- Mapping Mountain vegetation - 499 Applied Vegetation Science 11: 499-508, 2008 doi: 10.3170/2008-7-18560, published online 4 June 2008 © IAVS; Opulus Press Uppsala. Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classifcation Dobrowski, Solomon Z. 1* ; Safford, Hugh D. 2 ; Cheng, Yen Ben 2 & Ustin, Susan L. 3 1 Department of Forest Management, College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA; 2 Center for Spatial Technologies and Remote Sensing, One Shields Ave, The Barn, University of California, Davis, CA 95616, USA; 3 USDA-Forest Service Pacifc Southwest Region, 1323 Club Drive, Vallejo, CA 94592, USA; * Corresponding author; E-mail solomon.dobrowski@cfc.umt.edu Abstract Objective:The objective of this study was to map vegetation composition across a 24 000 ha watershed. Location: The study was conducted on the western slope of the Sierra Nevada mountain range of California, USA. Methods: Automated image segmentation was used to deline- ate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classifed us- ing a decision tree and data sources extracted from individual species distribution models, Landsat spectral data, and life form cover estimates derived from aerial image-based texture variables. Results: A total of 12 plant communities were mapped with an overall accuracy of 75% and a κ-value of 0.69. Species distribu- tion model inputs improved map accuracy by approximately 15% over maps derived solely from image data. Automated mapping of existing vegetation distributions, based solely on predictive distribution model results, proved to be more ac- curate than mapping based on Landsat data, and equivalent in accuracy to mapping based on all image data sources. Conclusions: Results highlight the importance of terrain, edaphic, and bioclimatic variables when mapping vegetation communities in complex terrain. Mapping errors stemmed from the lack of spectral discernability between vegetation classes, and the inability to account for the confounding effects of land use history and disturbance within a static distribution modeling framework. Keywords: Decision tree; GAM; Image segmentation; Sierra Nevada; Topographic convergence index; Vegetation map- ping. Nomenclature: Hickman (ed.) 1993. Abbreviations: DOQQ = Digital ortho-photo quarter quads; DT = Decision tree; GAM = General additive modeling; SDM = Species Distribution Modeling; TCI = Topographic convergence index. Introduction Image classifcation and predictive distribution modeling (referred to as species distribution modeling, gradient modeling, niche modeling, among others) are common approaches to mapping vegetation. Both ap- proaches have limitations, for instance, mapping based on the classifcation of spectral data is often hindered by the lack of spectral discernability between vegetation types (Dirnböck et al. 2003; Treitz et al. 1992), whereas predictive distribution modeling characterizes potential rather than actual vegetation distributions (Austin 2002; Guisan & Zimmerman 2000). When used in combina- tion, both image analysis and predictive modeling can be complementary. Terrain variables have long been used in image classifcation to improve map accuracies (J. Fran- klin 1995). Similarly, image variables have been used in predictive distribution modeling (Dirnböck et al. 2003; Lees & Ritman 1991) and are more recently referred to as functional gradients (Muller 1998). A challenge to auto- mated mapping of vegetation, is devising a methodology that leverages the strengths of both predictive modeling and image-based approaches, as well as divides fnite resources between tasks associated with each. Approaches to incorporating environmental variables into existing vegetation mapping efforts include but are not exclusive to: (1) the direct use of environment and image variables in a nominal classifer, (2) constrained ordination techniques using inventory data, image and environmental variables (e.g. Ohmann & Gregory 2002; Dirnböck et al. 2003), and (3) Probabilistic or consensus theoretic approaches to combining image analysis and gradient modeling results (e.g. Strahler 1980; Dobrowski et al. 2006). The use of environmental variables in these and others studies has been shown to improve map accuracies given that these types of data are related to biophysical factors that affect the distribution of species (J. Franklin 1995; Richards et al. 1982; Strahler 1980). Mapping vegetation at high spatial resolution presents