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

Duccio RocchiniElisa ThouveraiMatteo MarcantonioMartina IannacitoDaniele Da ReMichele TorresaniGiovanni BacaroManuele BazzichettoAlessandra BernardiGiles M FoodyReinhard FurrerDavid KleijnStefano LarsenJonathan LenoirMarco MalavasiElisa MarchettoFilippo MessoriAlessandro MontaghiVítězslav MoudrýBabak NaimiCarlo RicottaMicol RossiniFrancesco SantiMaria João SantosMichael E SchaepmanFabian D SchneiderLeila SchuhSonia SilvestriPetra ŜímováAndrew K SkidmoreClara TattoniEnrico TordoniSaverio VicarioPiero ZanniniMartin Wegmann
Published in: Methods in ecology and evolution (2021)
Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.
Keyphrases
  • single cell
  • climate change
  • human health
  • risk assessment
  • machine learning
  • deep learning
  • health information
  • healthcare
  • genome wide
  • electronic health record
  • copy number
  • big data
  • resting state
  • social media