Forest Fires

• overview
• subprojects
• Fire History Analysis
• Fire Management - National Resource Planning Under a Changing Climate
• Fire growth modelling and fire/weather data analysis and visualization
• Fire history analysis
• Fire-Weather Science
• Identification of Smoke Plumes from Satellite Images
• Lightning storm tracking using a data sharpening-based clustering algorithm
• Models for predicting fire occurrence
• Remote Sensing Analysis of Forest Fire Smoke for Investigating Health Effects
• Spatio-Temporal Models Examining Fire Frequency and Severity from Panel Data
• software
• publications

Spatio-Temporal Models Examining Fire Frequency and Severity from Panel Data

Researcher: Nathoo

With Steve Taylor (Pacific Forestry) and M.Sc. student Yolanda Li (UVic) I have been examining statistical methods for studying the spatial and temporal distribution of fire frequency and severity in British Columbia in addition to examining the interaction between wildfire and forest insect outbreaks. There is a widespread belief that forest insect outbreaks lead to an increased frequency of wildfires; however, empirical evidence to date has been limited. Li's thesis focuses on relating the frequency of wildfire to the severity of mountain pine beetle (MPB) outbreaks in British Columbia for data collected over a 46 year period across the province. The development of models relating fire frequency and severity to MPB outbreak is very important as British Columbia is currently experiencing a historically unprecedented MPB outbreak. Hierarchical spatio-temporal regression models for fire frequency based on the negative binomial distribution are applied for analysis in a Bayesian framework. Model adequacy and model selection will be investigated through methods based on the cross-validation predictive distribution with Markov chain Monte Carlo used for implementation.