Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification

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 delineate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classified using 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: Atotal of 12 plant communities were mapped with an overall accuracy of 75% and a /c-value of 0.69. Species distribution 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 accurate 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.