Year of Award
Master of Science (MS)
Department or School/College
College of Forestry and Conservation
Brady Allred, Matt Reeves
rangeland phenology, random forest model, spatial model, rangeland, climate model
University of Montana
Natural Resources and Conservation | Other Forestry and Forest Sciences | Statistical Models
Plant phenology has long been used as an indicator of climate. Recent changes in plant phenology are evidence of the influence of climate change. Modeling plant phenology has become an effective tool to understand the impacts of climate change. Using machine learning techniques I developed a modeling process for accurately predicting phenology across a diverse landscape. This model uses individual site data to set site specific climate thresholds for plant phenology. This model also identifies the limiting factors to vegetation phenology for rangelands in the western United States. NDVI remotely sensed data was used to quantify land surface phenology and DAYMET data was used to quantify climate variables. I found that random forest modeling can predict observed plant phenological dates across western rangelands to within a single day for start of season, end of season and day of max NDVI. The model can also identify the most highly correlated variables for phenological events in the study area and highlight which variables limit growth in different vegetative communities. These results confirm previous work on drivers of temperate phenology. This study’s results show that random forest modeling can accurately identify the most important climate variables for phenological events and use those variables to predict phenological events on a large spatial scale.
St. Peter, Joseph R., "A Model For Determining Drivers of Phenology in Western United States Rangelands" (2015). Graduate Student Theses, Dissertations, & Professional Papers. 4444.
Natural Resources and Conservation Commons, Other Forestry and Forest Sciences Commons, Statistical Models Commons
© Copyright 2015 Joseph R. St. Peter