Remote Sensing of Monitoring Forest Fire Dynamics in Near/Ultra Real Time

Bhuian, Md Hanif

Remote Sensing of Monitoring Forest Fire Dynamics in Near/Ultra Real Time

dc.contributor.advisor	Hassan, Quazi Khalid
dc.contributor.author	Bhuian, Md Hanif
dc.contributor.committeemember	Hassan, Quazi Khalid
dc.contributor.committeemember	Billah, Muntasir
dc.contributor.committeemember	Ahmed, Mohammad Razu
dc.date	2025-06-05
dc.date.accessioned	2024-12-09T18:52:56Z
dc.date.available	2024-12-09T18:52:56Z
dc.date.issued	2024-12-04
dc.description.abstract	Forest fires cause extensive environmental damage, significant carbon emissions, and economic losses, posing major challenges for disaster management and emergency response. Accurate and timely delineation of fire perimeters is essential for minimizing these impacts. The overall aim of this study was to develop an early forest fire monitoring model that enables automatic clustering of active fire points and precise delineation of fire perimeters using satellite-based active fire data from VIIRS and MODIS datasets. Various algorithms—buffer, concave, convex, and combination methods—are assessed for their efficiency in near real-time (NRT), real-time (RT), and ultra-real-time (URT) fire perimeter delineation. Additionally, this study also introduced the automated Timely Active Fire Progression (TAFP) model for clustering and calculating active fire perimeter consistently. The results indicate that increasing the concave α (alpha) values (e.g., 0.1 to 0.5) enhances the matching percentage with ground fire areas but also leads to higher commission errors, signifying a risk of overestimation. Combination methods achieved the highest matching percentages but were also associated with increased commission errors. However, the TAFP model demonstrated an 85.13% matching rate for fire perimeters across various size classes, with a 95.95% clustering accuracy for fires larger than 100 hectares. This research was the first to scientifically evaluate multiple algorithms, both individually and synergistically, using NRT/RT/URT active fire data. The insights gained underscore the trade-offs between enhancing perimeter accuracy and avoiding overestimation, providing a critical foundation for optimizing sensor data alignment techniques. By improving the timeliness and accuracy of forest fire monitoring, the proposed models can significantly bolster operational responses by fire management agencies, aiding in resource allocation, evacuation planning, and mitigation strategies. This study lays the groundwork for future advancements in automated forest fire perimeter assessment, essential for effective disaster management and rapid response efforts.
dc.identifier.citation	Bhuian, M. (2024). Remote sensing of monitoring forest fire dynamics in near/ultra real time (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/120186
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
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.
dc.subject	forest fire
dc.subject	remote sensing
dc.subject	active Fire
dc.subject	fire monitoring
dc.subject	wildfire
dc.subject	VIIRS
dc.subject	MODIS
dc.subject	satellite
dc.subject	gis
dc.subject	model
dc.subject.classification	Education--Sciences
dc.title	Remote Sensing of Monitoring Forest Fire Dynamics in Near/Ultra Real Time
dc.type	master thesis
thesis.degree.discipline	Engineering – Geomatics
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.