Forecasting spatio-temporal vegetation changes in the mealy mountains using a cellular automata-markov chain hybrid model

• Main Author: Bartlett, Zachary
• Format: Thesis
• Language: English
• Published: Memorial University of Newfoundland 2012
• Subjects: Arctic; Mealy Mountains; Tundra
• Online Access: https://research.library.mun.ca/6084/; https://research.library.mun.ca/6084/1/Bartlett_Zachary.pdf; https://research.library.mun.ca/6084/3/Bartlett_Zachary.pdf

Sub-arctic temperatures are expected to increase by approximately 4°C by 2050. These changes are having impacts on vegetation patterns in arctic and sub-arctic environments, particularly along transition areas between forested and tundra ecosystems. Using multi-temporal satellite imagery, in combination with topographic variables, the changes in vegetation patterns from 1983 to 2008 were explored in a small, diverse region of the Mealy Mountains, Labrador. Bayesian probabilities were created for each land cover class, with topographic variables used as a priori additions to the probabilities. Vegetation changes were related to topographic variables, climate, and Bayesian probabilities. The Bayesian probability layers demonstrate the propensity for change of each land cover class used in the study. Knowledge of these changes was used in a cellular automata-Markov chain model to predict vegetation changes to 2020 and 2032. The predictions suggest movement of deciduous shrub along valley floors and into toe-slopes, as well as on protected, south-facing slopes. Coniferous shrub is expected to expand in the lower elevations (where it is dominant), and advance marginally along the valley floors.