Vegetation and Forest Fire Dynamics in Alberta: A Ground and Remote Sensing Data Analysis

This thesis presents a comprehensive analysis of the interplay between forest fires and vegetation dynamics in Alberta, Canada, under the lens of climate change. By synthesizing data from remote sensing, climate records, and fire databases, the study reveals the intricate relationships between vegetation cover changes and climatic factors throughout 2001–2022. It highlights the significant lead and lag times between the Normalized Difference Vegetation Index (NDVI) and climate variables such as Land Surface Temperature (LST), relative humidity, and precipitation, offering insights into the temporal dynamics of vegetation response to climatic influences. The research further explores the patterns and trends of forest fires, correlating them with interpolated climate data across various subregions. Using trend analysis and anomaly detection methods, the study identifies significant warming and drying trends, alongside variable precipitation changes, which have influenced both human-caused and lightning-induced forest fires. The findings underscore the differential impact of climate variables on fire occurrence and source, with notable patterns emerging in subregions like Athabasca Plain and Central Mixedwood. Building on these insights, the thesis develops a robust forest fire spread model, validated through high-precision simulations of the 2011 Slave Lake and 2016 Fort McMurray wildfires. The model leverages regional physical features, climatic data, and MODIS datasets to offer accurate fire behavior predictions. The phased simulation approach adapts to dynamic factors such as weather conditions and firefighting strategies, enhancing the model's applicability for effective fire management. Ultimately, this thesis aims to bridge the gap between theoretical understanding and practical application, providing valuable contributions to Alberta's forest fire management and community protection strategies. The research paves the way for more informed decision-making in the face of climate change by offering a nuanced understanding of fire-vegetation-climate interactions and developing an advanced predictive model.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Engineering – Geomatics

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2024

Author dc:creator

• Dastour, Hatef

Advisor dc:contributor.advisor

• Hassan, Quazi K.

Committee members dc:contributor.committeemember

• Achari, Gopal
• Ahmed, M. Razu

• Remote Sensing
• Machine Learning
• Time Series Analysis
• Time Series Modeling
• Vegetation Greenness
• Fire Season
• Numerical Simulation

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

en

DOI

https://doi.org/10.11575/PRISM/46683

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/119087