Remote Sensing of Forest Fire Danger Forecasting

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dc.contributor.advisorHassan, Quazi K
dc.contributor.advisorHass
dc.contributor.authorAbdollahi, Masoud
dc.contributor.committeememberGupta, Anil
dc.contributor.committeememberIslam, Tanvir
dc.contributor.committeememberNowicki, Edwin Peter
dc.contributor.committeememberGovind, Ajit
dc.date2019-06
dc.date.accessioned2019-04-29T16:39:53Z
dc.date.available2019-04-29T16:39:53Z
dc.date.issued2019-04-26
dc.description.abstractForest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, my aim was to employ primarily remote sensing data in forecasting the forest fire danger conditions in the Canadian province of Alberta. Thus, I followed three specific objectives. Firstly, I generated topography-based static fire danger (SFD) map upon exploring the relationship between topographical elements (i.e., elevation, slope, and aspect) and fire occurrences. Since, the slope was found to be the best predictor for fire occurrences; I generated a slope-derived probability of forest fire occurrences. However, I did not incorporate the obtained map in the final specific objective as it had very small low fire danger areas. Secondly, I examined the possibility of lightning-caused fires modelling using remote sensing-derived vegetation moisture content in natural subregion level. I employed 8-day composite of normalized differences water index (NDWI) at 500 m spatial resolution along with historical lightning-caused fire occurrences during the 2005-2016 period. Employing the cumulative frequency cumulative-values of natural subregion-specific median NDWI and lightning-caused fire frequencies from snow disappearance date to the peak of the growing season, I found strong agreements (i.e., R2 ≥ 0.96) between these two frequencies for each of the subregions. Finally, I developed an advanced forest fire danger forecasting system upon applying three modifications on the exiting FFDFS, and incorporating the outcomes in the scope of the previous specific objectives. Then I examined the outcomes of the different combinations against the actual fire spots during the fire seasons of 2009–2011. Among all of the combinations, I found that the integration of modified FFDFS and human-caused SFD map demonstrated the most effective results in fire detection, i.e., about 82% on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. I strongly believe that my developments would be useful in the forest fire management.en_US
dc.identifier.citationAbdollahi, M. (2019). Remote Sensing of Forest Fire Danger Forecasting (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36411
dc.identifier.urihttp://hdl.handle.net/1880/110229
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity 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.en_US
dc.subjectforest fireen_US
dc.subjectremote sensingen_US
dc.subjectlightning-caused fire occurrencesen_US
dc.subjecthuman-caused fire occurrencesen_US
dc.subjectAlbertaen_US
dc.subjectCanadaen_US
dc.subjectModerate Resolution Imaging Spectroradiometeren_US
dc.subjectNormalized Difference Vegetation Indexen_US
dc.subjectNormalized Difference Water Indexen_US
dc.subjectPrecipitable Wateren_US
dc.subjectSurface Temperatureen_US
dc.subjectProbability Density Functionen_US
dc.subjectGeographic Information Systemen_US
dc.subject.classificationRemote Sensingen_US
dc.subject.classificationEnvironmental Sciencesen_US
dc.subject.classificationEnergyen_US
dc.subject.classificationEngineeringen_US
dc.titleRemote Sensing of Forest Fire Danger Forecastingen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Geomaticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
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