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Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

Journal Article


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Abstract


  • Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Authors


  •   Pottier, Julien (external author)
  •   Malenovky, Zbynek (external author)
  •   Psomas, Achilleas (external author)
  •   Homolova, Lucie (external author)
  •   Schaepman, Michael E. (external author)
  •   Choler, Philippe (external author)
  •   Thuiller, Wilfried (external author)
  •   Guisan, Antoine (external author)
  •   Zimmermann, Niklaus E. (external author)

Publication Date


  • 2014

Citation


  • Pottier, J., Malenovsky, Z., Psomas, A., Homolova, L., Schaepman, M. E., Choler, P., Thuiller, W., Guisan, A. & Zimmermann, N. E. (2014). Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy. Biology Letters, 10 (7), 1-4.

Scopus Eid


  • 2-s2.0-84906061903

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3275&context=smhpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smhpapers/2257

Number Of Pages


  • 3

Start Page


  • 1

End Page


  • 4

Volume


  • 10

Issue


  • 7

Abstract


  • Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Authors


  •   Pottier, Julien (external author)
  •   Malenovky, Zbynek (external author)
  •   Psomas, Achilleas (external author)
  •   Homolova, Lucie (external author)
  •   Schaepman, Michael E. (external author)
  •   Choler, Philippe (external author)
  •   Thuiller, Wilfried (external author)
  •   Guisan, Antoine (external author)
  •   Zimmermann, Niklaus E. (external author)

Publication Date


  • 2014

Citation


  • Pottier, J., Malenovsky, Z., Psomas, A., Homolova, L., Schaepman, M. E., Choler, P., Thuiller, W., Guisan, A. & Zimmermann, N. E. (2014). Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy. Biology Letters, 10 (7), 1-4.

Scopus Eid


  • 2-s2.0-84906061903

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3275&context=smhpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smhpapers/2257

Number Of Pages


  • 3

Start Page


  • 1

End Page


  • 4

Volume


  • 10

Issue


  • 7