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Duccio Rocchini
Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy

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Author correction
Published: 19 July 2021 in Nature Ecology & Evolution
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Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 2021, 1 -1.

AMA Style

Andrew K. Skidmore, Nicholas C. Coops, Elnaz Neinavaz, Abebe Ali, Michael E. Schaepman, Marc Paganini, W. Daniel Kissling, Petteri Vihervaara, Roshanak Darvishzadeh, Hannes Feilhauer, Miguel Fernandez, Néstor Fernández, Noel Gorelick, Ilse Geijzendorffer, Uta Heiden, Marco Heurich, Donald Hobern, Stefanie Holzwarth, Frank E. Muller-Karger, Ruben Van De Kerchove, Angela Lausch, Pedro J. Leitão, Marcelle C. Lock, Caspar A. Mücher, Brian O’Connor, Duccio Rocchini, Claudia Roeoesli, Woody Turner, Jan Kees Vis, Tiejun Wang, Martin Wegmann, Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution. 2021; ():1-1.

Chicago/Turabian Style

Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. 2021. "Author Correction: Priority list of biodiversity metrics to observe from space." Nature Ecology & Evolution , no. : 1-1.

Author correction
Published: 24 May 2021 in Nature Ecology & Evolution
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ACS Style

Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 2021, 1 -1.

AMA Style

Andrew K. Skidmore, Nicholas C. Coops, Elnaz Neinavaz, Abebe Ali, Michael E. Schaepman, Marc Paganini, W. Daniel Kissling, Petteri Vihervaara, Roshanak Darvishzadeh, Hannes Feilhauer, Miguel Fernandez, Néstor Fernández, Noel Gorelick, Ilse Geijzendorffer, Uta Heiden, Marco Heurich, Donald Hobern, Stefanie Holzwarth, Frank E. Muller-Karger, Ruben Van De Kerchove, Angela Lausch, Pedro J. Leitão, Marcelle C. Lock, Caspar A. Mücher, Brian O’Connor, Duccio Rocchini, Woody Turner, Jan Kees Vis, Tiejun Wang, Martin Wegmann, Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution. 2021; ():1-1.

Chicago/Turabian Style

Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. 2021. "Author Correction: Priority list of biodiversity metrics to observe from space." Nature Ecology & Evolution , no. : 1-1.

Perspective
Published: 13 May 2021 in Nature Ecology & Evolution
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Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales. Remote sensing of geospatial biodiversity patterns is an important complement to field observations. This priority list suggests how remote sensing observations can be better integrated into the essential biodiversity variables.

ACS Style

Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 2021, 5, 896 -906.

AMA Style

Andrew K. Skidmore, Nicholas C. Coops, Elnaz Neinavaz, Abebe Ali, Michael E. Schaepman, Marc Paganini, W. Daniel Kissling, Petteri Vihervaara, Roshanak Darvishzadeh, Hannes Feilhauer, Miguel Fernandez, Néstor Fernández, Noel Gorelick, Ilse Geijzendorffer, Uta Heiden, Marco Heurich, Donald Hobern, Stefanie Holzwarth, Frank E. Muller-Karger, Ruben Van De Kerchove, Angela Lausch, Pedro J. Leitão, Marcelle C. Lock, Caspar A. Mücher, Brian O’Connor, Duccio Rocchini, Claudia Roeoesli, Woody Turner, Jan Kees Vis, Tiejun Wang, Martin Wegmann, Vladimir Wingate. Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution. 2021; 5 (7):896-906.

Chicago/Turabian Style

Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. 2021. "Priority list of biodiversity metrics to observe from space." Nature Ecology & Evolution 5, no. 7: 896-906.

Special feature article
Published: 29 April 2021 in Applied Vegetation Science
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Question Which optical traits, retrieved from the biophysical models on Sentinel‐2 images, enable an estimation of tree species diversity based on the Spectral Variation Hypothesis? Location Coniferous mountain forest in the eastern Italian Alps. Methods We analyzed the PROSPECT‐5 and INFORM biophysical parameters as retrieved from canopy reflectance data of different forest plots (using Sentinel‐2 images for the years 2017, 2018 and 2019) as optical trait indicators (OTIs). We successively tested the Spectral Variation Hypothesis (SVH) for each retrieved OTI using the Rao’s Q as heterogeneity index validating them against Shannon’s H values calculated as tree species diversity index derived by in‐situ collected data. Results We evidenced differences among OTIs in terms of capacity to link their variations to species diversity. In particular the variation of brown pigments (Cbrown), carotenoids (Car) and chlorophyll content (Cab) can be considered the most relevant OTIs for the application of the SVH, when using Rao’s Q as a proxy for tree species diversity in our study site. Conclusions This research underlined that the OTIs contribute differently in the SVH for the estimation of tree species diversity, highlighting significant positive correlations between tree species diversity and the spatial heterogeneity of estimated pigment content (Cab, Car, Cbrown).

