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11 results on '"Foody, Giles M."'

1. The Spectral Species Concept in Living Color

Author

Rocchini, Duccio, Santos, Maria J, Ustin, Susan L, Féret, Jean‐Baptiste, Asner, Gregory P, Beierkuhnlein, Carl, Dalponte, Michele, Feilhauer, Hannes, Foody, Giles M, Geller, Gary N, Gillespie, Thomas W, He, Kate S, Kleijn, David, Leitão, Pedro J, Malavasi, Marco, Moudrý, Vítězslav, Müllerová, Jana, Nagendra, Harini, Normand, Signe, Ricotta, Carlo, Schaepman, Michael E, Schmidtlein, Sebastian, Skidmore, Andrew K, Šímová, Petra, Torresani, Michele, Townsend, Philip A, Turner, Woody, Vihervaara, Petteri, Wegmann, Martin, and Lenoir, Jonathan

Subjects
Life Below Water, airborne sensors, biodiversity, ecoinformatics, hyperspectral images, plant optical types, remote sensing, satellite imagery, vegetation communities, Geophysics
Abstract

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Published
2022

3. 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, Moudrý, Vítězslav, and Naimi, Babak

Subjects
INFORMATION theory, BIOINDICATORS, HETEROGENEITY, ECOSYSTEMS, REMOTE-sensing images
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. [ABSTRACT FROM AUTHOR]

Published
2021
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4. 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., and Thouverai, Elisa

Subjects
MAXIMA & minima, ENTROPY (Information theory), INFORMATION theory, BIOLOGICAL classification, REMOTE-sensing images, SPACE-time block codes
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. [ABSTRACT FROM AUTHOR]

Published
2021
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5. Measuring β‐diversity by remote sensing: A challenge for biodiversity monitoring.

Author

Rocchini, Duccio, Luque, Sandra, Pettorelli, Nathalie, Bastin, Lucy, Doktor, Daniel, Faedi, Nicolò, Feilhauer, Hannes, Féret, Jean‐Baptiste, Foody, Giles M., Gavish, Yoni, Godinho, Sergio, Kunin, William E., Lausch, Angela, Leitão, Pedro J., Marcantonio, Matteo, Neteler, Markus, Ricotta, Carlo, Schmidtlein, Sebastian, Vihervaara, Petteri, and Wegmann, Martin

Subjects
BIODIVERSITY monitoring, ENVIRONMENTAL monitoring, REMOTE sensing, REMOTE-sensing images, COMPARATIVE biology, ECOLOGICAL heterogeneity
Abstract

Abstract: Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field. [ABSTRACT FROM AUTHOR]

Published
2018
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6. Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges.

Author

Rocchini, Duccio, Balkenhol, Niko, Carter, Gregory A., Foody, Giles M., Gillespie, Thomas W., He, Kate S., Kark, Salit, Levin, Noam, Lucas, Kelly, Luoto, Miska, Nagendra, Harini, Oldeland, Jens, Ricotta, Carlo, Southworth, Jane, and Neteler, Markus

Subjects
REMOTE-sensing images, ECOLOGICAL heterogeneity, SPECIES diversity, BIODIVERSITY, SPATIAL variation, ECOLOGICAL genetics, TAXONOMY
Abstract

Abstract: Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time. [ABSTRACT FROM AUTHOR]

Published
2010
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7. Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study.

Author

Rocchini, Duccio, Bacaro, Giovanni, Chirici, Gherardo, Da Re, Daniele, Feilhauer, Hannes, Foody, Giles M., Galluzzi, Marta, Garzon-Lopez, Carol X., Gillespie, Thomas W., He, Kate S., Lenoir, Jonathan, Marcantonio, Matteo, Nagendra, Harini, Ricotta, Carlo, Rommel, Edvinas, Schmidtlein, Sebastian, Skidmore, Andrew K., Van De Kerchove, Ruben, Wegmann, Martin, and Rugani, Benedetto

Subjects
*BIODIVERSITY, *REMOTE sensing, *REMOTE-sensing images, *PLANT species, *TAXONOMY
Abstract

Assessing biodiversity from field-based data is difficult for a number of practical reasons: (i) establishing the total number of sampling units to be investigated and the sampling design (e.g. systematic, random, stratified) can be difficult; (ii) the choice of the sampling design can affect the results; and (iii) defining the focal population of interest can be challenging. Satellite remote sensing is one of the most cost-effective and comprehensive approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, satellite remote sensing provides repeated measures, thus making it possible to study temporal changes in biodiversity. While taxonomic diversity measures have long been established, problems arising from abundance related measures have not been yet disentangled. Moreover, little has been done to account for functional diversity besides taxonomic diversity measures. The aim of this manuscript is to propose robust measures of remotely sensed heterogeneity to perform exploratory analysis for the detection of hotspots of taxonomic and functional diversity of plant species. [ABSTRACT FROM AUTHOR]

