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

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

Author

Rocchini, Duccio, Nowosad, Jakub, D’Introno, Rossella, Chieffallo, Ludovico, Bacaro, Giovanni, Gatti, Roberto Cazzolla, Foody, Giles M, Furrer, Reinhard, Gábor, Lukáš, Malavasi, Marco, Marcantonio, Matteo, Marchetto, Elisa, Moudrý, Vítězslav, Ricotta, Carlo, Šímová, Petra, Torresani, Michele, Thouverai, Elisa, University of Zurich, Rocchini, Duccio, Rocchini D., Nowosad J., D'Introno R., Chieffallo L., Bacaro G., Gatti R.C., Foody G.M., Furrer R., Gabor L., Malavasi M., Marcantonio M., Marchetto E., Moudry V., Ricotta C., Simova P., Torresani M., and Thouverai E.

Subjects
Colour blindne, Evolution, Computational ecology, Ecological informatic, 510 Mathematics, Scientific communication, 2302 Ecological Modeling, 2604 Applied Mathematics, 1706 Computer Science Applications, Ecology, Evolution, Behavior and Systematics, colour blindness, Ecology, Applied Mathematics, Ecological Modeling, Ecological informatics, Behavior and Systematics Colour blindness, computational ecology, ecological informatics, mapping, R, scientific communication, Computer Science Applications, 10123 Institute of Mathematics, 1105 Ecology, Evolution, Behavior and Systematics, Computational Theory and Mathematics, Mapping, 10231 Institute for Computational Science, Modeling and Simulation, 2303 Ecology, 2611 Modeling and Simulation, 1703 Computational Theory and 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 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

2. The spectral species concept in living color

Author

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

Subjects
Atmospheric Science, Ecology, Paleontology, Soil Science, Forestry, Aquatic Science, Water Science and Technology
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. addressing mismatch in spatial units of analyses

Author

Chandler, Chris J., van der Heijden, Geertje M.F., Boyd, Doreen S., Cutler, Mark E.J., Costa, Hugo, Nilus, Reuben, Foody, Giles M., NOVA Information Management School (NOVA IMS), NOVA IMS Research and Development Center (MagIC), and Information Management Research Center (MagIC) - NOVA Information Management School

Subjects
LiDAR, Hyperspectral imaging, Ecology, neural network, segmentation, liana infestation, Computers in Earth Sciences, Ecology, Evolution, Behavior and Systematics, pixel-based soft classification, Nature and Landscape Conservation
Abstract

Chandler, C. J., van der Heijden, G. M. F., Boyd, D. S., Cutler, M. E. J., Costa, H., Nilus, R., & Foody, G. M. (2021). Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation, 7(3), 397-410. https://doi.org/10.1002/rse2.197 ---------------------------------------------------------------- The authors also thank the Natural Environment Research Council [NE/P004806/1 to MEJC, DSB, GMF, GMFvdH; NE/I528477/1 (ARSF MA14/11) to MEJC, DSB, GMF and NE/L002604/1 to CJC, GMF, GMFvdH]. The FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) to HC. The authors also thank the University of Nottingham for an Anne McLaren Research Fellowship to GMFvdH which funded the collection of the ground data. The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape-level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object-based classification was more effective at predicting liana infestation when compared to a pixel-based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel-based approach (RMSD = 27.0% ± 0.80) in comparison to an object-based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object- versus pixel-based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object-based approaches which require refinement in order to accurately segment imagery across contiguous closed-canopy forests. We conclude that the decision on whether to use a pixel- or object-based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management. publishersversion published

Published
2021

4. Non-stationarity and local approaches to modelling the distributions of wildlife.

Author

Osborne, Patrick E., Foody, Giles M., and Suárez-Seoane, Susana

Subjects
*ANIMALS, *AUTOCORRELATION (Statistics), *ECOLOGY, *ENVIRONMENTAL sciences, *HABITATS, *STATISTICAL correlation, *EXTRAPOLATION, *APPROXIMATION theory, *ANTHROPOGENIC effects on nature
Abstract

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss. [ABSTRACT FROM AUTHOR]

Published
2007
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5. Investigating spatial structure in specific tree species in ancient semi-natural woodland using remote sensing and marked point pattern analysis.