ACS Style

Michele Torresani; Hannes Feilhauer; Duccio Rocchini; Jean‐Baptiste Féret; Marc Zebisch; Giustino Tonon. Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis? Applied Vegetation Science 2021, 24, e12586 .

AMA Style

Michele Torresani, Hannes Feilhauer, Duccio Rocchini, Jean‐Baptiste Féret, Marc Zebisch, Giustino Tonon. Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis? Applied Vegetation Science. 2021; 24 (2):e12586.

Chicago/Turabian Style

Michele Torresani; Hannes Feilhauer; Duccio Rocchini; Jean‐Baptiste Féret; Marc Zebisch; Giustino Tonon. 2021. "Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis?" Applied Vegetation Science 24, no. 2: e12586.

Original article
Published: 08 April 2021 in Community Ecology
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The variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new package - - to calculate diversity indices based on remotely sensed data, by discussing the theory behind the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and biochemical), taxonomic, phylogenetic and genetic diversity. Making use of the package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

ACS Style

Elisa Thouverai; Matteo Marcantonio; Giovanni Bacaro; Daniele Da Re; Martina Iannacito; Elisa Marchetto; Carlo Ricotta; Clara Tattoni; Saverio Vicario; Duccio Rocchini. Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective. Community Ecology 2021, 22, 1 -11.

AMA Style

Elisa Thouverai, Matteo Marcantonio, Giovanni Bacaro, Daniele Da Re, Martina Iannacito, Elisa Marchetto, Carlo Ricotta, Clara Tattoni, Saverio Vicario, Duccio Rocchini. Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective. Community Ecology. 2021; 22 (1):1-11.

Chicago/Turabian Style

Elisa Thouverai; Matteo Marcantonio; Giovanni Bacaro; Daniele Da Re; Martina Iannacito; Elisa Marchetto; Carlo Ricotta; Clara Tattoni; Saverio Vicario; Duccio Rocchini. 2021. "Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective." Community Ecology 22, no. 1: 1-11.

Journal article
Published: 24 March 2021 in Remote Sensing
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Land use/land cover (LULC) maps are a key input in environmental evaluations for the sustainable planning and management of socio-ecological systems. While the impact of map spatial resolution on environmental assessments has been evaluated by several studies, the effect of thematic resolution (the level of detail of LU/LC typologies) is discordant and still poorly investigated. In this paper, four scenarios of thematic resolutions, corresponding to the four levels of the CORINE classification scheme, have been compared in a real case study of landscape connectivity assessment, a major aspect for the biodiversity conservation and ecosystem service provision. The PANDORA model has been employed to investigate the effects of LULC thematic resolution on Bio-Energy Landscape Connectivity (BELC) at the scale of the whole system, landscape units, and single land cover patches, also in terms of ecosystem services. The results show different types of impacts on landscape connectivity due to the changed spatial pattern of the LULC classes across the four thematic resolution scenarios. Moreover, the main priority areas for conservation objectives and future sustainable urban expansion have been identified. Finally, several indications are given for supporting practitioners and researchers faced with thematic resolution issues in environmental assessment and land use planning.

ACS Style

Raffaele Pelorosso; Ciro Apollonio; Duccio Rocchini; Andrea Petroselli. Effects of Land Use-Land Cover Thematic Resolution on Environmental Evaluations. Remote Sensing 2021, 13, 1232 .

AMA Style

Raffaele Pelorosso, Ciro Apollonio, Duccio Rocchini, Andrea Petroselli. Effects of Land Use-Land Cover Thematic Resolution on Environmental Evaluations. Remote Sensing. 2021; 13 (7):1232.

Chicago/Turabian Style

Raffaele Pelorosso; Ciro Apollonio; Duccio Rocchini; Andrea Petroselli. 2021. "Effects of Land Use-Land Cover Thematic Resolution on Environmental Evaluations." Remote Sensing 13, no. 7: 1232.