Published
2018
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8. From zero to infinity : Minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy

Author

Martina Iannacito, Vítězslav Moudry, Giovanni Bacaro, Robert K. Wayne, Jonathan Lenoir, Matteo Marcantonio, David Kleijn, Saverio Vicario, Andrew H. Thornhill, Enrico Feoli, Giles M. Foody, Elisa Thouverai, Petra Šímová, Marco Malavasi, Rachel S. Meyer, Duccio Rocchini, Fabian D. Schneider, Elisa Marchetto, Meixi Lin, Daniele Da Re, Reinhard Furrer, Carlo Ricotta, Ryan J. Harrigan, University of Zurich, Gillespie, Thomas, Rocchini, Duccio, Marcantonio, Matteo, Da Re, Daniele, Bacaro, Giovanni, Feoli, Enrico, Foody, Gile, Furrer, Reinhard, Harrigan, Ryan, Kleijn, David, Iannacito, Martina, Lenoir, Jonathan, Lin, Meixi, Malavasi, Marco, Marchetto, Elisa, Meyer, Rachel, Moudry, Vitezslav, Payne, Davnah, Schneider, Fabian, Simova, Petra, Thornhill, Andrew, Thouverai, Elisa, Vicario, Saverio, Wayne, Roberto, Ricotta, Carlo, Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), UCL - SST/ELI/ELIC - Earth & Climate, Foody, Giles M., Harrigan, Ryan J., Meyer, Rachel S., Moudry, Vítězslav, Schneider, Fabian D., Šímová, Petra, Thornhill, Andrew H., and Wayne, Robert K.

Subjects
0106 biological sciences, UFSP13-8 Global Change and Biodiversity, Evolution, [SDE.MCG]Environmental Sciences/Global Changes, satellite, ecological informatic, 340 Law, 2306 Global and Planetary Change, 610 Medicine & health, Plant Ecology and Nature Conservation, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Information theory, satellite imagery, 010603 evolutionary biology, 01 natural sciences, Measure (mathematics), modelling, ecological informatics, remote sensing, Quadratic equation, 510 Mathematics, Behavior and Systematics, Robustness (computer science), biodiversity, Entropy (information theory), Representation (mathematics), Ecology, Evolution, Behavior and Systematics, Parametric statistics, Mathematics, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Global and Planetary Change, Pixel, Ecology, 010604 marine biology & hydrobiology, PE&RC, 10123 Institute of Mathematics, 1105 Ecology, Evolution, Behavior and Systematics, 13. Climate action, 10231 Institute for Computational Science, Plantenecologie en Natuurbeheer, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Algorithm, 2303 Ecology
Abstract

International audience; 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

9. 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
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10. Measuring β-diversity by remote sensing: A challenge for biodiversity monitoring

Author

Sérgio Godinho, Angela Lausch, Yoni Gavish, Markus Neteler, Petteri Vihervaara, Nicolò Faedi, Harini Nagendra, Giles M. Foody, Sandra Luque, Daniel Doktor, Martin Wegmann, Pedro J. Leitão, Carlo Ricotta, William E. Kunin, Matteo Marcantonio, Hannes Feilhauer, Jean-Baptiste Féret, Nathalie Pettorelli, Sebastian Schmidtlein, Lucy Bastin, Duccio Rocchini, UNIVERSITY OF TRENTO SAN MICHELE ALL ADIGE ITA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), DEPARTMENT OF BIODIVERSITY AND MOLECULAR ECOLOGY RESEARCH AND INNOVATION CENTRE FONDAZIONE EDMUND MACH ITA, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), INSTITUTE OF ZOOLOGY LONDON GBR, SCHOOL OF COMPUTER SCIENCE ASTON UNIVERSITY BIRMINGHAM GBR, UFZ HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH LEIPZIG DEU, DEPARTMENT OF BIODIVERSITY AND MOLECULAR ECOLOGY FONDAZIONE EDMUND MACH SAN MICHELE ALL ADIGE ITA, INSTITUT FÜR GEOGRAPHIE FRIEDRICH-ALEXANDER UNIVERSIT, Rocchini, Duccio, Luque, Sandra, Pettorelli, Nathalie, Bastin, Lucy, Doktor, Daniel, Faedi, Nicolò, Feilhauer, Hanne, Féret, Jean-Baptiste, Foody, Giles M., Gavish, Yoni, Godinho, Sergio, Kunin, William E., Lausch, Angela, Leitão, Pedro J., Marcantonio, Matteo, Neteler, Marku, Ricotta, Carlo, Schmidtlein, Sebastian, Vihervaara, Petteri, Wegmann, Martin, and Nagendra, Harini

