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Full-Text Articles in Other Forestry and Forest Sciences
Lidar-Landsat Covariance For Predicting Canopy Fuels, Margaret D. Epstein
Lidar-Landsat Covariance For Predicting Canopy Fuels, Margaret D. Epstein
Graduate Student Theses, Dissertations, & Professional Papers
Managing wildfires in the western United States is becoming increasingly complex. Visualizing and quantifying canopy structures allows fire managers to both plan for fire and track recovery. Light detecting and ranging, or LiDAR can measure forests in three dimensions, but has limited spatial and temporal coverage. LiDAR-Landsat covariance uses machine learning to fill in the spatial and temporal gaps of LiDAR coverage with supplemental Landsat imagery. However, in order to capture real forest dynamics, a model needs to be stable enough to detect long term trends, sensitive to episodic disturbance, and general enough to work on multiple landcovers. The purpose …
Sula Study Revisited: 20-Year Post-Fire Regeneration In The Southern Bitterroot Valley, Montana., Luke Alan Rymniak
Sula Study Revisited: 20-Year Post-Fire Regeneration In The Southern Bitterroot Valley, Montana., Luke Alan Rymniak
Graduate Student Theses, Dissertations, & Professional Papers
In the summer of 2000, a number of large fires burned in the southern Bitterroot Valley near Sula, Montana. Research was conducted in 2001 and 2003 in the fire-affected areas of the French Basin and Larid Creek areas in order to investigate the effects of environmental variables, fire severity, and post-fire management on vegetation regeneration. In 2020 these areas were remeasured to understand trends over time by evaluating the impact of these same factors 20 years post fire. The results showed that the effects of environmental variables, fire severity, and post-fire management on vegetation regeneration were varied. The most influential …
A Model For Determining Drivers Of Phenology In Western United States Rangelands, Joseph R. St. Peter
A Model For Determining Drivers Of Phenology In Western United States Rangelands, Joseph R. St. Peter
Graduate Student Theses, Dissertations, & Professional Papers
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 …