Macroecological methods
Published: 15 March 2021 in Global Ecology and Biogeography
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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.

ACS Style

Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Giovanni Bacaro; Enrico Feoli; Giles M. Foody; Reinhard Furrer; Ryan J. Harrigan; David Kleijn; Martina Iannacito; Jonathan Lenoir; Meixi Lin; Marco Malavasi; Elisa Marchetto; Rachel S. Meyer; Vítězslav Moudry; Fabian D. Schneider; Petra Šímová; Andrew H. Thornhill; Elisa Thouverai; Saverio Vicario; Robert K. Wayne; Carlo Ricotta. From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy. Global Ecology and Biogeography 2021, 30, 1153 -1162.

AMA Style

Duccio Rocchini, Matteo Marcantonio, Daniele Da Re, Giovanni Bacaro, Enrico Feoli, Giles M. Foody, Reinhard Furrer, Ryan J. Harrigan, David Kleijn, Martina Iannacito, Jonathan Lenoir, Meixi Lin, Marco Malavasi, Elisa Marchetto, Rachel S. Meyer, Vítězslav Moudry, Fabian D. Schneider, Petra Šímová, Andrew H. Thornhill, Elisa Thouverai, Saverio Vicario, Robert K. Wayne, Carlo Ricotta. From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy. Global Ecology and Biogeography. 2021; 30 (5):1153-1162.

Chicago/Turabian Style

Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Giovanni Bacaro; Enrico Feoli; Giles M. Foody; Reinhard Furrer; Ryan J. Harrigan; David Kleijn; Martina Iannacito; Jonathan Lenoir; Meixi Lin; Marco Malavasi; Elisa Marchetto; Rachel S. Meyer; Vítězslav Moudry; Fabian D. Schneider; Petra Šímová; Andrew H. Thornhill; Elisa Thouverai; Saverio Vicario; Robert K. Wayne; Carlo Ricotta. 2021. "From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy." Global Ecology and Biogeography 30, no. 5: 1153-1162.

Application
Published: 27 February 2021 in Methods in Ecology and Evolution
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Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package ‐ rasterdiv ‐ to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.

ACS Style

Duccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael E. Schaepman; Fabian D. Schneider; Leila Schuh; Sonia Silvestri; Petra Ŝímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back. Methods in Ecology and Evolution 2021, 12, 1093 -1102.

AMA Style

Duccio Rocchini, Elisa Thouverai, Matteo Marcantonio, Martina Iannacito, Daniele Da Re, Michele Torresani, Giovanni Bacaro, Manuele Bazzichetto, Alessandra Bernardi, Giles M. Foody, Reinhard Furrer, David Kleijn, Stefano Larsen, Jonathan Lenoir, Marco Malavasi, Elisa Marchetto, Filippo Messori, Alessandro Montaghi, Vítězslav Moudrý, Babak Naimi, Carlo Ricotta, Micol Rossini, Francesco Santi, Maria J. Santos, Michael E. Schaepman, Fabian D. Schneider, Leila Schuh, Sonia Silvestri, Petra Ŝímová, Andrew K. Skidmore, Clara Tattoni, Enrico Tordoni, Saverio Vicario, Piero Zannini, Martin Wegmann. rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back. Methods in Ecology and Evolution. 2021; 12 (6):1093-1102.

Chicago/Turabian Style

Duccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael E. Schaepman; Fabian D. Schneider; Leila Schuh; Sonia Silvestri; Petra Ŝímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. 2021. "rasterdiv—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back." Methods in Ecology and Evolution 12, no. 6: 1093-1102.

Preprint content
Published: 10 February 2021
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Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.

ACS Style

Duccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael Schaepman; Fabian Schneider; Leila Schuh; Sonia Silvestri; Petra Šímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back. 2021, 1 .

AMA Style

Duccio Rocchini, Elisa Thouverai, Matteo Marcantonio, Martina Iannacito, Daniele Da Re, Michele Torresani, Giovanni Bacaro, Manuele Bazzichetto, Alessandra Bernardi, Giles M. Foody, Reinhard Furrer, David Kleijn, Stefano Larsen, Jonathan Lenoir, Marco Malavasi, Elisa Marchetto, Filippo Messori, Alessandro Montaghi, Vítězslav Moudrý, Babak Naimi, Carlo Ricotta, Micol Rossini, Francesco Santi, Maria J. Santos, Michael Schaepman, Fabian Schneider, Leila Schuh, Sonia Silvestri, Petra Šímová, Andrew K. Skidmore, Clara Tattoni, Enrico Tordoni, Saverio Vicario, Piero Zannini, Martin Wegmann. rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back. . 2021; ():1.

