Remote Sensing of Forest Fire Danger Forecasting
dc.contributor.advisor | Hassan, Quazi K | |
dc.contributor.advisor | Hass | |
dc.contributor.author | Abdollahi, Masoud | |
dc.contributor.committeemember | Gupta, Anil | |
dc.contributor.committeemember | Islam, Tanvir | |
dc.contributor.committeemember | Nowicki, Edwin Peter | |
dc.contributor.committeemember | Govind, Ajit | |
dc.date | 2019-06 | |
dc.date.accessioned | 2019-04-29T16:39:53Z | |
dc.date.available | 2019-04-29T16:39:53Z | |
dc.date.issued | 2019-04-26 | |
dc.description.abstract | Forest 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.citation | Abdollahi, 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.doi | http://dx.doi.org/10.11575/PRISM/36411 | |
dc.identifier.uri | http://hdl.handle.net/1880/110229 | |
dc.language.iso | eng | en_US |
dc.publisher.faculty | Schulich School of Engineering | en_US |
dc.publisher.institution | University of Calgary | en |
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. | en_US |
dc.subject | forest fire | en_US |
dc.subject | remote sensing | en_US |
dc.subject | lightning-caused fire occurrences | en_US |
dc.subject | human-caused fire occurrences | en_US |
dc.subject | Alberta | en_US |
dc.subject | Canada | en_US |
dc.subject | Moderate Resolution Imaging Spectroradiometer | en_US |
dc.subject | Normalized Difference Vegetation Index | en_US |
dc.subject | Normalized Difference Water Index | en_US |
dc.subject | Precipitable Water | en_US |
dc.subject | Surface Temperature | en_US |
dc.subject | Probability Density Function | en_US |
dc.subject | Geographic Information System | en_US |
dc.subject.classification | Remote Sensing | en_US |
dc.subject.classification | Environmental Sciences | en_US |
dc.subject.classification | Energy | en_US |
dc.subject.classification | Engineering | en_US |
dc.title | Remote Sensing of Forest Fire Danger Forecasting | en_US |
dc.type | doctoral thesis | en_US |
thesis.degree.discipline | Engineering – Geomatics | en_US |
thesis.degree.grantor | University of Calgary | en_US |
thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
ucalgary.item.requestcopy | true |
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