Author

Atkinson, Peter M., Foody, Giles M., Gething, Peter W., Mathur, Ajay, and Kelly, Colleen K.

Subjects
*TREES, *SPECIES distribution, *REMOTE sensing, *ECOLOGISTS, *SPECIES, *ECOLOGY, *SIMULATION methods & models, *ACER pseudoplatanus, *EUROPEAN ash
Abstract

Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi-natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter-dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists. [ABSTRACT FROM AUTHOR]

Published
2007
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6. Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna.

Author

Foody, Giles M.

Subjects
*BIRD populations, *SPECIES diversity, *ENVIRONMENTAL sciences, *REGRESSION analysis, *ECOLOGY
Abstract

This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Sub-Saharan Africa. Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub-Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale-dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling. [ABSTRACT FROM AUTHOR]

Published
2004
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7. 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

8. 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. 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

11. Calculating landscape diversity with information-theory based indices: A GRASS GIS solution

Author

Markus Neteler, Harini Nagendra, Giles M. Foody, Giovanni Bacaro, Kate S. He, Claudio Porta, Martin Wegmann, Sebastian Schmidtlein, Carlo Ricotta, Hannes Feilhauer, Luca Delucchi, Lucio Davide Spano, Duccio Rocchini, Paolo Cavallini, Rocchini D., Delucchi L., Bacaro G., Cavallini P., Feilhauer H., Foody G. M., He K. S., Nagendra H., Porta C., Ricotta C., Schmidtlein S., Spano L. D., Wegmann M., Neteler M., Rocchini, Duccio, Delucchi, Luca, Bacaro, Giovanni, Cavallini, Paolo, Feilhauer, Hanne, Foody, Giles M., He, Kate S., Nagendra, Harini, Porta, Claudio, Ricotta, Carlo, Schmidtlein, Sebastian, Spano, Lucio Davide, Wegmann, Martin, and Neteler, Markus

Subjects
Spectral variability, r.diversity, diversity metrics, biodiversity, landscape ecology, grass gis, spectral variability, Source code, Computer science, Diversity metrics, Evolution, media_common.quotation_subject, Landscape ecology, Biodiversity, Information theory, GRASS GIS, R.diversity, Ecological Modeling, Modeling and Simulation, Applied Mathematics, Computational Theory and Mathematics, Computer Science Applications1707 Computer Vision and Pattern Recognition, Ecology, Ecology, Evolution, Behavior and Systematics, Diversity index, Behavior and Systematics, Settore BIO/07 - ECOLOGIA, Computational Theory and Mathematic, media_common, Land use, business.industry, Environmental resource management, Species diversity, Variabilità spettrale, Ecologia del paesaggio, Computer Science Applications, Applied Mathematic, Biodiversità, Diversity metric, business, Completeness (statistics)
Abstract

The assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty to judge the completeness of species lists and to undertake sufficient and appropriate sampling. Since the variability of remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level. It has been demonstrated that the relation between species and landscape diversity measured from remotely sensed data or land use maps varies with scale. However, Free and Open Source tools (allowing an access to the source code) for assessing landscape diversity at different spatial scales are still lacking today. In this paper, we aim at: i) providing a theoretical background of the mostly used diversity indices stemmed from information theory that are commonly applied to quantify landscape diversity from remotely sensed data and ii) proposing a free and robust Open Source tool ( r.diversity ) with its source code for calculating diversity indices (and allowing an easy potential implementation of new metrics by multiple contributors globally) at different spatial scales from remotely-sensed imagery or land use maps, running under the widely used Open Source program GRASS GIS. r.diversity can be a valuable tool for calculating landscape diversity in an Open Source space given the availability of multiple indices at multiple spatial scales with the possibility to create new indices directly reusing the code. We expect that the subject of this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity indices.

Published
2013

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