Chicago/Turabian Style

Duccio Rocchini; Elisa Thouverai; Matteo Marcantonio; Martina Iannacito; Daniele Da Re; Michele Torresani; Giovanni Bacaro; Manuele Bazzichetto; Alessandra Bernardi; Giles M. Foody; Reinhard Furrer; David Kleijn; Stefano Larsen; Jonathan Lenoir; Marco Malavasi; Elisa Marchetto; Filippo Messori; Alessandro Montaghi; Vítězslav Moudrý; Babak Naimi; Carlo Ricotta; Micol Rossini; Francesco Santi; Maria J. Santos; Michael Schaepman; Fabian Schneider; Leila Schuh; Sonia Silvestri; Petra Šímová; Andrew K. Skidmore; Clara Tattoni; Enrico Tordoni; Saverio Vicario; Piero Zannini; Martin Wegmann. 2021. "rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back." , no. : 1.

Special feature
Published: 01 January 2021 in Applied Vegetation Science
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Question Does spectral diversity captured by unmanned aerial systems (UAS) provide reliable information for monitoring the eco‐geomorphological integrity of Mediterranean coastal dune ecosystems? Can this information discriminate between two coastal areas with low (LP) and high (HP) human pressure? Location Tyrrhenian coast, Central Italy. Methods By processing UAS images, we derived the normalized difference vegetation index (NDVI) and topographic variables at high spatial resolution (0.5 m) for 150 m wide strips starting from the coastline inland on two representative coastal tracts under low and high human pressure. We mapped the sea–inland heterogeneity applying Rao's Q index to the plant biomass (NDVI) and geomorphology variables (elevation and slope). Since Rao's Q index can be calculated in a multidimensional space, we summarized the variability of these three variables into a single eco‐geomorphological layer. We then inspected and compared how the Rao's Q index values for plant biomass, geomorphology and eco‐geomorphology change as a function of the distance from the sea between the two coastal sites. Results Rao's Q heterogeneity values vary along the sea–inland gradient of well‐preserved sites (LP). The maximum eco‐geomorphological heterogeneity was found at intermediate distances from the sea and decreased toward the inner sector where the dune geomorphology was more stable and vegetation more homogeneously distributed. Instead, Rao's Q heterogeneity values featured constant low values along the gradient on the HP site, highlighting a simplified eco‐geomorphological gradient related to the high human pressure. Conclusions Using UAS, the eco‐geomorphological gradient of coastal dunes can be quantified at a very fine spatial resolution over management‐relevant extents. Rao's Q index applied to sensing imagery successfully captured the differences in the eco‐geomorphological heterogeneity along the sea–inland dune gradient and among sites with different levels of anthropic pressure. This approach supports frequent surveys and is particularly suitable for spatial monitoring of key coastal functions and services.

ACS Style

Marco Malavasi; Manuele Bazzichetto; Jan Komárek; Vítězslav Moudrý; Duccio Rocchini; Simonetta Bagella; Alicia T. R. Acosta; Maria L. Carranza. Unmanned aerial systems‐based monitoring of the eco‐geomorphology of coastal dunes through spectral Rao's Q. Applied Vegetation Science 2021, 24, e12567 .

AMA Style

Marco Malavasi, Manuele Bazzichetto, Jan Komárek, Vítězslav Moudrý, Duccio Rocchini, Simonetta Bagella, Alicia T. R. Acosta, Maria L. Carranza. Unmanned aerial systems‐based monitoring of the eco‐geomorphology of coastal dunes through spectral Rao's Q. Applied Vegetation Science. 2021; 24 (1):e12567.

Chicago/Turabian Style

Marco Malavasi; Manuele Bazzichetto; Jan Komárek; Vítězslav Moudrý; Duccio Rocchini; Simonetta Bagella; Alicia T. R. Acosta; Maria L. Carranza. 2021. "Unmanned aerial systems‐based monitoring of the eco‐geomorphology of coastal dunes through spectral Rao's Q." Applied Vegetation Science 24, no. 1: e12567.

