Forest Growth Modelling for Balancing Wildfire Risk and Carbon Budgets in the Greater Victoria Water Supply Area
Forests are an important component of the global carbon cycle and have significant potential for aiding climate change mitigation efforts. As such, the loss of forests poses a critical challenge to addressing climate change on numerous fronts. As global temperature rises, forests are more prone to suffer from diseases, insect outbreaks and higher frequency of wildfires. Therefore, it is essential to focus efforts on the development and improvement of tools that can predict and mitigate future wildfire events and assist on the creation of more resilient forests. This project, led by Bruna Barusco (PhD student), is focused on evaluating available forest growth and yield models in relation to carbon budgets and wildfire risk in the Greater Victoria Water Supply Area on southeastern Vancouver Island. This research will focus on modelling climate scenarios using the Generic Carbon Budget Model (GCBM) to estimate fire effects on carbon budgets.
Remote Sensing Technologies for Monitoring Forest Attributes and Dynamics
Forests can play a critical role in mitigating future climate change effects, but our capacity to understand these potentials or monitor future forest changes is limited by our ability to estimate forest attributes. In a project led by Jason Kelley (MSc Student) in collaboration with the Canadian Forest Service, we investigate the use of remote sensing technologies to evaluate and improve how forest changes and conditions are monitored. One application of this work is with the use of lidar area-based modelling in attempt to estimate the amount of forest material that is traditionally non-merchantable that could be used as biofuel or other products for the bio-economy. This research hopes to improve the capacity and understanding for applying remote sensing methods and technologies to improve our knowledge and understanding of forests as they change in the future.
Extracting Wildfire Behaviour Data from Individually Georeferenced Oblique Aerial Images
Wildfires, despite their important role in maintaining healthy forest ecosystems, can have significant economic, environmental, human health and safety impacts. In recent years, these impacts have become more common and severe, calling for the advancement of wildfire behaviour modelling to better understand and predict fire behaviour. To address this challenge, the objective of this study led by Henry Hart (MSc Student) is to explore a novel method of extracting spatial wildfire data from oblique images taken during BC wildfire operations using a photogrammetric approach called monophotogrammetry. We demonstrate that monophotogrammetry can be effective on a subset of wildfire images and used to assess characteristics including fire front position, spread distance and ROS. This research introduces the opportunity to not only validate and improve the existing FBP System, but to also increase the overall understanding of wildfire behaviour as it known today.