TY - JOUR
T1 - rasterdiv ‐ an Information Theory tailored R package for measuring ecosystem heterogeneity from space
T2 - to the origin and back
AU - Rocchini, Duccio
AU - Thouverai, Elisa
AU - Marcantonio, Matteo
AU - Iannacito, Martina
AU - Da Re, Daniele
AU - Torresani, Michele
AU - Bacaro, Giovanni
AU - Bazzichetto, Manuele
AU - Bernardi, Alessandra
AU - Foody, Giles M.
AU - Furrer, Reinhard
AU - Kleijn, David
AU - Larsen, Stefano
AU - Lenoir, Jonathan
AU - Malavasi, Marco
AU - Marchetto, Elisa
AU - Messori, Filippo
AU - Montaghi, Alessandro
AU - Moudrý, Vítězslav
AU - Naimi, Babak
AU - Ricotta, Carlo
AU - Rossini, Micol
AU - Santi, Francesco
AU - Santos, Maria J.
AU - Schaepman, Michael
AU - Schneider, Fabian D.
AU - Schuh, Leila
AU - Silvestri, Sonia
AU - Šímová, Petra
AU - Skidmore, A.K.
AU - Tattoni, Clara
AU - Tordoni, Enrico
AU - Vicario, Saverio
AU - Zannini, Piero
AU - Wegmann, Martin
N1 - Funding Information:
We are grateful to the handling Editor and two anonymous reviewers who helped us improving a previous version of this manuscript with their precious suggestions. D.R. and D.K. were partially supported by the H2020 Project SHOWCASE (Grant agreement No 862480). D.R. was also partially supported by the H2020 COST Action CA17134 ‘Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENSECO)'. The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Government sponsorship is acknowledged. This work was supported by Friuli Venezia Giulia Region Operative Program, European Social Fund—2014/2020 Program, Specific Action n. 53/16: Integrative professional training courses within degree programs. RF was partially supported by SNSF‐175529. We thank the Czech University of Life Sciences Prague. Faculty of Environmental Sciences for supporting their research group Spatial Sciences in Ecology and Environment. The contribution of R.F. M.J.S., M.E.S. and L.S. is supported by the University of Zurich Research Priority Program on Global Change and Biodiversity (URPP GCB).
Funding Information:
We are grateful to the handling Editor and two anonymous reviewers who helped us improving a previous version of this manuscript with their precious suggestions. D.R. and D.K. were partially supported by the H2020 Project SHOWCASE (Grant agreement No 862480). D.R. was also partially supported by the H2020 COST Action CA17134 ?Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENSECO)'. The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Government sponsorship is acknowledged. This work was supported by Friuli Venezia Giulia Region Operative Program, European Social Fund?2014/2020 Program, Specific Action n. 53/16: Integrative professional training courses within degree programs. RF was partially supported by SNSF-175529. We thank the Czech University of Life Sciences Prague. Faculty of Environmental Sciences for supporting their research group Spatial Sciences in Ecology and Environment. The contribution of R.F. M.J.S., M.E.S. and L.S. is supported by the University of Zurich Research Priority Program on Global Change and Biodiversity (URPP GCB).
Publisher Copyright:
© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/skidmore_ras.pdf
U2 - 10.1111/2041-210X.13583
DO - 10.1111/2041-210X.13583
M3 - Article
SN - 2041-210X
VL - 12
SP - 1093
EP - 1102
JO - Methods in ecology and evolution
JF - Methods in ecology and evolution
IS - 6
ER -