixi, additional, Malavasi, Marco, additional, Marchetto, Elisa, additional, Meyer, Rachel S., additional, Moudry, Vítězslav, additional, Schneider, Fabian D., additional, Šímová, Petra, additional, Thornhill, Andrew H., additional, Thouverai, Elisa, additional, Vicario, Saverio, additional, Wayne, Robert K., additional, and Ricotta, Carlo, additional

Published
2021
Full Text
View/download PDF

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

Author

Rocchini, Duccio, primary, Thouverai, Elisa, additional, Marcantonio, Matteo, additional, Iannacito, Martina, additional, Da Re, Daniele, additional, Torresani, Michele, additional, Bacaro, Giovanni, additional, Bazzichetto, Manuele, additional, Bernardi, Alessandra, additional, Foody, Giles M., additional, Furrer, Reinhard, additional, Kleijn, David, additional, Larsen, Stefano, additional, Lenoir, Jonathan, additional, Malavasi, Marco, additional, Marchetto, Elisa, additional, Messori, Filippo, additional, Montaghi, Alessandro, additional, Moudrý, Vítězslav, additional, Naimi, Babak, additional, Ricotta, Carlo, additional, Rossini, Micol, additional, Santi, Francesco, additional, Santos, Maria J., additional, Schaepman, Michael, additional, Schneider, Fabian, additional, Schuh, Leila, additional, Silvestri, Sonia, additional, Šímová, Petra, additional, Skidmore, Andrew K., additional, Tattoni, Clara, additional, Tordoni, Enrico, additional, Vicario, Saverio, additional, Zannini, Piero, additional, and Wegmann, Martin, additional

Published
2021
Full Text
View/download PDF

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

Author

Rocchini, Duccio, Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, Da Re, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Messori, Filippo, Montaghi, Alessandro, Moudr'y, V'itvezslav, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael E., Schneider, Fabian D., Schuh, Leila, Silvestri, Sonia, 'imov'a, Petra, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, Wegmann, Martin, University of Zurich, Rocchini, Duccio, Department of Natural Resources, UT-I-ITC-FORAGES, Faculty of Geo-Information Science and Earth Observation, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Czech University of Life Sciences Prague (CZU), University of California [Davis] (UC Davis), University of California (UC), High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Catholique de Louvain = Catholic University of Louvain (UCL), Free University of Bozen-Bolzano, Università degli studi di Trieste = University of Trieste, Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Trento (UNITN), University of Nottingham, UK (UON), Universität Zürich [Zürich] = University of Zurich (UZH), Wageningen University and Research [Wageningen] (WUR), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Firenze = University of Florence (UniFI), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), California Institute of Technology (CALTECH), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Macquarie University, University of Twente, Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Julius-Maximilians-Universität Würzburg (JMU), H2020 Project SHOWCASE [862480], H2020 COST Action European Cooperation in Science and Technology (COST) [CA17134], Jet Propulsion Laboratory, California Institute of Technology, National Aeronautics and Space Administration National Aeronautics and Space Administration (NASA) [80NM0018D0004], University of Zurich Research Priority Program on Global Change and Biodiversity (URPP GCB), Friuli Venezia Giulia Region, Swiss National Science Foundation Swiss National Science Foundation (SNSF) [SNSF-175529], Czech University of Life Sciences Prague, European Project: 862480,SHOWCASE(2020), University of California, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Università degli studi di Trieste, Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS)-Université de Picardie Jules Verne (UPJV), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), University of Helsinki, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), University of Twente [Netherlands], Università degli studi di Bari Aldo Moro (UNIBA), Julius-Maximilians-Universität Würzburg [Wurtzbourg, Allemagne] (JMU), Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, DA RE, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Elisa, Messori, Filippo, Montaghi, Alessandro, Moudrý, Vı́tězslav, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael, Schneider, Fabian, Schuh, Leila, Silvestri, Sonia, Šı́mová, Petra, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, Wegmann, Martin, Rocchini D., Thouverai E., Marcantonio M., Iannacito M., Da Re D., Torresani M., Bacaro G., Bazzichetto M., Bernardi A., Foody G.M., Furrer R., Kleijn D., Larsen S., Lenoir J., Malavasi M., Marchetto E., Messori F., Montaghi A., Moudry V., Naimi B., Ricotta C., Rossini M., Santi F., Santos M.J., Schaepman M.E., Schneider F.D., Schuh L., Silvestri S., Simova P., Skidmore A.K., Tattoni C., Tordoni E., Vicario S., Zannini P., Wegmann M., UCL - SST/ELI - Earth and Life Institute, UCL - SST/ELI/ELIC - Earth & Climate, Department of Geosciences and Geography, Rocchini, D, Thouverai, E, Marcantonio, M, Iannacito, M, Da Re, D, Torresani, M, Bacaro, G, Bazzichetto, M, Bernardi, A, Foody, G, Furrer, R, Kleijn, D, Larsen, S, Lenoir, J, Malavasi, M, Marchetto, E, Messori, F, Montaghi, A, Moudry, V, Naimi, B, Ricotta, C, Rossini, M, Santi, F, Santos, M, Schaepman, M, Schneider, F, Schuh, L, Silvestri, S, Simova, P, Skidmore, A, Tattoni, C, Tordoni, E, Vicario, S, Zannini, P, and Wegmann, M

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Computer science, UFSP13-8 Global Change and Biodiversity, ecological informatic, 340 Law, Information theory, Space (mathematics), computer.software_genre, 01 natural sciences, remote sensing, Feature (machine learning), biodiversity, education.field_of_study, Ecology, Ecological Modeling, Spatial Heterogeneity, PE&RC, 10123 Institute of Mathematics, 10122 Institute of Geography, ecological informatics, modeling, satellite imagery, Plantenecologie en Natuurbeheer, Data mining, 1171 Geosciences, Application, Evolution, Ecology (disciplines), Population, Metapopulation, Plant Ecology and Nature Conservation, 610 Medicine & health, 85 satellite imagery, 010603 evolutionary biology, ITC-HYBRID, modelling, 510 Mathematics, 2302 Ecological Modeling, Behavior and Systematics, Settore BIO/07 - ECOLOGIA, Ecosystem, education, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, biodiversity, ecological informatics, modelling, remote sensing, satellite imagery, Ecological indicator, Ecological Modelling, 1105 Ecology, Evolution, Behavior and Systematics, Spatial Ecology, ITC-ISI-JOURNAL-ARTICLE, 10231 Institute for Computational Science, Key (cryptography), [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Scale (map), computer
Abstract

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.

Published
2020
Full Text
View/download PDF

21. rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Author

Rocchini D, Thouverai E, Marcantonio M, Iannacito M, Da Re D, Torresani M, Bacaro G, Bazzichetto M, Bernardi A, Foody GM, Furrer R, Kleijn D, Larsen S, Lenoir J, Malavasi M, Marchetto E, Messori F, Montaghi A, Moudrý V, Naimi B, Ricotta C, Rossini M, Santi F, Santos MJ, Schaepman ME, Schneider FD, Schuh L, Silvestri S, Ŝímová P, Skidmore AK, Tattoni C, Tordoni E, Vicario S, Zannini P, and Wegmann M

Abstract

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, 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., (© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results