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Assessing across-scale optical diversity and productivity relationships in grasslands of the Italian alps

Research output: Contribution to journalArticle

  • 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

Related Edinburgh Organisations

Original languageEnglish
Article number614
JournalRemote Sensing
Volume11
Issue number6
DOIs
Publication statusPublished - 1 Mar 2019

Abstract

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.

    Research areas

  • Grasslands, Optical diversity, Optical diversity-productivity relationships, Productivity, Sentinel-2

ID: 111838627