Browsing by Author "Ahmed, M. Razu"

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    Evaluation of Selected Mitigation Strategies for Reducing Forest Fire-induced Risk
    (2021-12) Ahmed, M. Razu; Rahman, Khan R; Hossain, Sheikh M; Hassan, Quazi K
    The 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.
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    Forest Fire Danger/Risk Forecasting: A Remote Sensing Approach
    (2020-03) Ahmed, M. Razu; Hassan, Quazi K.; Gupta, Anil; Kibria, Md Golam
    Forest/wildland fires are natural disasters that create a significant threat to the communities living in the vicinity of the forested landscape. To minimize the risk concerning resiliency of those urban communities to forest fires, my overall objective was to develop primarily remote sensing (RS)-based models assessing potential risks at the wildland-urban interface (WUI) and making predictions of danger conditions in the environs forest/vegetation. I investigated the risks associated with WUI for the Fort McMurray community and danger conditions in the northern part of Alberta, Canada. For developing the risk modelling framework at WUI, I employed primarily a WorldView-2 satellite image acquired on June 06, 2016. I estimated structural damages due to the devastating 2016 Horse River wildland fire (HRF) that entered the community on May 03, 2016. Besides, I analyzed the presence of vegetation at the WUI to identify the associated risks according to the FireSmart Canada guidelines. My remote sensing-based estimates of the number of structural damages identified a strong linear relationship (i.e., r2 value of 0.97) with the ground-based estimates. Besides, all damaged structures were found associated with the existence of vegetation within the 30m buffer/priority zone of the WUI. It was revealed that approximately 30% of the areas of the WUI were vulnerable due to the presence of vegetation, in which approximately 7% were burned during the 2016 HRF event that led the structural damages. In addition, I developed a new medium-term (i.e., four days) model to forecast forest fire danger conditions using RS-derived biophysical variables of vegetation. I primarily employed Terra MODIS (moderate resolution imaging spectroradiometer)-derived four-day composites of daily surface temperature, normalized difference vegetation index and normalized difference water index. The model was able to detect about 75% of the fire events in the top two danger classes (i.e., very high and high) when evaluated with the historical ground-based forest fire occurrences during the fire seasons of 2015–2017. Besides, the model was able to predict the 2016 HRF event with about 67% agreement. Finally, I developed an operational near real-time (NRT) model to forecast forest fire danger conditions for a day to the next 8 days. Here, I employed Terra MODIS-acquired NRT data from NASA's LANCE (land, atmosphere near real-time capability for earth observing system), where data are made available to the public domain within 2.5 hours of satellite observation. The NRT model was successful in producing forecasted forest fire danger maps at any given time. These developed risk/forecast models would be very useful for the stakeholders in the forest fires management strategies of saving life, property, and community.
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    Remote Sensing of Wildland Fire-induced Risk Assessment Framework
    (National Fire Information Database (NFID) Project, 2017-12) Hassan, Quazi K.; Ahmed, M. Razu; Rahaman, Khan Rubayet
    Wildland fire is one of the critical natural hazards that pose a significant threat to the communities located in the vicinity of forested/vegetated areas. In this report, our overall goal was to use very high spatial resolution (0.5-2.4m) satellite images to develop wildland fire-induced risk framework. We considered two extreme fire events, such as the 2016 HRF over Fort McMurray, and 2011 Lesser Slave Lake fire in Alberta. Thus, our activities included the: (i) estimation of the structural damages; and (ii) delineation of the wildland-urban interface (WUI) and its associated buffers at certain intervals, and their utilization in assessing potential risks. Our proposed method of remote sensing-based estimates was compared with the ground-based information available from the Planning and Development Recovery Committee Task Force of Regional Municipality of Wood Buffalo (RMWB) and National Fire Information Database (NFID); and found strong linear relationships (i.e., r2-value of 0.97 with a slope of 0.97 for the 2016 HRF over Fort McMurray; and 378 from satellite image vs. 407 from 378 from satellite image vs. 407 from NFID system for the 2011 Lesser Slave Lake fire). Upon delineating the WUI and its associated buffer zones at 10m, 30m, 50m, 70m and 100m distances; we found existence of vegetation within the 30m buffers from the WUI for all of the damaged structures. In addition, we noticed that the relevant authorities had removed vegetation in some areas between 30m and 70m buffers from the WUI in case of Fort McMurray area, which was proven to be effective in order to protect the structures in the adjacent communities. Furthermore, we mapped the wildland fire-induced vulnerable areas upon considering the WUI and its associated buffers. We found that there were still some communities that had the existence of vegetation within the buffer zones; thus such vegetation should be removed and monitored regularly in order to reduce the wildland fire-induced risks.
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    Vegetation and Forest Fire Dynamics in Alberta: A Ground and Remote Sensing Data Analysis
    (2024-07-02) Dastour, Hatef; Hassan, Quazi K.; Achari, Gopal; Ahmed, M. Razu
    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.