Journal article
Published: 13 December 2019 in ISPRS International Journal of Geo-Information
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Landscape metrics constitute one of the main tools for the study of the changes of the landscape and of the ecological structure of a region. The most popular software for landscape metrics evaluation is FRAGSTATS, which is free to use but does not have free or open source software (FOSS). Therefore, FOSS implementations, such as QGIS’s LecoS plugin and GRASS’ r.li modules suite, were developed. While metrics are defined in the same way, the “cell neighborhood” parameter, specifying the configuration of the moving window used for the analysis, is managed differently: FRAGSTATS can use values of 4 or 8 (8 is default), LecoS uses 8 and r.li 4. Tests were performed to evaluate the landscape metrics variability depending on the “cell neighborhood” values: some metrics, such as “edge density” and “landscape shape index”, do not change, other, for example “patch number”, “patch density”, and “mean patch area”, vary up to 100% for real maps and 500% for maps built to highlight this variation. A review of the scientific literature was carried out to check how often the value of the “cell neighborhood” parameter is explicitly declared. A method based on the “aggregation index” is proposed to estimate the effect of the uncertainty on the “cell neighborhood” parameter on landscape metrics for different maps.

ACS Style

Paolo Zatelli; Stefano Gobbi; Clara Tattoni; Maria Giulia Cantiani; Nicola La Porta; Duccio Rocchini; Nicola Zorzi; Marco Ciolli. Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations. ISPRS International Journal of Geo-Information 2019, 8, 586 .

AMA Style

Paolo Zatelli, Stefano Gobbi, Clara Tattoni, Maria Giulia Cantiani, Nicola La Porta, Duccio Rocchini, Nicola Zorzi, Marco Ciolli. Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations. ISPRS International Journal of Geo-Information. 2019; 8 (12):586.

Chicago/Turabian Style

Paolo Zatelli; Stefano Gobbi; Clara Tattoni; Maria Giulia Cantiani; Nicola La Porta; Duccio Rocchini; Nicola Zorzi; Marco Ciolli. 2019. "Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations." ISPRS International Journal of Geo-Information 8, no. 12: 586.

Preprint
Published: 29 November 2019
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We propose a new method to estimate plant biodiversity with R{\'e}nyi and Rao indexes through the so called High Order Singular Value Decomposition (HOSVD) of tensors. Starting from NASA multispectral images we evaluate biodiversity and we compare original biodiversity estimates with those realised via the HOSVD compression methods for big data. Our strategy turns out to be extremely powerful in terms of storage memory and precision of the outcome. The obtained results are so promising that we can support the efficiency of our method in the ecological framework.

ACS Style

Alessandra Bernardi; Martina Iannacito; Duccio Rocchini. High Order Singular Value Decomposition for Plant Biodiversity Estimation. 2019, 1 .

AMA Style

Alessandra Bernardi, Martina Iannacito, Duccio Rocchini. High Order Singular Value Decomposition for Plant Biodiversity Estimation. . 2019; ():1.

Chicago/Turabian Style

Alessandra Bernardi; Martina Iannacito; Duccio Rocchini. 2019. "High Order Singular Value Decomposition for Plant Biodiversity Estimation." , no. : 1.

Research
Published: 24 October 2019 in Ecography
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Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine‐resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine‐scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine‐scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.

ACS Style

Lukáš Gábor; Vítězslav Moudrý; Vincent Lecours; Marco Malavasi; Vojtěch Barták; Michal Fogl; Petra Šímová; Duccio Rocchini; Tomáš Václavík. The effect of positional error on fine scale species distribution models increases for specialist species. Ecography 2019, 43, 256 -269.

AMA Style

Lukáš Gábor, Vítězslav Moudrý, Vincent Lecours, Marco Malavasi, Vojtěch Barták, Michal Fogl, Petra Šímová, Duccio Rocchini, Tomáš Václavík. The effect of positional error on fine scale species distribution models increases for specialist species. Ecography. 2019; 43 (2):256-269.

Chicago/Turabian Style

Lukáš Gábor; Vítězslav Moudrý; Vincent Lecours; Marco Malavasi; Vojtěch Barták; Michal Fogl; Petra Šímová; Duccio Rocchini; Tomáš Václavík. 2019. "The effect of positional error on fine scale species distribution models increases for specialist species." Ecography 43, no. 2: 256-269.