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Computer science, Biodiversity, Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA, SATELLITE IMAGERY, KOHONEN SELF-ORGANIZING FEATURE MAPS, 010603 evolutionary biology, 01 natural sciences, β-diversity, RAO'S Q DIVERSITY, Satellite imagery, BETA-DIVERSITY, Ecology, Evolution, Behavior and Systematics, INDEX, SPECTRAL SPECIES CONCEPT, biodiversity, 0105 earth and related environmental sciences, Remote sensing, kohonen self-organizing feature maps, rao's Q diversity index, remote sensing, satellite imagery, sparse generalized dissimilarity model, spectral species concept, Ecological Modeling, Species diversity, Sampling (statistics), 15. Life on land, Field (geography), SPARSE GENERALIZED DISSIMILARITY MODEL, Rao's Q diversity index, Remote sensing (archaeology), [SDE]Environmental Sciences, Species richness, Distance matrices in phylogeny
Abstract

International audience; 1. Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. 2. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. 3. Extending on previous work, in this manuscript, we propose novel techniques to measure beta‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.

Published
2018
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11. Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study

Author

Edvinas Rommel, Gherardo Chirici, Sebastian Schmidtlein, Marta Galluzzi, Thomas W. Gillespie, Giovanni Bacaro, Martin Wegmann, Duccio Rocchini, Daniele Da Re, Giles M. Foody, Carlo Ricotta, Andrew K. Skidmore, Harini Nagendra, Benedetto Rugani, Matteo Marcantonio, Jonathan Lenoir, Kate S. He, Carol X. Garzon-Lopez, Ruben Van De Kerchove, Hannes Feilhauer, Rocchini, D, Bacaro, G, Chirici, G, Da Re, D, Feilhauer, H, Foody G., M, Galluzzi, M, Garzon-Lopez C., X, Gillespie T., W, He K., S, Lenoir, J, Marcantonio, M, Nagendra, H, Ricotta, C, Rommel, E, Schmidtlein, S, Skidmore A., K, Van De Kerchove, R, Wegmann, M, Rugani, B., Department of Biodiversity and Molecular Ecology, Fondazione Edmund Mach, Research and Innovation Centre, 38010, S. Micehle all’Adige (TN), Italy, Universidad de los Andes [Bogota] (UNIANDES), Department of Geography, Escuela Militar de Ingeniería (EMI), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Dpt of Environmental Biology [Rome], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Karlsruhe Institute of Technology (KIT), Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre de Recherche Public Henri Tudor [Technoport] (CRP Henri Tudor), Centre de Recherche Public Henri-Tudor [Luxembourg] (CRP Henri-Tudor), Department of Natural Resources, UT-I-ITC-FORAGES, Faculty of Geo-Information Science and Earth Observation, Rocchini, Duccio, Bacaro, Giovanni, Chirici, Gherardo, Da Re, Daniele, Feilhauer, Hanne, Foody, Giles M., Galluzzi, Marta, Garzon-Lopez, Carol X., Gillespie, Thomas W., He, Kate S., Lenoir, Jonathan, Marcantonio, Matteo, Nagendra, Harini, Ricotta, Carlo, Rommel, Edvina, Schmidtlein, Sebastian, Skidmore, Andrew K., Van De Kerchove, Ruben, Wegmann, Martin, and Rugani, Benedetto

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Taxonomic diversity, Computer science, [SDE.MCG]Environmental Sciences/Global Changes, Population, Biodiversity, Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA, General Decision Sciences, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Functional diversity, 010603 evolutionary biology, 01 natural sciences, evolution, Sampling design, Satellite imagery, education, Cartograms, Functional diversity, Ecology, Evolution, Behavior and Systematics, biodiversity, 0105 earth and related environmental sciences, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, education.field_of_study, Rao's quadratic diversity, Remote sensing, Spectral rarefaction, Ecology, Sampling (statistics), behavior and systematics, 15. Life on land, Biodiversity hotspot, Field (geography), Spatial heterogeneity, Cartograms, cartograms, functional diversity, remote sensing, satellite imagery, spectral rarefaction, taxonomic diversity, ecology, ITC-ISI-JOURNAL-ARTICLE, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Cartography
Abstract

International audience; Assessing biodiversity from field-based data is difficult for a number of practical reasons: (i) establishing the total number of sampling units to be investigated and the sampling design (e.g. systematic, random, stratified) can be difficult; (ii) the choice of the sampling design can affect the results; and (iii) defining the focal population of interest can be challenging. Satellite remote sensing is one of the most cost-effective and comprehensive approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, satellite remote sensing provides repeated measures, thus making it possible to study temporal changes in biodiversity. While taxonomic diversity measures have long been established, problems arising from abundance related measures have not been yet disentangled. Moreover, little has been done to account for functional diversity besides taxonomic diversity measures. The aim of this manuscript is to propose robust measures of remotely sensed heterogeneity to perform exploratory analysis for the detection of hotspots of taxonomic and functional diversity of plant species.

Published
2018

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