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PREDICTIVE VEGETATION MODELING FOR CONSERVATION: IMPACT OF ERROR PROPAGATION FROM DIGITAL ELEVATION DATA
Author(s) -
Van Niel Kimberly P.,
Austin Mike P.
Publication year - 2007
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(2007)017[0266:pvmfci]2.0.co;2
Subject(s) - digital elevation model , mean squared prediction error , propagation of uncertainty , statistics , generalized additive model , elevation (ballistics) , predictive modelling , generalized linear model , vegetation (pathology) , ecology , environmental niche modelling , sensitivity (control systems) , errors in variables models , reliability (semiconductor) , model selection , type i and type ii errors , mathematics , habitat , geography , remote sensing , ecological niche , biology , medicine , power (physics) , physics , geometry , pathology , quantum mechanics , electronic engineering , engineering
The effect of digital elevation model (DEM) error on environmental variables, and subsequently on predictive habitat models, has not been explored. Based on an error analysis of a DEM, multiple error realizations of the DEM were created and used to develop both direct and indirect environmental variables for input to predictive habitat models. The study explores the effects of DEM error and the resultant uncertainty of results on typical steps in the modeling procedure for prediction of vegetation species presence/absence. Results indicate that all of these steps and results, including the statistical significance of environmental variables, shapes of species response curves in generalized additive models (GAMs), stepwise model selection, coefficients and standard errors for generalized linear models (GLMs), prediction accuracy (Cohen's kappa and AUC), and spatial extent of predictions, were greatly affected by this type of error. Error in the DEM can affect the reliability of interpretations of model results and level of accuracy in predictions, as well as the spatial extent of the predictions. We suggest that the sensitivity of DEM‐derived environmental variables to error in the DEM should be considered before including them in the modeling processes.