Journal article
Published: 14 October 2019 in ISPRS International Journal of Geo-Information
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Historical maps constitute an essential information for investigating the ecological and landscape features of a region over time. The integration of heritage maps in GIS models requires their digitalization and classification. This paper presents a semi-automatic procedure for the digitalization of heritage maps and the successive filtering of undesirable features such as text, symbols and boundary lines. The digitalization step is carried out using Object-based Image Analysis (OBIA) in GRASS GIS and R, combining image segmentation and machine-learning classification. The filtering step is performed by two GRASS GIS modules developed during this study and made available as GRASS GIS add-ons. The first module evaluates the size of the filter window needed for the removal of text, symbols and lines; the second module replaces the values of pixels of the category to be removed with values of the surrounding pixels. The procedure has been tested on three maps with different characteristics, the “Historical Cadaster Map for the Province of Trento” (1859), the “Italian Kingdom Forest Map” (1926) and the “Map of the potential limit of the forest in Trentino” (1992), with an average classification accuracy of 97%. These results improve the performance of classification of heritage maps compared to more classical methods, making the proposed procedure that can be applied to heterogeneous sets of maps, a viable approach.

ACS Style

Stefano Gobbi; Marco Ciolli; Nicola La Porta; Duccio Rocchini; Clara Tattoni; Paolo Zatelli. New Tools for the Classification and Filtering of Historical Maps. ISPRS International Journal of Geo-Information 2019, 8, 455 .

AMA Style

Stefano Gobbi, Marco Ciolli, Nicola La Porta, Duccio Rocchini, Clara Tattoni, Paolo Zatelli. New Tools for the Classification and Filtering of Historical Maps. ISPRS International Journal of Geo-Information. 2019; 8 (10):455.

Chicago/Turabian Style

Stefano Gobbi; Marco Ciolli; Nicola La Porta; Duccio Rocchini; Clara Tattoni; Paolo Zatelli. 2019. "New Tools for the Classification and Filtering of Historical Maps." ISPRS International Journal of Geo-Information 8, no. 10: 455.

Review
Published: 29 September 2019 in Data
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We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) to humans and animals pose substantial health risks. Species distribution models that are informed using environmental Earth data are frequently employed to predict the distribution of invasive species, and to advise targeted mitigation strategies. However, there are currently substantial mismatches in the temporal and spatial resolution of these data and the environmental contexts which affect IAVPs. Consequently, targeted actions to control invasive species or to prepare the population for possible disease outbreaks may lack efficacy. Here, we identify and discuss how the currently available environmental Earth data are lacking with respect to their applications in species distribution modeling, particularly when predicting the potential distribution of IAVPs at meaningful space-time scales. For example, we examine the issues related to interpolation of weather station data and the lack of microclimatic data relevant to the environment experienced by IAVPs. In addition, we suggest how these data gaps can be filled, including through the possible development of a dedicated open access database, where data from both remotely- and proximally-sensed sources can be stored, shared, and accessed.

ACS Style

Emily L. Pascoe; Sajid Pareeth; Duccio Rocchini; Matteo Marcantonio. A Lack of “Environmental Earth Data” at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens. Data 2019, 4, 133 .

AMA Style

Emily L. Pascoe, Sajid Pareeth, Duccio Rocchini, Matteo Marcantonio. A Lack of “Environmental Earth Data” at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens. Data. 2019; 4 (4):133.

Chicago/Turabian Style

Emily L. Pascoe; Sajid Pareeth; Duccio Rocchini; Matteo Marcantonio. 2019. "A Lack of “Environmental Earth Data” at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens." Data 4, no. 4: 133.

