Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard
    Date: 2007
    Source: Journal of Applied Ecology. 44: 1057-1067.
    Publication Series: Miscellaneous Publication
    PDF: Download Publication  (320 B)


    Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Zimmermann, N. E.; Edwards, T. C., Jr.; Moisen, G. G.; Frescino, T. S.; Blackard, J. A. 2007. Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah. Journal of Applied Ecology. 44: 1057-1067.


    core-satellite species hypothesis, K-fold cross-validation, Landsat TM, partial regression, predictive habitat distribution models, species traits, variation partitioning

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page