Browsing by Author "Hassan, Quazi K"
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- ItemOpen AccessEvaluation of Selected Mitigation Strategies for Reducing Forest Fire-induced Risk(2021-12) Ahmed, M. Razu; Rahman, Khan R; Hossain, Sheikh M; Hassan, Quazi KThe aim was to study post-fire perceptions of selected mitigation strategies for wildland fire- induced risks proposed in a previous scientific study for the communities situated within the forested areas. Consequently, we considered engaging relevant professionals in the Regional Municipality of Wood Buffalo (RMWB), Alberta who experienced the costliest wildland fire occurrences in Canadian history known as the 2016 Horse River Fire (HRF). To meet our goal, we formulated a questionnaire based on the scientific evidence presented in a previous study and con-ducted a structured survey. Our results revealed that 24 professionals participated in the survey during the June 2020-April 2021 period, providing a 32% response rate. We observed that a high percentage of the participants agreed (i.e., between 63% and 80%) with the proposed wildland fire-induced risk mitigation strategies, including the presence of no to little vegetation in the 30 m buffer zone from the wildland–urban interface (WUI), extending the 30 m buffer zone to 70 m from the WUI, constructing a 70 m width ring road around the communities, and parking lots of the social infrastructures in the fringe of the communities encountering to the forest. We also found other views, including the use of non-combustible and fire-resistant construction materials, and developing the 70 m buffer zone as a recreational space.
- ItemOpen AccessRemote Sensing of Forest Fire Danger Forecasting(2019-04-26) Abdollahi, Masoud; Hassan, Quazi K; Hass; Gupta, Anil; Islam, Tanvir; Nowicki, Edwin Peter; Govind, AjitForest 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.