Journal article
Published: 23 August 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Trentino is an Italian alpine region (about 6200 km2) with a forest coverage exceeding 60% of its whole surface. In the past, forest landscape has changed dramatically, especially in periods of forest over-exploitation.Previous studies in some Trentino sub-regions (Val di Fassa, Paneveggio) have identified these changes and the current trend of forest growth at the expenses of open areas, such as pastures and grasslands, due to the abandonment of rural areas. This phenomenon leads to the rapid Alpine landscape change and profoundly affects the ecological features of mountain ecosystems. To be able to monitor and to take future actions about this trend it is fundamental to know in detail the historical situation of the progressive changes on the land use that occurred over Trentino.The work aims to comprehensively reconstruct the forest cover of whole Trentino at high resolution (5 m × 5 m pixels) using a series of maps spanning a long period, consisting in historical maps, aerial images, remote sensed information and historical archives. The datasets were archived, processed and analyzed using the Free and Open Source Software (FOSS) GIS GRASS and QGIS. Historical maps include Atlas Tyrolensis (dated 1770), Theresianischer Kataster (dated 1859) and Italian Kingdom Forest Map (IKFM) of 1936. The aerial imagery dataset includes aerial images taken in 1954, which have been orthorectified during this research, and orthophotos available for years 1973, 1994, 2000, 2006, 2010 and 2016. Remote sensed information includes Landsat and recent Lidar data, while historical archives consist mostly in Forest Management Plans available since around 1950.The versatility of the wide variety of modules supplied from the FOSS GRASS and QGIS enabled to perform a diverse set of analysis and pre-processing (e.g.:orthorectification) on a heterogeneous dataset of input images. We will focus on the different strategies and methodologies implemented in the FOSS GIS used to process the various types of geographic data, challenges for the future of the research and the fundamental role of the FOSS systems in this process.Quantifying forest change in the time-span of our dataset can be used to perform further analysis on ecosystem services, such as protection from soil erosion, and on modification of biome diversity and to create future change scenarios.

ACS Style

S. Gobbi; M. G. Cantiani; D. Rocchini; P. Zatelli; C. Tattoni; N. La Porta; M. Ciolli. FINE SPATIAL SCALE MODELLING OF TRENTINO PAST FOREST LANDSCAPE (TRENTINOLAND): A CASE STUDY OF FOSS APPLICATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W14, 71 -78.

AMA Style

S. Gobbi, M. G. Cantiani, D. Rocchini, P. Zatelli, C. Tattoni, N. La Porta, M. Ciolli. FINE SPATIAL SCALE MODELLING OF TRENTINO PAST FOREST LANDSCAPE (TRENTINOLAND): A CASE STUDY OF FOSS APPLICATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W14 ():71-78.

Chicago/Turabian Style

S. Gobbi; M. G. Cantiani; D. Rocchini; P. Zatelli; C. Tattoni; N. La Porta; M. Ciolli. 2019. "FINE SPATIAL SCALE MODELLING OF TRENTINO PAST FOREST LANDSCAPE (TRENTINOLAND): A CASE STUDY OF FOSS APPLICATION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W14, no. : 71-78.

Data paper
Published: 01 August 2019 in Annals of Forest Science
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We provided long-term stand and canopy structural data from permanent monitoring plots representative of some most diffuse temperate and Mediterranean forests, under different coppice management regimes. Periodic inventories were performed in the surveyed plots since the 1970s. Annual litterfall production and its partitioning (leaf, woody, reproductive parts) and optical canopy measurements using the LAI-2000 Plant Canopy Analyzer were performed every year in fully equipped plots since the 1990s. These data can be used for evaluating the influence of coppice management in the stand and canopy structure, the parametrization of radiative transfer models that require accurate ground truth data, and the calibration of high to medium resolution remotely sensed data. Dataset access is at https://doi.org/10.17632/z8zm3ytkcx.2. Associated metadata is available at https://agroenvgeo.data.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/2bd2d77f-3cf8-43da-b1b5-9f8196dc017f .

ACS Style

Francesco Chianucci; Carlotta Ferrara; Giada Bertini; Gianfranco Fabbio; Clara Tattoni; Duccio Rocchini; Piermaria Corona; Andrea Cutini. Multi-temporal dataset of stand and canopy structural data in temperate and Mediterranean coppice forests. Annals of Forest Science 2019, 76, 1 -6.

AMA Style

Francesco Chianucci, Carlotta Ferrara, Giada Bertini, Gianfranco Fabbio, Clara Tattoni, Duccio Rocchini, Piermaria Corona, Andrea Cutini. Multi-temporal dataset of stand and canopy structural data in temperate and Mediterranean coppice forests. Annals of Forest Science. 2019; 76 (3):1-6.

Chicago/Turabian Style

Francesco Chianucci; Carlotta Ferrara; Giada Bertini; Gianfranco Fabbio; Clara Tattoni; Duccio Rocchini; Piermaria Corona; Andrea Cutini. 2019. "Multi-temporal dataset of stand and canopy structural data in temperate and Mediterranean coppice forests." Annals of Forest Science 76, no. 3: 1-6.

