Date of Award:
5-1999
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Biology
Committee Chair(s)
Mary E. Barkworth
Committee
Mary E. Barkworth
Committee
Ted Evans
Committee
Janis Boettinger
Committee
David Roberts
Abstract
Shifting perspectives on restoration and management of public lands in the inland West have resulted in an increased need for maps of potential natural vegetation which cover large areas at sufficient scale to delineate individual stands. In this study, classification tree modeling was used to predictively model and map the plant association types of a relatively undisturbed wilderness area in the Blue Mountains of northeastern Oregon. Models were developed using field data and data derived from a geographic information system database. Elevation, slope, aspect, annual precipitation, solar radiation, soil type, and topographic position were important predictor variables. The model predicted plant association types with a relatively high degree of accuracy for most plant association types, with the lowest accuracy for the types within the grand fir series. Fuzzy confusion analysis was used to analyze model performance, and indicated the overall model accuracy was 72%.
Checksum
95dc48596e75671506229187570c86fa
Recommended Citation
Kelly, Alison M., "Predictively Mapping the Plant Associations of the North Fork John Day Wilderness in Northeastern Oregon Using Classification Tree Modeling" (1999). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 6572.
https://digitalcommons.usu.edu/etd/6572
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