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Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah


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.

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.

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


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http://www.fs.fed.us/rm/pubs_other/rmrs_2007_zimmermann_n001.pdf

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Title: RMRS Other Publications: Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah
Electronic Publish Date: April 28, 2008
Last Update:
April 28, 2008

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