Journal article
Published: 06 June 2019 in Remote Sensing of Environment
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Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is overall true when considering global scales, since human-induced global change is expected to make significant changes on the Earth's biota. Biodiversity management and planning is mainly based on field observations related to community diversity, considering different taxa. However, such methods are time and cost demanding and do not allow in most cases to get temporal replicates. In this view, remote sensing can provide a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly take into account differences in a neighbourhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coefficient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be beneficial in order to plan more efficient conservation practices starting from spectral diversity hotspots in space and time. In this paper, we delivered a highly reproducible approach to calculate spatio-temporal diversity in a robust and straightforward manner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers.

ACS Style

Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Gherardo Chirici; Marta Galluzzi; Jonathan Lenoir; Carlo Ricotta; Michele Torresani; Guy Ziv. Time-lapsing biodiversity: An open source method for measuring diversity changes by remote sensing. Remote Sensing of Environment 2019, 231, 111192 .

AMA Style

Duccio Rocchini, Matteo Marcantonio, Daniele Da Re, Gherardo Chirici, Marta Galluzzi, Jonathan Lenoir, Carlo Ricotta, Michele Torresani, Guy Ziv. Time-lapsing biodiversity: An open source method for measuring diversity changes by remote sensing. Remote Sensing of Environment. 2019; 231 ():111192.

Chicago/Turabian Style

Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Gherardo Chirici; Marta Galluzzi; Jonathan Lenoir; Carlo Ricotta; Michele Torresani; Guy Ziv. 2019. "Time-lapsing biodiversity: An open source method for measuring diversity changes by remote sensing." Remote Sensing of Environment 231, no. : 111192.

Journal article
Published: 01 April 2019 in Ecological Complexity
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Predicting the geographical distribution of a species is a central topic in ecology, conservation and management of natural resources especially for invasive organisms. Invasive species can modify the structure and function of invaded ecosystems, altering their biodiversity, and causing significant economic losses locally and globally. Therefore, measuring and visualizing the uncertainty inherent in species’ potential distributions is fundamental for effective biodiversity monitoring and planning conservation interventions. This paper discusses a new Bayesian approach to mapping this uncertainty using cartograms, previously published knowledge, and presence/absence data.

ACS Style

Duccio Rocchini; Matteo Marcantonio; George Arhonditsis; Alessandro Lo Cacciato; Heidi C. Hauffe; Kate S. He. Cartogramming uncertainty in species distribution models: A Bayesian approach. Ecological Complexity 2019, 38, 146 -155.

AMA Style

Duccio Rocchini, Matteo Marcantonio, George Arhonditsis, Alessandro Lo Cacciato, Heidi C. Hauffe, Kate S. He. Cartogramming uncertainty in species distribution models: A Bayesian approach. Ecological Complexity. 2019; 38 ():146-155.

Chicago/Turabian Style

Duccio Rocchini; Matteo Marcantonio; George Arhonditsis; Alessandro Lo Cacciato; Heidi C. Hauffe; Kate S. He. 2019. "Cartogramming uncertainty in species distribution models: A Bayesian approach." Ecological Complexity 38, no. : 146-155.

Journal article
Published: 13 March 2019 in Remote Sensing
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The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation.

ACS Style

Karolina Sakowska; Alasdair MacArthur; Damiano Gianelle; Michele Dalponte; Giorgio Alberti; Beniamino Gioli; Franco Miglietta; Andrea Pitacco; Franco Meggio; Francesco Fava; Tommaso Julitta; Micol Rossini; Duccio Rocchini; Loris Vescovo. Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps. Remote Sensing 2019, 11, 614 .

AMA Style

Karolina Sakowska, Alasdair MacArthur, Damiano Gianelle, Michele Dalponte, Giorgio Alberti, Beniamino Gioli, Franco Miglietta, Andrea Pitacco, Franco Meggio, Francesco Fava, Tommaso Julitta, Micol Rossini, Duccio Rocchini, Loris Vescovo. Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps. Remote Sensing. 2019; 11 (6):614.

Chicago/Turabian Style

Karolina Sakowska; Alasdair MacArthur; Damiano Gianelle; Michele Dalponte; Giorgio Alberti; Beniamino Gioli; Franco Miglietta; Andrea Pitacco; Franco Meggio; Francesco Fava; Tommaso Julitta; Micol Rossini; Duccio Rocchini; Loris Vescovo. 2019. "Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps." Remote Sensing 11, no. 6: 614.